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facexlib/visualization/vis_alignment.py delete mode 100644 facexlib/visualization/vis_detection.py delete mode 100644 facexlib/visualization/vis_headpose.py delete mode 100644 facexlib/weights/README.md diff --git a/basicsr/__init__.py b/basicsr/__init__.py deleted file mode 100644 index 94b2a25..0000000 --- a/basicsr/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# https://github.com/xinntao/BasicSR -# flake8: noqa -from .archs import * -from .data import * -from .losses import * -from .metrics import * -from .models import * -from .ops import * -from .test import * -from .train import * -from .utils import * diff --git a/basicsr/__pycache__/__init__.cpython-310.pyc b/basicsr/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index ed555c5ee726259121db53b9b7c6b019041bca12..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 296 zcmYk$!43g25C-6O*RCa8yv0g5i-vQN~v_};E}wH2bimqR}cq-9!%5u 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opt.pop('type') - net = ARCH_REGISTRY.get(network_type)(**opt) - logger = get_root_logger() - logger.info(f'Network [{net.__class__.__name__}] is created.') - return net diff --git a/basicsr/archs/__pycache__/__init__.cpython-310.pyc b/basicsr/archs/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index cc53df24c19031b72935dd761708f8b5511a4fd3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1136 zcmZWoL5~wR6!!B>GLvj3n_UnR5{JEpLsY4^YK5qvv>duZOD`RQtjXA$oy8MRZEtB- zNiWN>s?;+=N+d4)6MloQocb4f;@J}KKk7`j``#j<4D{e+>`GHRL1S3p=9_9q>Nyz9RV{ho@w`_b*}~Cu49a7osUuvqV2` z3VS}LZhtN;(@I$;)qF0rOH5fTc%|{1?EBLPKe5vveti7&m(yQeHoL4OXA5CpD*d7a zf9-BiTobehXyz_XbFw1O57w}P4RYOrrWQ8dq7^dO0*Bk=ANm_beDo@1g)Yx0%~Jb2 z$FWPrf}59>J$I2ZjY~}Cek2T3v|J zP9GJeI2Fb!E%X%ssY;`#K8cw!RxK*a7^ZXmV_6wns=B$CdE}nPU7|<0{EuE2Hv_#p z=mdKGHCVC%PFC=myrEmz5rV7XF6AVooFLdSq-1k}wz#gGvvF_Kf#u|034^h-i(gbAM7tG!I;$Wi19F?G#EsKP) zLQ2G(;6v~|aY->V{>69}p;Ij1^UY;vN^+s|VH;o%{pTjnf~0pnHrf;TjFeZb_C0m! 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z)MqA*Z9i^HC;glRaf1D1K@yUb5Mr&z& z{P1kIBT{QP zKC|FA@iuURqhf{Mw;Vjd^5T>8eZyJC5gVjQ*<`1IH@FS!yW>-L3&YWnLp3vn94^%a zawc%1i@)J8VvjJZO?He30I{_%1JmRGpGGH4YVh`VB!fju_cXacwQ2aS|o7!=( zJ>Mxvt34odjYcqR;&TBT$_gLT8H4Rq7rAt)+Ghp>WpK;I$>W*XxsvGf-Jd|h<1;ia zWuNJtfdk)*1ScVv57EwuumxvZ+z?!%)Co7c9eCda8EBS8wsBIoT&dMmT@uk!vwmvy zD?Oa*QJDYi6e)9>ZFs>cG-m+r`#bvC(v{UBj_Fy=qs_ XS0AVr@jF?4ta{ctx1>84R`ve}j#G)6 diff --git a/basicsr/archs/arch_util.py b/basicsr/archs/arch_util.py deleted file mode 100644 index 493e566..0000000 --- a/basicsr/archs/arch_util.py +++ /dev/null @@ -1,313 +0,0 @@ -import collections.abc -import math -import torch -import torchvision -import warnings -from distutils.version import LooseVersion -from itertools import repeat -from torch import nn as nn -from torch.nn import functional as F -from torch.nn import init as init -from torch.nn.modules.batchnorm import _BatchNorm - -from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv -from basicsr.utils import get_root_logger - - -@torch.no_grad() -def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): - """Initialize network weights. - - Args: - module_list (list[nn.Module] | nn.Module): Modules to be initialized. - scale (float): Scale initialized weights, especially for residual - blocks. Default: 1. - bias_fill (float): The value to fill bias. Default: 0 - kwargs (dict): Other arguments for initialization function. - """ - if not isinstance(module_list, list): - module_list = [module_list] - for module in module_list: - for m in module.modules(): - if isinstance(m, nn.Conv2d): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, nn.Linear): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, _BatchNorm): - init.constant_(m.weight, 1) - if m.bias is not None: - m.bias.data.fill_(bias_fill) - - -def make_layer(basic_block, num_basic_block, **kwarg): - """Make layers by stacking the same blocks. - - Args: - basic_block (nn.module): nn.module class for basic block. - num_basic_block (int): number of blocks. - - Returns: - nn.Sequential: Stacked blocks in nn.Sequential. - """ - layers = [] - for _ in range(num_basic_block): - layers.append(basic_block(**kwarg)) - return nn.Sequential(*layers) - - -class ResidualBlockNoBN(nn.Module): - """Residual block without BN. - - Args: - num_feat (int): Channel number of intermediate features. - Default: 64. - res_scale (float): Residual scale. Default: 1. - pytorch_init (bool): If set to True, use pytorch default init, - otherwise, use default_init_weights. Default: False. - """ - - def __init__(self, num_feat=64, res_scale=1, pytorch_init=False): - super(ResidualBlockNoBN, self).__init__() - self.res_scale = res_scale - self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) - self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) - self.relu = nn.ReLU(inplace=True) - - if not pytorch_init: - default_init_weights([self.conv1, self.conv2], 0.1) - - def forward(self, x): - identity = x - out = self.conv2(self.relu(self.conv1(x))) - return identity + out * self.res_scale - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.') - super(Upsample, self).__init__(*m) - - -def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True): - """Warp an image or feature map with optical flow. - - Args: - x (Tensor): Tensor with size (n, c, h, w). - flow (Tensor): Tensor with size (n, h, w, 2), normal value. - interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'. - padding_mode (str): 'zeros' or 'border' or 'reflection'. - Default: 'zeros'. - align_corners (bool): Before pytorch 1.3, the default value is - align_corners=True. After pytorch 1.3, the default value is - align_corners=False. Here, we use the True as default. - - Returns: - Tensor: Warped image or feature map. - """ - assert x.size()[-2:] == flow.size()[1:3] - _, _, h, w = x.size() - # create mesh grid - grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x)) - grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2 - grid.requires_grad = False - - vgrid = grid + flow - # scale grid to [-1,1] - vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0 - vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0 - vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3) - output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners) - - # TODO, what if align_corners=False - return output - - -def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False): - """Resize a flow according to ratio or shape. - - Args: - flow (Tensor): Precomputed flow. shape [N, 2, H, W]. - size_type (str): 'ratio' or 'shape'. - sizes (list[int | float]): the ratio for resizing or the final output - shape. - 1) The order of ratio should be [ratio_h, ratio_w]. For - downsampling, the ratio should be smaller than 1.0 (i.e., ratio - < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e., - ratio > 1.0). - 2) The order of output_size should be [out_h, out_w]. - interp_mode (str): The mode of interpolation for resizing. - Default: 'bilinear'. - align_corners (bool): Whether align corners. Default: False. - - Returns: - Tensor: Resized flow. - """ - _, _, flow_h, flow_w = flow.size() - if size_type == 'ratio': - output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1]) - elif size_type == 'shape': - output_h, output_w = sizes[0], sizes[1] - else: - raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.') - - input_flow = flow.clone() - ratio_h = output_h / flow_h - ratio_w = output_w / flow_w - input_flow[:, 0, :, :] *= ratio_w - input_flow[:, 1, :, :] *= ratio_h - resized_flow = F.interpolate( - input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners) - return resized_flow - - -# TODO: may write a cpp file -def pixel_unshuffle(x, scale): - """ Pixel unshuffle. - - Args: - x (Tensor): Input feature with shape (b, c, hh, hw). - scale (int): Downsample ratio. - - Returns: - Tensor: the pixel unshuffled feature. - """ - b, c, hh, hw = x.size() - out_channel = c * (scale**2) - assert hh % scale == 0 and hw % scale == 0 - h = hh // scale - w = hw // scale - x_view = x.view(b, c, h, scale, w, scale) - return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) - - -class DCNv2Pack(ModulatedDeformConvPack): - """Modulated deformable conv for deformable alignment. - - Different from the official DCNv2Pack, which generates offsets and masks - from the preceding features, this DCNv2Pack takes another different - features to generate offsets and masks. - - ``Paper: Delving Deep into Deformable Alignment in Video Super-Resolution`` - """ - - def forward(self, x, feat): - out = self.conv_offset(feat) - o1, o2, mask = torch.chunk(out, 3, dim=1) - offset = torch.cat((o1, o2), dim=1) - mask = torch.sigmoid(mask) - - offset_absmean = torch.mean(torch.abs(offset)) - if offset_absmean > 50: - logger = get_root_logger() - logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.') - - if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'): - return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, - self.dilation, mask) - else: - return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, - self.dilation, self.groups, self.deformable_groups) - - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py - # Cut & paste from PyTorch official master until it's in a few official releases - RW - # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf - def norm_cdf(x): - # Computes standard normal cumulative distribution function - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn( - 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' - 'The distribution of values may be incorrect.', - stacklevel=2) - - with torch.no_grad(): - # Values are generated by using a truncated uniform distribution and - # then using the inverse CDF for the normal distribution. - # Get upper and lower cdf values - low = norm_cdf((a - mean) / std) - up = norm_cdf((b - mean) / std) - - # Uniformly fill tensor with values from [low, up], then translate to - # [2l-1, 2u-1]. - tensor.uniform_(2 * low - 1, 2 * up - 1) - - # Use inverse cdf transform for normal distribution to get truncated - # standard normal - tensor.erfinv_() - - # Transform to proper mean, std - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - - # Clamp to ensure it's in the proper range - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - r"""Fills the input Tensor with values drawn from a truncated - normal distribution. - - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py - - The values are effectively drawn from the - normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` - with values outside :math:`[a, b]` redrawn until they are within - the bounds. The method used for generating the random values works - best when :math:`a \leq \text{mean} \leq b`. - - Args: - tensor: an n-dimensional `torch.Tensor` - mean: the mean of the normal distribution - std: the standard deviation of the normal distribution - a: the minimum cutoff value - b: the maximum cutoff value - - Examples: - >>> w = torch.empty(3, 5) - >>> nn.init.trunc_normal_(w) - """ - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -# From PyTorch -def _ntuple(n): - - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - - return parse - - -to_1tuple = _ntuple(1) -to_2tuple = _ntuple(2) -to_3tuple = _ntuple(3) -to_4tuple = _ntuple(4) -to_ntuple = _ntuple diff --git a/basicsr/archs/basicvsr_arch.py b/basicsr/archs/basicvsr_arch.py deleted file mode 100644 index ed7b824..0000000 --- a/basicsr/archs/basicvsr_arch.py +++ /dev/null @@ -1,336 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import ResidualBlockNoBN, flow_warp, make_layer -from .edvr_arch import PCDAlignment, TSAFusion -from .spynet_arch import SpyNet - - -@ARCH_REGISTRY.register() -class BasicVSR(nn.Module): - """A recurrent network for video SR. Now only x4 is supported. - - Args: - num_feat (int): Number of channels. Default: 64. - num_block (int): Number of residual blocks for each branch. Default: 15 - spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. - """ - - def __init__(self, num_feat=64, num_block=15, spynet_path=None): - super().__init__() - self.num_feat = num_feat - - # alignment - self.spynet = SpyNet(spynet_path) - - # propagation - self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) - self.forward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) - - # reconstruction - self.fusion = nn.Conv2d(num_feat * 2, num_feat, 1, 1, 0, bias=True) - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True) - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - - self.pixel_shuffle = nn.PixelShuffle(2) - - # activation functions - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def get_flow(self, x): - b, n, c, h, w = x.size() - - x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w) - x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w) - - flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w) - flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w) - - return flows_forward, flows_backward - - def forward(self, x): - """Forward function of BasicVSR. - - Args: - x: Input frames with shape (b, n, c, h, w). n is the temporal dimension / number of frames. - """ - flows_forward, flows_backward = self.get_flow(x) - b, n, _, h, w = x.size() - - # backward branch - out_l = [] - feat_prop = x.new_zeros(b, self.num_feat, h, w) - for i in range(n - 1, -1, -1): - x_i = x[:, i, :, :, :] - if i < n - 1: - flow = flows_backward[:, i, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - feat_prop = torch.cat([x_i, feat_prop], dim=1) - feat_prop = self.backward_trunk(feat_prop) - out_l.insert(0, feat_prop) - - # forward branch - feat_prop = torch.zeros_like(feat_prop) - for i in range(0, n): - x_i = x[:, i, :, :, :] - if i > 0: - flow = flows_forward[:, i - 1, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - - feat_prop = torch.cat([x_i, feat_prop], dim=1) - feat_prop = self.forward_trunk(feat_prop) - - # upsample - out = torch.cat([out_l[i], feat_prop], dim=1) - out = self.lrelu(self.fusion(out)) - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - out = self.lrelu(self.conv_hr(out)) - out = self.conv_last(out) - base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False) - out += base - out_l[i] = out - - return torch.stack(out_l, dim=1) - - -class ConvResidualBlocks(nn.Module): - """Conv and residual block used in BasicVSR. - - Args: - num_in_ch (int): Number of input channels. Default: 3. - num_out_ch (int): Number of output channels. Default: 64. - num_block (int): Number of residual blocks. Default: 15. - """ - - def __init__(self, num_in_ch=3, num_out_ch=64, num_block=15): - super().__init__() - self.main = nn.Sequential( - nn.Conv2d(num_in_ch, num_out_ch, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.1, inplace=True), - make_layer(ResidualBlockNoBN, num_block, num_feat=num_out_ch)) - - def forward(self, fea): - return self.main(fea) - - -@ARCH_REGISTRY.register() -class IconVSR(nn.Module): - """IconVSR, proposed also in the BasicVSR paper. - - Args: - num_feat (int): Number of channels. Default: 64. - num_block (int): Number of residual blocks for each branch. Default: 15. - keyframe_stride (int): Keyframe stride. Default: 5. - temporal_padding (int): Temporal padding. Default: 2. - spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. - edvr_path (str): Path to the pretrained EDVR model. Default: None. - """ - - def __init__(self, - num_feat=64, - num_block=15, - keyframe_stride=5, - temporal_padding=2, - spynet_path=None, - edvr_path=None): - super().__init__() - - self.num_feat = num_feat - self.temporal_padding = temporal_padding - self.keyframe_stride = keyframe_stride - - # keyframe_branch - self.edvr = EDVRFeatureExtractor(temporal_padding * 2 + 1, num_feat, edvr_path) - # alignment - self.spynet = SpyNet(spynet_path) - - # propagation - self.backward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True) - self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) - - self.forward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True) - self.forward_trunk = ConvResidualBlocks(2 * num_feat + 3, num_feat, num_block) - - # reconstruction - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True) - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - - self.pixel_shuffle = nn.PixelShuffle(2) - - # activation functions - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def pad_spatial(self, x): - """Apply padding spatially. - - Since the PCD module in EDVR requires that the resolution is a multiple - of 4, we apply padding to the input LR images if their resolution is - not divisible by 4. - - Args: - x (Tensor): Input LR sequence with shape (n, t, c, h, w). - Returns: - Tensor: Padded LR sequence with shape (n, t, c, h_pad, w_pad). - """ - n, t, c, h, w = x.size() - - pad_h = (4 - h % 4) % 4 - pad_w = (4 - w % 4) % 4 - - # padding - x = x.view(-1, c, h, w) - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - - return x.view(n, t, c, h + pad_h, w + pad_w) - - def get_flow(self, x): - b, n, c, h, w = x.size() - - x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w) - x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w) - - flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w) - flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w) - - return flows_forward, flows_backward - - def get_keyframe_feature(self, x, keyframe_idx): - if self.temporal_padding == 2: - x = [x[:, [4, 3]], x, x[:, [-4, -5]]] - elif self.temporal_padding == 3: - x = [x[:, [6, 5, 4]], x, x[:, [-5, -6, -7]]] - x = torch.cat(x, dim=1) - - num_frames = 2 * self.temporal_padding + 1 - feats_keyframe = {} - for i in keyframe_idx: - feats_keyframe[i] = self.edvr(x[:, i:i + num_frames].contiguous()) - return feats_keyframe - - def forward(self, x): - b, n, _, h_input, w_input = x.size() - - x = self.pad_spatial(x) - h, w = x.shape[3:] - - keyframe_idx = list(range(0, n, self.keyframe_stride)) - if keyframe_idx[-1] != n - 1: - keyframe_idx.append(n - 1) # last frame is a keyframe - - # compute flow and keyframe features - flows_forward, flows_backward = self.get_flow(x) - feats_keyframe = self.get_keyframe_feature(x, keyframe_idx) - - # backward branch - out_l = [] - feat_prop = x.new_zeros(b, self.num_feat, h, w) - for i in range(n - 1, -1, -1): - x_i = x[:, i, :, :, :] - if i < n - 1: - flow = flows_backward[:, i, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - if i in keyframe_idx: - feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1) - feat_prop = self.backward_fusion(feat_prop) - feat_prop = torch.cat([x_i, feat_prop], dim=1) - feat_prop = self.backward_trunk(feat_prop) - out_l.insert(0, feat_prop) - - # forward branch - feat_prop = torch.zeros_like(feat_prop) - for i in range(0, n): - x_i = x[:, i, :, :, :] - if i > 0: - flow = flows_forward[:, i - 1, :, :, :] - feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) - if i in keyframe_idx: - feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1) - feat_prop = self.forward_fusion(feat_prop) - - feat_prop = torch.cat([x_i, out_l[i], feat_prop], dim=1) - feat_prop = self.forward_trunk(feat_prop) - - # upsample - out = self.lrelu(self.pixel_shuffle(self.upconv1(feat_prop))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - out = self.lrelu(self.conv_hr(out)) - out = self.conv_last(out) - base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False) - out += base - out_l[i] = out - - return torch.stack(out_l, dim=1)[..., :4 * h_input, :4 * w_input] - - -class EDVRFeatureExtractor(nn.Module): - """EDVR feature extractor used in IconVSR. - - Args: - num_input_frame (int): Number of input frames. - num_feat (int): Number of feature channels - load_path (str): Path to the pretrained weights of EDVR. Default: None. - """ - - def __init__(self, num_input_frame, num_feat, load_path): - - super(EDVRFeatureExtractor, self).__init__() - - self.center_frame_idx = num_input_frame // 2 - - # extract pyramid features - self.conv_first = nn.Conv2d(3, num_feat, 3, 1, 1) - self.feature_extraction = make_layer(ResidualBlockNoBN, 5, num_feat=num_feat) - self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - - # pcd and tsa module - self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=8) - self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_input_frame, center_frame_idx=self.center_frame_idx) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - if load_path: - self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) - - def forward(self, x): - b, n, c, h, w = x.size() - - # extract features for each frame - # L1 - feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w))) - feat_l1 = self.feature_extraction(feat_l1) - # L2 - feat_l2 = self.lrelu(self.conv_l2_1(feat_l1)) - feat_l2 = self.lrelu(self.conv_l2_2(feat_l2)) - # L3 - feat_l3 = self.lrelu(self.conv_l3_1(feat_l2)) - feat_l3 = self.lrelu(self.conv_l3_2(feat_l3)) - - feat_l1 = feat_l1.view(b, n, -1, h, w) - feat_l2 = feat_l2.view(b, n, -1, h // 2, w // 2) - feat_l3 = feat_l3.view(b, n, -1, h // 4, w // 4) - - # PCD alignment - ref_feat_l = [ # reference feature list - feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(), - feat_l3[:, self.center_frame_idx, :, :, :].clone() - ] - aligned_feat = [] - for i in range(n): - nbr_feat_l = [ # neighboring feature list - feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone() - ] - aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l)) - aligned_feat = torch.stack(aligned_feat, dim=1) # (b, t, c, h, w) - - # TSA fusion - return self.fusion(aligned_feat) diff --git a/basicsr/archs/basicvsrpp_arch.py b/basicsr/archs/basicvsrpp_arch.py deleted file mode 100644 index 2a9952e..0000000 --- a/basicsr/archs/basicvsrpp_arch.py +++ /dev/null @@ -1,417 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision -import warnings - -from basicsr.archs.arch_util import flow_warp -from basicsr.archs.basicvsr_arch import ConvResidualBlocks -from basicsr.archs.spynet_arch import SpyNet -from basicsr.ops.dcn import ModulatedDeformConvPack -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register() -class BasicVSRPlusPlus(nn.Module): - """BasicVSR++ network structure. - - Support either x4 upsampling or same size output. Since DCN is used in this - model, it can only be used with CUDA enabled. If CUDA is not enabled, - feature alignment will be skipped. Besides, we adopt the official DCN - implementation and the version of torch need to be higher than 1.9. - - ``Paper: BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment`` - - Args: - mid_channels (int, optional): Channel number of the intermediate - features. Default: 64. - num_blocks (int, optional): The number of residual blocks in each - propagation branch. Default: 7. - max_residue_magnitude (int): The maximum magnitude of the offset - residue (Eq. 6 in paper). Default: 10. - is_low_res_input (bool, optional): Whether the input is low-resolution - or not. If False, the output resolution is equal to the input - resolution. Default: True. - spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. - cpu_cache_length (int, optional): When the length of sequence is larger - than this value, the intermediate features are sent to CPU. This - saves GPU memory, but slows down the inference speed. You can - increase this number if you have a GPU with large memory. - Default: 100. - """ - - def __init__(self, - mid_channels=64, - num_blocks=7, - max_residue_magnitude=10, - is_low_res_input=True, - spynet_path=None, - cpu_cache_length=100): - - super().__init__() - self.mid_channels = mid_channels - self.is_low_res_input = is_low_res_input - self.cpu_cache_length = cpu_cache_length - - # optical flow - self.spynet = SpyNet(spynet_path) - - # feature extraction module - if is_low_res_input: - self.feat_extract = ConvResidualBlocks(3, mid_channels, 5) - else: - self.feat_extract = nn.Sequential( - nn.Conv2d(3, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(mid_channels, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), - ConvResidualBlocks(mid_channels, mid_channels, 5)) - - # propagation branches - self.deform_align = nn.ModuleDict() - self.backbone = nn.ModuleDict() - modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2'] - for i, module in enumerate(modules): - if torch.cuda.is_available(): - self.deform_align[module] = SecondOrderDeformableAlignment( - 2 * mid_channels, - mid_channels, - 3, - padding=1, - deformable_groups=16, - max_residue_magnitude=max_residue_magnitude) - self.backbone[module] = ConvResidualBlocks((2 + i) * mid_channels, mid_channels, num_blocks) - - # upsampling module - self.reconstruction = ConvResidualBlocks(5 * mid_channels, mid_channels, 5) - - self.upconv1 = nn.Conv2d(mid_channels, mid_channels * 4, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(mid_channels, 64 * 4, 3, 1, 1, bias=True) - - self.pixel_shuffle = nn.PixelShuffle(2) - - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - self.img_upsample = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - # check if the sequence is augmented by flipping - self.is_mirror_extended = False - - if len(self.deform_align) > 0: - self.is_with_alignment = True - else: - self.is_with_alignment = False - warnings.warn('Deformable alignment module is not added. ' - 'Probably your CUDA is not configured correctly. DCN can only ' - 'be used with CUDA enabled. Alignment is skipped now.') - - def check_if_mirror_extended(self, lqs): - """Check whether the input is a mirror-extended sequence. - - If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the (t-1-i)-th frame. - - Args: - lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w). - """ - - if lqs.size(1) % 2 == 0: - lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1) - if torch.norm(lqs_1 - lqs_2.flip(1)) == 0: - self.is_mirror_extended = True - - def compute_flow(self, lqs): - """Compute optical flow using SPyNet for feature alignment. - - Note that if the input is an mirror-extended sequence, 'flows_forward' - is not needed, since it is equal to 'flows_backward.flip(1)'. - - Args: - lqs (tensor): Input low quality (LQ) sequence with - shape (n, t, c, h, w). - - Return: - tuple(Tensor): Optical flow. 'flows_forward' corresponds to the flows used for forward-time propagation \ - (current to previous). 'flows_backward' corresponds to the flows used for backward-time \ - propagation (current to next). - """ - - n, t, c, h, w = lqs.size() - lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w) - lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w) - - flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w) - - if self.is_mirror_extended: # flows_forward = flows_backward.flip(1) - flows_forward = flows_backward.flip(1) - else: - flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w) - - if self.cpu_cache: - flows_backward = flows_backward.cpu() - flows_forward = flows_forward.cpu() - - return flows_forward, flows_backward - - def propagate(self, feats, flows, module_name): - """Propagate the latent features throughout the sequence. - - Args: - feats dict(list[tensor]): Features from previous branches. Each - component is a list of tensors with shape (n, c, h, w). - flows (tensor): Optical flows with shape (n, t - 1, 2, h, w). - module_name (str): The name of the propgation branches. Can either - be 'backward_1', 'forward_1', 'backward_2', 'forward_2'. - - Return: - dict(list[tensor]): A dictionary containing all the propagated \ - features. Each key in the dictionary corresponds to a \ - propagation branch, which is represented by a list of tensors. - """ - - n, t, _, h, w = flows.size() - - frame_idx = range(0, t + 1) - flow_idx = range(-1, t) - mapping_idx = list(range(0, len(feats['spatial']))) - mapping_idx += mapping_idx[::-1] - - if 'backward' in module_name: - frame_idx = frame_idx[::-1] - flow_idx = frame_idx - - feat_prop = flows.new_zeros(n, self.mid_channels, h, w) - for i, idx in enumerate(frame_idx): - feat_current = feats['spatial'][mapping_idx[idx]] - if self.cpu_cache: - feat_current = feat_current.cuda() - feat_prop = feat_prop.cuda() - # second-order deformable alignment - if i > 0 and self.is_with_alignment: - flow_n1 = flows[:, flow_idx[i], :, :, :] - if self.cpu_cache: - flow_n1 = flow_n1.cuda() - - cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1)) - - # initialize second-order features - feat_n2 = torch.zeros_like(feat_prop) - flow_n2 = torch.zeros_like(flow_n1) - cond_n2 = torch.zeros_like(cond_n1) - - if i > 1: # second-order features - feat_n2 = feats[module_name][-2] - if self.cpu_cache: - feat_n2 = feat_n2.cuda() - - flow_n2 = flows[:, flow_idx[i - 1], :, :, :] - if self.cpu_cache: - flow_n2 = flow_n2.cuda() - - flow_n2 = flow_n1 + flow_warp(flow_n2, flow_n1.permute(0, 2, 3, 1)) - cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1)) - - # flow-guided deformable convolution - cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1) - feat_prop = torch.cat([feat_prop, feat_n2], dim=1) - feat_prop = self.deform_align[module_name](feat_prop, cond, flow_n1, flow_n2) - - # concatenate and residual blocks - feat = [feat_current] + [feats[k][idx] for k in feats if k not in ['spatial', module_name]] + [feat_prop] - if self.cpu_cache: - feat = [f.cuda() for f in feat] - - feat = torch.cat(feat, dim=1) - feat_prop = feat_prop + self.backbone[module_name](feat) - feats[module_name].append(feat_prop) - - if self.cpu_cache: - feats[module_name][-1] = feats[module_name][-1].cpu() - torch.cuda.empty_cache() - - if 'backward' in module_name: - feats[module_name] = feats[module_name][::-1] - - return feats - - def upsample(self, lqs, feats): - """Compute the output image given the features. - - Args: - lqs (tensor): Input low quality (LQ) sequence with - shape (n, t, c, h, w). - feats (dict): The features from the propagation branches. - - Returns: - Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). - """ - - outputs = [] - num_outputs = len(feats['spatial']) - - mapping_idx = list(range(0, num_outputs)) - mapping_idx += mapping_idx[::-1] - - for i in range(0, lqs.size(1)): - hr = [feats[k].pop(0) for k in feats if k != 'spatial'] - hr.insert(0, feats['spatial'][mapping_idx[i]]) - hr = torch.cat(hr, dim=1) - if self.cpu_cache: - hr = hr.cuda() - - hr = self.reconstruction(hr) - hr = self.lrelu(self.pixel_shuffle(self.upconv1(hr))) - hr = self.lrelu(self.pixel_shuffle(self.upconv2(hr))) - hr = self.lrelu(self.conv_hr(hr)) - hr = self.conv_last(hr) - if self.is_low_res_input: - hr += self.img_upsample(lqs[:, i, :, :, :]) - else: - hr += lqs[:, i, :, :, :] - - if self.cpu_cache: - hr = hr.cpu() - torch.cuda.empty_cache() - - outputs.append(hr) - - return torch.stack(outputs, dim=1) - - def forward(self, lqs): - """Forward function for BasicVSR++. - - Args: - lqs (tensor): Input low quality (LQ) sequence with - shape (n, t, c, h, w). - - Returns: - Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). - """ - - n, t, c, h, w = lqs.size() - - # whether to cache the features in CPU - self.cpu_cache = True if t > self.cpu_cache_length else False - - if self.is_low_res_input: - lqs_downsample = lqs.clone() - else: - lqs_downsample = F.interpolate( - lqs.view(-1, c, h, w), scale_factor=0.25, mode='bicubic').view(n, t, c, h // 4, w // 4) - - # check whether the input is an extended sequence - self.check_if_mirror_extended(lqs) - - feats = {} - # compute spatial features - if self.cpu_cache: - feats['spatial'] = [] - for i in range(0, t): - feat = self.feat_extract(lqs[:, i, :, :, :]).cpu() - feats['spatial'].append(feat) - torch.cuda.empty_cache() - else: - feats_ = self.feat_extract(lqs.view(-1, c, h, w)) - h, w = feats_.shape[2:] - feats_ = feats_.view(n, t, -1, h, w) - feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)] - - # compute optical flow using the low-res inputs - assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, ( - 'The height and width of low-res inputs must be at least 64, ' - f'but got {h} and {w}.') - flows_forward, flows_backward = self.compute_flow(lqs_downsample) - - # feature propgation - for iter_ in [1, 2]: - for direction in ['backward', 'forward']: - module = f'{direction}_{iter_}' - - feats[module] = [] - - if direction == 'backward': - flows = flows_backward - elif flows_forward is not None: - flows = flows_forward - else: - flows = flows_backward.flip(1) - - feats = self.propagate(feats, flows, module) - if self.cpu_cache: - del flows - torch.cuda.empty_cache() - - return self.upsample(lqs, feats) - - -class SecondOrderDeformableAlignment(ModulatedDeformConvPack): - """Second-order deformable alignment module. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int or tuple[int]): Same as nn.Conv2d. - padding (int or tuple[int]): Same as nn.Conv2d. - dilation (int or tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - max_residue_magnitude (int): The maximum magnitude of the offset - residue (Eq. 6 in paper). Default: 10. - """ - - def __init__(self, *args, **kwargs): - self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10) - - super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Sequential( - nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, 27 * self.deformable_groups, 3, 1, 1), - ) - - self.init_offset() - - def init_offset(self): - - def _constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - _constant_init(self.conv_offset[-1], val=0, bias=0) - - def forward(self, x, extra_feat, flow_1, flow_2): - extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1) - out = self.conv_offset(extra_feat) - o1, o2, mask = torch.chunk(out, 3, dim=1) - - # offset - offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1)) - offset_1, offset_2 = torch.chunk(offset, 2, dim=1) - offset_1 = offset_1 + flow_1.flip(1).repeat(1, offset_1.size(1) // 2, 1, 1) - offset_2 = offset_2 + flow_2.flip(1).repeat(1, offset_2.size(1) // 2, 1, 1) - offset = torch.cat([offset_1, offset_2], dim=1) - - # mask - mask = torch.sigmoid(mask) - - return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, - self.dilation, mask) - - -# if __name__ == '__main__': -# spynet_path = 'experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth' -# model = BasicVSRPlusPlus(spynet_path=spynet_path).cuda() -# input = torch.rand(1, 2, 3, 64, 64).cuda() -# output = model(input) -# print('===================') -# print(output.shape) diff --git a/basicsr/archs/codeformer_arch.py b/basicsr/archs/codeformer_arch.py deleted file mode 100644 index 4d0d802..0000000 --- a/basicsr/archs/codeformer_arch.py +++ /dev/null @@ -1,276 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn, Tensor -import torch.nn.functional as F -from typing import Optional, List - -from basicsr.archs.vqgan_arch import * -from basicsr.utils import get_root_logger -from basicsr.utils.registry import ARCH_REGISTRY - -def calc_mean_std(feat, eps=1e-5): - """Calculate mean and std for adaptive_instance_normalization. - - Args: - feat (Tensor): 4D tensor. - eps (float): A small value added to the variance to avoid - divide-by-zero. Default: 1e-5. - """ - size = feat.size() - assert len(size) == 4, 'The input feature should be 4D tensor.' - b, c = size[:2] - feat_var = feat.view(b, c, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(b, c, 1, 1) - feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - """Adaptive instance normalization. - - Adjust the reference features to have the similar color and illuminations - as those in the degradate features. - - Args: - content_feat (Tensor): The reference feature. - style_feat (Tensor): The degradate features. - """ - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, x, mask=None): - if mask is None: - mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") - - -class TransformerSALayer(nn.Module): - def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"): - super().__init__() - self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) - # Implementation of Feedforward model - MLP - self.linear1 = nn.Linear(embed_dim, dim_mlp) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_mlp, embed_dim) - - self.norm1 = nn.LayerNorm(embed_dim) - self.norm2 = nn.LayerNorm(embed_dim) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward(self, tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - - # self attention - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - - # ffn - tgt2 = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout2(tgt2) - return tgt - -class Fuse_sft_block(nn.Module): - def __init__(self, in_ch, out_ch): - super().__init__() - self.encode_enc = ResBlock(2*in_ch, out_ch) - - self.scale = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) - - self.shift = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) - - def forward(self, enc_feat, dec_feat, w=1): - enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) - scale = self.scale(enc_feat) - shift = self.shift(enc_feat) - residual = w * (dec_feat * scale + shift) - out = dec_feat + residual - return out - - -@ARCH_REGISTRY.register() -class CodeFormer(VQAutoEncoder): - def __init__(self, dim_embd=512, n_head=8, n_layers=9, - codebook_size=1024, latent_size=256, - connect_list=['32', '64', '128', '256'], - fix_modules=['quantize','generator']): - super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size) - - if fix_modules is not None: - for module in fix_modules: - for param in getattr(self, module).parameters(): - param.requires_grad = False - - self.connect_list = connect_list - self.n_layers = n_layers - self.dim_embd = dim_embd - self.dim_mlp = dim_embd*2 - - self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) - self.feat_emb = nn.Linear(256, self.dim_embd) - - # transformer - self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) - for _ in range(self.n_layers)]) - - # logits_predict head - self.idx_pred_layer = nn.Sequential( - nn.LayerNorm(dim_embd), - nn.Linear(dim_embd, codebook_size, bias=False)) - - self.channels = { - '16': 512, - '32': 256, - '64': 256, - '128': 128, - '256': 128, - '512': 64, - } - - # after second residual block for > 16, before attn layer for ==16 - self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18} - # after first residual block for > 16, before attn layer for ==16 - self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21} - - # fuse_convs_dict - self.fuse_convs_dict = nn.ModuleDict() - for f_size in self.connect_list: - in_ch = self.channels[f_size] - self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) - - def _init_weights(self, module): - if isinstance(module, (nn.Linear, nn.Embedding)): - module.weight.data.normal_(mean=0.0, std=0.02) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - - def forward(self, x, w=0, detach_16=True, code_only=False, adain=False): - # ################### Encoder ##################### - enc_feat_dict = {} - out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] - for i, block in enumerate(self.encoder.blocks): - x = block(x) - if i in out_list: - enc_feat_dict[str(x.shape[-1])] = x.clone() - - lq_feat = x - # ################# Transformer ################### - # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) - pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1) - # BCHW -> BC(HW) -> (HW)BC - feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1)) - query_emb = feat_emb - # Transformer encoder - for layer in self.ft_layers: - query_emb = layer(query_emb, query_pos=pos_emb) - - # output logits - logits = self.idx_pred_layer(query_emb) # (hw)bn - logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n - - if code_only: # for training stage II - # logits doesn't need softmax before cross_entropy loss - return logits, lq_feat - - # ################# Quantization ################### - # if self.training: - # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) - # # b(hw)c -> bc(hw) -> bchw - # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) - # ------------ - soft_one_hot = F.softmax(logits, dim=2) - _, top_idx = torch.topk(soft_one_hot, 1, dim=2) - quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256]) - # preserve gradients - # quant_feat = lq_feat + (quant_feat - lq_feat).detach() - - if detach_16: - quant_feat = quant_feat.detach() # for training stage III - if adain: - quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) - - # ################## Generator #################### - x = quant_feat - fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] - - for i, block in enumerate(self.generator.blocks): - x = block(x) - if i in fuse_list: # fuse after i-th block - f_size = str(x.shape[-1]) - if w>0: - x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w) - out = x - # logits doesn't need softmax before cross_entropy loss - return out, logits, lq_feat \ No newline at end of file diff --git a/basicsr/archs/dfdnet_arch.py b/basicsr/archs/dfdnet_arch.py deleted file mode 100644 index 4751434..0000000 --- a/basicsr/archs/dfdnet_arch.py +++ /dev/null @@ -1,169 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils.spectral_norm import spectral_norm - -from basicsr.utils.registry import ARCH_REGISTRY -from .dfdnet_util import AttentionBlock, Blur, MSDilationBlock, UpResBlock, adaptive_instance_normalization -from .vgg_arch import VGGFeatureExtractor - - -class SFTUpBlock(nn.Module): - """Spatial feature transform (SFT) with upsampling block. - - Args: - in_channel (int): Number of input channels. - out_channel (int): Number of output channels. - kernel_size (int): Kernel size in convolutions. Default: 3. - padding (int): Padding in convolutions. Default: 1. - """ - - def __init__(self, in_channel, out_channel, kernel_size=3, padding=1): - super(SFTUpBlock, self).__init__() - self.conv1 = nn.Sequential( - Blur(in_channel), - spectral_norm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)), - nn.LeakyReLU(0.04, True), - # The official codes use two LeakyReLU here, so 0.04 for equivalent - ) - self.convup = nn.Sequential( - nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), - spectral_norm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)), - nn.LeakyReLU(0.2, True), - ) - - # for SFT scale and shift - self.scale_block = nn.Sequential( - spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), - spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1))) - self.shift_block = nn.Sequential( - spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), - spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)), nn.Sigmoid()) - # The official codes use sigmoid for shift block, do not know why - - def forward(self, x, updated_feat): - out = self.conv1(x) - # SFT - scale = self.scale_block(updated_feat) - shift = self.shift_block(updated_feat) - out = out * scale + shift - # upsample - out = self.convup(out) - return out - - -@ARCH_REGISTRY.register() -class DFDNet(nn.Module): - """DFDNet: Deep Face Dictionary Network. - - It only processes faces with 512x512 size. - - Args: - num_feat (int): Number of feature channels. - dict_path (str): Path to the facial component dictionary. - """ - - def __init__(self, num_feat, dict_path): - super().__init__() - self.parts = ['left_eye', 'right_eye', 'nose', 'mouth'] - # part_sizes: [80, 80, 50, 110] - channel_sizes = [128, 256, 512, 512] - self.feature_sizes = np.array([256, 128, 64, 32]) - self.vgg_layers = ['relu2_2', 'relu3_4', 'relu4_4', 'conv5_4'] - self.flag_dict_device = False - - # dict - self.dict = torch.load(dict_path) - - # vgg face extractor - self.vgg_extractor = VGGFeatureExtractor( - layer_name_list=self.vgg_layers, - vgg_type='vgg19', - use_input_norm=True, - range_norm=True, - requires_grad=False) - - # attention block for fusing dictionary features and input features - self.attn_blocks = nn.ModuleDict() - for idx, feat_size in enumerate(self.feature_sizes): - for name in self.parts: - self.attn_blocks[f'{name}_{feat_size}'] = AttentionBlock(channel_sizes[idx]) - - # multi scale dilation block - self.multi_scale_dilation = MSDilationBlock(num_feat * 8, dilation=[4, 3, 2, 1]) - - # upsampling and reconstruction - self.upsample0 = SFTUpBlock(num_feat * 8, num_feat * 8) - self.upsample1 = SFTUpBlock(num_feat * 8, num_feat * 4) - self.upsample2 = SFTUpBlock(num_feat * 4, num_feat * 2) - self.upsample3 = SFTUpBlock(num_feat * 2, num_feat) - self.upsample4 = nn.Sequential( - spectral_norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1)), nn.LeakyReLU(0.2, True), UpResBlock(num_feat), - UpResBlock(num_feat), nn.Conv2d(num_feat, 3, kernel_size=3, stride=1, padding=1), nn.Tanh()) - - def swap_feat(self, vgg_feat, updated_feat, dict_feat, location, part_name, f_size): - """swap the features from the dictionary.""" - # get the original vgg features - part_feat = vgg_feat[:, :, location[1]:location[3], location[0]:location[2]].clone() - # resize original vgg features - part_resize_feat = F.interpolate(part_feat, dict_feat.size()[2:4], mode='bilinear', align_corners=False) - # use adaptive instance normalization to adjust color and illuminations - dict_feat = adaptive_instance_normalization(dict_feat, part_resize_feat) - # get similarity scores - similarity_score = F.conv2d(part_resize_feat, dict_feat) - similarity_score = F.softmax(similarity_score.view(-1), dim=0) - # select the most similar features in the dict (after norm) - select_idx = torch.argmax(similarity_score) - swap_feat = F.interpolate(dict_feat[select_idx:select_idx + 1], part_feat.size()[2:4]) - # attention - attn = self.attn_blocks[f'{part_name}_' + str(f_size)](swap_feat - part_feat) - attn_feat = attn * swap_feat - # update features - updated_feat[:, :, location[1]:location[3], location[0]:location[2]] = attn_feat + part_feat - return updated_feat - - def put_dict_to_device(self, x): - if self.flag_dict_device is False: - for k, v in self.dict.items(): - for kk, vv in v.items(): - self.dict[k][kk] = vv.to(x) - self.flag_dict_device = True - - def forward(self, x, part_locations): - """ - Now only support testing with batch size = 0. - - Args: - x (Tensor): Input faces with shape (b, c, 512, 512). - part_locations (list[Tensor]): Part locations. - """ - self.put_dict_to_device(x) - # extract vggface features - vgg_features = self.vgg_extractor(x) - # update vggface features using the dictionary for each part - updated_vgg_features = [] - batch = 0 # only supports testing with batch size = 0 - for vgg_layer, f_size in zip(self.vgg_layers, self.feature_sizes): - dict_features = self.dict[f'{f_size}'] - vgg_feat = vgg_features[vgg_layer] - updated_feat = vgg_feat.clone() - - # swap features from dictionary - for part_idx, part_name in enumerate(self.parts): - location = (part_locations[part_idx][batch] // (512 / f_size)).int() - updated_feat = self.swap_feat(vgg_feat, updated_feat, dict_features[part_name], location, part_name, - f_size) - - updated_vgg_features.append(updated_feat) - - vgg_feat_dilation = self.multi_scale_dilation(vgg_features['conv5_4']) - # use updated vgg features to modulate the upsampled features with - # SFT (Spatial Feature Transform) scaling and shifting manner. - upsampled_feat = self.upsample0(vgg_feat_dilation, updated_vgg_features[3]) - upsampled_feat = self.upsample1(upsampled_feat, updated_vgg_features[2]) - upsampled_feat = self.upsample2(upsampled_feat, updated_vgg_features[1]) - upsampled_feat = self.upsample3(upsampled_feat, updated_vgg_features[0]) - out = self.upsample4(upsampled_feat) - - return out diff --git a/basicsr/archs/dfdnet_util.py b/basicsr/archs/dfdnet_util.py deleted file mode 100644 index b4dc0ff..0000000 --- a/basicsr/archs/dfdnet_util.py +++ /dev/null @@ -1,162 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Function -from torch.nn.utils.spectral_norm import spectral_norm - - -class BlurFunctionBackward(Function): - - @staticmethod - def forward(ctx, grad_output, kernel, kernel_flip): - ctx.save_for_backward(kernel, kernel_flip) - grad_input = F.conv2d(grad_output, kernel_flip, padding=1, groups=grad_output.shape[1]) - return grad_input - - @staticmethod - def backward(ctx, gradgrad_output): - kernel, _ = ctx.saved_tensors - grad_input = F.conv2d(gradgrad_output, kernel, padding=1, groups=gradgrad_output.shape[1]) - return grad_input, None, None - - -class BlurFunction(Function): - - @staticmethod - def forward(ctx, x, kernel, kernel_flip): - ctx.save_for_backward(kernel, kernel_flip) - output = F.conv2d(x, kernel, padding=1, groups=x.shape[1]) - return output - - @staticmethod - def backward(ctx, grad_output): - kernel, kernel_flip = ctx.saved_tensors - grad_input = BlurFunctionBackward.apply(grad_output, kernel, kernel_flip) - return grad_input, None, None - - -blur = BlurFunction.apply - - -class Blur(nn.Module): - - def __init__(self, channel): - super().__init__() - kernel = torch.tensor([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=torch.float32) - kernel = kernel.view(1, 1, 3, 3) - kernel = kernel / kernel.sum() - kernel_flip = torch.flip(kernel, [2, 3]) - - self.kernel = kernel.repeat(channel, 1, 1, 1) - self.kernel_flip = kernel_flip.repeat(channel, 1, 1, 1) - - def forward(self, x): - return blur(x, self.kernel.type_as(x), self.kernel_flip.type_as(x)) - - -def calc_mean_std(feat, eps=1e-5): - """Calculate mean and std for adaptive_instance_normalization. - - Args: - feat (Tensor): 4D tensor. - eps (float): A small value added to the variance to avoid - divide-by-zero. Default: 1e-5. - """ - size = feat.size() - assert len(size) == 4, 'The input feature should be 4D tensor.' - n, c = size[:2] - feat_var = feat.view(n, c, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(n, c, 1, 1) - feat_mean = feat.view(n, c, -1).mean(dim=2).view(n, c, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - """Adaptive instance normalization. - - Adjust the reference features to have the similar color and illuminations - as those in the degradate features. - - Args: - content_feat (Tensor): The reference feature. - style_feat (Tensor): The degradate features. - """ - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -def AttentionBlock(in_channel): - return nn.Sequential( - spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), - spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1))) - - -def conv_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bias=True): - """Conv block used in MSDilationBlock.""" - - return nn.Sequential( - spectral_norm( - nn.Conv2d( - in_channels, - out_channels, - kernel_size=kernel_size, - stride=stride, - dilation=dilation, - padding=((kernel_size - 1) // 2) * dilation, - bias=bias)), - nn.LeakyReLU(0.2), - spectral_norm( - nn.Conv2d( - out_channels, - out_channels, - kernel_size=kernel_size, - stride=stride, - dilation=dilation, - padding=((kernel_size - 1) // 2) * dilation, - bias=bias)), - ) - - -class MSDilationBlock(nn.Module): - """Multi-scale dilation block.""" - - def __init__(self, in_channels, kernel_size=3, dilation=(1, 1, 1, 1), bias=True): - super(MSDilationBlock, self).__init__() - - self.conv_blocks = nn.ModuleList() - for i in range(4): - self.conv_blocks.append(conv_block(in_channels, in_channels, kernel_size, dilation=dilation[i], bias=bias)) - self.conv_fusion = spectral_norm( - nn.Conv2d( - in_channels * 4, - in_channels, - kernel_size=kernel_size, - stride=1, - padding=(kernel_size - 1) // 2, - bias=bias)) - - def forward(self, x): - out = [] - for i in range(4): - out.append(self.conv_blocks[i](x)) - out = torch.cat(out, 1) - out = self.conv_fusion(out) + x - return out - - -class UpResBlock(nn.Module): - - def __init__(self, in_channel): - super(UpResBlock, self).__init__() - self.body = nn.Sequential( - nn.Conv2d(in_channel, in_channel, 3, 1, 1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(in_channel, in_channel, 3, 1, 1), - ) - - def forward(self, x): - out = x + self.body(x) - return out diff --git a/basicsr/archs/discriminator_arch.py b/basicsr/archs/discriminator_arch.py deleted file mode 100644 index 33f9a8f..0000000 --- a/basicsr/archs/discriminator_arch.py +++ /dev/null @@ -1,150 +0,0 @@ -from torch import nn as nn -from torch.nn import functional as F -from torch.nn.utils import spectral_norm - -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register() -class VGGStyleDiscriminator(nn.Module): - """VGG style discriminator with input size 128 x 128 or 256 x 256. - - It is used to train SRGAN, ESRGAN, and VideoGAN. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_feat (int): Channel number of base intermediate features.Default: 64. - """ - - def __init__(self, num_in_ch, num_feat, input_size=128): - super(VGGStyleDiscriminator, self).__init__() - self.input_size = input_size - assert self.input_size == 128 or self.input_size == 256, ( - f'input size must be 128 or 256, but received {input_size}') - - self.conv0_0 = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True) - self.conv0_1 = nn.Conv2d(num_feat, num_feat, 4, 2, 1, bias=False) - self.bn0_1 = nn.BatchNorm2d(num_feat, affine=True) - - self.conv1_0 = nn.Conv2d(num_feat, num_feat * 2, 3, 1, 1, bias=False) - self.bn1_0 = nn.BatchNorm2d(num_feat * 2, affine=True) - self.conv1_1 = nn.Conv2d(num_feat * 2, num_feat * 2, 4, 2, 1, bias=False) - self.bn1_1 = nn.BatchNorm2d(num_feat * 2, affine=True) - - self.conv2_0 = nn.Conv2d(num_feat * 2, num_feat * 4, 3, 1, 1, bias=False) - self.bn2_0 = nn.BatchNorm2d(num_feat * 4, affine=True) - self.conv2_1 = nn.Conv2d(num_feat * 4, num_feat * 4, 4, 2, 1, bias=False) - self.bn2_1 = nn.BatchNorm2d(num_feat * 4, affine=True) - - self.conv3_0 = nn.Conv2d(num_feat * 4, num_feat * 8, 3, 1, 1, bias=False) - self.bn3_0 = nn.BatchNorm2d(num_feat * 8, affine=True) - self.conv3_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) - self.bn3_1 = nn.BatchNorm2d(num_feat * 8, affine=True) - - self.conv4_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) - self.bn4_0 = nn.BatchNorm2d(num_feat * 8, affine=True) - self.conv4_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) - self.bn4_1 = nn.BatchNorm2d(num_feat * 8, affine=True) - - if self.input_size == 256: - self.conv5_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) - self.bn5_0 = nn.BatchNorm2d(num_feat * 8, affine=True) - self.conv5_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) - self.bn5_1 = nn.BatchNorm2d(num_feat * 8, affine=True) - - self.linear1 = nn.Linear(num_feat * 8 * 4 * 4, 100) - self.linear2 = nn.Linear(100, 1) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - assert x.size(2) == self.input_size, (f'Input size must be identical to input_size, but received {x.size()}.') - - feat = self.lrelu(self.conv0_0(x)) - feat = self.lrelu(self.bn0_1(self.conv0_1(feat))) # output spatial size: /2 - - feat = self.lrelu(self.bn1_0(self.conv1_0(feat))) - feat = self.lrelu(self.bn1_1(self.conv1_1(feat))) # output spatial size: /4 - - feat = self.lrelu(self.bn2_0(self.conv2_0(feat))) - feat = self.lrelu(self.bn2_1(self.conv2_1(feat))) # output spatial size: /8 - - feat = self.lrelu(self.bn3_0(self.conv3_0(feat))) - feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: /16 - - feat = self.lrelu(self.bn4_0(self.conv4_0(feat))) - feat = self.lrelu(self.bn4_1(self.conv4_1(feat))) # output spatial size: /32 - - if self.input_size == 256: - feat = self.lrelu(self.bn5_0(self.conv5_0(feat))) - feat = self.lrelu(self.bn5_1(self.conv5_1(feat))) # output spatial size: / 64 - - # spatial size: (4, 4) - feat = feat.view(feat.size(0), -1) - feat = self.lrelu(self.linear1(feat)) - out = self.linear2(feat) - return out - - -@ARCH_REGISTRY.register(suffix='basicsr') -class UNetDiscriminatorSN(nn.Module): - """Defines a U-Net discriminator with spectral normalization (SN) - - It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - Arg: - num_in_ch (int): Channel number of inputs. Default: 3. - num_feat (int): Channel number of base intermediate features. Default: 64. - skip_connection (bool): Whether to use skip connections between U-Net. Default: True. - """ - - def __init__(self, num_in_ch, num_feat=64, skip_connection=True): - super(UNetDiscriminatorSN, self).__init__() - self.skip_connection = skip_connection - norm = spectral_norm - # the first convolution - self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1) - # downsample - self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False)) - self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False)) - self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False)) - # upsample - self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False)) - self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False)) - self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False)) - # extra convolutions - self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) - self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) - self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1) - - def forward(self, x): - # downsample - x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True) - x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True) - x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True) - x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True) - - # upsample - x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False) - x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x4 = x4 + x2 - x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False) - x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x5 = x5 + x1 - x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False) - x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x6 = x6 + x0 - - # extra convolutions - out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True) - out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True) - out = self.conv9(out) - - return out diff --git a/basicsr/archs/duf_arch.py b/basicsr/archs/duf_arch.py deleted file mode 100644 index e2b3ab7..0000000 --- a/basicsr/archs/duf_arch.py +++ /dev/null @@ -1,276 +0,0 @@ -import numpy as np -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY - - -class DenseBlocksTemporalReduce(nn.Module): - """A concatenation of 3 dense blocks with reduction in temporal dimension. - - Note that the output temporal dimension is 6 fewer the input temporal dimension, since there are 3 blocks. - - Args: - num_feat (int): Number of channels in the blocks. Default: 64. - num_grow_ch (int): Growing factor of the dense blocks. Default: 32 - adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation. - Set to false if you want to train from scratch. Default: False. - """ - - def __init__(self, num_feat=64, num_grow_ch=32, adapt_official_weights=False): - super(DenseBlocksTemporalReduce, self).__init__() - if adapt_official_weights: - eps = 1e-3 - momentum = 1e-3 - else: # pytorch default values - eps = 1e-05 - momentum = 0.1 - - self.temporal_reduce1 = nn.Sequential( - nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d(num_feat, num_feat, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True), - nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d(num_feat, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) - - self.temporal_reduce2 = nn.Sequential( - nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + num_grow_ch, - num_feat + num_grow_ch, (1, 1, 1), - stride=(1, 1, 1), - padding=(0, 0, 0), - bias=True), nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d(num_feat + num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) - - self.temporal_reduce3 = nn.Sequential( - nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + 2 * num_grow_ch, - num_feat + 2 * num_grow_ch, (1, 1, 1), - stride=(1, 1, 1), - padding=(0, 0, 0), - bias=True), nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), - nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + 2 * num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) - - def forward(self, x): - """ - Args: - x (Tensor): Input tensor with shape (b, num_feat, t, h, w). - - Returns: - Tensor: Output with shape (b, num_feat + num_grow_ch * 3, 1, h, w). - """ - x1 = self.temporal_reduce1(x) - x1 = torch.cat((x[:, :, 1:-1, :, :], x1), 1) - - x2 = self.temporal_reduce2(x1) - x2 = torch.cat((x1[:, :, 1:-1, :, :], x2), 1) - - x3 = self.temporal_reduce3(x2) - x3 = torch.cat((x2[:, :, 1:-1, :, :], x3), 1) - - return x3 - - -class DenseBlocks(nn.Module): - """ A concatenation of N dense blocks. - - Args: - num_feat (int): Number of channels in the blocks. Default: 64. - num_grow_ch (int): Growing factor of the dense blocks. Default: 32. - num_block (int): Number of dense blocks. The values are: - DUF-S (16 layers): 3 - DUF-M (18 layers): 9 - DUF-L (52 layers): 21 - adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation. - Set to false if you want to train from scratch. Default: False. - """ - - def __init__(self, num_block, num_feat=64, num_grow_ch=16, adapt_official_weights=False): - super(DenseBlocks, self).__init__() - if adapt_official_weights: - eps = 1e-3 - momentum = 1e-3 - else: # pytorch default values - eps = 1e-05 - momentum = 0.1 - - self.dense_blocks = nn.ModuleList() - for i in range(0, num_block): - self.dense_blocks.append( - nn.Sequential( - nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + i * num_grow_ch, - num_feat + i * num_grow_ch, (1, 1, 1), - stride=(1, 1, 1), - padding=(0, 0, 0), - bias=True), nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), - nn.ReLU(inplace=True), - nn.Conv3d( - num_feat + i * num_grow_ch, - num_grow_ch, (3, 3, 3), - stride=(1, 1, 1), - padding=(1, 1, 1), - bias=True))) - - def forward(self, x): - """ - Args: - x (Tensor): Input tensor with shape (b, num_feat, t, h, w). - - Returns: - Tensor: Output with shape (b, num_feat + num_block * num_grow_ch, t, h, w). - """ - for i in range(0, len(self.dense_blocks)): - y = self.dense_blocks[i](x) - x = torch.cat((x, y), 1) - return x - - -class DynamicUpsamplingFilter(nn.Module): - """Dynamic upsampling filter used in DUF. - - Reference: https://github.com/yhjo09/VSR-DUF - - It only supports input with 3 channels. And it applies the same filters to 3 channels. - - Args: - filter_size (tuple): Filter size of generated filters. The shape is (kh, kw). Default: (5, 5). - """ - - def __init__(self, filter_size=(5, 5)): - super(DynamicUpsamplingFilter, self).__init__() - if not isinstance(filter_size, tuple): - raise TypeError(f'The type of filter_size must be tuple, but got type{filter_size}') - if len(filter_size) != 2: - raise ValueError(f'The length of filter size must be 2, but got {len(filter_size)}.') - # generate a local expansion filter, similar to im2col - self.filter_size = filter_size - filter_prod = np.prod(filter_size) - expansion_filter = torch.eye(int(filter_prod)).view(filter_prod, 1, *filter_size) # (kh*kw, 1, kh, kw) - self.expansion_filter = expansion_filter.repeat(3, 1, 1, 1) # repeat for all the 3 channels - - def forward(self, x, filters): - """Forward function for DynamicUpsamplingFilter. - - Args: - x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w). - filters (Tensor): Generated dynamic filters. The shape is (n, filter_prod, upsampling_square, h, w). - filter_prod: prod of filter kernel size, e.g., 1*5*5=25. - upsampling_square: similar to pixel shuffle, upsampling_square = upsampling * upsampling. - e.g., for x 4 upsampling, upsampling_square= 4*4 = 16 - - Returns: - Tensor: Filtered image with shape (n, 3*upsampling_square, h, w) - """ - n, filter_prod, upsampling_square, h, w = filters.size() - kh, kw = self.filter_size - expanded_input = F.conv2d( - x, self.expansion_filter.to(x), padding=(kh // 2, kw // 2), groups=3) # (n, 3*filter_prod, h, w) - expanded_input = expanded_input.view(n, 3, filter_prod, h, w).permute(0, 3, 4, 1, - 2) # (n, h, w, 3, filter_prod) - filters = filters.permute(0, 3, 4, 1, 2) # (n, h, w, filter_prod, upsampling_square] - out = torch.matmul(expanded_input, filters) # (n, h, w, 3, upsampling_square) - return out.permute(0, 3, 4, 1, 2).view(n, 3 * upsampling_square, h, w) - - -@ARCH_REGISTRY.register() -class DUF(nn.Module): - """Network architecture for DUF - - ``Paper: Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation`` - - Reference: https://github.com/yhjo09/VSR-DUF - - For all the models below, 'adapt_official_weights' is only necessary when - loading the weights converted from the official TensorFlow weights. - Please set it to False if you are training the model from scratch. - - There are three models with different model size: DUF16Layers, DUF28Layers, - and DUF52Layers. This class is the base class for these models. - - Args: - scale (int): The upsampling factor. Default: 4. - num_layer (int): The number of layers. Default: 52. - adapt_official_weights_weights (bool): Whether to adapt the weights - translated from the official implementation. Set to false if you - want to train from scratch. Default: False. - """ - - def __init__(self, scale=4, num_layer=52, adapt_official_weights=False): - super(DUF, self).__init__() - self.scale = scale - if adapt_official_weights: - eps = 1e-3 - momentum = 1e-3 - else: # pytorch default values - eps = 1e-05 - momentum = 0.1 - - self.conv3d1 = nn.Conv3d(3, 64, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True) - self.dynamic_filter = DynamicUpsamplingFilter((5, 5)) - - if num_layer == 16: - num_block = 3 - num_grow_ch = 32 - elif num_layer == 28: - num_block = 9 - num_grow_ch = 16 - elif num_layer == 52: - num_block = 21 - num_grow_ch = 16 - else: - raise ValueError(f'Only supported (16, 28, 52) layers, but got {num_layer}.') - - self.dense_block1 = DenseBlocks( - num_block=num_block, num_feat=64, num_grow_ch=num_grow_ch, - adapt_official_weights=adapt_official_weights) # T = 7 - self.dense_block2 = DenseBlocksTemporalReduce( - 64 + num_grow_ch * num_block, num_grow_ch, adapt_official_weights=adapt_official_weights) # T = 1 - channels = 64 + num_grow_ch * num_block + num_grow_ch * 3 - self.bn3d2 = nn.BatchNorm3d(channels, eps=eps, momentum=momentum) - self.conv3d2 = nn.Conv3d(channels, 256, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True) - - self.conv3d_r1 = nn.Conv3d(256, 256, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - self.conv3d_r2 = nn.Conv3d(256, 3 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - - self.conv3d_f1 = nn.Conv3d(256, 512, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - self.conv3d_f2 = nn.Conv3d( - 512, 1 * 5 * 5 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) - - def forward(self, x): - """ - Args: - x (Tensor): Input with shape (b, 7, c, h, w) - - Returns: - Tensor: Output with shape (b, c, h * scale, w * scale) - """ - num_batches, num_imgs, _, h, w = x.size() - - x = x.permute(0, 2, 1, 3, 4) # (b, c, 7, h, w) for Conv3D - x_center = x[:, :, num_imgs // 2, :, :] - - x = self.conv3d1(x) - x = self.dense_block1(x) - x = self.dense_block2(x) - x = F.relu(self.bn3d2(x), inplace=True) - x = F.relu(self.conv3d2(x), inplace=True) - - # residual image - res = self.conv3d_r2(F.relu(self.conv3d_r1(x), inplace=True)) - - # filter - filter_ = self.conv3d_f2(F.relu(self.conv3d_f1(x), inplace=True)) - filter_ = F.softmax(filter_.view(num_batches, 25, self.scale**2, h, w), dim=1) - - # dynamic filter - out = self.dynamic_filter(x_center, filter_) - out += res.squeeze_(2) - out = F.pixel_shuffle(out, self.scale) - - return out diff --git a/basicsr/archs/ecbsr_arch.py b/basicsr/archs/ecbsr_arch.py deleted file mode 100644 index fe20e77..0000000 --- a/basicsr/archs/ecbsr_arch.py +++ /dev/null @@ -1,275 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from basicsr.utils.registry import ARCH_REGISTRY - - -class SeqConv3x3(nn.Module): - """The re-parameterizable block used in the ECBSR architecture. - - ``Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices`` - - Reference: https://github.com/xindongzhang/ECBSR - - Args: - seq_type (str): Sequence type, option: conv1x1-conv3x3 | conv1x1-sobelx | conv1x1-sobely | conv1x1-laplacian. - in_channels (int): Channel number of input. - out_channels (int): Channel number of output. - depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. - """ - - def __init__(self, seq_type, in_channels, out_channels, depth_multiplier=1): - super(SeqConv3x3, self).__init__() - self.seq_type = seq_type - self.in_channels = in_channels - self.out_channels = out_channels - - if self.seq_type == 'conv1x1-conv3x3': - self.mid_planes = int(out_channels * depth_multiplier) - conv0 = torch.nn.Conv2d(self.in_channels, self.mid_planes, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - conv1 = torch.nn.Conv2d(self.mid_planes, self.out_channels, kernel_size=3) - self.k1 = conv1.weight - self.b1 = conv1.bias - - elif self.seq_type == 'conv1x1-sobelx': - conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - # init scale and bias - scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 - self.scale = nn.Parameter(scale) - bias = torch.randn(self.out_channels) * 1e-3 - bias = torch.reshape(bias, (self.out_channels, )) - self.bias = nn.Parameter(bias) - # init mask - self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) - for i in range(self.out_channels): - self.mask[i, 0, 0, 0] = 1.0 - self.mask[i, 0, 1, 0] = 2.0 - self.mask[i, 0, 2, 0] = 1.0 - self.mask[i, 0, 0, 2] = -1.0 - self.mask[i, 0, 1, 2] = -2.0 - self.mask[i, 0, 2, 2] = -1.0 - self.mask = nn.Parameter(data=self.mask, requires_grad=False) - - elif self.seq_type == 'conv1x1-sobely': - conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - # init scale and bias - scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 - self.scale = nn.Parameter(torch.FloatTensor(scale)) - bias = torch.randn(self.out_channels) * 1e-3 - bias = torch.reshape(bias, (self.out_channels, )) - self.bias = nn.Parameter(torch.FloatTensor(bias)) - # init mask - self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) - for i in range(self.out_channels): - self.mask[i, 0, 0, 0] = 1.0 - self.mask[i, 0, 0, 1] = 2.0 - self.mask[i, 0, 0, 2] = 1.0 - self.mask[i, 0, 2, 0] = -1.0 - self.mask[i, 0, 2, 1] = -2.0 - self.mask[i, 0, 2, 2] = -1.0 - self.mask = nn.Parameter(data=self.mask, requires_grad=False) - - elif self.seq_type == 'conv1x1-laplacian': - conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) - self.k0 = conv0.weight - self.b0 = conv0.bias - - # init scale and bias - scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 - self.scale = nn.Parameter(torch.FloatTensor(scale)) - bias = torch.randn(self.out_channels) * 1e-3 - bias = torch.reshape(bias, (self.out_channels, )) - self.bias = nn.Parameter(torch.FloatTensor(bias)) - # init mask - self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) - for i in range(self.out_channels): - self.mask[i, 0, 0, 1] = 1.0 - self.mask[i, 0, 1, 0] = 1.0 - self.mask[i, 0, 1, 2] = 1.0 - self.mask[i, 0, 2, 1] = 1.0 - self.mask[i, 0, 1, 1] = -4.0 - self.mask = nn.Parameter(data=self.mask, requires_grad=False) - else: - raise ValueError('The type of seqconv is not supported!') - - def forward(self, x): - if self.seq_type == 'conv1x1-conv3x3': - # conv-1x1 - y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1) - # explicitly padding with bias - y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0) - b0_pad = self.b0.view(1, -1, 1, 1) - y0[:, :, 0:1, :] = b0_pad - y0[:, :, -1:, :] = b0_pad - y0[:, :, :, 0:1] = b0_pad - y0[:, :, :, -1:] = b0_pad - # conv-3x3 - y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1) - else: - y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1) - # explicitly padding with bias - y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0) - b0_pad = self.b0.view(1, -1, 1, 1) - y0[:, :, 0:1, :] = b0_pad - y0[:, :, -1:, :] = b0_pad - y0[:, :, :, 0:1] = b0_pad - y0[:, :, :, -1:] = b0_pad - # conv-3x3 - y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_channels) - return y1 - - def rep_params(self): - device = self.k0.get_device() - if device < 0: - device = None - - if self.seq_type == 'conv1x1-conv3x3': - # re-param conv kernel - rep_weight = F.conv2d(input=self.k1, weight=self.k0.permute(1, 0, 2, 3)) - # re-param conv bias - rep_bias = torch.ones(1, self.mid_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1) - rep_bias = F.conv2d(input=rep_bias, weight=self.k1).view(-1, ) + self.b1 - else: - tmp = self.scale * self.mask - k1 = torch.zeros((self.out_channels, self.out_channels, 3, 3), device=device) - for i in range(self.out_channels): - k1[i, i, :, :] = tmp[i, 0, :, :] - b1 = self.bias - # re-param conv kernel - rep_weight = F.conv2d(input=k1, weight=self.k0.permute(1, 0, 2, 3)) - # re-param conv bias - rep_bias = torch.ones(1, self.out_channels, 3, 3, device=device) * self.b0.view(1, -1, 1, 1) - rep_bias = F.conv2d(input=rep_bias, weight=k1).view(-1, ) + b1 - return rep_weight, rep_bias - - -class ECB(nn.Module): - """The ECB block used in the ECBSR architecture. - - Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices - Ref git repo: https://github.com/xindongzhang/ECBSR - - Args: - in_channels (int): Channel number of input. - out_channels (int): Channel number of output. - depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. - act_type (str): Activation type. Option: prelu | relu | rrelu | softplus | linear. Default: prelu. - with_idt (bool): Whether to use identity connection. Default: False. - """ - - def __init__(self, in_channels, out_channels, depth_multiplier, act_type='prelu', with_idt=False): - super(ECB, self).__init__() - - self.depth_multiplier = depth_multiplier - self.in_channels = in_channels - self.out_channels = out_channels - self.act_type = act_type - - if with_idt and (self.in_channels == self.out_channels): - self.with_idt = True - else: - self.with_idt = False - - self.conv3x3 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=3, padding=1) - self.conv1x1_3x3 = SeqConv3x3('conv1x1-conv3x3', self.in_channels, self.out_channels, self.depth_multiplier) - self.conv1x1_sbx = SeqConv3x3('conv1x1-sobelx', self.in_channels, self.out_channels) - self.conv1x1_sby = SeqConv3x3('conv1x1-sobely', self.in_channels, self.out_channels) - self.conv1x1_lpl = SeqConv3x3('conv1x1-laplacian', self.in_channels, self.out_channels) - - if self.act_type == 'prelu': - self.act = nn.PReLU(num_parameters=self.out_channels) - elif self.act_type == 'relu': - self.act = nn.ReLU(inplace=True) - elif self.act_type == 'rrelu': - self.act = nn.RReLU(lower=-0.05, upper=0.05) - elif self.act_type == 'softplus': - self.act = nn.Softplus() - elif self.act_type == 'linear': - pass - else: - raise ValueError('The type of activation if not support!') - - def forward(self, x): - if self.training: - y = self.conv3x3(x) + self.conv1x1_3x3(x) + self.conv1x1_sbx(x) + self.conv1x1_sby(x) + self.conv1x1_lpl(x) - if self.with_idt: - y += x - else: - rep_weight, rep_bias = self.rep_params() - y = F.conv2d(input=x, weight=rep_weight, bias=rep_bias, stride=1, padding=1) - if self.act_type != 'linear': - y = self.act(y) - return y - - def rep_params(self): - weight0, bias0 = self.conv3x3.weight, self.conv3x3.bias - weight1, bias1 = self.conv1x1_3x3.rep_params() - weight2, bias2 = self.conv1x1_sbx.rep_params() - weight3, bias3 = self.conv1x1_sby.rep_params() - weight4, bias4 = self.conv1x1_lpl.rep_params() - rep_weight, rep_bias = (weight0 + weight1 + weight2 + weight3 + weight4), ( - bias0 + bias1 + bias2 + bias3 + bias4) - - if self.with_idt: - device = rep_weight.get_device() - if device < 0: - device = None - weight_idt = torch.zeros(self.out_channels, self.out_channels, 3, 3, device=device) - for i in range(self.out_channels): - weight_idt[i, i, 1, 1] = 1.0 - bias_idt = 0.0 - rep_weight, rep_bias = rep_weight + weight_idt, rep_bias + bias_idt - return rep_weight, rep_bias - - -@ARCH_REGISTRY.register() -class ECBSR(nn.Module): - """ECBSR architecture. - - Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices - Ref git repo: https://github.com/xindongzhang/ECBSR - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_block (int): Block number in the trunk network. - num_channel (int): Channel number. - with_idt (bool): Whether use identity in convolution layers. - act_type (str): Activation type. - scale (int): Upsampling factor. - """ - - def __init__(self, num_in_ch, num_out_ch, num_block, num_channel, with_idt, act_type, scale): - super(ECBSR, self).__init__() - self.num_in_ch = num_in_ch - self.scale = scale - - backbone = [] - backbone += [ECB(num_in_ch, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)] - for _ in range(num_block): - backbone += [ECB(num_channel, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)] - backbone += [ - ECB(num_channel, num_out_ch * scale * scale, depth_multiplier=2.0, act_type='linear', with_idt=with_idt) - ] - - self.backbone = nn.Sequential(*backbone) - self.upsampler = nn.PixelShuffle(scale) - - def forward(self, x): - if self.num_in_ch > 1: - shortcut = torch.repeat_interleave(x, self.scale * self.scale, dim=1) - else: - shortcut = x # will repeat the input in the channel dimension (repeat scale * scale times) - y = self.backbone(x) + shortcut - y = self.upsampler(y) - return y diff --git a/basicsr/archs/edsr_arch.py b/basicsr/archs/edsr_arch.py deleted file mode 100644 index b80566f..0000000 --- a/basicsr/archs/edsr_arch.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch import nn as nn - -from basicsr.archs.arch_util import ResidualBlockNoBN, Upsample, make_layer -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register() -class EDSR(nn.Module): - """EDSR network structure. - - Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution. - Ref git repo: https://github.com/thstkdgus35/EDSR-PyTorch - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64. - num_block (int): Block number in the trunk network. Default: 16. - upscale (int): Upsampling factor. Support 2^n and 3. - Default: 4. - res_scale (float): Used to scale the residual in residual block. - Default: 1. - img_range (float): Image range. Default: 255. - rgb_mean (tuple[float]): Image mean in RGB orders. - Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. - """ - - def __init__(self, - num_in_ch, - num_out_ch, - num_feat=64, - num_block=16, - upscale=4, - res_scale=1, - img_range=255., - rgb_mean=(0.4488, 0.4371, 0.4040)): - super(EDSR, self).__init__() - - self.img_range = img_range - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat, res_scale=res_scale, pytorch_init=True) - self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - def forward(self, x): - self.mean = self.mean.type_as(x) - - x = (x - self.mean) * self.img_range - x = self.conv_first(x) - res = self.conv_after_body(self.body(x)) - res += x - - x = self.conv_last(self.upsample(res)) - x = x / self.img_range + self.mean - - return x diff --git a/basicsr/archs/edvr_arch.py b/basicsr/archs/edvr_arch.py deleted file mode 100644 index b0c4f47..0000000 --- a/basicsr/archs/edvr_arch.py +++ /dev/null @@ -1,382 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import DCNv2Pack, ResidualBlockNoBN, make_layer - - -class PCDAlignment(nn.Module): - """Alignment module using Pyramid, Cascading and Deformable convolution - (PCD). It is used in EDVR. - - ``Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks`` - - Args: - num_feat (int): Channel number of middle features. Default: 64. - deformable_groups (int): Deformable groups. Defaults: 8. - """ - - def __init__(self, num_feat=64, deformable_groups=8): - super(PCDAlignment, self).__init__() - - # Pyramid has three levels: - # L3: level 3, 1/4 spatial size - # L2: level 2, 1/2 spatial size - # L1: level 1, original spatial size - self.offset_conv1 = nn.ModuleDict() - self.offset_conv2 = nn.ModuleDict() - self.offset_conv3 = nn.ModuleDict() - self.dcn_pack = nn.ModuleDict() - self.feat_conv = nn.ModuleDict() - - # Pyramids - for i in range(3, 0, -1): - level = f'l{i}' - self.offset_conv1[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - if i == 3: - self.offset_conv2[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - else: - self.offset_conv2[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - self.offset_conv3[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.dcn_pack[level] = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups) - - if i < 3: - self.feat_conv[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - - # Cascading dcn - self.cas_offset_conv1 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - self.cas_offset_conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.cas_dcnpack = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups) - - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, nbr_feat_l, ref_feat_l): - """Align neighboring frame features to the reference frame features. - - Args: - nbr_feat_l (list[Tensor]): Neighboring feature list. It - contains three pyramid levels (L1, L2, L3), - each with shape (b, c, h, w). - ref_feat_l (list[Tensor]): Reference feature list. It - contains three pyramid levels (L1, L2, L3), - each with shape (b, c, h, w). - - Returns: - Tensor: Aligned features. - """ - # Pyramids - upsampled_offset, upsampled_feat = None, None - for i in range(3, 0, -1): - level = f'l{i}' - offset = torch.cat([nbr_feat_l[i - 1], ref_feat_l[i - 1]], dim=1) - offset = self.lrelu(self.offset_conv1[level](offset)) - if i == 3: - offset = self.lrelu(self.offset_conv2[level](offset)) - else: - offset = self.lrelu(self.offset_conv2[level](torch.cat([offset, upsampled_offset], dim=1))) - offset = self.lrelu(self.offset_conv3[level](offset)) - - feat = self.dcn_pack[level](nbr_feat_l[i - 1], offset) - if i < 3: - feat = self.feat_conv[level](torch.cat([feat, upsampled_feat], dim=1)) - if i > 1: - feat = self.lrelu(feat) - - if i > 1: # upsample offset and features - # x2: when we upsample the offset, we should also enlarge - # the magnitude. - upsampled_offset = self.upsample(offset) * 2 - upsampled_feat = self.upsample(feat) - - # Cascading - offset = torch.cat([feat, ref_feat_l[0]], dim=1) - offset = self.lrelu(self.cas_offset_conv2(self.lrelu(self.cas_offset_conv1(offset)))) - feat = self.lrelu(self.cas_dcnpack(feat, offset)) - return feat - - -class TSAFusion(nn.Module): - """Temporal Spatial Attention (TSA) fusion module. - - Temporal: Calculate the correlation between center frame and - neighboring frames; - Spatial: It has 3 pyramid levels, the attention is similar to SFT. - (SFT: Recovering realistic texture in image super-resolution by deep - spatial feature transform.) - - Args: - num_feat (int): Channel number of middle features. Default: 64. - num_frame (int): Number of frames. Default: 5. - center_frame_idx (int): The index of center frame. Default: 2. - """ - - def __init__(self, num_feat=64, num_frame=5, center_frame_idx=2): - super(TSAFusion, self).__init__() - self.center_frame_idx = center_frame_idx - # temporal attention (before fusion conv) - self.temporal_attn1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.temporal_attn2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.feat_fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1) - - # spatial attention (after fusion conv) - self.max_pool = nn.MaxPool2d(3, stride=2, padding=1) - self.avg_pool = nn.AvgPool2d(3, stride=2, padding=1) - self.spatial_attn1 = nn.Conv2d(num_frame * num_feat, num_feat, 1) - self.spatial_attn2 = nn.Conv2d(num_feat * 2, num_feat, 1) - self.spatial_attn3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.spatial_attn4 = nn.Conv2d(num_feat, num_feat, 1) - self.spatial_attn5 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.spatial_attn_l1 = nn.Conv2d(num_feat, num_feat, 1) - self.spatial_attn_l2 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) - self.spatial_attn_l3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.spatial_attn_add1 = nn.Conv2d(num_feat, num_feat, 1) - self.spatial_attn_add2 = nn.Conv2d(num_feat, num_feat, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) - - def forward(self, aligned_feat): - """ - Args: - aligned_feat (Tensor): Aligned features with shape (b, t, c, h, w). - - Returns: - Tensor: Features after TSA with the shape (b, c, h, w). - """ - b, t, c, h, w = aligned_feat.size() - # temporal attention - embedding_ref = self.temporal_attn1(aligned_feat[:, self.center_frame_idx, :, :, :].clone()) - embedding = self.temporal_attn2(aligned_feat.view(-1, c, h, w)) - embedding = embedding.view(b, t, -1, h, w) # (b, t, c, h, w) - - corr_l = [] # correlation list - for i in range(t): - emb_neighbor = embedding[:, i, :, :, :] - corr = torch.sum(emb_neighbor * embedding_ref, 1) # (b, h, w) - corr_l.append(corr.unsqueeze(1)) # (b, 1, h, w) - corr_prob = torch.sigmoid(torch.cat(corr_l, dim=1)) # (b, t, h, w) - corr_prob = corr_prob.unsqueeze(2).expand(b, t, c, h, w) - corr_prob = corr_prob.contiguous().view(b, -1, h, w) # (b, t*c, h, w) - aligned_feat = aligned_feat.view(b, -1, h, w) * corr_prob - - # fusion - feat = self.lrelu(self.feat_fusion(aligned_feat)) - - # spatial attention - attn = self.lrelu(self.spatial_attn1(aligned_feat)) - attn_max = self.max_pool(attn) - attn_avg = self.avg_pool(attn) - attn = self.lrelu(self.spatial_attn2(torch.cat([attn_max, attn_avg], dim=1))) - # pyramid levels - attn_level = self.lrelu(self.spatial_attn_l1(attn)) - attn_max = self.max_pool(attn_level) - attn_avg = self.avg_pool(attn_level) - attn_level = self.lrelu(self.spatial_attn_l2(torch.cat([attn_max, attn_avg], dim=1))) - attn_level = self.lrelu(self.spatial_attn_l3(attn_level)) - attn_level = self.upsample(attn_level) - - attn = self.lrelu(self.spatial_attn3(attn)) + attn_level - attn = self.lrelu(self.spatial_attn4(attn)) - attn = self.upsample(attn) - attn = self.spatial_attn5(attn) - attn_add = self.spatial_attn_add2(self.lrelu(self.spatial_attn_add1(attn))) - attn = torch.sigmoid(attn) - - # after initialization, * 2 makes (attn * 2) to be close to 1. - feat = feat * attn * 2 + attn_add - return feat - - -class PredeblurModule(nn.Module): - """Pre-dublur module. - - Args: - num_in_ch (int): Channel number of input image. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - hr_in (bool): Whether the input has high resolution. Default: False. - """ - - def __init__(self, num_in_ch=3, num_feat=64, hr_in=False): - super(PredeblurModule, self).__init__() - self.hr_in = hr_in - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - if self.hr_in: - # downsample x4 by stride conv - self.stride_conv_hr1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.stride_conv_hr2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - - # generate feature pyramid - self.stride_conv_l2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.stride_conv_l3 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - - self.resblock_l3 = ResidualBlockNoBN(num_feat=num_feat) - self.resblock_l2_1 = ResidualBlockNoBN(num_feat=num_feat) - self.resblock_l2_2 = ResidualBlockNoBN(num_feat=num_feat) - self.resblock_l1 = nn.ModuleList([ResidualBlockNoBN(num_feat=num_feat) for i in range(5)]) - - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - feat_l1 = self.lrelu(self.conv_first(x)) - if self.hr_in: - feat_l1 = self.lrelu(self.stride_conv_hr1(feat_l1)) - feat_l1 = self.lrelu(self.stride_conv_hr2(feat_l1)) - - # generate feature pyramid - feat_l2 = self.lrelu(self.stride_conv_l2(feat_l1)) - feat_l3 = self.lrelu(self.stride_conv_l3(feat_l2)) - - feat_l3 = self.upsample(self.resblock_l3(feat_l3)) - feat_l2 = self.resblock_l2_1(feat_l2) + feat_l3 - feat_l2 = self.upsample(self.resblock_l2_2(feat_l2)) - - for i in range(2): - feat_l1 = self.resblock_l1[i](feat_l1) - feat_l1 = feat_l1 + feat_l2 - for i in range(2, 5): - feat_l1 = self.resblock_l1[i](feat_l1) - return feat_l1 - - -@ARCH_REGISTRY.register() -class EDVR(nn.Module): - """EDVR network structure for video super-resolution. - - Now only support X4 upsampling factor. - - ``Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks`` - - Args: - num_in_ch (int): Channel number of input image. Default: 3. - num_out_ch (int): Channel number of output image. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - num_frame (int): Number of input frames. Default: 5. - deformable_groups (int): Deformable groups. Defaults: 8. - num_extract_block (int): Number of blocks for feature extraction. - Default: 5. - num_reconstruct_block (int): Number of blocks for reconstruction. - Default: 10. - center_frame_idx (int): The index of center frame. Frame counting from - 0. Default: Middle of input frames. - hr_in (bool): Whether the input has high resolution. Default: False. - with_predeblur (bool): Whether has predeblur module. - Default: False. - with_tsa (bool): Whether has TSA module. Default: True. - """ - - def __init__(self, - num_in_ch=3, - num_out_ch=3, - num_feat=64, - num_frame=5, - deformable_groups=8, - num_extract_block=5, - num_reconstruct_block=10, - center_frame_idx=None, - hr_in=False, - with_predeblur=False, - with_tsa=True): - super(EDVR, self).__init__() - if center_frame_idx is None: - self.center_frame_idx = num_frame // 2 - else: - self.center_frame_idx = center_frame_idx - self.hr_in = hr_in - self.with_predeblur = with_predeblur - self.with_tsa = with_tsa - - # extract features for each frame - if self.with_predeblur: - self.predeblur = PredeblurModule(num_feat=num_feat, hr_in=self.hr_in) - self.conv_1x1 = nn.Conv2d(num_feat, num_feat, 1, 1) - else: - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - - # extract pyramid features - self.feature_extraction = make_layer(ResidualBlockNoBN, num_extract_block, num_feat=num_feat) - self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) - self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - - # pcd and tsa module - self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=deformable_groups) - if self.with_tsa: - self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_frame, center_frame_idx=self.center_frame_idx) - else: - self.fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1) - - # reconstruction - self.reconstruction = make_layer(ResidualBlockNoBN, num_reconstruct_block, num_feat=num_feat) - # upsample - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) - self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1) - self.pixel_shuffle = nn.PixelShuffle(2) - self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) - self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - b, t, c, h, w = x.size() - if self.hr_in: - assert h % 16 == 0 and w % 16 == 0, ('The height and width must be multiple of 16.') - else: - assert h % 4 == 0 and w % 4 == 0, ('The height and width must be multiple of 4.') - - x_center = x[:, self.center_frame_idx, :, :, :].contiguous() - - # extract features for each frame - # L1 - if self.with_predeblur: - feat_l1 = self.conv_1x1(self.predeblur(x.view(-1, c, h, w))) - if self.hr_in: - h, w = h // 4, w // 4 - else: - feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w))) - - feat_l1 = self.feature_extraction(feat_l1) - # L2 - feat_l2 = self.lrelu(self.conv_l2_1(feat_l1)) - feat_l2 = self.lrelu(self.conv_l2_2(feat_l2)) - # L3 - feat_l3 = self.lrelu(self.conv_l3_1(feat_l2)) - feat_l3 = self.lrelu(self.conv_l3_2(feat_l3)) - - feat_l1 = feat_l1.view(b, t, -1, h, w) - feat_l2 = feat_l2.view(b, t, -1, h // 2, w // 2) - feat_l3 = feat_l3.view(b, t, -1, h // 4, w // 4) - - # PCD alignment - ref_feat_l = [ # reference feature list - feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(), - feat_l3[:, self.center_frame_idx, :, :, :].clone() - ] - aligned_feat = [] - for i in range(t): - nbr_feat_l = [ # neighboring feature list - feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone() - ] - aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l)) - aligned_feat = torch.stack(aligned_feat, dim=1) # (b, t, c, h, w) - - if not self.with_tsa: - aligned_feat = aligned_feat.view(b, -1, h, w) - feat = self.fusion(aligned_feat) - - out = self.reconstruction(feat) - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - out = self.lrelu(self.conv_hr(out)) - out = self.conv_last(out) - if self.hr_in: - base = x_center - else: - base = F.interpolate(x_center, scale_factor=4, mode='bilinear', align_corners=False) - out += base - return out diff --git a/basicsr/archs/hifacegan_arch.py b/basicsr/archs/hifacegan_arch.py deleted file mode 100644 index 098e3ed..0000000 --- a/basicsr/archs/hifacegan_arch.py +++ /dev/null @@ -1,260 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .hifacegan_util import BaseNetwork, LIPEncoder, SPADEResnetBlock, get_nonspade_norm_layer - - -class SPADEGenerator(BaseNetwork): - """Generator with SPADEResBlock""" - - def __init__(self, - num_in_ch=3, - num_feat=64, - use_vae=False, - z_dim=256, - crop_size=512, - norm_g='spectralspadesyncbatch3x3', - is_train=True, - init_train_phase=3): # progressive training disabled - super().__init__() - self.nf = num_feat - self.input_nc = num_in_ch - self.is_train = is_train - self.train_phase = init_train_phase - - self.scale_ratio = 5 # hardcoded now - self.sw = crop_size // (2**self.scale_ratio) - self.sh = self.sw # 20210519: By default use square image, aspect_ratio = 1.0 - - if use_vae: - # In case of VAE, we will sample from random z vector - self.fc = nn.Linear(z_dim, 16 * self.nf * self.sw * self.sh) - else: - # Otherwise, we make the network deterministic by starting with - # downsampled segmentation map instead of random z - self.fc = nn.Conv2d(num_in_ch, 16 * self.nf, 3, padding=1) - - self.head_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) - - self.g_middle_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) - self.g_middle_1 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) - - self.ups = nn.ModuleList([ - SPADEResnetBlock(16 * self.nf, 8 * self.nf, norm_g), - SPADEResnetBlock(8 * self.nf, 4 * self.nf, norm_g), - SPADEResnetBlock(4 * self.nf, 2 * self.nf, norm_g), - SPADEResnetBlock(2 * self.nf, 1 * self.nf, norm_g) - ]) - - self.to_rgbs = nn.ModuleList([ - nn.Conv2d(8 * self.nf, 3, 3, padding=1), - nn.Conv2d(4 * self.nf, 3, 3, padding=1), - nn.Conv2d(2 * self.nf, 3, 3, padding=1), - nn.Conv2d(1 * self.nf, 3, 3, padding=1) - ]) - - self.up = nn.Upsample(scale_factor=2) - - def encode(self, input_tensor): - """ - Encode input_tensor into feature maps, can be overridden in derived classes - Default: nearest downsampling of 2**5 = 32 times - """ - h, w = input_tensor.size()[-2:] - sh, sw = h // 2**self.scale_ratio, w // 2**self.scale_ratio - x = F.interpolate(input_tensor, size=(sh, sw)) - return self.fc(x) - - def forward(self, x): - # In oroginal SPADE, seg means a segmentation map, but here we use x instead. - seg = x - - x = self.encode(x) - x = self.head_0(x, seg) - - x = self.up(x) - x = self.g_middle_0(x, seg) - x = self.g_middle_1(x, seg) - - if self.is_train: - phase = self.train_phase + 1 - else: - phase = len(self.to_rgbs) - - for i in range(phase): - x = self.up(x) - x = self.ups[i](x, seg) - - x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1)) - x = torch.tanh(x) - - return x - - def mixed_guidance_forward(self, input_x, seg=None, n=0, mode='progressive'): - """ - A helper class for subspace visualization. Input and seg are different images. - For the first n levels (including encoder) we use input, for the rest we use seg. - - If mode = 'progressive', the output's like: AAABBB - If mode = 'one_plug', the output's like: AAABAA - If mode = 'one_ablate', the output's like: BBBABB - """ - - if seg is None: - return self.forward(input_x) - - if self.is_train: - phase = self.train_phase + 1 - else: - phase = len(self.to_rgbs) - - if mode == 'progressive': - n = max(min(n, 4 + phase), 0) - guide_list = [input_x] * n + [seg] * (4 + phase - n) - elif mode == 'one_plug': - n = max(min(n, 4 + phase - 1), 0) - guide_list = [seg] * (4 + phase) - guide_list[n] = input_x - elif mode == 'one_ablate': - if n > 3 + phase: - return self.forward(input_x) - guide_list = [input_x] * (4 + phase) - guide_list[n] = seg - - x = self.encode(guide_list[0]) - x = self.head_0(x, guide_list[1]) - - x = self.up(x) - x = self.g_middle_0(x, guide_list[2]) - x = self.g_middle_1(x, guide_list[3]) - - for i in range(phase): - x = self.up(x) - x = self.ups[i](x, guide_list[4 + i]) - - x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1)) - x = torch.tanh(x) - - return x - - -@ARCH_REGISTRY.register() -class HiFaceGAN(SPADEGenerator): - """ - HiFaceGAN: SPADEGenerator with a learnable feature encoder - Current encoder design: LIPEncoder - """ - - def __init__(self, - num_in_ch=3, - num_feat=64, - use_vae=False, - z_dim=256, - crop_size=512, - norm_g='spectralspadesyncbatch3x3', - is_train=True, - init_train_phase=3): - super().__init__(num_in_ch, num_feat, use_vae, z_dim, crop_size, norm_g, is_train, init_train_phase) - self.lip_encoder = LIPEncoder(num_in_ch, num_feat, self.sw, self.sh, self.scale_ratio) - - def encode(self, input_tensor): - return self.lip_encoder(input_tensor) - - -@ARCH_REGISTRY.register() -class HiFaceGANDiscriminator(BaseNetwork): - """ - Inspired by pix2pixHD multiscale discriminator. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_out_ch (int): Channel number of outputs. Default: 3. - conditional_d (bool): Whether use conditional discriminator. - Default: True. - num_d (int): Number of Multiscale discriminators. Default: 3. - n_layers_d (int): Number of downsample layers in each D. Default: 4. - num_feat (int): Channel number of base intermediate features. - Default: 64. - norm_d (str): String to determine normalization layers in D. - Choices: [spectral][instance/batch/syncbatch] - Default: 'spectralinstance'. - keep_features (bool): Keep intermediate features for matching loss, etc. - Default: True. - """ - - def __init__(self, - num_in_ch=3, - num_out_ch=3, - conditional_d=True, - num_d=2, - n_layers_d=4, - num_feat=64, - norm_d='spectralinstance', - keep_features=True): - super().__init__() - self.num_d = num_d - - input_nc = num_in_ch - if conditional_d: - input_nc += num_out_ch - - for i in range(num_d): - subnet_d = NLayerDiscriminator(input_nc, n_layers_d, num_feat, norm_d, keep_features) - self.add_module(f'discriminator_{i}', subnet_d) - - def downsample(self, x): - return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False) - - # Returns list of lists of discriminator outputs. - # The final result is of size opt.num_d x opt.n_layers_D - def forward(self, x): - result = [] - for _, _net_d in self.named_children(): - out = _net_d(x) - result.append(out) - x = self.downsample(x) - - return result - - -class NLayerDiscriminator(BaseNetwork): - """Defines the PatchGAN discriminator with the specified arguments.""" - - def __init__(self, input_nc, n_layers_d, num_feat, norm_d, keep_features): - super().__init__() - kw = 4 - padw = int(np.ceil((kw - 1.0) / 2)) - nf = num_feat - self.keep_features = keep_features - - norm_layer = get_nonspade_norm_layer(norm_d) - sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, False)]] - - for n in range(1, n_layers_d): - nf_prev = nf - nf = min(nf * 2, 512) - stride = 1 if n == n_layers_d - 1 else 2 - sequence += [[ - norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=stride, padding=padw)), - nn.LeakyReLU(0.2, False) - ]] - - sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] - - # We divide the layers into groups to extract intermediate layer outputs - for n in range(len(sequence)): - self.add_module('model' + str(n), nn.Sequential(*sequence[n])) - - def forward(self, x): - results = [x] - for submodel in self.children(): - intermediate_output = submodel(results[-1]) - results.append(intermediate_output) - - if self.keep_features: - return results[1:] - else: - return results[-1] diff --git a/basicsr/archs/hifacegan_util.py b/basicsr/archs/hifacegan_util.py deleted file mode 100644 index 35cbef3..0000000 --- a/basicsr/archs/hifacegan_util.py +++ /dev/null @@ -1,255 +0,0 @@ -import re -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn import init -# Warning: spectral norm could be buggy -# under eval mode and multi-GPU inference -# A workaround is sticking to single-GPU inference and train mode -from torch.nn.utils import spectral_norm - - -class SPADE(nn.Module): - - def __init__(self, config_text, norm_nc, label_nc): - super().__init__() - - assert config_text.startswith('spade') - parsed = re.search('spade(\\D+)(\\d)x\\d', config_text) - param_free_norm_type = str(parsed.group(1)) - ks = int(parsed.group(2)) - - if param_free_norm_type == 'instance': - self.param_free_norm = nn.InstanceNorm2d(norm_nc) - elif param_free_norm_type == 'syncbatch': - print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') - self.param_free_norm = nn.InstanceNorm2d(norm_nc) - elif param_free_norm_type == 'batch': - self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False) - else: - raise ValueError(f'{param_free_norm_type} is not a recognized param-free norm type in SPADE') - - # The dimension of the intermediate embedding space. Yes, hardcoded. - nhidden = 128 if norm_nc > 128 else norm_nc - - pw = ks // 2 - self.mlp_shared = nn.Sequential(nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), nn.ReLU()) - self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) - self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) - - def forward(self, x, segmap): - - # Part 1. generate parameter-free normalized activations - normalized = self.param_free_norm(x) - - # Part 2. produce scaling and bias conditioned on semantic map - segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') - actv = self.mlp_shared(segmap) - gamma = self.mlp_gamma(actv) - beta = self.mlp_beta(actv) - - # apply scale and bias - out = normalized * gamma + beta - - return out - - -class SPADEResnetBlock(nn.Module): - """ - ResNet block that uses SPADE. It differs from the ResNet block of pix2pixHD in that - it takes in the segmentation map as input, learns the skip connection if necessary, - and applies normalization first and then convolution. - This architecture seemed like a standard architecture for unconditional or - class-conditional GAN architecture using residual block. - The code was inspired from https://github.com/LMescheder/GAN_stability. - """ - - def __init__(self, fin, fout, norm_g='spectralspadesyncbatch3x3', semantic_nc=3): - super().__init__() - # Attributes - self.learned_shortcut = (fin != fout) - fmiddle = min(fin, fout) - - # create conv layers - self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) - self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) - if self.learned_shortcut: - self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) - - # apply spectral norm if specified - if 'spectral' in norm_g: - self.conv_0 = spectral_norm(self.conv_0) - self.conv_1 = spectral_norm(self.conv_1) - if self.learned_shortcut: - self.conv_s = spectral_norm(self.conv_s) - - # define normalization layers - spade_config_str = norm_g.replace('spectral', '') - self.norm_0 = SPADE(spade_config_str, fin, semantic_nc) - self.norm_1 = SPADE(spade_config_str, fmiddle, semantic_nc) - if self.learned_shortcut: - self.norm_s = SPADE(spade_config_str, fin, semantic_nc) - - # note the resnet block with SPADE also takes in |seg|, - # the semantic segmentation map as input - def forward(self, x, seg): - x_s = self.shortcut(x, seg) - dx = self.conv_0(self.act(self.norm_0(x, seg))) - dx = self.conv_1(self.act(self.norm_1(dx, seg))) - out = x_s + dx - return out - - def shortcut(self, x, seg): - if self.learned_shortcut: - x_s = self.conv_s(self.norm_s(x, seg)) - else: - x_s = x - return x_s - - def act(self, x): - return F.leaky_relu(x, 2e-1) - - -class BaseNetwork(nn.Module): - """ A basis for hifacegan archs with custom initialization """ - - def init_weights(self, init_type='normal', gain=0.02): - - def init_func(m): - classname = m.__class__.__name__ - if classname.find('BatchNorm2d') != -1: - if hasattr(m, 'weight') and m.weight is not None: - init.normal_(m.weight.data, 1.0, gain) - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): - if init_type == 'normal': - init.normal_(m.weight.data, 0.0, gain) - elif init_type == 'xavier': - init.xavier_normal_(m.weight.data, gain=gain) - elif init_type == 'xavier_uniform': - init.xavier_uniform_(m.weight.data, gain=1.0) - elif init_type == 'kaiming': - init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') - elif init_type == 'orthogonal': - init.orthogonal_(m.weight.data, gain=gain) - elif init_type == 'none': # uses pytorch's default init method - m.reset_parameters() - else: - raise NotImplementedError(f'initialization method [{init_type}] is not implemented') - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - - self.apply(init_func) - - # propagate to children - for m in self.children(): - if hasattr(m, 'init_weights'): - m.init_weights(init_type, gain) - - def forward(self, x): - pass - - -def lip2d(x, logit, kernel=3, stride=2, padding=1): - weight = logit.exp() - return F.avg_pool2d(x * weight, kernel, stride, padding) / F.avg_pool2d(weight, kernel, stride, padding) - - -class SoftGate(nn.Module): - COEFF = 12.0 - - def forward(self, x): - return torch.sigmoid(x).mul(self.COEFF) - - -class SimplifiedLIP(nn.Module): - - def __init__(self, channels): - super(SimplifiedLIP, self).__init__() - self.logit = nn.Sequential( - nn.Conv2d(channels, channels, 3, padding=1, bias=False), nn.InstanceNorm2d(channels, affine=True), - SoftGate()) - - def init_layer(self): - self.logit[0].weight.data.fill_(0.0) - - def forward(self, x): - frac = lip2d(x, self.logit(x)) - return frac - - -class LIPEncoder(BaseNetwork): - """Local Importance-based Pooling (Ziteng Gao et.al.,ICCV 2019)""" - - def __init__(self, input_nc, ngf, sw, sh, n_2xdown, norm_layer=nn.InstanceNorm2d): - super().__init__() - self.sw = sw - self.sh = sh - self.max_ratio = 16 - # 20200310: Several Convolution (stride 1) + LIP blocks, 4 fold - kw = 3 - pw = (kw - 1) // 2 - - model = [ - nn.Conv2d(input_nc, ngf, kw, stride=1, padding=pw, bias=False), - norm_layer(ngf), - nn.ReLU(), - ] - cur_ratio = 1 - for i in range(n_2xdown): - next_ratio = min(cur_ratio * 2, self.max_ratio) - model += [ - SimplifiedLIP(ngf * cur_ratio), - nn.Conv2d(ngf * cur_ratio, ngf * next_ratio, kw, stride=1, padding=pw), - norm_layer(ngf * next_ratio), - ] - cur_ratio = next_ratio - if i < n_2xdown - 1: - model += [nn.ReLU(inplace=True)] - - self.model = nn.Sequential(*model) - - def forward(self, x): - return self.model(x) - - -def get_nonspade_norm_layer(norm_type='instance'): - # helper function to get # output channels of the previous layer - def get_out_channel(layer): - if hasattr(layer, 'out_channels'): - return getattr(layer, 'out_channels') - return layer.weight.size(0) - - # this function will be returned - def add_norm_layer(layer): - nonlocal norm_type - if norm_type.startswith('spectral'): - layer = spectral_norm(layer) - subnorm_type = norm_type[len('spectral'):] - - if subnorm_type == 'none' or len(subnorm_type) == 0: - return layer - - # remove bias in the previous layer, which is meaningless - # since it has no effect after normalization - if getattr(layer, 'bias', None) is not None: - delattr(layer, 'bias') - layer.register_parameter('bias', None) - - if subnorm_type == 'batch': - norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True) - elif subnorm_type == 'sync_batch': - print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') - # norm_layer = SynchronizedBatchNorm2d( - # get_out_channel(layer), affine=True) - norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) - elif subnorm_type == 'instance': - norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) - else: - raise ValueError(f'normalization layer {subnorm_type} is not recognized') - - return nn.Sequential(layer, norm_layer) - - print('This is a legacy from nvlabs/SPADE, and will be removed in future versions.') - return add_norm_layer diff --git a/basicsr/archs/inception.py b/basicsr/archs/inception.py deleted file mode 100644 index de1abef..0000000 --- a/basicsr/archs/inception.py +++ /dev/null @@ -1,307 +0,0 @@ -# Modified from https://github.com/mseitzer/pytorch-fid/blob/master/pytorch_fid/inception.py # noqa: E501 -# For FID metric - -import os -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.model_zoo import load_url -from torchvision import models - -# Inception weights ported to Pytorch from -# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 -LOCAL_FID_WEIGHTS = 'experiments/pretrained_models/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 - - -class InceptionV3(nn.Module): - """Pretrained InceptionV3 network returning feature maps""" - - # Index of default block of inception to return, - # corresponds to output of final average pooling - DEFAULT_BLOCK_INDEX = 3 - - # Maps feature dimensionality to their output blocks indices - BLOCK_INDEX_BY_DIM = { - 64: 0, # First max pooling features - 192: 1, # Second max pooling features - 768: 2, # Pre-aux classifier features - 2048: 3 # Final average pooling features - } - - def __init__(self, - output_blocks=(DEFAULT_BLOCK_INDEX), - resize_input=True, - normalize_input=True, - requires_grad=False, - use_fid_inception=True): - """Build pretrained InceptionV3. - - Args: - output_blocks (list[int]): Indices of blocks to return features of. - Possible values are: - - 0: corresponds to output of first max pooling - - 1: corresponds to output of second max pooling - - 2: corresponds to output which is fed to aux classifier - - 3: corresponds to output of final average pooling - resize_input (bool): If true, bilinearly resizes input to width and - height 299 before feeding input to model. As the network - without fully connected layers is fully convolutional, it - should be able to handle inputs of arbitrary size, so resizing - might not be strictly needed. Default: True. - normalize_input (bool): If true, scales the input from range (0, 1) - to the range the pretrained Inception network expects, - namely (-1, 1). Default: True. - requires_grad (bool): If true, parameters of the model require - gradients. Possibly useful for finetuning the network. - Default: False. - use_fid_inception (bool): If true, uses the pretrained Inception - model used in Tensorflow's FID implementation. - If false, uses the pretrained Inception model available in - torchvision. The FID Inception model has different weights - and a slightly different structure from torchvision's - Inception model. If you want to compute FID scores, you are - strongly advised to set this parameter to true to get - comparable results. Default: True. - """ - super(InceptionV3, self).__init__() - - self.resize_input = resize_input - self.normalize_input = normalize_input - self.output_blocks = sorted(output_blocks) - self.last_needed_block = max(output_blocks) - - assert self.last_needed_block <= 3, ('Last possible output block index is 3') - - self.blocks = nn.ModuleList() - - if use_fid_inception: - inception = fid_inception_v3() - else: - try: - inception = models.inception_v3(pretrained=True, init_weights=False) - except TypeError: - # pytorch < 1.5 does not have init_weights for inception_v3 - inception = models.inception_v3(pretrained=True) - - # Block 0: input to maxpool1 - block0 = [ - inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, - nn.MaxPool2d(kernel_size=3, stride=2) - ] - self.blocks.append(nn.Sequential(*block0)) - - # Block 1: maxpool1 to maxpool2 - if self.last_needed_block >= 1: - block1 = [inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2)] - self.blocks.append(nn.Sequential(*block1)) - - # Block 2: maxpool2 to aux classifier - if self.last_needed_block >= 2: - block2 = [ - inception.Mixed_5b, - inception.Mixed_5c, - inception.Mixed_5d, - inception.Mixed_6a, - inception.Mixed_6b, - inception.Mixed_6c, - inception.Mixed_6d, - inception.Mixed_6e, - ] - self.blocks.append(nn.Sequential(*block2)) - - # Block 3: aux classifier to final avgpool - if self.last_needed_block >= 3: - block3 = [ - inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, - nn.AdaptiveAvgPool2d(output_size=(1, 1)) - ] - self.blocks.append(nn.Sequential(*block3)) - - for param in self.parameters(): - param.requires_grad = requires_grad - - def forward(self, x): - """Get Inception feature maps. - - Args: - x (Tensor): Input tensor of shape (b, 3, h, w). - Values are expected to be in range (-1, 1). You can also input - (0, 1) with setting normalize_input = True. - - Returns: - list[Tensor]: Corresponding to the selected output block, sorted - ascending by index. - """ - output = [] - - if self.resize_input: - x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False) - - if self.normalize_input: - x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) - - for idx, block in enumerate(self.blocks): - x = block(x) - if idx in self.output_blocks: - output.append(x) - - if idx == self.last_needed_block: - break - - return output - - -def fid_inception_v3(): - """Build pretrained Inception model for FID computation. - - The Inception model for FID computation uses a different set of weights - and has a slightly different structure than torchvision's Inception. - - This method first constructs torchvision's Inception and then patches the - necessary parts that are different in the FID Inception model. - """ - try: - inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False, init_weights=False) - except TypeError: - # pytorch < 1.5 does not have init_weights for inception_v3 - inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False) - - inception.Mixed_5b = FIDInceptionA(192, pool_features=32) - inception.Mixed_5c = FIDInceptionA(256, pool_features=64) - inception.Mixed_5d = FIDInceptionA(288, pool_features=64) - inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) - inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) - inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) - inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) - inception.Mixed_7b = FIDInceptionE_1(1280) - inception.Mixed_7c = FIDInceptionE_2(2048) - - if os.path.exists(LOCAL_FID_WEIGHTS): - state_dict = torch.load(LOCAL_FID_WEIGHTS, map_location=lambda storage, loc: storage) - else: - state_dict = load_url(FID_WEIGHTS_URL, progress=True) - - inception.load_state_dict(state_dict) - return inception - - -class FIDInceptionA(models.inception.InceptionA): - """InceptionA block patched for FID computation""" - - def __init__(self, in_channels, pool_features): - super(FIDInceptionA, self).__init__(in_channels, pool_features) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch5x5 = self.branch5x5_1(x) - branch5x5 = self.branch5x5_2(branch5x5) - - branch3x3dbl = self.branch3x3dbl_1(x) - branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) - branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) - - # Patch: Tensorflow's average pool does not use the padded zero's in - # its average calculation - branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] - return torch.cat(outputs, 1) - - -class FIDInceptionC(models.inception.InceptionC): - """InceptionC block patched for FID computation""" - - def __init__(self, in_channels, channels_7x7): - super(FIDInceptionC, self).__init__(in_channels, channels_7x7) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch7x7 = self.branch7x7_1(x) - branch7x7 = self.branch7x7_2(branch7x7) - branch7x7 = self.branch7x7_3(branch7x7) - - branch7x7dbl = self.branch7x7dbl_1(x) - branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) - branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) - branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) - branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) - - # Patch: Tensorflow's average pool does not use the padded zero's in - # its average calculation - branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] - return torch.cat(outputs, 1) - - -class FIDInceptionE_1(models.inception.InceptionE): - """First InceptionE block patched for FID computation""" - - def __init__(self, in_channels): - super(FIDInceptionE_1, self).__init__(in_channels) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch3x3 = self.branch3x3_1(x) - branch3x3 = [ - self.branch3x3_2a(branch3x3), - self.branch3x3_2b(branch3x3), - ] - branch3x3 = torch.cat(branch3x3, 1) - - branch3x3dbl = self.branch3x3dbl_1(x) - branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) - branch3x3dbl = [ - self.branch3x3dbl_3a(branch3x3dbl), - self.branch3x3dbl_3b(branch3x3dbl), - ] - branch3x3dbl = torch.cat(branch3x3dbl, 1) - - # Patch: Tensorflow's average pool does not use the padded zero's in - # its average calculation - branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] - return torch.cat(outputs, 1) - - -class FIDInceptionE_2(models.inception.InceptionE): - """Second InceptionE block patched for FID computation""" - - def __init__(self, in_channels): - super(FIDInceptionE_2, self).__init__(in_channels) - - def forward(self, x): - branch1x1 = self.branch1x1(x) - - branch3x3 = self.branch3x3_1(x) - branch3x3 = [ - self.branch3x3_2a(branch3x3), - self.branch3x3_2b(branch3x3), - ] - branch3x3 = torch.cat(branch3x3, 1) - - branch3x3dbl = self.branch3x3dbl_1(x) - branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) - branch3x3dbl = [ - self.branch3x3dbl_3a(branch3x3dbl), - self.branch3x3dbl_3b(branch3x3dbl), - ] - branch3x3dbl = torch.cat(branch3x3dbl, 1) - - # Patch: The FID Inception model uses max pooling instead of average - # pooling. This is likely an error in this specific Inception - # implementation, as other Inception models use average pooling here - # (which matches the description in the paper). - branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) - branch_pool = self.branch_pool(branch_pool) - - outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] - return torch.cat(outputs, 1) diff --git a/basicsr/archs/rcan_arch.py b/basicsr/archs/rcan_arch.py deleted file mode 100644 index 48872e6..0000000 --- a/basicsr/archs/rcan_arch.py +++ /dev/null @@ -1,135 +0,0 @@ -import torch -from torch import nn as nn - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import Upsample, make_layer - - -class ChannelAttention(nn.Module): - """Channel attention used in RCAN. - - Args: - num_feat (int): Channel number of intermediate features. - squeeze_factor (int): Channel squeeze factor. Default: 16. - """ - - def __init__(self, num_feat, squeeze_factor=16): - super(ChannelAttention, self).__init__() - self.attention = nn.Sequential( - nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), - nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid()) - - def forward(self, x): - y = self.attention(x) - return x * y - - -class RCAB(nn.Module): - """Residual Channel Attention Block (RCAB) used in RCAN. - - Args: - num_feat (int): Channel number of intermediate features. - squeeze_factor (int): Channel squeeze factor. Default: 16. - res_scale (float): Scale the residual. Default: 1. - """ - - def __init__(self, num_feat, squeeze_factor=16, res_scale=1): - super(RCAB, self).__init__() - self.res_scale = res_scale - - self.rcab = nn.Sequential( - nn.Conv2d(num_feat, num_feat, 3, 1, 1), nn.ReLU(True), nn.Conv2d(num_feat, num_feat, 3, 1, 1), - ChannelAttention(num_feat, squeeze_factor)) - - def forward(self, x): - res = self.rcab(x) * self.res_scale - return res + x - - -class ResidualGroup(nn.Module): - """Residual Group of RCAB. - - Args: - num_feat (int): Channel number of intermediate features. - num_block (int): Block number in the body network. - squeeze_factor (int): Channel squeeze factor. Default: 16. - res_scale (float): Scale the residual. Default: 1. - """ - - def __init__(self, num_feat, num_block, squeeze_factor=16, res_scale=1): - super(ResidualGroup, self).__init__() - - self.residual_group = make_layer( - RCAB, num_block, num_feat=num_feat, squeeze_factor=squeeze_factor, res_scale=res_scale) - self.conv = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - - def forward(self, x): - res = self.conv(self.residual_group(x)) - return res + x - - -@ARCH_REGISTRY.register() -class RCAN(nn.Module): - """Residual Channel Attention Networks. - - ``Paper: Image Super-Resolution Using Very Deep Residual Channel Attention Networks`` - - Reference: https://github.com/yulunzhang/RCAN - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64. - num_group (int): Number of ResidualGroup. Default: 10. - num_block (int): Number of RCAB in ResidualGroup. Default: 16. - squeeze_factor (int): Channel squeeze factor. Default: 16. - upscale (int): Upsampling factor. Support 2^n and 3. - Default: 4. - res_scale (float): Used to scale the residual in residual block. - Default: 1. - img_range (float): Image range. Default: 255. - rgb_mean (tuple[float]): Image mean in RGB orders. - Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. - """ - - def __init__(self, - num_in_ch, - num_out_ch, - num_feat=64, - num_group=10, - num_block=16, - squeeze_factor=16, - upscale=4, - res_scale=1, - img_range=255., - rgb_mean=(0.4488, 0.4371, 0.4040)): - super(RCAN, self).__init__() - - self.img_range = img_range - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer( - ResidualGroup, - num_group, - num_feat=num_feat, - num_block=num_block, - squeeze_factor=squeeze_factor, - res_scale=res_scale) - self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - def forward(self, x): - self.mean = self.mean.type_as(x) - - x = (x - self.mean) * self.img_range - x = self.conv_first(x) - res = self.conv_after_body(self.body(x)) - res += x - - x = self.conv_last(self.upsample(res)) - x = x / self.img_range + self.mean - - return x diff --git a/basicsr/archs/ridnet_arch.py b/basicsr/archs/ridnet_arch.py deleted file mode 100644 index 85bb9ae..0000000 --- a/basicsr/archs/ridnet_arch.py +++ /dev/null @@ -1,180 +0,0 @@ -import torch -import torch.nn as nn - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import ResidualBlockNoBN, make_layer - - -class MeanShift(nn.Conv2d): - """ Data normalization with mean and std. - - Args: - rgb_range (int): Maximum value of RGB. - rgb_mean (list[float]): Mean for RGB channels. - rgb_std (list[float]): Std for RGB channels. - sign (int): For subtraction, sign is -1, for addition, sign is 1. - Default: -1. - requires_grad (bool): Whether to update the self.weight and self.bias. - Default: True. - """ - - def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1, requires_grad=True): - super(MeanShift, self).__init__(3, 3, kernel_size=1) - std = torch.Tensor(rgb_std) - self.weight.data = torch.eye(3).view(3, 3, 1, 1) - self.weight.data.div_(std.view(3, 1, 1, 1)) - self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) - self.bias.data.div_(std) - self.requires_grad = requires_grad - - -class EResidualBlockNoBN(nn.Module): - """Enhanced Residual block without BN. - - There are three convolution layers in residual branch. - """ - - def __init__(self, in_channels, out_channels): - super(EResidualBlockNoBN, self).__init__() - - self.body = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, 1, 1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 3, 1, 1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 1, 1, 0), - ) - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - out = self.body(x) - out = self.relu(out + x) - return out - - -class MergeRun(nn.Module): - """ Merge-and-run unit. - - This unit contains two branches with different dilated convolutions, - followed by a convolution to process the concatenated features. - - Paper: Real Image Denoising with Feature Attention - Ref git repo: https://github.com/saeed-anwar/RIDNet - """ - - def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1): - super(MergeRun, self).__init__() - - self.dilation1 = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, kernel_size, stride, 2, 2), nn.ReLU(inplace=True)) - self.dilation2 = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size, stride, 3, 3), nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, kernel_size, stride, 4, 4), nn.ReLU(inplace=True)) - - self.aggregation = nn.Sequential( - nn.Conv2d(out_channels * 2, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True)) - - def forward(self, x): - dilation1 = self.dilation1(x) - dilation2 = self.dilation2(x) - out = torch.cat([dilation1, dilation2], dim=1) - out = self.aggregation(out) - out = out + x - return out - - -class ChannelAttention(nn.Module): - """Channel attention. - - Args: - num_feat (int): Channel number of intermediate features. - squeeze_factor (int): Channel squeeze factor. Default: - """ - - def __init__(self, mid_channels, squeeze_factor=16): - super(ChannelAttention, self).__init__() - self.attention = nn.Sequential( - nn.AdaptiveAvgPool2d(1), nn.Conv2d(mid_channels, mid_channels // squeeze_factor, 1, padding=0), - nn.ReLU(inplace=True), nn.Conv2d(mid_channels // squeeze_factor, mid_channels, 1, padding=0), nn.Sigmoid()) - - def forward(self, x): - y = self.attention(x) - return x * y - - -class EAM(nn.Module): - """Enhancement attention modules (EAM) in RIDNet. - - This module contains a merge-and-run unit, a residual block, - an enhanced residual block and a feature attention unit. - - Attributes: - merge: The merge-and-run unit. - block1: The residual block. - block2: The enhanced residual block. - ca: The feature/channel attention unit. - """ - - def __init__(self, in_channels, mid_channels, out_channels): - super(EAM, self).__init__() - - self.merge = MergeRun(in_channels, mid_channels) - self.block1 = ResidualBlockNoBN(mid_channels) - self.block2 = EResidualBlockNoBN(mid_channels, out_channels) - self.ca = ChannelAttention(out_channels) - # The residual block in the paper contains a relu after addition. - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - out = self.merge(x) - out = self.relu(self.block1(out)) - out = self.block2(out) - out = self.ca(out) - return out - - -@ARCH_REGISTRY.register() -class RIDNet(nn.Module): - """RIDNet: Real Image Denoising with Feature Attention. - - Ref git repo: https://github.com/saeed-anwar/RIDNet - - Args: - in_channels (int): Channel number of inputs. - mid_channels (int): Channel number of EAM modules. - Default: 64. - out_channels (int): Channel number of outputs. - num_block (int): Number of EAM. Default: 4. - img_range (float): Image range. Default: 255. - rgb_mean (tuple[float]): Image mean in RGB orders. - Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. - """ - - def __init__(self, - in_channels, - mid_channels, - out_channels, - num_block=4, - img_range=255., - rgb_mean=(0.4488, 0.4371, 0.4040), - rgb_std=(1.0, 1.0, 1.0)): - super(RIDNet, self).__init__() - - self.sub_mean = MeanShift(img_range, rgb_mean, rgb_std) - self.add_mean = MeanShift(img_range, rgb_mean, rgb_std, 1) - - self.head = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) - self.body = make_layer( - EAM, num_block, in_channels=mid_channels, mid_channels=mid_channels, out_channels=mid_channels) - self.tail = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - res = self.sub_mean(x) - res = self.tail(self.body(self.relu(self.head(res)))) - res = self.add_mean(res) - - out = x + res - return out diff --git a/basicsr/archs/rrdbnet_arch.py b/basicsr/archs/rrdbnet_arch.py deleted file mode 100644 index 63d0708..0000000 --- a/basicsr/archs/rrdbnet_arch.py +++ /dev/null @@ -1,119 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import default_init_weights, make_layer, pixel_unshuffle - - -class ResidualDenseBlock(nn.Module): - """Residual Dense Block. - - Used in RRDB block in ESRGAN. - - Args: - num_feat (int): Channel number of intermediate features. - num_grow_ch (int): Channels for each growth. - """ - - def __init__(self, num_feat=64, num_grow_ch=32): - super(ResidualDenseBlock, self).__init__() - self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) - self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - # initialization - default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) - - def forward(self, x): - x1 = self.lrelu(self.conv1(x)) - x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) - x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) - x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - # Empirically, we use 0.2 to scale the residual for better performance - return x5 * 0.2 + x - - -class RRDB(nn.Module): - """Residual in Residual Dense Block. - - Used in RRDB-Net in ESRGAN. - - Args: - num_feat (int): Channel number of intermediate features. - num_grow_ch (int): Channels for each growth. - """ - - def __init__(self, num_feat, num_grow_ch=32): - super(RRDB, self).__init__() - self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) - self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) - self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) - - def forward(self, x): - out = self.rdb1(x) - out = self.rdb2(out) - out = self.rdb3(out) - # Empirically, we use 0.2 to scale the residual for better performance - return out * 0.2 + x - - -@ARCH_REGISTRY.register() -class RRDBNet(nn.Module): - """Networks consisting of Residual in Residual Dense Block, which is used - in ESRGAN. - - ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. - - We extend ESRGAN for scale x2 and scale x1. - Note: This is one option for scale 1, scale 2 in RRDBNet. - We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size - and enlarge the channel size before feeding inputs into the main ESRGAN architecture. - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64 - num_block (int): Block number in the trunk network. Defaults: 23 - num_grow_ch (int): Channels for each growth. Default: 32. - """ - - def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32): - super(RRDBNet, self).__init__() - self.scale = scale - if scale == 2: - num_in_ch = num_in_ch * 4 - elif scale == 1: - num_in_ch = num_in_ch * 16 - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch) - self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - # upsample - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - if self.scale == 2: - feat = pixel_unshuffle(x, scale=2) - elif self.scale == 1: - feat = pixel_unshuffle(x, scale=4) - else: - feat = x - feat = self.conv_first(feat) - body_feat = self.conv_body(self.body(feat)) - feat = feat + body_feat - # upsample - feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) - feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) - out = self.conv_last(self.lrelu(self.conv_hr(feat))) - return out diff --git a/basicsr/archs/spynet_arch.py b/basicsr/archs/spynet_arch.py deleted file mode 100644 index 4c7af13..0000000 --- a/basicsr/archs/spynet_arch.py +++ /dev/null @@ -1,96 +0,0 @@ -import math -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import flow_warp - - -class BasicModule(nn.Module): - """Basic Module for SpyNet. - """ - - def __init__(self): - super(BasicModule, self).__init__() - - self.basic_module = nn.Sequential( - nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), - nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3)) - - def forward(self, tensor_input): - return self.basic_module(tensor_input) - - -@ARCH_REGISTRY.register() -class SpyNet(nn.Module): - """SpyNet architecture. - - Args: - load_path (str): path for pretrained SpyNet. Default: None. - """ - - def __init__(self, load_path=None): - super(SpyNet, self).__init__() - self.basic_module = nn.ModuleList([BasicModule() for _ in range(6)]) - if load_path: - self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) - - self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) - self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) - - def preprocess(self, tensor_input): - tensor_output = (tensor_input - self.mean) / self.std - return tensor_output - - def process(self, ref, supp): - flow = [] - - ref = [self.preprocess(ref)] - supp = [self.preprocess(supp)] - - for level in range(5): - ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False)) - supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False)) - - flow = ref[0].new_zeros( - [ref[0].size(0), 2, - int(math.floor(ref[0].size(2) / 2.0)), - int(math.floor(ref[0].size(3) / 2.0))]) - - for level in range(len(ref)): - upsampled_flow = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0 - - if upsampled_flow.size(2) != ref[level].size(2): - upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 0, 0, 1], mode='replicate') - if upsampled_flow.size(3) != ref[level].size(3): - upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 1, 0, 0], mode='replicate') - - flow = self.basic_module[level](torch.cat([ - ref[level], - flow_warp( - supp[level], upsampled_flow.permute(0, 2, 3, 1), interp_mode='bilinear', padding_mode='border'), - upsampled_flow - ], 1)) + upsampled_flow - - return flow - - def forward(self, ref, supp): - assert ref.size() == supp.size() - - h, w = ref.size(2), ref.size(3) - w_floor = math.floor(math.ceil(w / 32.0) * 32.0) - h_floor = math.floor(math.ceil(h / 32.0) * 32.0) - - ref = F.interpolate(input=ref, size=(h_floor, w_floor), mode='bilinear', align_corners=False) - supp = F.interpolate(input=supp, size=(h_floor, w_floor), mode='bilinear', align_corners=False) - - flow = F.interpolate(input=self.process(ref, supp), size=(h, w), mode='bilinear', align_corners=False) - - flow[:, 0, :, :] *= float(w) / float(w_floor) - flow[:, 1, :, :] *= float(h) / float(h_floor) - - return flow diff --git a/basicsr/archs/srresnet_arch.py b/basicsr/archs/srresnet_arch.py deleted file mode 100644 index 7f57155..0000000 --- a/basicsr/archs/srresnet_arch.py +++ /dev/null @@ -1,65 +0,0 @@ -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import ResidualBlockNoBN, default_init_weights, make_layer - - -@ARCH_REGISTRY.register() -class MSRResNet(nn.Module): - """Modified SRResNet. - - A compacted version modified from SRResNet in - "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" - It uses residual blocks without BN, similar to EDSR. - Currently, it supports x2, x3 and x4 upsampling scale factor. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_out_ch (int): Channel number of outputs. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - num_block (int): Block number in the body network. Default: 16. - upscale (int): Upsampling factor. Support x2, x3 and x4. Default: 4. - """ - - def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4): - super(MSRResNet, self).__init__() - self.upscale = upscale - - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat) - - # upsampling - if self.upscale in [2, 3]: - self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1) - self.pixel_shuffle = nn.PixelShuffle(self.upscale) - elif self.upscale == 4: - self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) - self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) - self.pixel_shuffle = nn.PixelShuffle(2) - - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - # activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - # initialization - default_init_weights([self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1) - if self.upscale == 4: - default_init_weights(self.upconv2, 0.1) - - def forward(self, x): - feat = self.lrelu(self.conv_first(x)) - out = self.body(feat) - - if self.upscale == 4: - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) - elif self.upscale in [2, 3]: - out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) - - out = self.conv_last(self.lrelu(self.conv_hr(out))) - base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False) - out += base - return out diff --git a/basicsr/archs/srvgg_arch.py b/basicsr/archs/srvgg_arch.py deleted file mode 100644 index d8fe5ce..0000000 --- a/basicsr/archs/srvgg_arch.py +++ /dev/null @@ -1,70 +0,0 @@ -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY - - -@ARCH_REGISTRY.register(suffix='basicsr') -class SRVGGNetCompact(nn.Module): - """A compact VGG-style network structure for super-resolution. - - It is a compact network structure, which performs upsampling in the last layer and no convolution is - conducted on the HR feature space. - - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_out_ch (int): Channel number of outputs. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - num_conv (int): Number of convolution layers in the body network. Default: 16. - upscale (int): Upsampling factor. Default: 4. - act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. - """ - - def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'): - super(SRVGGNetCompact, self).__init__() - self.num_in_ch = num_in_ch - self.num_out_ch = num_out_ch - self.num_feat = num_feat - self.num_conv = num_conv - self.upscale = upscale - self.act_type = act_type - - self.body = nn.ModuleList() - # the first conv - self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)) - # the first activation - if act_type == 'relu': - activation = nn.ReLU(inplace=True) - elif act_type == 'prelu': - activation = nn.PReLU(num_parameters=num_feat) - elif act_type == 'leakyrelu': - activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.body.append(activation) - - # the body structure - for _ in range(num_conv): - self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1)) - # activation - if act_type == 'relu': - activation = nn.ReLU(inplace=True) - elif act_type == 'prelu': - activation = nn.PReLU(num_parameters=num_feat) - elif act_type == 'leakyrelu': - activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.body.append(activation) - - # the last conv - self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1)) - # upsample - self.upsampler = nn.PixelShuffle(upscale) - - def forward(self, x): - out = x - for i in range(0, len(self.body)): - out = self.body[i](out) - - out = self.upsampler(out) - # add the nearest upsampled image, so that the network learns the residual - base = F.interpolate(x, scale_factor=self.upscale, mode='nearest') - out += base - return out diff --git a/basicsr/archs/stylegan2_arch.py b/basicsr/archs/stylegan2_arch.py deleted file mode 100644 index 9ab37f5..0000000 --- a/basicsr/archs/stylegan2_arch.py +++ /dev/null @@ -1,799 +0,0 @@ -import math -import random -import torch -from torch import nn -from torch.nn import functional as F - -from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu -from basicsr.ops.upfirdn2d import upfirdn2d -from basicsr.utils.registry import ARCH_REGISTRY - - -class NormStyleCode(nn.Module): - - def forward(self, x): - """Normalize the style codes. - - Args: - x (Tensor): Style codes with shape (b, c). - - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -def make_resample_kernel(k): - """Make resampling kernel for UpFirDn. - - Args: - k (list[int]): A list indicating the 1D resample kernel magnitude. - - Returns: - Tensor: 2D resampled kernel. - """ - k = torch.tensor(k, dtype=torch.float32) - if k.ndim == 1: - k = k[None, :] * k[:, None] # to 2D kernel, outer product - # normalize - k /= k.sum() - return k - - -class UpFirDnUpsample(nn.Module): - """Upsample, FIR filter, and downsample (upsampole version). - - References: - 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 - 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - factor (int): Upsampling scale factor. Default: 2. - """ - - def __init__(self, resample_kernel, factor=2): - super(UpFirDnUpsample, self).__init__() - self.kernel = make_resample_kernel(resample_kernel) * (factor**2) - self.factor = factor - - pad = self.kernel.shape[0] - factor - self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(factor={self.factor})') - - -class UpFirDnDownsample(nn.Module): - """Upsample, FIR filter, and downsample (downsampole version). - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - factor (int): Downsampling scale factor. Default: 2. - """ - - def __init__(self, resample_kernel, factor=2): - super(UpFirDnDownsample, self).__init__() - self.kernel = make_resample_kernel(resample_kernel) - self.factor = factor - - pad = self.kernel.shape[0] - factor - self.pad = ((pad + 1) // 2, pad // 2) - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(factor={self.factor})') - - -class UpFirDnSmooth(nn.Module): - """Upsample, FIR filter, and downsample (smooth version). - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - upsample_factor (int): Upsampling scale factor. Default: 1. - downsample_factor (int): Downsampling scale factor. Default: 1. - kernel_size (int): Kernel size: Default: 1. - """ - - def __init__(self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1): - super(UpFirDnSmooth, self).__init__() - self.upsample_factor = upsample_factor - self.downsample_factor = downsample_factor - self.kernel = make_resample_kernel(resample_kernel) - if upsample_factor > 1: - self.kernel = self.kernel * (upsample_factor**2) - - if upsample_factor > 1: - pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) - self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) - elif downsample_factor > 1: - pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) - self.pad = ((pad + 1) // 2, pad // 2) - else: - raise NotImplementedError - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(upsample_factor={self.upsample_factor}' - f', downsample_factor={self.downsample_factor})') - - -class EqualLinear(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Size of each sample. - out_channels (int): Size of each output sample. - bias (bool): If set to ``False``, the layer will not learn an additive - bias. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - lr_mul (float): Learning rate multiplier. Default: 1. - activation (None | str): The activation after ``linear`` operation. - Supported: 'fused_lrelu', None. Default: None. - """ - - def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): - super(EqualLinear, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.lr_mul = lr_mul - self.activation = activation - if self.activation not in ['fused_lrelu', None]: - raise ValueError(f'Wrong activation value in EqualLinear: {activation}' - "Supported ones are: ['fused_lrelu', None].") - self.scale = (1 / math.sqrt(in_channels)) * lr_mul - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - if self.bias is None: - bias = None - else: - bias = self.bias * self.lr_mul - if self.activation == 'fused_lrelu': - out = F.linear(x, self.weight * self.scale) - out = fused_leaky_relu(out, bias) - else: - out = F.linear(x, self.weight * self.scale, bias=bias) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, bias={self.bias is not None})') - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - - There is no bias in ModulatedConv2d. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. - Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - eps (float): A value added to the denominator for numerical stability. - Default: 1e-8. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=(1, 3, 3, 1), - eps=1e-8): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - - if self.sample_mode == 'upsample': - self.smooth = UpFirDnSmooth( - resample_kernel, upsample_factor=2, downsample_factor=1, kernel_size=kernel_size) - elif self.sample_mode == 'downsample': - self.smooth = UpFirDnSmooth( - resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size) - elif self.sample_mode is None: - pass - else: - raise ValueError(f'Wrong sample mode {self.sample_mode}, ' - "supported ones are ['upsample', 'downsample', None].") - - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - # modulation inside each modulated conv - self.modulation = EqualLinear( - num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) - - self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) - - if self.sample_mode == 'upsample': - x = x.view(1, b * c, h, w) - weight = weight.view(b, self.out_channels, c, self.kernel_size, self.kernel_size) - weight = weight.transpose(1, 2).reshape(b * c, self.out_channels, self.kernel_size, self.kernel_size) - out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - out = self.smooth(out) - elif self.sample_mode == 'downsample': - x = self.smooth(x) - x = x.view(1, b * c, *x.shape[2:4]) - out = F.conv2d(x, weight, padding=0, stride=2, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - else: - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size}, ' - f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') - - -class StyleConv(nn.Module): - """Style conv. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=(1, 3, 3, 1)): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - resample_kernel=resample_kernel) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.activate = FusedLeakyReLU(out_channels) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # activation (with bias) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB from features. - - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - """ - - def __init__(self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)): - super(ToRGB, self).__init__() - if upsample: - self.upsample = UpFirDnUpsample(resample_kernel, factor=2) - else: - self.upsample = None - self.modulated_conv = ModulatedConv2d( - in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = self.upsample(skip) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -@ARCH_REGISTRY.register() -class StyleGAN2Generator(nn.Module): - """StyleGAN2 Generator. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. A cross production will be applied to extent 1D resample - kernel to 2D resample kernel. Default: (1, 3, 3, 1). - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - resample_kernel=(1, 3, 3, 1), - lr_mlp=0.01, - narrow=1): - super(StyleGAN2Generator, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.append( - EqualLinear( - num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, - activation='fused_lrelu')) - self.style_mlp = nn.Sequential(*style_mlp_layers) - - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - self.channels = channels - - self.constant_input = ConstantInput(channels['4'], size=4) - self.style_conv1 = StyleConv( - channels['4'], - channels['4'], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=resample_kernel) - self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, resample_kernel=resample_kernel) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels['4'] - # noise - for layer_idx in range(self.num_layers): - resolution = 2**((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode='upsample', - resample_kernel=resample_kernel, - )) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=resample_kernel)) - self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True, resample_kernel=resample_kernel)) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward(self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2Generator. - - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. - Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is - False. Default: True. - truncation (float): TODO. Default: 1. - truncation_latent (Tensor | None): TODO. Default: None. - inject_index (int | None): The injection index for mixing noise. - Default: None. - return_latents (bool): Whether to return style latents. - Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latent with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ScaledLeakyReLU(nn.Module): - """Scaled LeakyReLU. - - Args: - negative_slope (float): Negative slope. Default: 0.2. - """ - - def __init__(self, negative_slope=0.2): - super(ScaledLeakyReLU, self).__init__() - self.negative_slope = negative_slope - - def forward(self, x): - out = F.leaky_relu(x, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class EqualConv2d(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. - Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. - Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - """ - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): - super(EqualConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - out = F.conv2d( - x, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size},' - f' stride={self.stride}, padding={self.padding}, ' - f'bias={self.bias is not None})') - - -class ConvLayer(nn.Sequential): - """Conv Layer used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Kernel size. - downsample (bool): Whether downsample by a factor of 2. - Default: False. - resample_kernel (list[int]): A list indicating the 1D resample - kernel magnitude. A cross production will be applied to - extent 1D resample kernel to 2D resample kernel. - Default: (1, 3, 3, 1). - bias (bool): Whether with bias. Default: True. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - downsample=False, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True): - layers = [] - # downsample - if downsample: - layers.append( - UpFirDnSmooth(resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size)) - stride = 2 - self.padding = 0 - else: - stride = 1 - self.padding = kernel_size // 2 - # conv - layers.append( - EqualConv2d( - in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias - and not activate)) - # activation - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channels)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super(ConvLayer, self).__init__(*layers) - - -class ResBlock(nn.Module): - """Residual block used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - resample_kernel (list[int]): A list indicating the 1D resample - kernel magnitude. A cross production will be applied to - extent 1D resample kernel to 2D resample kernel. - Default: (1, 3, 3, 1). - """ - - def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): - super(ResBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvLayer( - in_channels, out_channels, 3, downsample=True, resample_kernel=resample_kernel, bias=True, activate=True) - self.skip = ConvLayer( - in_channels, out_channels, 1, downsample=True, resample_kernel=resample_kernel, bias=False, activate=False) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out - - -@ARCH_REGISTRY.register() -class StyleGAN2Discriminator(nn.Module): - """StyleGAN2 Discriminator. - - Args: - out_size (int): The spatial size of outputs. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. A cross production will be applied to extent 1D resample - kernel to 2D resample kernel. Default: (1, 3, 3, 1). - stddev_group (int): For group stddev statistics. Default: 4. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, out_size, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), stddev_group=4, narrow=1): - super(StyleGAN2Discriminator, self).__init__() - - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - - log_size = int(math.log(out_size, 2)) - - conv_body = [ConvLayer(3, channels[f'{out_size}'], 1, bias=True, activate=True)] - - in_channels = channels[f'{out_size}'] - for i in range(log_size, 2, -1): - out_channels = channels[f'{2**(i - 1)}'] - conv_body.append(ResBlock(in_channels, out_channels, resample_kernel)) - in_channels = out_channels - self.conv_body = nn.Sequential(*conv_body) - - self.final_conv = ConvLayer(in_channels + 1, channels['4'], 3, bias=True, activate=True) - self.final_linear = nn.Sequential( - EqualLinear( - channels['4'] * 4 * 4, channels['4'], bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'), - EqualLinear(channels['4'], 1, bias=True, bias_init_val=0, lr_mul=1, activation=None), - ) - self.stddev_group = stddev_group - self.stddev_feat = 1 - - def forward(self, x): - out = self.conv_body(x) - - b, c, h, w = out.shape - # concatenate a group stddev statistics to out - group = min(b, self.stddev_group) # Minibatch must be divisible by (or smaller than) group_size - stddev = out.view(group, -1, self.stddev_feat, c // self.stddev_feat, h, w) - stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) - stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) - stddev = stddev.repeat(group, 1, h, w) - out = torch.cat([out, stddev], 1) - - out = self.final_conv(out) - out = out.view(b, -1) - out = self.final_linear(out) - - return out diff --git a/basicsr/archs/stylegan2_bilinear_arch.py b/basicsr/archs/stylegan2_bilinear_arch.py deleted file mode 100644 index 2395170..0000000 --- a/basicsr/archs/stylegan2_bilinear_arch.py +++ /dev/null @@ -1,614 +0,0 @@ -import math -import random -import torch -from torch import nn -from torch.nn import functional as F - -from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu -from basicsr.utils.registry import ARCH_REGISTRY - - -class NormStyleCode(nn.Module): - - def forward(self, x): - """Normalize the style codes. - - Args: - x (Tensor): Style codes with shape (b, c). - - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -class EqualLinear(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Size of each sample. - out_channels (int): Size of each output sample. - bias (bool): If set to ``False``, the layer will not learn an additive - bias. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - lr_mul (float): Learning rate multiplier. Default: 1. - activation (None | str): The activation after ``linear`` operation. - Supported: 'fused_lrelu', None. Default: None. - """ - - def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): - super(EqualLinear, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.lr_mul = lr_mul - self.activation = activation - if self.activation not in ['fused_lrelu', None]: - raise ValueError(f'Wrong activation value in EqualLinear: {activation}' - "Supported ones are: ['fused_lrelu', None].") - self.scale = (1 / math.sqrt(in_channels)) * lr_mul - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - if self.bias is None: - bias = None - else: - bias = self.bias * self.lr_mul - if self.activation == 'fused_lrelu': - out = F.linear(x, self.weight * self.scale) - out = fused_leaky_relu(out, bias) - else: - out = F.linear(x, self.weight * self.scale, bias=bias) - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, bias={self.bias is not None})') - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - - There is no bias in ModulatedConv2d. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. - Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - eps (float): A value added to the denominator for numerical stability. - Default: 1e-8. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - eps=1e-8, - interpolation_mode='bilinear'): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - # modulation inside each modulated conv - self.modulation = EqualLinear( - num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) - - self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) - - if self.sample_mode == 'upsample': - x = F.interpolate(x, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) - elif self.sample_mode == 'downsample': - x = F.interpolate(x, scale_factor=0.5, mode=self.interpolation_mode, align_corners=self.align_corners) - - b, c, h, w = x.shape - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size}, ' - f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') - - -class StyleConv(nn.Module): - """Style conv. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode='bilinear'): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - interpolation_mode=interpolation_mode) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.activate = FusedLeakyReLU(out_channels) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # activation (with bias) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB from features. - - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - """ - - def __init__(self, in_channels, num_style_feat, upsample=True, interpolation_mode='bilinear'): - super(ToRGB, self).__init__() - self.upsample = upsample - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - self.modulated_conv = ModulatedConv2d( - in_channels, - 3, - kernel_size=1, - num_style_feat=num_style_feat, - demodulate=False, - sample_mode=None, - interpolation_mode=interpolation_mode) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = F.interpolate( - skip, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -@ARCH_REGISTRY.register(suffix='basicsr') -class StyleGAN2GeneratorBilinear(nn.Module): - """StyleGAN2 Generator. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__(self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - lr_mlp=0.01, - narrow=1, - interpolation_mode='bilinear'): - super(StyleGAN2GeneratorBilinear, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.append( - EqualLinear( - num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, - activation='fused_lrelu')) - self.style_mlp = nn.Sequential(*style_mlp_layers) - - channels = { - '4': int(512 * narrow), - '8': int(512 * narrow), - '16': int(512 * narrow), - '32': int(512 * narrow), - '64': int(256 * channel_multiplier * narrow), - '128': int(128 * channel_multiplier * narrow), - '256': int(64 * channel_multiplier * narrow), - '512': int(32 * channel_multiplier * narrow), - '1024': int(16 * channel_multiplier * narrow) - } - self.channels = channels - - self.constant_input = ConstantInput(channels['4'], size=4) - self.style_conv1 = StyleConv( - channels['4'], - channels['4'], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode) - self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, interpolation_mode=interpolation_mode) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels['4'] - # noise - for layer_idx in range(self.num_layers): - resolution = 2**((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f'{2**i}'] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode='upsample', - interpolation_mode=interpolation_mode)) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode)) - self.to_rgbs.append( - ToRGB(out_channels, num_style_feat, upsample=True, interpolation_mode=interpolation_mode)) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward(self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False): - """Forward function for StyleGAN2Generator. - - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. - Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is - False. Default: True. - truncation (float): TODO. Default: 1. - truncation_latent (Tensor | None): TODO. Default: None. - inject_index (int | None): The injection index for mixing noise. - Default: None. - return_latents (bool): Whether to return style latents. - Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) - styles = style_truncation - # get style latent with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], - noise[2::2], self.to_rgbs): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ScaledLeakyReLU(nn.Module): - """Scaled LeakyReLU. - - Args: - negative_slope (float): Negative slope. Default: 0.2. - """ - - def __init__(self, negative_slope=0.2): - super(ScaledLeakyReLU, self).__init__() - self.negative_slope = negative_slope - - def forward(self, x): - out = F.leaky_relu(x, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class EqualConv2d(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. - Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. - Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - """ - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): - super(EqualConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter('bias', None) - - def forward(self, x): - out = F.conv2d( - x, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - - return out - - def __repr__(self): - return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' - f'out_channels={self.out_channels}, ' - f'kernel_size={self.kernel_size},' - f' stride={self.stride}, padding={self.padding}, ' - f'bias={self.bias is not None})') - - -class ConvLayer(nn.Sequential): - """Conv Layer used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Kernel size. - downsample (bool): Whether downsample by a factor of 2. - Default: False. - bias (bool): Whether with bias. Default: True. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - downsample=False, - bias=True, - activate=True, - interpolation_mode='bilinear'): - layers = [] - self.interpolation_mode = interpolation_mode - # downsample - if downsample: - if self.interpolation_mode == 'nearest': - self.align_corners = None - else: - self.align_corners = False - - layers.append( - torch.nn.Upsample(scale_factor=0.5, mode=interpolation_mode, align_corners=self.align_corners)) - stride = 1 - self.padding = kernel_size // 2 - # conv - layers.append( - EqualConv2d( - in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias - and not activate)) - # activation - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channels)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super(ConvLayer, self).__init__(*layers) - - -class ResBlock(nn.Module): - """Residual block used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - """ - - def __init__(self, in_channels, out_channels, interpolation_mode='bilinear'): - super(ResBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvLayer( - in_channels, - out_channels, - 3, - downsample=True, - interpolation_mode=interpolation_mode, - bias=True, - activate=True) - self.skip = ConvLayer( - in_channels, - out_channels, - 1, - downsample=True, - interpolation_mode=interpolation_mode, - bias=False, - activate=False) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out diff --git a/basicsr/archs/swinir_arch.py b/basicsr/archs/swinir_arch.py deleted file mode 100644 index 3917fa2..0000000 --- a/basicsr/archs/swinir_arch.py +++ /dev/null @@ -1,956 +0,0 @@ -# Modified from https://github.com/JingyunLiang/SwinIR -# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 -# Originally Written by Ze Liu, Modified by Jingyun Liang. - -import math -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import to_2tuple, trunc_normal_ - - -def drop_path(x, drop_prob: float = 0., training: bool = False): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py - """ - if drop_prob == 0. or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py - """ - - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) - - -class Mlp(nn.Module): - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (b, h, w, c) - window_size (int): window size - - Returns: - windows: (num_windows*b, window_size, window_size, c) - """ - b, h, w, c = x.shape - x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) - return windows - - -def window_reverse(windows, window_size, h, w): - """ - Args: - windows: (num_windows*b, window_size, window_size, c) - window_size (int): Window size - h (int): Height of image - w (int): Width of image - - Returns: - x: (b, h, w, c) - """ - b = int(windows.shape[0] / (h * w / window_size / window_size)) - x = windows.view(b, h // window_size, w // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) - return x - - -class WindowAttention(nn.Module): - r""" Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer('relative_position_index', relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*b, n, c) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - b_, n, c = x.shape - qkv = self.qkv(x).reshape(b_, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nw = mask.shape[0] - attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, n, n) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(b_, n, c) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' - - def flops(self, n): - # calculate flops for 1 window with token length of n - flops = 0 - # qkv = self.qkv(x) - flops += n * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * n * (self.dim // self.num_heads) * n - # x = (attn @ v) - flops += self.num_heads * n * n * (self.dim // self.num_heads) - # x = self.proj(x) - flops += n * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r""" Swin Transformer Block. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, - dim, - input_resolution, - num_heads, - window_size=7, - shift_size=0, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - act_layer=nn.GELU, - norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size' - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, - window_size=to_2tuple(self.window_size), - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if self.shift_size > 0: - attn_mask = self.calculate_mask(self.input_resolution) - else: - attn_mask = None - - self.register_buffer('attn_mask', attn_mask) - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - h, w = x_size - img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 - h_slices = (slice(0, -self.window_size), slice(-self.window_size, - -self.shift_size), slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), slice(-self.window_size, - -self.shift_size), slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nw, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - def forward(self, x, x_size): - h, w = x_size - b, _, c = x.shape - # assert seq_len == h * w, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(b, h, w, c) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nw*b, window_size, window_size, c - x_windows = x_windows.view(-1, self.window_size * self.window_size, c) # nw*b, window_size*window_size, c - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - if self.input_resolution == x_size: - attn_windows = self.attn(x_windows, mask=self.attn_mask) # nw*b, window_size*window_size, c - else: - attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) - shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(b, h * w, c) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - def extra_repr(self) -> str: - return (f'dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, ' - f'window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}') - - def flops(self): - flops = 0 - h, w = self.input_resolution - # norm1 - flops += self.dim * h * w - # W-MSA/SW-MSA - nw = h * w / self.window_size / self.window_size - flops += nw * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * h * w * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * h * w - return flops - - -class PatchMerging(nn.Module): - r""" Patch Merging Layer. - - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: b, h*w, c - """ - h, w = self.input_resolution - b, seq_len, c = x.shape - assert seq_len == h * w, 'input feature has wrong size' - assert h % 2 == 0 and w % 2 == 0, f'x size ({h}*{w}) are not even.' - - x = x.view(b, h, w, c) - - x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c - x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c - x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c - x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c - x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c - x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c - - x = self.norm(x) - x = self.reduction(x) - - return x - - def extra_repr(self) -> str: - return f'input_resolution={self.input_resolution}, dim={self.dim}' - - def flops(self): - h, w = self.input_resolution - flops = h * w * self.dim - flops += (h // 2) * (w // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False): - - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock( - dim=dim, - input_resolution=input_resolution, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) for i in range(depth) - ]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, x_size): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x, x_size) - if self.downsample is not None: - x = self.downsample(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}' - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class RSTB(nn.Module): - """Residual Swin Transformer Block (RSTB). - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - img_size: Input image size. - patch_size: Patch size. - resi_connection: The convolutional block before residual connection. - """ - - def __init__(self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - img_size=224, - patch_size=4, - resi_connection='1conv'): - super(RSTB, self).__init__() - - self.dim = dim - self.input_resolution = input_resolution - - self.residual_group = BasicLayer( - dim=dim, - input_resolution=input_resolution, - depth=depth, - num_heads=num_heads, - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path, - norm_layer=norm_layer, - downsample=downsample, - use_checkpoint=use_checkpoint) - - if resi_connection == '1conv': - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv = nn.Sequential( - nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim, 3, 1, 1)) - - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) - - self.patch_unembed = PatchUnEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) - - def forward(self, x, x_size): - return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x - - def flops(self): - flops = 0 - flops += self.residual_group.flops() - h, w = self.input_resolution - flops += h * w * self.dim * self.dim * 9 - flops += self.patch_embed.flops() - flops += self.patch_unembed.flops() - - return flops - - -class PatchEmbed(nn.Module): - r""" Image to Patch Embedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - x = x.flatten(2).transpose(1, 2) # b Ph*Pw c - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - flops = 0 - h, w = self.img_size - if self.norm is not None: - flops += h * w * self.embed_dim - return flops - - -class PatchUnEmbed(nn.Module): - r""" Image to Patch Unembedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - def forward(self, x, x_size): - x = x.transpose(1, 2).view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) # b Ph*Pw c - return x - - def flops(self): - flops = 0 - return flops - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.') - super(Upsample, self).__init__(*m) - - -class UpsampleOneStep(nn.Sequential): - """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) - Used in lightweight SR to save parameters. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - - """ - - def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): - self.num_feat = num_feat - self.input_resolution = input_resolution - m = [] - m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) - m.append(nn.PixelShuffle(scale)) - super(UpsampleOneStep, self).__init__(*m) - - def flops(self): - h, w = self.input_resolution - flops = h * w * self.num_feat * 3 * 9 - return flops - - -@ARCH_REGISTRY.register() -class SwinIR(nn.Module): - r""" SwinIR - A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. - - Args: - img_size (int | tuple(int)): Input image size. Default 64 - patch_size (int | tuple(int)): Patch size. Default: 1 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction - img_range: Image range. 1. or 255. - upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__(self, - img_size=64, - patch_size=1, - in_chans=3, - embed_dim=96, - depths=(6, 6, 6, 6), - num_heads=(6, 6, 6, 6), - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.1, - norm_layer=nn.LayerNorm, - ape=False, - patch_norm=True, - use_checkpoint=False, - upscale=2, - img_range=1., - upsampler='', - resi_connection='1conv', - **kwargs): - super(SwinIR, self).__init__() - num_in_ch = in_chans - num_out_ch = in_chans - num_feat = 64 - self.img_range = img_range - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - - # ------------------------- 1, shallow feature extraction ------------------------- # - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - # ------------------------- 2, deep feature extraction ------------------------- # - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = embed_dim - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # merge non-overlapping patches into image - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build Residual Swin Transformer blocks (RSTB) - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RSTB( - dim=embed_dim, - input_resolution=(patches_resolution[0], patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection) - self.layers.append(layer) - self.norm = norm_layer(self.num_features) - - # build the last conv layer in deep feature extraction - if resi_connection == '1conv': - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv_after_body = nn.Sequential( - nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) - - # ------------------------- 3, high quality image reconstruction ------------------------- # - if self.upsampler == 'pixelshuffle': - # for classical SR - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR (to save parameters) - self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, - (patches_resolution[0], patches_resolution[1])) - elif self.upsampler == 'nearest+conv': - # for real-world SR (less artifacts) - assert self.upscale == 4, 'only support x4 now.' - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - else: - # for image denoising and JPEG compression artifact reduction - self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'absolute_pos_embed'} - - @torch.jit.ignore - def no_weight_decay_keywords(self): - return {'relative_position_bias_table'} - - def forward_features(self, x): - x_size = (x.shape[2], x.shape[3]) - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x, x_size) - - x = self.norm(x) # b seq_len c - x = self.patch_unembed(x, x_size) - - return x - - def forward(self, x): - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - - if self.upsampler == 'pixelshuffle': - # for classical SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.upsample(x) - elif self.upsampler == 'nearest+conv': - # for real-world SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.conv_last(self.lrelu(self.conv_hr(x))) - else: - # for image denoising and JPEG compression artifact reduction - x_first = self.conv_first(x) - res = self.conv_after_body(self.forward_features(x_first)) + x_first - x = x + self.conv_last(res) - - x = x / self.img_range + self.mean - - return x - - def flops(self): - flops = 0 - h, w = self.patches_resolution - flops += h * w * 3 * self.embed_dim * 9 - flops += self.patch_embed.flops() - for layer in self.layers: - flops += layer.flops() - flops += h * w * 3 * self.embed_dim * self.embed_dim - flops += self.upsample.flops() - return flops - - -if __name__ == '__main__': - upscale = 4 - window_size = 8 - height = (1024 // upscale // window_size + 1) * window_size - width = (720 // upscale // window_size + 1) * window_size - model = SwinIR( - upscale=2, - img_size=(height, width), - window_size=window_size, - img_range=1., - depths=[6, 6, 6, 6], - embed_dim=60, - num_heads=[6, 6, 6, 6], - mlp_ratio=2, - upsampler='pixelshuffledirect') - print(model) - print(height, width, model.flops() / 1e9) - - x = torch.randn((1, 3, height, width)) - x = model(x) - print(x.shape) diff --git a/basicsr/archs/tof_arch.py b/basicsr/archs/tof_arch.py deleted file mode 100644 index a90a64d..0000000 --- a/basicsr/archs/tof_arch.py +++ /dev/null @@ -1,172 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import flow_warp - - -class BasicModule(nn.Module): - """Basic module of SPyNet. - - Note that unlike the architecture in spynet_arch.py, the basic module - here contains batch normalization. - """ - - def __init__(self): - super(BasicModule, self).__init__() - self.basic_module = nn.Sequential( - nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(32), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(64), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(32), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3, bias=False), - nn.BatchNorm2d(16), nn.ReLU(inplace=True), - nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3)) - - def forward(self, tensor_input): - """ - Args: - tensor_input (Tensor): Input tensor with shape (b, 8, h, w). - 8 channels contain: - [reference image (3), neighbor image (3), initial flow (2)]. - - Returns: - Tensor: Estimated flow with shape (b, 2, h, w) - """ - return self.basic_module(tensor_input) - - -class SPyNetTOF(nn.Module): - """SPyNet architecture for TOF. - - Note that this implementation is specifically for TOFlow. Please use :file:`spynet_arch.py` for general use. - They differ in the following aspects: - - 1. The basic modules here contain BatchNorm. - 2. Normalization and denormalization are not done here, as they are done in TOFlow. - - ``Paper: Optical Flow Estimation using a Spatial Pyramid Network`` - - Reference: https://github.com/Coldog2333/pytoflow - - Args: - load_path (str): Path for pretrained SPyNet. Default: None. - """ - - def __init__(self, load_path=None): - super(SPyNetTOF, self).__init__() - - self.basic_module = nn.ModuleList([BasicModule() for _ in range(4)]) - if load_path: - self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) - - def forward(self, ref, supp): - """ - Args: - ref (Tensor): Reference image with shape of (b, 3, h, w). - supp: The supporting image to be warped: (b, 3, h, w). - - Returns: - Tensor: Estimated optical flow: (b, 2, h, w). - """ - num_batches, _, h, w = ref.size() - ref = [ref] - supp = [supp] - - # generate downsampled frames - for _ in range(3): - ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False)) - supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False)) - - # flow computation - flow = ref[0].new_zeros(num_batches, 2, h // 16, w // 16) - for i in range(4): - flow_up = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0 - flow = flow_up + self.basic_module[i]( - torch.cat([ref[i], flow_warp(supp[i], flow_up.permute(0, 2, 3, 1)), flow_up], 1)) - return flow - - -@ARCH_REGISTRY.register() -class TOFlow(nn.Module): - """PyTorch implementation of TOFlow. - - In TOFlow, the LR frames are pre-upsampled and have the same size with the GT frames. - - ``Paper: Video Enhancement with Task-Oriented Flow`` - - Reference: https://github.com/anchen1011/toflow - - Reference: https://github.com/Coldog2333/pytoflow - - Args: - adapt_official_weights (bool): Whether to adapt the weights translated - from the official implementation. Set to false if you want to - train from scratch. Default: False - """ - - def __init__(self, adapt_official_weights=False): - super(TOFlow, self).__init__() - self.adapt_official_weights = adapt_official_weights - self.ref_idx = 0 if adapt_official_weights else 3 - - self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) - self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) - - # flow estimation module - self.spynet = SPyNetTOF() - - # reconstruction module - self.conv_1 = nn.Conv2d(3 * 7, 64, 9, 1, 4) - self.conv_2 = nn.Conv2d(64, 64, 9, 1, 4) - self.conv_3 = nn.Conv2d(64, 64, 1) - self.conv_4 = nn.Conv2d(64, 3, 1) - - # activation function - self.relu = nn.ReLU(inplace=True) - - def normalize(self, img): - return (img - self.mean) / self.std - - def denormalize(self, img): - return img * self.std + self.mean - - def forward(self, lrs): - """ - Args: - lrs: Input lr frames: (b, 7, 3, h, w). - - Returns: - Tensor: SR frame: (b, 3, h, w). - """ - # In the official implementation, the 0-th frame is the reference frame - if self.adapt_official_weights: - lrs = lrs[:, [3, 0, 1, 2, 4, 5, 6], :, :, :] - - num_batches, num_lrs, _, h, w = lrs.size() - - lrs = self.normalize(lrs.view(-1, 3, h, w)) - lrs = lrs.view(num_batches, num_lrs, 3, h, w) - - lr_ref = lrs[:, self.ref_idx, :, :, :] - lr_aligned = [] - for i in range(7): # 7 frames - if i == self.ref_idx: - lr_aligned.append(lr_ref) - else: - lr_supp = lrs[:, i, :, :, :] - flow = self.spynet(lr_ref, lr_supp) - lr_aligned.append(flow_warp(lr_supp, flow.permute(0, 2, 3, 1))) - - # reconstruction - hr = torch.stack(lr_aligned, dim=1) - hr = hr.view(num_batches, -1, h, w) - hr = self.relu(self.conv_1(hr)) - hr = self.relu(self.conv_2(hr)) - hr = self.relu(self.conv_3(hr)) - hr = self.conv_4(hr) + lr_ref - - return self.denormalize(hr) diff --git a/basicsr/archs/vgg_arch.py b/basicsr/archs/vgg_arch.py deleted file mode 100644 index 0520033..0000000 --- a/basicsr/archs/vgg_arch.py +++ /dev/null @@ -1,161 +0,0 @@ -import os -import torch -from collections import OrderedDict -from torch import nn as nn -from torchvision.models import vgg as vgg - -from basicsr.utils.registry import ARCH_REGISTRY - -VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth' -NAMES = { - 'vgg11': [ - 'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', - 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', - 'pool5' - ], - 'vgg13': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', - 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', - 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' - ], - 'vgg16': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', - 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', - 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', - 'pool5' - ], - 'vgg19': [ - 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', - 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', - 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', - 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5' - ] -} - - -def insert_bn(names): - """Insert bn layer after each conv. - - Args: - names (list): The list of layer names. - - Returns: - list: The list of layer names with bn layers. - """ - names_bn = [] - for name in names: - names_bn.append(name) - if 'conv' in name: - position = name.replace('conv', '') - names_bn.append('bn' + position) - return names_bn - - -@ARCH_REGISTRY.register() -class VGGFeatureExtractor(nn.Module): - """VGG network for feature extraction. - - In this implementation, we allow users to choose whether use normalization - in the input feature and the type of vgg network. Note that the pretrained - path must fit the vgg type. - - Args: - layer_name_list (list[str]): Forward function returns the corresponding - features according to the layer_name_list. - Example: {'relu1_1', 'relu2_1', 'relu3_1'}. - vgg_type (str): Set the type of vgg network. Default: 'vgg19'. - use_input_norm (bool): If True, normalize the input image. Importantly, - the input feature must in the range [0, 1]. Default: True. - range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. - Default: False. - requires_grad (bool): If true, the parameters of VGG network will be - optimized. Default: False. - remove_pooling (bool): If true, the max pooling operations in VGG net - will be removed. Default: False. - pooling_stride (int): The stride of max pooling operation. Default: 2. - """ - - def __init__(self, - layer_name_list, - vgg_type='vgg19', - use_input_norm=True, - range_norm=False, - requires_grad=False, - remove_pooling=False, - pooling_stride=2): - super(VGGFeatureExtractor, self).__init__() - - self.layer_name_list = layer_name_list - self.use_input_norm = use_input_norm - self.range_norm = range_norm - - self.names = NAMES[vgg_type.replace('_bn', '')] - if 'bn' in vgg_type: - self.names = insert_bn(self.names) - - # only borrow layers that will be used to avoid unused params - max_idx = 0 - for v in layer_name_list: - idx = self.names.index(v) - if idx > max_idx: - max_idx = idx - - if os.path.exists(VGG_PRETRAIN_PATH): - vgg_net = getattr(vgg, vgg_type)(pretrained=False) - state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage) - vgg_net.load_state_dict(state_dict) - else: - vgg_net = getattr(vgg, vgg_type)(pretrained=True) - - features = vgg_net.features[:max_idx + 1] - - modified_net = OrderedDict() - for k, v in zip(self.names, features): - if 'pool' in k: - # if remove_pooling is true, pooling operation will be removed - if remove_pooling: - continue - else: - # in some cases, we may want to change the default stride - modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride) - else: - modified_net[k] = v - - self.vgg_net = nn.Sequential(modified_net) - - if not requires_grad: - self.vgg_net.eval() - for param in self.parameters(): - param.requires_grad = False - else: - self.vgg_net.train() - for param in self.parameters(): - param.requires_grad = True - - if self.use_input_norm: - # the mean is for image with range [0, 1] - self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) - # the std is for image with range [0, 1] - self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) - - def forward(self, x): - """Forward function. - - Args: - x (Tensor): Input tensor with shape (n, c, h, w). - - Returns: - Tensor: Forward results. - """ - if self.range_norm: - x = (x + 1) / 2 - if self.use_input_norm: - x = (x - self.mean) / self.std - - output = {} - for key, layer in self.vgg_net._modules.items(): - x = layer(x) - if key in self.layer_name_list: - output[key] = x.clone() - - return output diff --git a/basicsr/archs/vqgan_arch.py b/basicsr/archs/vqgan_arch.py deleted file mode 100644 index f6dfcf4..0000000 --- a/basicsr/archs/vqgan_arch.py +++ /dev/null @@ -1,435 +0,0 @@ -''' -VQGAN code, adapted from the original created by the Unleashing Transformers authors: -https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py - -''' -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import copy -from basicsr.utils import get_root_logger -from basicsr.utils.registry import ARCH_REGISTRY - -def normalize(in_channels): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - - -@torch.jit.script -def swish(x): - return x*torch.sigmoid(x) - - -# Define VQVAE classes -class VectorQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, beta): - super(VectorQuantizer, self).__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) - self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) - - def forward(self, z): - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.emb_dim) - - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) - - mean_distance = torch.mean(d) - # find closest encodings - # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) - min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) - # [0-1], higher score, higher confidence - min_encoding_scores = torch.exp(-min_encoding_scores/10) - - min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - # compute loss for embedding - loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2) - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q, loss, { - "perplexity": perplexity, - "min_encodings": min_encodings, - "min_encoding_indices": min_encoding_indices, - "min_encoding_scores": min_encoding_scores, - "mean_distance": mean_distance - } - - def get_codebook_feat(self, indices, shape): - # input indices: batch*token_num -> (batch*token_num)*1 - # shape: batch, height, width, channel - indices = indices.view(-1,1) - min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) - min_encodings.scatter_(1, indices, 1) - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: # reshape back to match original input shape - z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() - - return z_q - - -class GumbelQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): - super().__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.straight_through = straight_through - self.temperature = temp_init - self.kl_weight = kl_weight - self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits - self.embed = nn.Embedding(codebook_size, emb_dim) - - def forward(self, z): - hard = self.straight_through if self.training else True - - logits = self.proj(z) - - soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) - - z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) - - # + kl divergence to the prior loss - qy = F.softmax(logits, dim=1) - diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() - min_encoding_indices = soft_one_hot.argmax(dim=1) - - return z_q, diff, { - "min_encoding_indices": min_encoding_indices - } - - -class Downsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) - - def forward(self, x): - pad = (0, 1, 0, 1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - return x - - -class Upsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) - - def forward(self, x): - x = F.interpolate(x, scale_factor=2.0, mode="nearest") - x = self.conv(x) - - return x - - -class ResBlock(nn.Module): - def __init__(self, in_channels, out_channels=None): - super(ResBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = in_channels if out_channels is None else out_channels - self.norm1 = normalize(in_channels) - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) - self.norm2 = normalize(out_channels) - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) - if self.in_channels != self.out_channels: - self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - - def forward(self, x_in): - x = x_in - x = self.norm1(x) - x = swish(x) - x = self.conv1(x) - x = self.norm2(x) - x = swish(x) - x = self.conv2(x) - if self.in_channels != self.out_channels: - x_in = self.conv_out(x_in) - - return x + x_in - - -class AttnBlock(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = normalize(in_channels) - self.q = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.k = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.v = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.proj_out = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b, c, h, w = q.shape - q = q.reshape(b, c, h*w) - q = q.permute(0, 2, 1) - k = k.reshape(b, c, h*w) - w_ = torch.bmm(q, k) - w_ = w_ * (int(c)**(-0.5)) - w_ = F.softmax(w_, dim=2) - - # attend to values - v = v.reshape(b, c, h*w) - w_ = w_.permute(0, 2, 1) - h_ = torch.bmm(v, w_) - h_ = h_.reshape(b, c, h, w) - - h_ = self.proj_out(h_) - - return x+h_ - - -class Encoder(nn.Module): - def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions): - super().__init__() - self.nf = nf - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.attn_resolutions = attn_resolutions - - curr_res = self.resolution - in_ch_mult = (1,)+tuple(ch_mult) - - blocks = [] - # initial convultion - blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) - - # residual and downsampling blocks, with attention on smaller res (16x16) - for i in range(self.num_resolutions): - block_in_ch = nf * in_ch_mult[i] - block_out_ch = nf * ch_mult[i] - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - if curr_res in attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != self.num_resolutions - 1: - blocks.append(Downsample(block_in_ch)) - curr_res = curr_res // 2 - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - # normalise and convert to latent size - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)) - self.blocks = nn.ModuleList(blocks) - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -class Generator(nn.Module): - def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): - super().__init__() - self.nf = nf - self.ch_mult = ch_mult - self.num_resolutions = len(self.ch_mult) - self.num_res_blocks = res_blocks - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.in_channels = emb_dim - self.out_channels = 3 - block_in_ch = self.nf * self.ch_mult[-1] - curr_res = self.resolution // 2 ** (self.num_resolutions-1) - - blocks = [] - # initial conv - blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - for i in reversed(range(self.num_resolutions)): - block_out_ch = self.nf * self.ch_mult[i] - - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - - if curr_res in self.attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != 0: - blocks.append(Upsample(block_in_ch)) - curr_res = curr_res * 2 - - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) - - self.blocks = nn.ModuleList(blocks) - - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -@ARCH_REGISTRY.register() -class VQAutoEncoder(nn.Module): - def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, - beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): - super().__init__() - logger = get_root_logger() - self.in_channels = 3 - self.nf = nf - self.n_blocks = res_blocks - self.codebook_size = codebook_size - self.embed_dim = emb_dim - self.ch_mult = ch_mult - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.quantizer_type = quantizer - self.encoder = Encoder( - self.in_channels, - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - if self.quantizer_type == "nearest": - self.beta = beta #0.25 - self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) - elif self.quantizer_type == "gumbel": - self.gumbel_num_hiddens = emb_dim - self.straight_through = gumbel_straight_through - self.kl_weight = gumbel_kl_weight - self.quantize = GumbelQuantizer( - self.codebook_size, - self.embed_dim, - self.gumbel_num_hiddens, - self.straight_through, - self.kl_weight - ) - self.generator = Generator( - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_ema' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) - logger.info(f'vqgan is loaded from: {model_path} [params_ema]') - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - logger.info(f'vqgan is loaded from: {model_path} [params]') - else: - raise ValueError(f'Wrong params!') - - - def forward(self, x): - x = self.encoder(x) - quant, codebook_loss, quant_stats = self.quantize(x) - x = self.generator(quant) - return x, codebook_loss, quant_stats - - - -# patch based discriminator -@ARCH_REGISTRY.register() -class VQGANDiscriminator(nn.Module): - def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None): - super().__init__() - - layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] - ndf_mult = 1 - ndf_mult_prev = 1 - for n in range(1, n_layers): # gradually increase the number of filters - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n, 8) - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n_layers, 8) - - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - layers += [ - nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map - self.main = nn.Sequential(*layers) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_d' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d']) - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - else: - raise ValueError(f'Wrong params!') - - def forward(self, x): - return self.main(x) \ No newline at end of file diff --git a/basicsr/data/__init__.py b/basicsr/data/__init__.py deleted file mode 100644 index 510df16..0000000 --- a/basicsr/data/__init__.py +++ /dev/null @@ -1,101 +0,0 @@ -import importlib -import numpy as np -import random -import torch -import torch.utils.data -from copy import deepcopy -from functools import partial -from os import path as osp - -from basicsr.data.prefetch_dataloader import PrefetchDataLoader -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import DATASET_REGISTRY - -__all__ = ['build_dataset', 'build_dataloader'] - -# automatically scan and import dataset modules for registry -# scan all the files under the data folder with '_dataset' in file names -data_folder = osp.dirname(osp.abspath(__file__)) -dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] -# import all the dataset modules -_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames] - - -def build_dataset(dataset_opt): - """Build dataset from options. - - Args: - dataset_opt (dict): Configuration for dataset. It must contain: - name (str): Dataset name. - type (str): Dataset type. - """ - dataset_opt = deepcopy(dataset_opt) - dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt) - logger = get_root_logger() - logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} is built.') - return dataset - - -def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None): - """Build dataloader. - - Args: - dataset (torch.utils.data.Dataset): Dataset. - dataset_opt (dict): Dataset options. It contains the following keys: - phase (str): 'train' or 'val'. - num_worker_per_gpu (int): Number of workers for each GPU. - batch_size_per_gpu (int): Training batch size for each GPU. - num_gpu (int): Number of GPUs. Used only in the train phase. - Default: 1. - dist (bool): Whether in distributed training. Used only in the train - phase. Default: False. - sampler (torch.utils.data.sampler): Data sampler. Default: None. - seed (int | None): Seed. Default: None - """ - phase = dataset_opt['phase'] - rank, _ = get_dist_info() - if phase == 'train': - if dist: # distributed training - batch_size = dataset_opt['batch_size_per_gpu'] - num_workers = dataset_opt['num_worker_per_gpu'] - else: # non-distributed training - multiplier = 1 if num_gpu == 0 else num_gpu - batch_size = dataset_opt['batch_size_per_gpu'] * multiplier - num_workers = dataset_opt['num_worker_per_gpu'] * multiplier - dataloader_args = dict( - dataset=dataset, - batch_size=batch_size, - shuffle=False, - num_workers=num_workers, - sampler=sampler, - drop_last=True) - if sampler is None: - dataloader_args['shuffle'] = True - dataloader_args['worker_init_fn'] = partial( - worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None - elif phase in ['val', 'test']: # validation - dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0) - else: - raise ValueError(f"Wrong dataset phase: {phase}. Supported ones are 'train', 'val' and 'test'.") - - dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False) - dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', False) - - prefetch_mode = dataset_opt.get('prefetch_mode') - if prefetch_mode == 'cpu': # CPUPrefetcher - num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1) - logger = get_root_logger() - logger.info(f'Use {prefetch_mode} prefetch dataloader: num_prefetch_queue = {num_prefetch_queue}') - return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args) - else: - # prefetch_mode=None: Normal dataloader - # prefetch_mode='cuda': dataloader for CUDAPrefetcher - return torch.utils.data.DataLoader(**dataloader_args) - - -def worker_init_fn(worker_id, num_workers, rank, seed): - # Set the worker seed to num_workers * rank + worker_id + seed - worker_seed = num_workers * rank + worker_id + seed - np.random.seed(worker_seed) - random.seed(worker_seed) diff --git 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It is usually the world_size. - rank (int | None): Rank of the current process within num_replicas. - ratio (int): Enlarging ratio. Default: 1. - """ - - def __init__(self, dataset, num_replicas, rank, ratio=1): - self.dataset = dataset - self.num_replicas = num_replicas - self.rank = rank - self.epoch = 0 - self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas) - self.total_size = self.num_samples * self.num_replicas - - def __iter__(self): - # deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.epoch) - indices = torch.randperm(self.total_size, generator=g).tolist() - - dataset_size = len(self.dataset) - indices = [v % dataset_size for v in indices] - - # subsample - indices = indices[self.rank:self.total_size:self.num_replicas] - assert len(indices) == self.num_samples - - return iter(indices) - - def __len__(self): - return self.num_samples - - def set_epoch(self, epoch): - self.epoch = epoch diff --git a/basicsr/data/data_util.py b/basicsr/data/data_util.py deleted file mode 100644 index bf4c494..0000000 --- a/basicsr/data/data_util.py +++ /dev/null @@ -1,315 +0,0 @@ -import cv2 -import numpy as np -import torch -from os import path as osp -from torch.nn import functional as F - -from basicsr.data.transforms import mod_crop -from basicsr.utils import img2tensor, scandir - - -def read_img_seq(path, require_mod_crop=False, scale=1, return_imgname=False): - """Read a sequence of images from a given folder path. - - Args: - path (list[str] | str): List of image paths or image folder path. - require_mod_crop (bool): Require mod crop for each image. - Default: False. - scale (int): Scale factor for mod_crop. Default: 1. - return_imgname(bool): Whether return image names. Default False. - - Returns: - Tensor: size (t, c, h, w), RGB, [0, 1]. - list[str]: Returned image name list. - """ - if isinstance(path, list): - img_paths = path - else: - img_paths = sorted(list(scandir(path, full_path=True))) - imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths] - - if require_mod_crop: - imgs = [mod_crop(img, scale) for img in imgs] - imgs = img2tensor(imgs, bgr2rgb=True, float32=True) - imgs = torch.stack(imgs, dim=0) - - if return_imgname: - imgnames = [osp.splitext(osp.basename(path))[0] for path in img_paths] - return imgs, imgnames - else: - return imgs - - -def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'): - """Generate an index list for reading `num_frames` frames from a sequence - of images. - - Args: - crt_idx (int): Current center index. - max_frame_num (int): Max number of the sequence of images (from 1). - num_frames (int): Reading num_frames frames. - padding (str): Padding mode, one of - 'replicate' | 'reflection' | 'reflection_circle' | 'circle' - Examples: current_idx = 0, num_frames = 5 - The generated frame indices under different padding mode: - replicate: [0, 0, 0, 1, 2] - reflection: [2, 1, 0, 1, 2] - reflection_circle: [4, 3, 0, 1, 2] - circle: [3, 4, 0, 1, 2] - - Returns: - list[int]: A list of indices. - """ - assert num_frames % 2 == 1, 'num_frames should be an odd number.' - assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.' - - max_frame_num = max_frame_num - 1 # start from 0 - num_pad = num_frames // 2 - - indices = [] - for i in range(crt_idx - num_pad, crt_idx + num_pad + 1): - if i < 0: - if padding == 'replicate': - pad_idx = 0 - elif padding == 'reflection': - pad_idx = -i - elif padding == 'reflection_circle': - pad_idx = crt_idx + num_pad - i - else: - pad_idx = num_frames + i - elif i > max_frame_num: - if padding == 'replicate': - pad_idx = max_frame_num - elif padding == 'reflection': - pad_idx = max_frame_num * 2 - i - elif padding == 'reflection_circle': - pad_idx = (crt_idx - num_pad) - (i - max_frame_num) - else: - pad_idx = i - num_frames - else: - pad_idx = i - indices.append(pad_idx) - return indices - - -def paired_paths_from_lmdb(folders, keys): - """Generate paired paths from lmdb files. - - Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is: - - :: - - lq.lmdb - ├── data.mdb - ├── lock.mdb - ├── meta_info.txt - - The data.mdb and lock.mdb are standard lmdb files and you can refer to - https://lmdb.readthedocs.io/en/release/ for more details. - - The meta_info.txt is a specified txt file to record the meta information - of our datasets. It will be automatically created when preparing - datasets by our provided dataset tools. - Each line in the txt file records - 1)image name (with extension), - 2)image shape, - 3)compression level, separated by a white space. - Example: `baboon.png (120,125,3) 1` - - We use the image name without extension as the lmdb key. - Note that we use the same key for the corresponding lq and gt images. - - Args: - folders (list[str]): A list of folder path. The order of list should - be [input_folder, gt_folder]. - keys (list[str]): A list of keys identifying folders. The order should - be in consistent with folders, e.g., ['lq', 'gt']. - Note that this key is different from lmdb keys. - - Returns: - list[str]: Returned path list. - """ - assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' - f'But got {len(folders)}') - assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' - input_folder, gt_folder = folders - input_key, gt_key = keys - - if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')): - raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb ' - f'formats. But received {input_key}: {input_folder}; ' - f'{gt_key}: {gt_folder}') - # ensure that the two meta_info files are the same - with open(osp.join(input_folder, 'meta_info.txt')) as fin: - input_lmdb_keys = [line.split('.')[0] for line in fin] - with open(osp.join(gt_folder, 'meta_info.txt')) as fin: - gt_lmdb_keys = [line.split('.')[0] for line in fin] - if set(input_lmdb_keys) != set(gt_lmdb_keys): - raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.') - else: - paths = [] - for lmdb_key in sorted(input_lmdb_keys): - paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)])) - return paths - - -def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl): - """Generate paired paths from an meta information file. - - Each line in the meta information file contains the image names and - image shape (usually for gt), separated by a white space. - - Example of an meta information file: - ``` - 0001_s001.png (480,480,3) - 0001_s002.png (480,480,3) - ``` - - Args: - folders (list[str]): A list of folder path. The order of list should - be [input_folder, gt_folder]. - keys (list[str]): A list of keys identifying folders. The order should - be in consistent with folders, e.g., ['lq', 'gt']. - meta_info_file (str): Path to the meta information file. - filename_tmpl (str): Template for each filename. Note that the - template excludes the file extension. Usually the filename_tmpl is - for files in the input folder. - - Returns: - list[str]: Returned path list. - """ - assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' - f'But got {len(folders)}') - assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' - input_folder, gt_folder = folders - input_key, gt_key = keys - - with open(meta_info_file, 'r') as fin: - gt_names = [line.strip().split(' ')[0] for line in fin] - - paths = [] - for gt_name in gt_names: - basename, ext = osp.splitext(osp.basename(gt_name)) - input_name = f'{filename_tmpl.format(basename)}{ext}' - input_path = osp.join(input_folder, input_name) - gt_path = osp.join(gt_folder, gt_name) - paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)])) - return paths - - -def paired_paths_from_folder(folders, keys, filename_tmpl): - """Generate paired paths from folders. - - Args: - folders (list[str]): A list of folder path. The order of list should - be [input_folder, gt_folder]. - keys (list[str]): A list of keys identifying folders. The order should - be in consistent with folders, e.g., ['lq', 'gt']. - filename_tmpl (str): Template for each filename. Note that the - template excludes the file extension. Usually the filename_tmpl is - for files in the input folder. - - Returns: - list[str]: Returned path list. - """ - assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' - f'But got {len(folders)}') - assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' - input_folder, gt_folder = folders - input_key, gt_key = keys - - input_paths = list(scandir(input_folder)) - gt_paths = list(scandir(gt_folder)) - assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: ' - f'{len(input_paths)}, {len(gt_paths)}.') - paths = [] - for gt_path in gt_paths: - basename, ext = osp.splitext(osp.basename(gt_path)) - input_name = f'{filename_tmpl.format(basename)}{ext}' - input_path = osp.join(input_folder, input_name) - assert input_name in input_paths, f'{input_name} is not in {input_key}_paths.' - gt_path = osp.join(gt_folder, gt_path) - paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)])) - return paths - - -def paths_from_folder(folder): - """Generate paths from folder. - - Args: - folder (str): Folder path. - - Returns: - list[str]: Returned path list. - """ - - paths = list(scandir(folder)) - paths = [osp.join(folder, path) for path in paths] - return paths - - -def paths_from_lmdb(folder): - """Generate paths from lmdb. - - Args: - folder (str): Folder path. - - Returns: - list[str]: Returned path list. - """ - if not folder.endswith('.lmdb'): - raise ValueError(f'Folder {folder}folder should in lmdb format.') - with open(osp.join(folder, 'meta_info.txt')) as fin: - paths = [line.split('.')[0] for line in fin] - return paths - - -def generate_gaussian_kernel(kernel_size=13, sigma=1.6): - """Generate Gaussian kernel used in `duf_downsample`. - - Args: - kernel_size (int): Kernel size. Default: 13. - sigma (float): Sigma of the Gaussian kernel. Default: 1.6. - - Returns: - np.array: The Gaussian kernel. - """ - from scipy.ndimage import filters as filters - kernel = np.zeros((kernel_size, kernel_size)) - # set element at the middle to one, a dirac delta - kernel[kernel_size // 2, kernel_size // 2] = 1 - # gaussian-smooth the dirac, resulting in a gaussian filter - return filters.gaussian_filter(kernel, sigma) - - -def duf_downsample(x, kernel_size=13, scale=4): - """Downsamping with Gaussian kernel used in the DUF official code. - - Args: - x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w). - kernel_size (int): Kernel size. Default: 13. - scale (int): Downsampling factor. Supported scale: (2, 3, 4). - Default: 4. - - Returns: - Tensor: DUF downsampled frames. - """ - assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.' - - squeeze_flag = False - if x.ndim == 4: - squeeze_flag = True - x = x.unsqueeze(0) - b, t, c, h, w = x.size() - x = x.view(-1, 1, h, w) - pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2 - x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect') - - gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale) - gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0) - x = F.conv2d(x, gaussian_filter, stride=scale) - x = x[:, :, 2:-2, 2:-2] - x = x.view(b, t, c, x.size(2), x.size(3)) - if squeeze_flag: - x = x.squeeze(0) - return x diff --git a/basicsr/data/degradations.py b/basicsr/data/degradations.py deleted file mode 100644 index 1431960..0000000 --- a/basicsr/data/degradations.py +++ /dev/null @@ -1,764 +0,0 @@ -import cv2 -import math -import numpy as np -import random -import torch -from scipy import special -from scipy.stats import multivariate_normal -from torchvision.transforms.functional import rgb_to_grayscale - -# -------------------------------------------------------------------- # -# --------------------------- blur kernels --------------------------- # -# -------------------------------------------------------------------- # - - -# --------------------------- util functions --------------------------- # -def sigma_matrix2(sig_x, sig_y, theta): - """Calculate the rotated sigma matrix (two dimensional matrix). - - Args: - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - - Returns: - ndarray: Rotated sigma matrix. - """ - d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]]) - u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) - return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T)) - - -def mesh_grid(kernel_size): - """Generate the mesh grid, centering at zero. - - Args: - kernel_size (int): - - Returns: - xy (ndarray): with the shape (kernel_size, kernel_size, 2) - xx (ndarray): with the shape (kernel_size, kernel_size) - yy (ndarray): with the shape (kernel_size, kernel_size) - """ - ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.) - xx, yy = np.meshgrid(ax, ax) - xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size, - 1))).reshape(kernel_size, kernel_size, 2) - return xy, xx, yy - - -def pdf2(sigma_matrix, grid): - """Calculate PDF of the bivariate Gaussian distribution. - - Args: - sigma_matrix (ndarray): with the shape (2, 2) - grid (ndarray): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. - - Returns: - kernel (ndarrray): un-normalized kernel. - """ - inverse_sigma = np.linalg.inv(sigma_matrix) - kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2)) - return kernel - - -def cdf2(d_matrix, grid): - """Calculate the CDF of the standard bivariate Gaussian distribution. - Used in skewed Gaussian distribution. - - Args: - d_matrix (ndarrasy): skew matrix. - grid (ndarray): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. - - Returns: - cdf (ndarray): skewed cdf. - """ - rv = multivariate_normal([0, 0], [[1, 0], [0, 1]]) - grid = np.dot(grid, d_matrix) - cdf = rv.cdf(grid) - return cdf - - -def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True): - """Generate a bivariate isotropic or anisotropic Gaussian kernel. - - In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. - - Args: - kernel_size (int): - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - grid (ndarray, optional): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. Default: None - isotropic (bool): - - Returns: - kernel (ndarray): normalized kernel. - """ - if grid is None: - grid, _, _ = mesh_grid(kernel_size) - if isotropic: - sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) - else: - sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) - kernel = pdf2(sigma_matrix, grid) - kernel = kernel / np.sum(kernel) - return kernel - - -def bivariate_generalized_Gaussian(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): - """Generate a bivariate generalized Gaussian kernel. - - ``Paper: Parameter Estimation For Multivariate Generalized Gaussian Distributions`` - - In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. - - Args: - kernel_size (int): - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - beta (float): shape parameter, beta = 1 is the normal distribution. - grid (ndarray, optional): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. Default: None - - Returns: - kernel (ndarray): normalized kernel. - """ - if grid is None: - grid, _, _ = mesh_grid(kernel_size) - if isotropic: - sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) - else: - sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) - inverse_sigma = np.linalg.inv(sigma_matrix) - kernel = np.exp(-0.5 * np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta)) - kernel = kernel / np.sum(kernel) - return kernel - - -def bivariate_plateau(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): - """Generate a plateau-like anisotropic kernel. - - 1 / (1+x^(beta)) - - Reference: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-shaped-distribution - - In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. - - Args: - kernel_size (int): - sig_x (float): - sig_y (float): - theta (float): Radian measurement. - beta (float): shape parameter, beta = 1 is the normal distribution. - grid (ndarray, optional): generated by :func:`mesh_grid`, - with the shape (K, K, 2), K is the kernel size. Default: None - - Returns: - kernel (ndarray): normalized kernel. - """ - if grid is None: - grid, _, _ = mesh_grid(kernel_size) - if isotropic: - sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) - else: - sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) - inverse_sigma = np.linalg.inv(sigma_matrix) - kernel = np.reciprocal(np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta) + 1) - kernel = kernel / np.sum(kernel) - return kernel - - -def random_bivariate_Gaussian(kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - noise_range=None, - isotropic=True): - """Randomly generate bivariate isotropic or anisotropic Gaussian kernels. - - In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. - - Args: - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi, math.pi] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' - sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) - if isotropic is False: - assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' - assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' - sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) - rotation = np.random.uniform(rotation_range[0], rotation_range[1]) - else: - sigma_y = sigma_x - rotation = 0 - - kernel = bivariate_Gaussian(kernel_size, sigma_x, sigma_y, rotation, isotropic=isotropic) - - # add multiplicative noise - if noise_range is not None: - assert noise_range[0] < noise_range[1], 'Wrong noise range.' - noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) - kernel = kernel * noise - kernel = kernel / np.sum(kernel) - return kernel - - -def random_bivariate_generalized_Gaussian(kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - beta_range, - noise_range=None, - isotropic=True): - """Randomly generate bivariate generalized Gaussian kernels. - - In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. - - Args: - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi, math.pi] - beta_range (tuple): [0.5, 8] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' - sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) - if isotropic is False: - assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' - assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' - sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) - rotation = np.random.uniform(rotation_range[0], rotation_range[1]) - else: - sigma_y = sigma_x - rotation = 0 - - # assume beta_range[0] < 1 < beta_range[1] - if np.random.uniform() < 0.5: - beta = np.random.uniform(beta_range[0], 1) - else: - beta = np.random.uniform(1, beta_range[1]) - - kernel = bivariate_generalized_Gaussian(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) - - # add multiplicative noise - if noise_range is not None: - assert noise_range[0] < noise_range[1], 'Wrong noise range.' - noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) - kernel = kernel * noise - kernel = kernel / np.sum(kernel) - return kernel - - -def random_bivariate_plateau(kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - beta_range, - noise_range=None, - isotropic=True): - """Randomly generate bivariate plateau kernels. - - In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. - - Args: - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi/2, math.pi/2] - beta_range (tuple): [1, 4] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' - sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) - if isotropic is False: - assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' - assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' - sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) - rotation = np.random.uniform(rotation_range[0], rotation_range[1]) - else: - sigma_y = sigma_x - rotation = 0 - - # TODO: this may be not proper - if np.random.uniform() < 0.5: - beta = np.random.uniform(beta_range[0], 1) - else: - beta = np.random.uniform(1, beta_range[1]) - - kernel = bivariate_plateau(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) - # add multiplicative noise - if noise_range is not None: - assert noise_range[0] < noise_range[1], 'Wrong noise range.' - noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) - kernel = kernel * noise - kernel = kernel / np.sum(kernel) - - return kernel - - -def random_mixed_kernels(kernel_list, - kernel_prob, - kernel_size=21, - sigma_x_range=(0.6, 5), - sigma_y_range=(0.6, 5), - rotation_range=(-math.pi, math.pi), - betag_range=(0.5, 8), - betap_range=(0.5, 8), - noise_range=None): - """Randomly generate mixed kernels. - - Args: - kernel_list (tuple): a list name of kernel types, - support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso', - 'plateau_aniso'] - kernel_prob (tuple): corresponding kernel probability for each - kernel type - kernel_size (int): - sigma_x_range (tuple): [0.6, 5] - sigma_y_range (tuple): [0.6, 5] - rotation range (tuple): [-math.pi, math.pi] - beta_range (tuple): [0.5, 8] - noise_range(tuple, optional): multiplicative kernel noise, - [0.75, 1.25]. Default: None - - Returns: - kernel (ndarray): - """ - kernel_type = random.choices(kernel_list, kernel_prob)[0] - if kernel_type == 'iso': - kernel = random_bivariate_Gaussian( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True) - elif kernel_type == 'aniso': - kernel = random_bivariate_Gaussian( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False) - elif kernel_type == 'generalized_iso': - kernel = random_bivariate_generalized_Gaussian( - kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - betag_range, - noise_range=noise_range, - isotropic=True) - elif kernel_type == 'generalized_aniso': - kernel = random_bivariate_generalized_Gaussian( - kernel_size, - sigma_x_range, - sigma_y_range, - rotation_range, - betag_range, - noise_range=noise_range, - isotropic=False) - elif kernel_type == 'plateau_iso': - kernel = random_bivariate_plateau( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True) - elif kernel_type == 'plateau_aniso': - kernel = random_bivariate_plateau( - kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False) - return kernel - - -np.seterr(divide='ignore', invalid='ignore') - - -def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0): - """2D sinc filter - - Reference: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter - - Args: - cutoff (float): cutoff frequency in radians (pi is max) - kernel_size (int): horizontal and vertical size, must be odd. - pad_to (int): pad kernel size to desired size, must be odd or zero. - """ - assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' - kernel = np.fromfunction( - lambda x, y: cutoff * special.j1(cutoff * np.sqrt( - (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt( - (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size]) - kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi) - kernel = kernel / np.sum(kernel) - if pad_to > kernel_size: - pad_size = (pad_to - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - return kernel - - -# ------------------------------------------------------------- # -# --------------------------- noise --------------------------- # -# ------------------------------------------------------------- # - -# ----------------------- Gaussian Noise ----------------------- # - - -def generate_gaussian_noise(img, sigma=10, gray_noise=False): - """Generate Gaussian noise. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - sigma (float): Noise scale (measured in range 255). Default: 10. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - if gray_noise: - noise = np.float32(np.random.randn(*(img.shape[0:2]))) * sigma / 255. - noise = np.expand_dims(noise, axis=2).repeat(3, axis=2) - else: - noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255. - return noise - - -def add_gaussian_noise(img, sigma=10, clip=True, rounds=False, gray_noise=False): - """Add Gaussian noise. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - sigma (float): Noise scale (measured in range 255). Default: 10. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - noise = generate_gaussian_noise(img, sigma, gray_noise) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def generate_gaussian_noise_pt(img, sigma=10, gray_noise=0): - """Add Gaussian noise (PyTorch version). - - Args: - img (Tensor): Shape (b, c, h, w), range[0, 1], float32. - scale (float | Tensor): Noise scale. Default: 1.0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - b, _, h, w = img.size() - if not isinstance(sigma, (float, int)): - sigma = sigma.view(img.size(0), 1, 1, 1) - if isinstance(gray_noise, (float, int)): - cal_gray_noise = gray_noise > 0 - else: - gray_noise = gray_noise.view(b, 1, 1, 1) - cal_gray_noise = torch.sum(gray_noise) > 0 - - if cal_gray_noise: - noise_gray = torch.randn(*img.size()[2:4], dtype=img.dtype, device=img.device) * sigma / 255. - noise_gray = noise_gray.view(b, 1, h, w) - - # always calculate color noise - noise = torch.randn(*img.size(), dtype=img.dtype, device=img.device) * sigma / 255. - - if cal_gray_noise: - noise = noise * (1 - gray_noise) + noise_gray * gray_noise - return noise - - -def add_gaussian_noise_pt(img, sigma=10, gray_noise=0, clip=True, rounds=False): - """Add Gaussian noise (PyTorch version). - - Args: - img (Tensor): Shape (b, c, h, w), range[0, 1], float32. - scale (float | Tensor): Noise scale. Default: 1.0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - noise = generate_gaussian_noise_pt(img, sigma, gray_noise) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ----------------------- Random Gaussian Noise ----------------------- # -def random_generate_gaussian_noise(img, sigma_range=(0, 10), gray_prob=0): - sigma = np.random.uniform(sigma_range[0], sigma_range[1]) - if np.random.uniform() < gray_prob: - gray_noise = True - else: - gray_noise = False - return generate_gaussian_noise(img, sigma, gray_noise) - - -def random_add_gaussian_noise(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_gaussian_noise(img, sigma_range, gray_prob) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def random_generate_gaussian_noise_pt(img, sigma_range=(0, 10), gray_prob=0): - sigma = torch.rand( - img.size(0), dtype=img.dtype, device=img.device) * (sigma_range[1] - sigma_range[0]) + sigma_range[0] - gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) - gray_noise = (gray_noise < gray_prob).float() - return generate_gaussian_noise_pt(img, sigma, gray_noise) - - -def random_add_gaussian_noise_pt(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_gaussian_noise_pt(img, sigma_range, gray_prob) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ----------------------- Poisson (Shot) Noise ----------------------- # - - -def generate_poisson_noise(img, scale=1.0, gray_noise=False): - """Generate poisson noise. - - Reference: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/noise.py#L37-L219 - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - scale (float): Noise scale. Default: 1.0. - gray_noise (bool): Whether generate gray noise. Default: False. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - if gray_noise: - img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - # round and clip image for counting vals correctly - img = np.clip((img * 255.0).round(), 0, 255) / 255. - vals = len(np.unique(img)) - vals = 2**np.ceil(np.log2(vals)) - out = np.float32(np.random.poisson(img * vals) / float(vals)) - noise = out - img - if gray_noise: - noise = np.repeat(noise[:, :, np.newaxis], 3, axis=2) - return noise * scale - - -def add_poisson_noise(img, scale=1.0, clip=True, rounds=False, gray_noise=False): - """Add poisson noise. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - scale (float): Noise scale. Default: 1.0. - gray_noise (bool): Whether generate gray noise. Default: False. - - Returns: - (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], - float32. - """ - noise = generate_poisson_noise(img, scale, gray_noise) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def generate_poisson_noise_pt(img, scale=1.0, gray_noise=0): - """Generate a batch of poisson noise (PyTorch version) - - Args: - img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. - scale (float | Tensor): Noise scale. Number or Tensor with shape (b). - Default: 1.0. - gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). - 0 for False, 1 for True. Default: 0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - b, _, h, w = img.size() - if isinstance(gray_noise, (float, int)): - cal_gray_noise = gray_noise > 0 - else: - gray_noise = gray_noise.view(b, 1, 1, 1) - cal_gray_noise = torch.sum(gray_noise) > 0 - if cal_gray_noise: - img_gray = rgb_to_grayscale(img, num_output_channels=1) - # round and clip image for counting vals correctly - img_gray = torch.clamp((img_gray * 255.0).round(), 0, 255) / 255. - # use for-loop to get the unique values for each sample - vals_list = [len(torch.unique(img_gray[i, :, :, :])) for i in range(b)] - vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] - vals = img_gray.new_tensor(vals_list).view(b, 1, 1, 1) - out = torch.poisson(img_gray * vals) / vals - noise_gray = out - img_gray - noise_gray = noise_gray.expand(b, 3, h, w) - - # always calculate color noise - # round and clip image for counting vals correctly - img = torch.clamp((img * 255.0).round(), 0, 255) / 255. - # use for-loop to get the unique values for each sample - vals_list = [len(torch.unique(img[i, :, :, :])) for i in range(b)] - vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] - vals = img.new_tensor(vals_list).view(b, 1, 1, 1) - out = torch.poisson(img * vals) / vals - noise = out - img - if cal_gray_noise: - noise = noise * (1 - gray_noise) + noise_gray * gray_noise - if not isinstance(scale, (float, int)): - scale = scale.view(b, 1, 1, 1) - return noise * scale - - -def add_poisson_noise_pt(img, scale=1.0, clip=True, rounds=False, gray_noise=0): - """Add poisson noise to a batch of images (PyTorch version). - - Args: - img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. - scale (float | Tensor): Noise scale. Number or Tensor with shape (b). - Default: 1.0. - gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). - 0 for False, 1 for True. Default: 0. - - Returns: - (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], - float32. - """ - noise = generate_poisson_noise_pt(img, scale, gray_noise) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ----------------------- Random Poisson (Shot) Noise ----------------------- # - - -def random_generate_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0): - scale = np.random.uniform(scale_range[0], scale_range[1]) - if np.random.uniform() < gray_prob: - gray_noise = True - else: - gray_noise = False - return generate_poisson_noise(img, scale, gray_noise) - - -def random_add_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_poisson_noise(img, scale_range, gray_prob) - out = img + noise - if clip and rounds: - out = np.clip((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = np.clip(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -def random_generate_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0): - scale = torch.rand( - img.size(0), dtype=img.dtype, device=img.device) * (scale_range[1] - scale_range[0]) + scale_range[0] - gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) - gray_noise = (gray_noise < gray_prob).float() - return generate_poisson_noise_pt(img, scale, gray_noise) - - -def random_add_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): - noise = random_generate_poisson_noise_pt(img, scale_range, gray_prob) - out = img + noise - if clip and rounds: - out = torch.clamp((out * 255.0).round(), 0, 255) / 255. - elif clip: - out = torch.clamp(out, 0, 1) - elif rounds: - out = (out * 255.0).round() / 255. - return out - - -# ------------------------------------------------------------------------ # -# --------------------------- JPEG compression --------------------------- # -# ------------------------------------------------------------------------ # - - -def add_jpg_compression(img, quality=90): - """Add JPG compression artifacts. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - quality (float): JPG compression quality. 0 for lowest quality, 100 for - best quality. Default: 90. - - Returns: - (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], - float32. - """ - img = np.clip(img, 0, 1) - encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality] - _, encimg = cv2.imencode('.jpg', img * 255., encode_param) - img = np.float32(cv2.imdecode(encimg, 1)) / 255. - return img - - -def random_add_jpg_compression(img, quality_range=(90, 100)): - """Randomly add JPG compression artifacts. - - Args: - img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. - quality_range (tuple[float] | list[float]): JPG compression quality - range. 0 for lowest quality, 100 for best quality. - Default: (90, 100). - - Returns: - (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], - float32. - """ - quality = np.random.uniform(quality_range[0], quality_range[1]) - return add_jpg_compression(img, quality) diff --git a/basicsr/data/ffhq_dataset.py b/basicsr/data/ffhq_dataset.py deleted file mode 100644 index 23992eb..0000000 --- a/basicsr/data/ffhq_dataset.py +++ /dev/null @@ -1,80 +0,0 @@ -import random -import time -from os import path as osp -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class FFHQDataset(data.Dataset): - """FFHQ dataset for StyleGAN. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - io_backend (dict): IO backend type and other kwarg. - mean (list | tuple): Image mean. - std (list | tuple): Image std. - use_hflip (bool): Whether to horizontally flip. - - """ - - def __init__(self, opt): - super(FFHQDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - - self.gt_folder = opt['dataroot_gt'] - self.mean = opt['mean'] - self.std = opt['std'] - - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = self.gt_folder - if not self.gt_folder.endswith('.lmdb'): - raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # FFHQ has 70000 images in total - self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)] - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # load gt image - gt_path = self.paths[index] - # avoid errors caused by high latency in reading files - retry = 3 - while retry > 0: - try: - img_bytes = self.file_client.get(gt_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'File client error: {e}, remaining retry times: {retry - 1}') - # change another file to read - index = random.randint(0, self.__len__()) - gt_path = self.paths[index] - time.sleep(1) # sleep 1s for occasional server congestion - else: - break - finally: - retry -= 1 - img_gt = imfrombytes(img_bytes, float32=True) - - # random horizontal flip - img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False) - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) - # normalize - normalize(img_gt, self.mean, self.std, inplace=True) - return {'gt': img_gt, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt b/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt deleted file mode 100644 index 0819690..0000000 --- a/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt +++ /dev/null @@ -1,32592 +0,0 @@ -0001_s001.png (480,480,3) -0001_s002.png (480,480,3) -0001_s003.png (480,480,3) -0001_s004.png (480,480,3) -0001_s005.png (480,480,3) -0001_s006.png (480,480,3) -0001_s007.png (480,480,3) -0001_s008.png (480,480,3) -0001_s009.png (480,480,3) -0001_s010.png (480,480,3) -0001_s011.png (480,480,3) -0001_s012.png (480,480,3) -0001_s013.png (480,480,3) -0001_s014.png (480,480,3) -0001_s015.png (480,480,3) -0001_s016.png (480,480,3) -0001_s017.png (480,480,3) -0001_s018.png (480,480,3) -0001_s019.png (480,480,3) -0001_s020.png (480,480,3) -0001_s021.png (480,480,3) -0001_s022.png (480,480,3) -0001_s023.png (480,480,3) -0001_s024.png (480,480,3) 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-0799_s036.png (480,480,3) -0799_s037.png (480,480,3) -0799_s038.png (480,480,3) -0799_s039.png (480,480,3) -0799_s040.png (480,480,3) -0800_s001.png (480,480,3) -0800_s002.png (480,480,3) -0800_s003.png (480,480,3) -0800_s004.png (480,480,3) -0800_s005.png (480,480,3) -0800_s006.png (480,480,3) -0800_s007.png (480,480,3) -0800_s008.png (480,480,3) -0800_s009.png (480,480,3) -0800_s010.png (480,480,3) -0800_s011.png (480,480,3) -0800_s012.png (480,480,3) -0800_s013.png (480,480,3) -0800_s014.png (480,480,3) -0800_s015.png (480,480,3) -0800_s016.png (480,480,3) -0800_s017.png (480,480,3) -0800_s018.png (480,480,3) -0800_s019.png (480,480,3) -0800_s020.png (480,480,3) -0800_s021.png (480,480,3) -0800_s022.png (480,480,3) -0800_s023.png (480,480,3) -0800_s024.png (480,480,3) -0800_s025.png (480,480,3) -0800_s026.png (480,480,3) -0800_s027.png (480,480,3) -0800_s028.png (480,480,3) -0800_s029.png (480,480,3) -0800_s030.png (480,480,3) -0800_s031.png (480,480,3) -0800_s032.png (480,480,3) -0800_s033.png (480,480,3) -0800_s034.png (480,480,3) -0800_s035.png (480,480,3) -0800_s036.png (480,480,3) -0800_s037.png (480,480,3) -0800_s038.png (480,480,3) -0800_s039.png (480,480,3) -0800_s040.png (480,480,3) diff --git a/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt b/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt deleted file mode 100644 index 45badd0..0000000 --- a/basicsr/data/meta_info/meta_info_REDS4_test_GT.txt +++ /dev/null @@ -1,4 +0,0 @@ -000 100 (720,1280,3) -011 100 (720,1280,3) -015 100 (720,1280,3) -020 100 (720,1280,3) diff --git a/basicsr/data/meta_info/meta_info_REDS_GT.txt b/basicsr/data/meta_info/meta_info_REDS_GT.txt deleted file mode 100644 index 34fb398..0000000 --- a/basicsr/data/meta_info/meta_info_REDS_GT.txt +++ /dev/null @@ -1,270 +0,0 @@ -000 100 (720,1280,3) -001 100 (720,1280,3) -002 100 (720,1280,3) -003 100 (720,1280,3) -004 100 (720,1280,3) -005 100 (720,1280,3) -006 100 (720,1280,3) -007 100 (720,1280,3) -008 100 (720,1280,3) -009 100 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(720,1280,3) -055 100 (720,1280,3) -056 100 (720,1280,3) -057 100 (720,1280,3) -058 100 (720,1280,3) -059 100 (720,1280,3) -060 100 (720,1280,3) -061 100 (720,1280,3) -062 100 (720,1280,3) -063 100 (720,1280,3) -064 100 (720,1280,3) -065 100 (720,1280,3) -066 100 (720,1280,3) -067 100 (720,1280,3) -068 100 (720,1280,3) -069 100 (720,1280,3) -070 100 (720,1280,3) -071 100 (720,1280,3) -072 100 (720,1280,3) -073 100 (720,1280,3) -074 100 (720,1280,3) -075 100 (720,1280,3) -076 100 (720,1280,3) -077 100 (720,1280,3) -078 100 (720,1280,3) -079 100 (720,1280,3) -080 100 (720,1280,3) -081 100 (720,1280,3) -082 100 (720,1280,3) -083 100 (720,1280,3) -084 100 (720,1280,3) -085 100 (720,1280,3) -086 100 (720,1280,3) -087 100 (720,1280,3) -088 100 (720,1280,3) -089 100 (720,1280,3) -090 100 (720,1280,3) -091 100 (720,1280,3) -092 100 (720,1280,3) -093 100 (720,1280,3) -094 100 (720,1280,3) -095 100 (720,1280,3) -096 100 (720,1280,3) -097 100 (720,1280,3) -098 100 (720,1280,3) -099 100 (720,1280,3) -100 100 (720,1280,3) -101 100 (720,1280,3) -102 100 (720,1280,3) -103 100 (720,1280,3) -104 100 (720,1280,3) -105 100 (720,1280,3) -106 100 (720,1280,3) -107 100 (720,1280,3) -108 100 (720,1280,3) -109 100 (720,1280,3) -110 100 (720,1280,3) -111 100 (720,1280,3) -112 100 (720,1280,3) -113 100 (720,1280,3) -114 100 (720,1280,3) -115 100 (720,1280,3) -116 100 (720,1280,3) -117 100 (720,1280,3) -118 100 (720,1280,3) -119 100 (720,1280,3) -120 100 (720,1280,3) -121 100 (720,1280,3) -122 100 (720,1280,3) -123 100 (720,1280,3) -124 100 (720,1280,3) -125 100 (720,1280,3) -126 100 (720,1280,3) -127 100 (720,1280,3) -128 100 (720,1280,3) -129 100 (720,1280,3) -130 100 (720,1280,3) -131 100 (720,1280,3) -132 100 (720,1280,3) -133 100 (720,1280,3) -134 100 (720,1280,3) -135 100 (720,1280,3) -136 100 (720,1280,3) -137 100 (720,1280,3) -138 100 (720,1280,3) -139 100 (720,1280,3) -140 100 (720,1280,3) -141 100 (720,1280,3) -142 100 (720,1280,3) -143 100 (720,1280,3) -144 100 (720,1280,3) -145 100 (720,1280,3) -146 100 (720,1280,3) -147 100 (720,1280,3) -148 100 (720,1280,3) -149 100 (720,1280,3) -150 100 (720,1280,3) -151 100 (720,1280,3) -152 100 (720,1280,3) -153 100 (720,1280,3) -154 100 (720,1280,3) -155 100 (720,1280,3) -156 100 (720,1280,3) -157 100 (720,1280,3) -158 100 (720,1280,3) -159 100 (720,1280,3) -160 100 (720,1280,3) -161 100 (720,1280,3) -162 100 (720,1280,3) -163 100 (720,1280,3) -164 100 (720,1280,3) -165 100 (720,1280,3) -166 100 (720,1280,3) -167 100 (720,1280,3) -168 100 (720,1280,3) -169 100 (720,1280,3) -170 100 (720,1280,3) -171 100 (720,1280,3) -172 100 (720,1280,3) -173 100 (720,1280,3) -174 100 (720,1280,3) -175 100 (720,1280,3) -176 100 (720,1280,3) -177 100 (720,1280,3) -178 100 (720,1280,3) -179 100 (720,1280,3) -180 100 (720,1280,3) -181 100 (720,1280,3) -182 100 (720,1280,3) -183 100 (720,1280,3) -184 100 (720,1280,3) -185 100 (720,1280,3) -186 100 (720,1280,3) -187 100 (720,1280,3) -188 100 (720,1280,3) -189 100 (720,1280,3) -190 100 (720,1280,3) -191 100 (720,1280,3) -192 100 (720,1280,3) -193 100 (720,1280,3) -194 100 (720,1280,3) -195 100 (720,1280,3) -196 100 (720,1280,3) -197 100 (720,1280,3) -198 100 (720,1280,3) -199 100 (720,1280,3) -200 100 (720,1280,3) -201 100 (720,1280,3) -202 100 (720,1280,3) -203 100 (720,1280,3) -204 100 (720,1280,3) -205 100 (720,1280,3) -206 100 (720,1280,3) -207 100 (720,1280,3) -208 100 (720,1280,3) -209 100 (720,1280,3) -210 100 (720,1280,3) -211 100 (720,1280,3) -212 100 (720,1280,3) -213 100 (720,1280,3) -214 100 (720,1280,3) -215 100 (720,1280,3) -216 100 (720,1280,3) -217 100 (720,1280,3) -218 100 (720,1280,3) -219 100 (720,1280,3) -220 100 (720,1280,3) -221 100 (720,1280,3) -222 100 (720,1280,3) -223 100 (720,1280,3) -224 100 (720,1280,3) -225 100 (720,1280,3) -226 100 (720,1280,3) -227 100 (720,1280,3) -228 100 (720,1280,3) -229 100 (720,1280,3) -230 100 (720,1280,3) -231 100 (720,1280,3) -232 100 (720,1280,3) -233 100 (720,1280,3) -234 100 (720,1280,3) -235 100 (720,1280,3) -236 100 (720,1280,3) -237 100 (720,1280,3) -238 100 (720,1280,3) -239 100 (720,1280,3) -240 100 (720,1280,3) -241 100 (720,1280,3) -242 100 (720,1280,3) -243 100 (720,1280,3) -244 100 (720,1280,3) -245 100 (720,1280,3) -246 100 (720,1280,3) -247 100 (720,1280,3) -248 100 (720,1280,3) -249 100 (720,1280,3) -250 100 (720,1280,3) -251 100 (720,1280,3) -252 100 (720,1280,3) -253 100 (720,1280,3) -254 100 (720,1280,3) -255 100 (720,1280,3) -256 100 (720,1280,3) -257 100 (720,1280,3) -258 100 (720,1280,3) -259 100 (720,1280,3) -260 100 (720,1280,3) -261 100 (720,1280,3) -262 100 (720,1280,3) -263 100 (720,1280,3) -264 100 (720,1280,3) -265 100 (720,1280,3) -266 100 (720,1280,3) -267 100 (720,1280,3) -268 100 (720,1280,3) -269 100 (720,1280,3) diff --git a/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt b/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt deleted file mode 100644 index b407561..0000000 --- a/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt +++ /dev/null @@ -1,4 +0,0 @@ -240 100 (720,1280,3) -241 100 (720,1280,3) -246 100 (720,1280,3) -257 100 (720,1280,3) diff --git a/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt b/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt deleted file mode 100644 index 1112131..0000000 --- a/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt +++ /dev/null @@ -1,30 +0,0 @@ -240 100 (720,1280,3) -241 100 (720,1280,3) -242 100 (720,1280,3) -243 100 (720,1280,3) -244 100 (720,1280,3) -245 100 (720,1280,3) -246 100 (720,1280,3) -247 100 (720,1280,3) -248 100 (720,1280,3) -249 100 (720,1280,3) -250 100 (720,1280,3) -251 100 (720,1280,3) -252 100 (720,1280,3) -253 100 (720,1280,3) -254 100 (720,1280,3) -255 100 (720,1280,3) -256 100 (720,1280,3) -257 100 (720,1280,3) -258 100 (720,1280,3) -259 100 (720,1280,3) -260 100 (720,1280,3) -261 100 (720,1280,3) -262 100 (720,1280,3) -263 100 (720,1280,3) -264 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a/basicsr/data/paired_image_dataset.py b/basicsr/data/paired_image_dataset.py deleted file mode 100644 index 9f5c8c6..0000000 --- a/basicsr/data/paired_image_dataset.py +++ /dev/null @@ -1,106 +0,0 @@ -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb, paired_paths_from_meta_info_file -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, bgr2ycbcr, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class PairedImageDataset(data.Dataset): - """Paired image dataset for image restoration. - - Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. - - There are three modes: - - 1. **lmdb**: Use lmdb files. If opt['io_backend'] == lmdb. - 2. **meta_info_file**: Use meta information file to generate paths. \ - If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None. - 3. **folder**: Scan folders to generate paths. The rest. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - meta_info_file (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. - Default: '{}'. - gt_size (int): Cropped patched size for gt patches. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - phase (str): 'train' or 'val'. - """ - - def __init__(self, opt): - super(PairedImageDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.mean = opt['mean'] if 'mean' in opt else None - self.std = opt['std'] if 'std' in opt else None - - self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] - if 'filename_tmpl' in opt: - self.filename_tmpl = opt['filename_tmpl'] - else: - self.filename_tmpl = '{}' - - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) - elif 'meta_info_file' in self.opt and self.opt['meta_info_file'] is not None: - self.paths = paired_paths_from_meta_info_file([self.lq_folder, self.gt_folder], ['lq', 'gt'], - self.opt['meta_info_file'], self.filename_tmpl) - else: - self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - - # Load gt and lq images. Dimension order: HWC; channel order: BGR; - # image range: [0, 1], float32. - gt_path = self.paths[index]['gt_path'] - img_bytes = self.file_client.get(gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - lq_path = self.paths[index]['lq_path'] - img_bytes = self.file_client.get(lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - - # augmentation for training - if self.opt['phase'] == 'train': - gt_size = self.opt['gt_size'] - # random crop - img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) - # flip, rotation - img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) - - # color space transform - if 'color' in self.opt and self.opt['color'] == 'y': - img_gt = bgr2ycbcr(img_gt, y_only=True)[..., None] - img_lq = bgr2ycbcr(img_lq, y_only=True)[..., None] - - # crop the unmatched GT images during validation or testing, especially for SR benchmark datasets - # TODO: It is better to update the datasets, rather than force to crop - if self.opt['phase'] != 'train': - img_gt = img_gt[0:img_lq.shape[0] * scale, 0:img_lq.shape[1] * scale, :] - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) - # normalize - if self.mean is not None or self.std is not None: - normalize(img_lq, self.mean, self.std, inplace=True) - normalize(img_gt, self.mean, self.std, inplace=True) - - return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/prefetch_dataloader.py b/basicsr/data/prefetch_dataloader.py deleted file mode 100644 index 332abd3..0000000 --- a/basicsr/data/prefetch_dataloader.py +++ /dev/null @@ -1,122 +0,0 @@ -import queue as Queue -import threading -import torch -from torch.utils.data import DataLoader - - -class PrefetchGenerator(threading.Thread): - """A general prefetch generator. - - Reference: https://stackoverflow.com/questions/7323664/python-generator-pre-fetch - - Args: - generator: Python generator. - num_prefetch_queue (int): Number of prefetch queue. - """ - - def __init__(self, generator, num_prefetch_queue): - threading.Thread.__init__(self) - self.queue = Queue.Queue(num_prefetch_queue) - self.generator = generator - self.daemon = True - self.start() - - def run(self): - for item in self.generator: - self.queue.put(item) - self.queue.put(None) - - def __next__(self): - next_item = self.queue.get() - if next_item is None: - raise StopIteration - return next_item - - def __iter__(self): - return self - - -class PrefetchDataLoader(DataLoader): - """Prefetch version of dataloader. - - Reference: https://github.com/IgorSusmelj/pytorch-styleguide/issues/5# - - TODO: - Need to test on single gpu and ddp (multi-gpu). There is a known issue in - ddp. - - Args: - num_prefetch_queue (int): Number of prefetch queue. - kwargs (dict): Other arguments for dataloader. - """ - - def __init__(self, num_prefetch_queue, **kwargs): - self.num_prefetch_queue = num_prefetch_queue - super(PrefetchDataLoader, self).__init__(**kwargs) - - def __iter__(self): - return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue) - - -class CPUPrefetcher(): - """CPU prefetcher. - - Args: - loader: Dataloader. - """ - - def __init__(self, loader): - self.ori_loader = loader - self.loader = iter(loader) - - def next(self): - try: - return next(self.loader) - except StopIteration: - return None - - def reset(self): - self.loader = iter(self.ori_loader) - - -class CUDAPrefetcher(): - """CUDA prefetcher. - - Reference: https://github.com/NVIDIA/apex/issues/304# - - It may consume more GPU memory. - - Args: - loader: Dataloader. - opt (dict): Options. - """ - - def __init__(self, loader, opt): - self.ori_loader = loader - self.loader = iter(loader) - self.opt = opt - self.stream = torch.cuda.Stream() - self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') - self.preload() - - def preload(self): - try: - self.batch = next(self.loader) # self.batch is a dict - except StopIteration: - self.batch = None - return None - # put tensors to gpu - with torch.cuda.stream(self.stream): - for k, v in self.batch.items(): - if torch.is_tensor(v): - self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True) - - def next(self): - torch.cuda.current_stream().wait_stream(self.stream) - batch = self.batch - self.preload() - return batch - - def reset(self): - self.loader = iter(self.ori_loader) - self.preload() diff --git a/basicsr/data/realesrgan_dataset.py b/basicsr/data/realesrgan_dataset.py deleted file mode 100644 index 1616e9b..0000000 --- a/basicsr/data/realesrgan_dataset.py +++ /dev/null @@ -1,193 +0,0 @@ -import cv2 -import math -import numpy as np -import os -import os.path as osp -import random -import time -import torch -from torch.utils import data as data - -from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register(suffix='basicsr') -class RealESRGANDataset(data.Dataset): - """Dataset used for Real-ESRGAN model: - Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It loads gt (Ground-Truth) images, and augments them. - It also generates blur kernels and sinc kernels for generating low-quality images. - Note that the low-quality images are processed in tensors on GPUS for faster processing. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - meta_info (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - Please see more options in the codes. - """ - - def __init__(self, opt): - super(RealESRGANDataset, self).__init__() - self.opt = opt - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.gt_folder = opt['dataroot_gt'] - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.gt_folder] - self.io_backend_opt['client_keys'] = ['gt'] - if not self.gt_folder.endswith('.lmdb'): - raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # disk backend with meta_info - # Each line in the meta_info describes the relative path to an image - with open(self.opt['meta_info']) as fin: - paths = [line.strip().split(' ')[0] for line in fin] - self.paths = [os.path.join(self.gt_folder, v) for v in paths] - - # blur settings for the first degradation - self.blur_kernel_size = opt['blur_kernel_size'] - self.kernel_list = opt['kernel_list'] - self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability - self.blur_sigma = opt['blur_sigma'] - self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels - self.betap_range = opt['betap_range'] # betap used in plateau blur kernels - self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters - - # blur settings for the second degradation - self.blur_kernel_size2 = opt['blur_kernel_size2'] - self.kernel_list2 = opt['kernel_list2'] - self.kernel_prob2 = opt['kernel_prob2'] - self.blur_sigma2 = opt['blur_sigma2'] - self.betag_range2 = opt['betag_range2'] - self.betap_range2 = opt['betap_range2'] - self.sinc_prob2 = opt['sinc_prob2'] - - # a final sinc filter - self.final_sinc_prob = opt['final_sinc_prob'] - - self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21 - # TODO: kernel range is now hard-coded, should be in the configure file - self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect - self.pulse_tensor[10, 10] = 1 - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # -------------------------------- Load gt images -------------------------------- # - # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. - gt_path = self.paths[index] - # avoid errors caused by high latency in reading files - retry = 3 - while retry > 0: - try: - img_bytes = self.file_client.get(gt_path, 'gt') - except (IOError, OSError) as e: - logger = get_root_logger() - logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}') - # change another file to read - index = random.randint(0, self.__len__()) - gt_path = self.paths[index] - time.sleep(1) # sleep 1s for occasional server congestion - else: - break - finally: - retry -= 1 - img_gt = imfrombytes(img_bytes, float32=True) - - # -------------------- Do augmentation for training: flip, rotation -------------------- # - img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot']) - - # crop or pad to 400 - # TODO: 400 is hard-coded. You may change it accordingly - h, w = img_gt.shape[0:2] - crop_pad_size = 400 - # pad - if h < crop_pad_size or w < crop_pad_size: - pad_h = max(0, crop_pad_size - h) - pad_w = max(0, crop_pad_size - w) - img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101) - # crop - if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size: - h, w = img_gt.shape[0:2] - # randomly choose top and left coordinates - top = random.randint(0, h - crop_pad_size) - left = random.randint(0, w - crop_pad_size) - img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...] - - # ------------------------ Generate kernels (used in the first degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob']: - # this sinc filter setting is for kernels ranging from [7, 21] - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel = random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - kernel_size, - self.blur_sigma, - self.blur_sigma, [-math.pi, math.pi], - self.betag_range, - self.betap_range, - noise_range=None) - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------ Generate kernels (used in the second degradation) ------------------------ # - kernel_size = random.choice(self.kernel_range) - if np.random.uniform() < self.opt['sinc_prob2']: - if kernel_size < 13: - omega_c = np.random.uniform(np.pi / 3, np.pi) - else: - omega_c = np.random.uniform(np.pi / 5, np.pi) - kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) - else: - kernel2 = random_mixed_kernels( - self.kernel_list2, - self.kernel_prob2, - kernel_size, - self.blur_sigma2, - self.blur_sigma2, [-math.pi, math.pi], - self.betag_range2, - self.betap_range2, - noise_range=None) - - # pad kernel - pad_size = (21 - kernel_size) // 2 - kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) - - # ------------------------------------- the final sinc kernel ------------------------------------- # - if np.random.uniform() < self.opt['final_sinc_prob']: - kernel_size = random.choice(self.kernel_range) - omega_c = np.random.uniform(np.pi / 3, np.pi) - sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21) - sinc_kernel = torch.FloatTensor(sinc_kernel) - else: - sinc_kernel = self.pulse_tensor - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0] - kernel = torch.FloatTensor(kernel) - kernel2 = torch.FloatTensor(kernel2) - - return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path} - return return_d - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/realesrgan_paired_dataset.py b/basicsr/data/realesrgan_paired_dataset.py deleted file mode 100644 index 604b026..0000000 --- a/basicsr/data/realesrgan_paired_dataset.py +++ /dev/null @@ -1,106 +0,0 @@ -import os -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register(suffix='basicsr') -class RealESRGANPairedDataset(data.Dataset): - """Paired image dataset for image restoration. - - Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. - - There are three modes: - - 1. **lmdb**: Use lmdb files. If opt['io_backend'] == lmdb. - 2. **meta_info_file**: Use meta information file to generate paths. \ - If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None. - 3. **folder**: Scan folders to generate paths. The rest. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - meta_info (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. - Default: '{}'. - gt_size (int): Cropped patched size for gt patches. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - phase (str): 'train' or 'val'. - """ - - def __init__(self, opt): - super(RealESRGANPairedDataset, self).__init__() - self.opt = opt - self.file_client = None - self.io_backend_opt = opt['io_backend'] - # mean and std for normalizing the input images - self.mean = opt['mean'] if 'mean' in opt else None - self.std = opt['std'] if 'std' in opt else None - - self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] - self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}' - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) - elif 'meta_info' in self.opt and self.opt['meta_info'] is not None: - # disk backend with meta_info - # Each line in the meta_info describes the relative path to an image - with open(self.opt['meta_info']) as fin: - paths = [line.strip() for line in fin] - self.paths = [] - for path in paths: - gt_path, lq_path = path.split(', ') - gt_path = os.path.join(self.gt_folder, gt_path) - lq_path = os.path.join(self.lq_folder, lq_path) - self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)])) - else: - # disk backend - # it will scan the whole folder to get meta info - # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file - self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - - # Load gt and lq images. Dimension order: HWC; channel order: BGR; - # image range: [0, 1], float32. - gt_path = self.paths[index]['gt_path'] - img_bytes = self.file_client.get(gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - lq_path = self.paths[index]['lq_path'] - img_bytes = self.file_client.get(lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - - # augmentation for training - if self.opt['phase'] == 'train': - gt_size = self.opt['gt_size'] - # random crop - img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) - # flip, rotation - img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) - # normalize - if self.mean is not None or self.std is not None: - normalize(img_lq, self.mean, self.std, inplace=True) - normalize(img_gt, self.mean, self.std, inplace=True) - - return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/reds_dataset.py b/basicsr/data/reds_dataset.py deleted file mode 100644 index fabef1d..0000000 --- a/basicsr/data/reds_dataset.py +++ /dev/null @@ -1,352 +0,0 @@ -import numpy as np -import random -import torch -from pathlib import Path -from torch.utils import data as data - -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.flow_util import dequantize_flow -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class REDSDataset(data.Dataset): - """REDS dataset for training. - - The keys are generated from a meta info txt file. - basicsr/data/meta_info/meta_info_REDS_GT.txt - - Each line contains: - 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by - a white space. - Examples: - 000 100 (720,1280,3) - 001 100 (720,1280,3) - ... - - Key examples: "000/00000000" - GT (gt): Ground-Truth; - LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - dataroot_flow (str, optional): Data root path for flow. - meta_info_file (str): Path for meta information file. - val_partition (str): Validation partition types. 'REDS4' or 'official'. - io_backend (dict): IO backend type and other kwarg. - num_frame (int): Window size for input frames. - gt_size (int): Cropped patched size for gt patches. - interval_list (list): Interval list for temporal augmentation. - random_reverse (bool): Random reverse input frames. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - """ - - def __init__(self, opt): - super(REDSDataset, self).__init__() - self.opt = opt - self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) - self.flow_root = Path(opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None - assert opt['num_frame'] % 2 == 1, (f'num_frame should be odd number, but got {opt["num_frame"]}') - self.num_frame = opt['num_frame'] - self.num_half_frames = opt['num_frame'] // 2 - - self.keys = [] - with open(opt['meta_info_file'], 'r') as fin: - for line in fin: - folder, frame_num, _ = line.split(' ') - self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))]) - - # remove the video clips used in validation - if opt['val_partition'] == 'REDS4': - val_partition = ['000', '011', '015', '020'] - elif opt['val_partition'] == 'official': - val_partition = [f'{v:03d}' for v in range(240, 270)] - else: - raise ValueError(f'Wrong validation partition {opt["val_partition"]}.' - f"Supported ones are ['official', 'REDS4'].") - self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.is_lmdb = False - if self.io_backend_opt['type'] == 'lmdb': - self.is_lmdb = True - if self.flow_root is not None: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] - else: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - - # temporal augmentation configs - self.interval_list = opt['interval_list'] - self.random_reverse = opt['random_reverse'] - interval_str = ','.join(str(x) for x in opt['interval_list']) - logger = get_root_logger() - logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' - f'random reverse is {self.random_reverse}.') - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip_name, frame_name = key.split('/') # key example: 000/00000000 - center_frame_idx = int(frame_name) - - # determine the neighboring frames - interval = random.choice(self.interval_list) - - # ensure not exceeding the borders - start_frame_idx = center_frame_idx - self.num_half_frames * interval - end_frame_idx = center_frame_idx + self.num_half_frames * interval - # each clip has 100 frames starting from 0 to 99 - while (start_frame_idx < 0) or (end_frame_idx > 99): - center_frame_idx = random.randint(0, 99) - start_frame_idx = (center_frame_idx - self.num_half_frames * interval) - end_frame_idx = center_frame_idx + self.num_half_frames * interval - frame_name = f'{center_frame_idx:08d}' - neighbor_list = list(range(start_frame_idx, end_frame_idx + 1, interval)) - # random reverse - if self.random_reverse and random.random() < 0.5: - neighbor_list.reverse() - - assert len(neighbor_list) == self.num_frame, (f'Wrong length of neighbor list: {len(neighbor_list)}') - - # get the GT frame (as the center frame) - if self.is_lmdb: - img_gt_path = f'{clip_name}/{frame_name}' - else: - img_gt_path = self.gt_root / clip_name / f'{frame_name}.png' - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - - # get the neighboring LQ frames - img_lqs = [] - for neighbor in neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip_name}/{neighbor:08d}' - else: - img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - img_lqs.append(img_lq) - - # get flows - if self.flow_root is not None: - img_flows = [] - # read previous flows - for i in range(self.num_half_frames, 0, -1): - if self.is_lmdb: - flow_path = f'{clip_name}/{frame_name}_p{i}' - else: - flow_path = (self.flow_root / clip_name / f'{frame_name}_p{i}.png') - img_bytes = self.file_client.get(flow_path, 'flow') - cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] - dx, dy = np.split(cat_flow, 2, axis=0) - flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. - img_flows.append(flow) - # read next flows - for i in range(1, self.num_half_frames + 1): - if self.is_lmdb: - flow_path = f'{clip_name}/{frame_name}_n{i}' - else: - flow_path = (self.flow_root / clip_name / f'{frame_name}_n{i}.png') - img_bytes = self.file_client.get(flow_path, 'flow') - cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] - dx, dy = np.split(cat_flow, 2, axis=0) - flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. - img_flows.append(flow) - - # for random crop, here, img_flows and img_lqs have the same - # spatial size - img_lqs.extend(img_flows) - - # randomly crop - img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) - if self.flow_root is not None: - img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.num_frame:] - - # augmentation - flip, rotate - img_lqs.append(img_gt) - if self.flow_root is not None: - img_results, img_flows = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'], img_flows) - else: - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_lqs = torch.stack(img_results[0:-1], dim=0) - img_gt = img_results[-1] - - if self.flow_root is not None: - img_flows = img2tensor(img_flows) - # add the zero center flow - img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0])) - img_flows = torch.stack(img_flows, dim=0) - - # img_lqs: (t, c, h, w) - # img_flows: (t, 2, h, w) - # img_gt: (c, h, w) - # key: str - if self.flow_root is not None: - return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key} - else: - return {'lq': img_lqs, 'gt': img_gt, 'key': key} - - def __len__(self): - return len(self.keys) - - -@DATASET_REGISTRY.register() -class REDSRecurrentDataset(data.Dataset): - """REDS dataset for training recurrent networks. - - The keys are generated from a meta info txt file. - basicsr/data/meta_info/meta_info_REDS_GT.txt - - Each line contains: - 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by - a white space. - Examples: - 000 100 (720,1280,3) - 001 100 (720,1280,3) - ... - - Key examples: "000/00000000" - GT (gt): Ground-Truth; - LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - dataroot_flow (str, optional): Data root path for flow. - meta_info_file (str): Path for meta information file. - val_partition (str): Validation partition types. 'REDS4' or 'official'. - io_backend (dict): IO backend type and other kwarg. - num_frame (int): Window size for input frames. - gt_size (int): Cropped patched size for gt patches. - interval_list (list): Interval list for temporal augmentation. - random_reverse (bool): Random reverse input frames. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - """ - - def __init__(self, opt): - super(REDSRecurrentDataset, self).__init__() - self.opt = opt - self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) - self.num_frame = opt['num_frame'] - - self.keys = [] - with open(opt['meta_info_file'], 'r') as fin: - for line in fin: - folder, frame_num, _ = line.split(' ') - self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))]) - - # remove the video clips used in validation - if opt['val_partition'] == 'REDS4': - val_partition = ['000', '011', '015', '020'] - elif opt['val_partition'] == 'official': - val_partition = [f'{v:03d}' for v in range(240, 270)] - else: - raise ValueError(f'Wrong validation partition {opt["val_partition"]}.' - f"Supported ones are ['official', 'REDS4'].") - if opt['test_mode']: - self.keys = [v for v in self.keys if v.split('/')[0] in val_partition] - else: - self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.is_lmdb = False - if self.io_backend_opt['type'] == 'lmdb': - self.is_lmdb = True - if hasattr(self, 'flow_root') and self.flow_root is not None: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] - else: - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - - # temporal augmentation configs - self.interval_list = opt.get('interval_list', [1]) - self.random_reverse = opt.get('random_reverse', False) - interval_str = ','.join(str(x) for x in self.interval_list) - logger = get_root_logger() - logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' - f'random reverse is {self.random_reverse}.') - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip_name, frame_name = key.split('/') # key example: 000/00000000 - - # determine the neighboring frames - interval = random.choice(self.interval_list) - - # ensure not exceeding the borders - start_frame_idx = int(frame_name) - if start_frame_idx > 100 - self.num_frame * interval: - start_frame_idx = random.randint(0, 100 - self.num_frame * interval) - end_frame_idx = start_frame_idx + self.num_frame * interval - - neighbor_list = list(range(start_frame_idx, end_frame_idx, interval)) - - # random reverse - if self.random_reverse and random.random() < 0.5: - neighbor_list.reverse() - - # get the neighboring LQ and GT frames - img_lqs = [] - img_gts = [] - for neighbor in neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip_name}/{neighbor:08d}' - img_gt_path = f'{clip_name}/{neighbor:08d}' - else: - img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' - img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png' - - # get LQ - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - img_lqs.append(img_lq) - - # get GT - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - img_gts.append(img_gt) - - # randomly crop - img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) - - # augmentation - flip, rotate - img_lqs.extend(img_gts) - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_gts = torch.stack(img_results[len(img_lqs) // 2:], dim=0) - img_lqs = torch.stack(img_results[:len(img_lqs) // 2], dim=0) - - # img_lqs: (t, c, h, w) - # img_gts: (t, c, h, w) - # key: str - return {'lq': img_lqs, 'gt': img_gts, 'key': key} - - def __len__(self): - return len(self.keys) diff --git a/basicsr/data/single_image_dataset.py b/basicsr/data/single_image_dataset.py deleted file mode 100644 index acbc7d9..0000000 --- a/basicsr/data/single_image_dataset.py +++ /dev/null @@ -1,68 +0,0 @@ -from os import path as osp -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.data_util import paths_from_lmdb -from basicsr.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class SingleImageDataset(data.Dataset): - """Read only lq images in the test phase. - - Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc). - - There are two modes: - 1. 'meta_info_file': Use meta information file to generate paths. - 2. 'folder': Scan folders to generate paths. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_lq (str): Data root path for lq. - meta_info_file (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - """ - - def __init__(self, opt): - super(SingleImageDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.mean = opt['mean'] if 'mean' in opt else None - self.std = opt['std'] if 'std' in opt else None - self.lq_folder = opt['dataroot_lq'] - - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.lq_folder] - self.io_backend_opt['client_keys'] = ['lq'] - self.paths = paths_from_lmdb(self.lq_folder) - elif 'meta_info_file' in self.opt: - with open(self.opt['meta_info_file'], 'r') as fin: - self.paths = [osp.join(self.lq_folder, line.rstrip().split(' ')[0]) for line in fin] - else: - self.paths = sorted(list(scandir(self.lq_folder, full_path=True))) - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # load lq image - lq_path = self.paths[index] - img_bytes = self.file_client.get(lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - - # color space transform - if 'color' in self.opt and self.opt['color'] == 'y': - img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] - - # BGR to RGB, HWC to CHW, numpy to tensor - img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) - # normalize - if self.mean is not None or self.std is not None: - normalize(img_lq, self.mean, self.std, inplace=True) - return {'lq': img_lq, 'lq_path': lq_path} - - def __len__(self): - return len(self.paths) diff --git a/basicsr/data/transforms.py b/basicsr/data/transforms.py deleted file mode 100644 index d9bbb5f..0000000 --- a/basicsr/data/transforms.py +++ /dev/null @@ -1,179 +0,0 @@ -import cv2 -import random -import torch - - -def mod_crop(img, scale): - """Mod crop images, used during testing. - - Args: - img (ndarray): Input image. - scale (int): Scale factor. - - Returns: - ndarray: Result image. - """ - img = img.copy() - if img.ndim in (2, 3): - h, w = img.shape[0], img.shape[1] - h_remainder, w_remainder = h % scale, w % scale - img = img[:h - h_remainder, :w - w_remainder, ...] - else: - raise ValueError(f'Wrong img ndim: {img.ndim}.') - return img - - -def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None): - """Paired random crop. Support Numpy array and Tensor inputs. - - It crops lists of lq and gt images with corresponding locations. - - Args: - img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images - should have the same shape. If the input is an ndarray, it will - be transformed to a list containing itself. - img_lqs (list[ndarray] | ndarray): LQ images. Note that all images - should have the same shape. If the input is an ndarray, it will - be transformed to a list containing itself. - gt_patch_size (int): GT patch size. - scale (int): Scale factor. - gt_path (str): Path to ground-truth. Default: None. - - Returns: - list[ndarray] | ndarray: GT images and LQ images. If returned results - only have one element, just return ndarray. - """ - - if not isinstance(img_gts, list): - img_gts = [img_gts] - if not isinstance(img_lqs, list): - img_lqs = [img_lqs] - - # determine input type: Numpy array or Tensor - input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy' - - if input_type == 'Tensor': - h_lq, w_lq = img_lqs[0].size()[-2:] - h_gt, w_gt = img_gts[0].size()[-2:] - else: - h_lq, w_lq = img_lqs[0].shape[0:2] - h_gt, w_gt = img_gts[0].shape[0:2] - lq_patch_size = gt_patch_size // scale - - if h_gt != h_lq * scale or w_gt != w_lq * scale: - raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ', - f'multiplication of LQ ({h_lq}, {w_lq}).') - if h_lq < lq_patch_size or w_lq < lq_patch_size: - raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size ' - f'({lq_patch_size}, {lq_patch_size}). ' - f'Please remove {gt_path}.') - - # randomly choose top and left coordinates for lq patch - top = random.randint(0, h_lq - lq_patch_size) - left = random.randint(0, w_lq - lq_patch_size) - - # crop lq patch - if input_type == 'Tensor': - img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs] - else: - img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs] - - # crop corresponding gt patch - top_gt, left_gt = int(top * scale), int(left * scale) - if input_type == 'Tensor': - img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts] - else: - img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts] - if len(img_gts) == 1: - img_gts = img_gts[0] - if len(img_lqs) == 1: - img_lqs = img_lqs[0] - return img_gts, img_lqs - - -def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False): - """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees). - - We use vertical flip and transpose for rotation implementation. - All the images in the list use the same augmentation. - - Args: - imgs (list[ndarray] | ndarray): Images to be augmented. If the input - is an ndarray, it will be transformed to a list. - hflip (bool): Horizontal flip. Default: True. - rotation (bool): Ratotation. Default: True. - flows (list[ndarray]: Flows to be augmented. If the input is an - ndarray, it will be transformed to a list. - Dimension is (h, w, 2). Default: None. - return_status (bool): Return the status of flip and rotation. - Default: False. - - Returns: - list[ndarray] | ndarray: Augmented images and flows. If returned - results only have one element, just return ndarray. - - """ - hflip = hflip and random.random() < 0.5 - vflip = rotation and random.random() < 0.5 - rot90 = rotation and random.random() < 0.5 - - def _augment(img): - if hflip: # horizontal - cv2.flip(img, 1, img) - if vflip: # vertical - cv2.flip(img, 0, img) - if rot90: - img = img.transpose(1, 0, 2) - return img - - def _augment_flow(flow): - if hflip: # horizontal - cv2.flip(flow, 1, flow) - flow[:, :, 0] *= -1 - if vflip: # vertical - cv2.flip(flow, 0, flow) - flow[:, :, 1] *= -1 - if rot90: - flow = flow.transpose(1, 0, 2) - flow = flow[:, :, [1, 0]] - return flow - - if not isinstance(imgs, list): - imgs = [imgs] - imgs = [_augment(img) for img in imgs] - if len(imgs) == 1: - imgs = imgs[0] - - if flows is not None: - if not isinstance(flows, list): - flows = [flows] - flows = [_augment_flow(flow) for flow in flows] - if len(flows) == 1: - flows = flows[0] - return imgs, flows - else: - if return_status: - return imgs, (hflip, vflip, rot90) - else: - return imgs - - -def img_rotate(img, angle, center=None, scale=1.0): - """Rotate image. - - Args: - img (ndarray): Image to be rotated. - angle (float): Rotation angle in degrees. Positive values mean - counter-clockwise rotation. - center (tuple[int]): Rotation center. If the center is None, - initialize it as the center of the image. Default: None. - scale (float): Isotropic scale factor. Default: 1.0. - """ - (h, w) = img.shape[:2] - - if center is None: - center = (w // 2, h // 2) - - matrix = cv2.getRotationMatrix2D(center, angle, scale) - rotated_img = cv2.warpAffine(img, matrix, (w, h)) - return rotated_img diff --git a/basicsr/data/video_test_dataset.py b/basicsr/data/video_test_dataset.py deleted file mode 100644 index 929f7d9..0000000 --- a/basicsr/data/video_test_dataset.py +++ /dev/null @@ -1,283 +0,0 @@ -import glob -import torch -from os import path as osp -from torch.utils import data as data - -from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class VideoTestDataset(data.Dataset): - """Video test dataset. - - Supported datasets: Vid4, REDS4, REDSofficial. - More generally, it supports testing dataset with following structures: - - :: - - dataroot - ├── subfolder1 - ├── frame000 - ├── frame001 - ├── ... - ├── subfolder2 - ├── frame000 - ├── frame001 - ├── ... - ├── ... - - For testing datasets, there is no need to prepare LMDB files. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - io_backend (dict): IO backend type and other kwarg. - cache_data (bool): Whether to cache testing datasets. - name (str): Dataset name. - meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders - in the dataroot will be used. - num_frame (int): Window size for input frames. - padding (str): Padding mode. - """ - - def __init__(self, opt): - super(VideoTestDataset, self).__init__() - self.opt = opt - self.cache_data = opt['cache_data'] - self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] - self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' - - logger = get_root_logger() - logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') - self.imgs_lq, self.imgs_gt = {}, {} - if 'meta_info_file' in opt: - with open(opt['meta_info_file'], 'r') as fin: - subfolders = [line.split(' ')[0] for line in fin] - subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders] - subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders] - else: - subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*'))) - subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*'))) - - if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']: - for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt): - # get frame list for lq and gt - subfolder_name = osp.basename(subfolder_lq) - img_paths_lq = sorted(list(scandir(subfolder_lq, full_path=True))) - img_paths_gt = sorted(list(scandir(subfolder_gt, full_path=True))) - - max_idx = len(img_paths_lq) - assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})' - f' and gt folders ({len(img_paths_gt)})') - - self.data_info['lq_path'].extend(img_paths_lq) - self.data_info['gt_path'].extend(img_paths_gt) - self.data_info['folder'].extend([subfolder_name] * max_idx) - for i in range(max_idx): - self.data_info['idx'].append(f'{i}/{max_idx}') - border_l = [0] * max_idx - for i in range(self.opt['num_frame'] // 2): - border_l[i] = 1 - border_l[max_idx - i - 1] = 1 - self.data_info['border'].extend(border_l) - - # cache data or save the frame list - if self.cache_data: - logger.info(f'Cache {subfolder_name} for VideoTestDataset...') - self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq) - self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt) - else: - self.imgs_lq[subfolder_name] = img_paths_lq - self.imgs_gt[subfolder_name] = img_paths_gt - else: - raise ValueError(f'Non-supported video test dataset: {type(opt["name"])}') - - def __getitem__(self, index): - folder = self.data_info['folder'][index] - idx, max_idx = self.data_info['idx'][index].split('/') - idx, max_idx = int(idx), int(max_idx) - border = self.data_info['border'][index] - lq_path = self.data_info['lq_path'][index] - - select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) - - if self.cache_data: - imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) - img_gt = self.imgs_gt[folder][idx] - else: - img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] - imgs_lq = read_img_seq(img_paths_lq) - img_gt = read_img_seq([self.imgs_gt[folder][idx]]) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': folder, # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/99 - 'border': border, # 1 for border, 0 for non-border - 'lq_path': lq_path # center frame - } - - def __len__(self): - return len(self.data_info['gt_path']) - - -@DATASET_REGISTRY.register() -class VideoTestVimeo90KDataset(data.Dataset): - """Video test dataset for Vimeo90k-Test dataset. - - It only keeps the center frame for testing. - For testing datasets, there is no need to prepare LMDB files. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - io_backend (dict): IO backend type and other kwarg. - cache_data (bool): Whether to cache testing datasets. - name (str): Dataset name. - meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders - in the dataroot will be used. - num_frame (int): Window size for input frames. - padding (str): Padding mode. - """ - - def __init__(self, opt): - super(VideoTestVimeo90KDataset, self).__init__() - self.opt = opt - self.cache_data = opt['cache_data'] - if self.cache_data: - raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.') - self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] - self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} - neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' - - logger = get_root_logger() - logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') - with open(opt['meta_info_file'], 'r') as fin: - subfolders = [line.split(' ')[0] for line in fin] - for idx, subfolder in enumerate(subfolders): - gt_path = osp.join(self.gt_root, subfolder, 'im4.png') - self.data_info['gt_path'].append(gt_path) - lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list] - self.data_info['lq_path'].append(lq_paths) - self.data_info['folder'].append('vimeo90k') - self.data_info['idx'].append(f'{idx}/{len(subfolders)}') - self.data_info['border'].append(0) - - def __getitem__(self, index): - lq_path = self.data_info['lq_path'][index] - gt_path = self.data_info['gt_path'][index] - imgs_lq = read_img_seq(lq_path) - img_gt = read_img_seq([gt_path]) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': self.data_info['folder'][index], # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/843 - 'border': self.data_info['border'][index], # 0 for non-border - 'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame - } - - def __len__(self): - return len(self.data_info['gt_path']) - - -@DATASET_REGISTRY.register() -class VideoTestDUFDataset(VideoTestDataset): - """ Video test dataset for DUF dataset. - - Args: - opt (dict): Config for train dataset. Most of keys are the same as VideoTestDataset. - It has the following extra keys: - use_duf_downsampling (bool): Whether to use duf downsampling to generate low-resolution frames. - scale (bool): Scale, which will be added automatically. - """ - - def __getitem__(self, index): - folder = self.data_info['folder'][index] - idx, max_idx = self.data_info['idx'][index].split('/') - idx, max_idx = int(idx), int(max_idx) - border = self.data_info['border'][index] - lq_path = self.data_info['lq_path'][index] - - select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) - - if self.cache_data: - if self.opt['use_duf_downsampling']: - # read imgs_gt to generate low-resolution frames - imgs_lq = self.imgs_gt[folder].index_select(0, torch.LongTensor(select_idx)) - imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) - else: - imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) - img_gt = self.imgs_gt[folder][idx] - else: - if self.opt['use_duf_downsampling']: - img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx] - # read imgs_gt to generate low-resolution frames - imgs_lq = read_img_seq(img_paths_lq, require_mod_crop=True, scale=self.opt['scale']) - imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) - else: - img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] - imgs_lq = read_img_seq(img_paths_lq) - img_gt = read_img_seq([self.imgs_gt[folder][idx]], require_mod_crop=True, scale=self.opt['scale']) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': folder, # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/99 - 'border': border, # 1 for border, 0 for non-border - 'lq_path': lq_path # center frame - } - - -@DATASET_REGISTRY.register() -class VideoRecurrentTestDataset(VideoTestDataset): - """Video test dataset for recurrent architectures, which takes LR video - frames as input and output corresponding HR video frames. - - Args: - opt (dict): Same as VideoTestDataset. Unused opt: - padding (str): Padding mode. - - """ - - def __init__(self, opt): - super(VideoRecurrentTestDataset, self).__init__(opt) - # Find unique folder strings - self.folders = sorted(list(set(self.data_info['folder']))) - - def __getitem__(self, index): - folder = self.folders[index] - - if self.cache_data: - imgs_lq = self.imgs_lq[folder] - imgs_gt = self.imgs_gt[folder] - else: - raise NotImplementedError('Without cache_data is not implemented.') - - return { - 'lq': imgs_lq, - 'gt': imgs_gt, - 'folder': folder, - } - - def __len__(self): - return len(self.folders) diff --git a/basicsr/data/vimeo90k_dataset.py b/basicsr/data/vimeo90k_dataset.py deleted file mode 100644 index e5e33e1..0000000 --- a/basicsr/data/vimeo90k_dataset.py +++ /dev/null @@ -1,199 +0,0 @@ -import random -import torch -from pathlib import Path -from torch.utils import data as data - -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class Vimeo90KDataset(data.Dataset): - """Vimeo90K dataset for training. - - The keys are generated from a meta info txt file. - basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt - - Each line contains the following items, separated by a white space. - - 1. clip name; - 2. frame number; - 3. image shape - - Examples: - - :: - - 00001/0001 7 (256,448,3) - 00001/0002 7 (256,448,3) - - - Key examples: "00001/0001" - - GT (gt): Ground-Truth; - - LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. - - The neighboring frame list for different num_frame: - - :: - - num_frame | frame list - 1 | 4 - 3 | 3,4,5 - 5 | 2,3,4,5,6 - 7 | 1,2,3,4,5,6,7 - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - meta_info_file (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - num_frame (int): Window size for input frames. - gt_size (int): Cropped patched size for gt patches. - random_reverse (bool): Random reverse input frames. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). - scale (bool): Scale, which will be added automatically. - """ - - def __init__(self, opt): - super(Vimeo90KDataset, self).__init__() - self.opt = opt - self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) - - with open(opt['meta_info_file'], 'r') as fin: - self.keys = [line.split(' ')[0] for line in fin] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - self.is_lmdb = False - if self.io_backend_opt['type'] == 'lmdb': - self.is_lmdb = True - self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - - # indices of input images - self.neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] - - # temporal augmentation configs - self.random_reverse = opt['random_reverse'] - logger = get_root_logger() - logger.info(f'Random reverse is {self.random_reverse}.') - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # random reverse - if self.random_reverse and random.random() < 0.5: - self.neighbor_list.reverse() - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip, seq = key.split('/') # key example: 00001/0001 - - # get the GT frame (im4.png) - if self.is_lmdb: - img_gt_path = f'{key}/im4' - else: - img_gt_path = self.gt_root / clip / seq / 'im4.png' - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - - # get the neighboring LQ frames - img_lqs = [] - for neighbor in self.neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip}/{seq}/im{neighbor}' - else: - img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - img_lqs.append(img_lq) - - # randomly crop - img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) - - # augmentation - flip, rotate - img_lqs.append(img_gt) - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_lqs = torch.stack(img_results[0:-1], dim=0) - img_gt = img_results[-1] - - # img_lqs: (t, c, h, w) - # img_gt: (c, h, w) - # key: str - return {'lq': img_lqs, 'gt': img_gt, 'key': key} - - def __len__(self): - return len(self.keys) - - -@DATASET_REGISTRY.register() -class Vimeo90KRecurrentDataset(Vimeo90KDataset): - - def __init__(self, opt): - super(Vimeo90KRecurrentDataset, self).__init__(opt) - - self.flip_sequence = opt['flip_sequence'] - self.neighbor_list = [1, 2, 3, 4, 5, 6, 7] - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # random reverse - if self.random_reverse and random.random() < 0.5: - self.neighbor_list.reverse() - - scale = self.opt['scale'] - gt_size = self.opt['gt_size'] - key = self.keys[index] - clip, seq = key.split('/') # key example: 00001/0001 - - # get the neighboring LQ and GT frames - img_lqs = [] - img_gts = [] - for neighbor in self.neighbor_list: - if self.is_lmdb: - img_lq_path = f'{clip}/{seq}/im{neighbor}' - img_gt_path = f'{clip}/{seq}/im{neighbor}' - else: - img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' - img_gt_path = self.gt_root / clip / seq / f'im{neighbor}.png' - # LQ - img_bytes = self.file_client.get(img_lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - # GT - img_bytes = self.file_client.get(img_gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - - img_lqs.append(img_lq) - img_gts.append(img_gt) - - # randomly crop - img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) - - # augmentation - flip, rotate - img_lqs.extend(img_gts) - img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) - - img_results = img2tensor(img_results) - img_lqs = torch.stack(img_results[:7], dim=0) - img_gts = torch.stack(img_results[7:], dim=0) - - if self.flip_sequence: # flip the sequence: 7 frames to 14 frames - img_lqs = torch.cat([img_lqs, img_lqs.flip(0)], dim=0) - img_gts = torch.cat([img_gts, img_gts.flip(0)], dim=0) - - # img_lqs: (t, c, h, w) - # img_gt: (c, h, w) - # key: str - return {'lq': img_lqs, 'gt': img_gts, 'key': key} - - def __len__(self): - return len(self.keys) diff --git a/basicsr/losses/__init__.py b/basicsr/losses/__init__.py deleted file mode 100644 index 70a172a..0000000 --- a/basicsr/losses/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -import importlib -from copy import deepcopy -from os import path as osp - -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import LOSS_REGISTRY -from .gan_loss import g_path_regularize, gradient_penalty_loss, r1_penalty - -__all__ = ['build_loss', 'gradient_penalty_loss', 'r1_penalty', 'g_path_regularize'] - -# automatically scan and import loss modules for registry -# scan all the files under the 'losses' folder and collect files ending with '_loss.py' -loss_folder = osp.dirname(osp.abspath(__file__)) -loss_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(loss_folder) if v.endswith('_loss.py')] -# import all the loss modules -_model_modules = [importlib.import_module(f'basicsr.losses.{file_name}') for file_name in loss_filenames] - - -def build_loss(opt): - """Build loss from options. - - Args: - opt (dict): Configuration. 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Default: 1.0. - style_weight (float): If `style_weight > 0`, the style loss will be - calculated and the loss will multiplied by the weight. - Default: 0. - criterion (str): Criterion used for perceptual loss. Default: 'l1'. - """ - - def __init__(self, - layer_weights, - vgg_type='vgg19', - use_input_norm=True, - range_norm=False, - perceptual_weight=1.0, - style_weight=0., - criterion='l1'): - super(PerceptualLoss, self).__init__() - self.perceptual_weight = perceptual_weight - self.style_weight = style_weight - self.layer_weights = layer_weights - self.vgg = VGGFeatureExtractor( - layer_name_list=list(layer_weights.keys()), - vgg_type=vgg_type, - use_input_norm=use_input_norm, - range_norm=range_norm) - - self.criterion_type = criterion - if self.criterion_type == 'l1': - self.criterion = torch.nn.L1Loss() - elif self.criterion_type == 'l2': - self.criterion = torch.nn.MSELoss() - elif self.criterion_type == 'fro': - self.criterion = None - else: - raise NotImplementedError(f'{criterion} criterion has not been supported.') - - def forward(self, x, gt): - """Forward function. - - Args: - x (Tensor): Input tensor with shape (n, c, h, w). - gt (Tensor): Ground-truth tensor with shape (n, c, h, w). - - Returns: - Tensor: Forward results. - """ - # extract vgg features - x_features = self.vgg(x) - gt_features = self.vgg(gt.detach()) - - # calculate perceptual loss - if self.perceptual_weight > 0: - percep_loss = 0 - for k in x_features.keys(): - if self.criterion_type == 'fro': - percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k] - else: - percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k] - percep_loss *= self.perceptual_weight - else: - percep_loss = None - - # calculate style loss - if self.style_weight > 0: - style_loss = 0 - for k in x_features.keys(): - if self.criterion_type == 'fro': - style_loss += torch.norm( - self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k] - else: - style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat( - gt_features[k])) * self.layer_weights[k] - style_loss *= self.style_weight - else: - style_loss = None - - return percep_loss, style_loss - - def _gram_mat(self, x): - """Calculate Gram matrix. - - Args: - x (torch.Tensor): Tensor with shape of (n, c, h, w). - - Returns: - torch.Tensor: Gram matrix. - """ - n, c, h, w = x.size() - features = x.view(n, c, w * h) - features_t = features.transpose(1, 2) - gram = features.bmm(features_t) / (c * h * w) - return gram diff --git a/basicsr/losses/gan_loss.py b/basicsr/losses/gan_loss.py deleted file mode 100644 index 870baa2..0000000 --- a/basicsr/losses/gan_loss.py +++ /dev/null @@ -1,207 +0,0 @@ -import math -import torch -from torch import autograd as autograd -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import LOSS_REGISTRY - - -@LOSS_REGISTRY.register() -class GANLoss(nn.Module): - """Define GAN loss. - - Args: - gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'. - real_label_val (float): The value for real label. Default: 1.0. - fake_label_val (float): The value for fake label. Default: 0.0. - loss_weight (float): Loss weight. Default: 1.0. - Note that loss_weight is only for generators; and it is always 1.0 - for discriminators. - """ - - def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): - super(GANLoss, self).__init__() - self.gan_type = gan_type - self.loss_weight = loss_weight - self.real_label_val = real_label_val - self.fake_label_val = fake_label_val - - if self.gan_type == 'vanilla': - self.loss = nn.BCEWithLogitsLoss() - elif self.gan_type == 'lsgan': - self.loss = nn.MSELoss() - elif self.gan_type == 'wgan': - self.loss = self._wgan_loss - elif self.gan_type == 'wgan_softplus': - self.loss = self._wgan_softplus_loss - elif self.gan_type == 'hinge': - self.loss = nn.ReLU() - else: - raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.') - - def _wgan_loss(self, input, target): - """wgan loss. - - Args: - input (Tensor): Input tensor. - target (bool): Target label. - - Returns: - Tensor: wgan loss. - """ - return -input.mean() if target else input.mean() - - def _wgan_softplus_loss(self, input, target): - """wgan loss with soft plus. softplus is a smooth approximation to the - ReLU function. - - In StyleGAN2, it is called: - Logistic loss for discriminator; - Non-saturating loss for generator. - - Args: - input (Tensor): Input tensor. - target (bool): Target label. - - Returns: - Tensor: wgan loss. - """ - return F.softplus(-input).mean() if target else F.softplus(input).mean() - - def get_target_label(self, input, target_is_real): - """Get target label. - - Args: - input (Tensor): Input tensor. - target_is_real (bool): Whether the target is real or fake. - - Returns: - (bool | Tensor): Target tensor. Return bool for wgan, otherwise, - return Tensor. - """ - - if self.gan_type in ['wgan', 'wgan_softplus']: - return target_is_real - target_val = (self.real_label_val if target_is_real else self.fake_label_val) - return input.new_ones(input.size()) * target_val - - def forward(self, input, target_is_real, is_disc=False): - """ - Args: - input (Tensor): The input for the loss module, i.e., the network - prediction. - target_is_real (bool): Whether the targe is real or fake. - is_disc (bool): Whether the loss for discriminators or not. - Default: False. - - Returns: - Tensor: GAN loss value. - """ - target_label = self.get_target_label(input, target_is_real) - if self.gan_type == 'hinge': - if is_disc: # for discriminators in hinge-gan - input = -input if target_is_real else input - loss = self.loss(1 + input).mean() - else: # for generators in hinge-gan - loss = -input.mean() - else: # other gan types - loss = self.loss(input, target_label) - - # loss_weight is always 1.0 for discriminators - return loss if is_disc else loss * self.loss_weight - - -@LOSS_REGISTRY.register() -class MultiScaleGANLoss(GANLoss): - """ - MultiScaleGANLoss accepts a list of predictions - """ - - def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): - super(MultiScaleGANLoss, self).__init__(gan_type, real_label_val, fake_label_val, loss_weight) - - def forward(self, input, target_is_real, is_disc=False): - """ - The input is a list of tensors, or a list of (a list of tensors) - """ - if isinstance(input, list): - loss = 0 - for pred_i in input: - if isinstance(pred_i, list): - # Only compute GAN loss for the last layer - # in case of multiscale feature matching - pred_i = pred_i[-1] - # Safe operation: 0-dim tensor calling self.mean() does nothing - loss_tensor = super().forward(pred_i, target_is_real, is_disc).mean() - loss += loss_tensor - return loss / len(input) - else: - return super().forward(input, target_is_real, is_disc) - - -def r1_penalty(real_pred, real_img): - """R1 regularization for discriminator. The core idea is to - penalize the gradient on real data alone: when the - generator distribution produces the true data distribution - and the discriminator is equal to 0 on the data manifold, the - gradient penalty ensures that the discriminator cannot create - a non-zero gradient orthogonal to the data manifold without - suffering a loss in the GAN game. - - Reference: Eq. 9 in Which training methods for GANs do actually converge. - """ - grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0] - grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean() - return grad_penalty - - -def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): - noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3]) - grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0] - path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) - - path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) - - path_penalty = (path_lengths - path_mean).pow(2).mean() - - return path_penalty, path_lengths.detach().mean(), path_mean.detach() - - -def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None): - """Calculate gradient penalty for wgan-gp. - - Args: - discriminator (nn.Module): Network for the discriminator. - real_data (Tensor): Real input data. - fake_data (Tensor): Fake input data. - weight (Tensor): Weight tensor. Default: None. - - Returns: - Tensor: A tensor for gradient penalty. - """ - - batch_size = real_data.size(0) - alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1)) - - # interpolate between real_data and fake_data - interpolates = alpha * real_data + (1. - alpha) * fake_data - interpolates = autograd.Variable(interpolates, requires_grad=True) - - disc_interpolates = discriminator(interpolates) - gradients = autograd.grad( - outputs=disc_interpolates, - inputs=interpolates, - grad_outputs=torch.ones_like(disc_interpolates), - create_graph=True, - retain_graph=True, - only_inputs=True)[0] - - if weight is not None: - gradients = gradients * weight - - gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean() - if weight is not None: - gradients_penalty /= torch.mean(weight) - - return gradients_penalty diff --git a/basicsr/losses/loss_util.py b/basicsr/losses/loss_util.py deleted file mode 100644 index fd293ff..0000000 --- a/basicsr/losses/loss_util.py +++ /dev/null @@ -1,145 +0,0 @@ -import functools -import torch -from torch.nn import functional as F - - -def reduce_loss(loss, reduction): - """Reduce loss as specified. - - Args: - loss (Tensor): Elementwise loss tensor. - reduction (str): Options are 'none', 'mean' and 'sum'. - - Returns: - Tensor: Reduced loss tensor. - """ - reduction_enum = F._Reduction.get_enum(reduction) - # none: 0, elementwise_mean:1, sum: 2 - if reduction_enum == 0: - return loss - elif reduction_enum == 1: - return loss.mean() - else: - return loss.sum() - - -def weight_reduce_loss(loss, weight=None, reduction='mean'): - """Apply element-wise weight and reduce loss. - - Args: - loss (Tensor): Element-wise loss. - weight (Tensor): Element-wise weights. Default: None. - reduction (str): Same as built-in losses of PyTorch. Options are - 'none', 'mean' and 'sum'. Default: 'mean'. - - Returns: - Tensor: Loss values. - """ - # if weight is specified, apply element-wise weight - if weight is not None: - assert weight.dim() == loss.dim() - assert weight.size(1) == 1 or weight.size(1) == loss.size(1) - loss = loss * weight - - # if weight is not specified or reduction is sum, just reduce the loss - if weight is None or reduction == 'sum': - loss = reduce_loss(loss, reduction) - # if reduction is mean, then compute mean over weight region - elif reduction == 'mean': - if weight.size(1) > 1: - weight = weight.sum() - else: - weight = weight.sum() * loss.size(1) - loss = loss.sum() / weight - - return loss - - -def weighted_loss(loss_func): - """Create a weighted version of a given loss function. - - To use this decorator, the loss function must have the signature like - `loss_func(pred, target, **kwargs)`. The function only needs to compute - element-wise loss without any reduction. This decorator will add weight - and reduction arguments to the function. The decorated function will have - the signature like `loss_func(pred, target, weight=None, reduction='mean', - **kwargs)`. - - :Example: - - >>> import torch - >>> @weighted_loss - >>> def l1_loss(pred, target): - >>> return (pred - target).abs() - - >>> pred = torch.Tensor([0, 2, 3]) - >>> target = torch.Tensor([1, 1, 1]) - >>> weight = torch.Tensor([1, 0, 1]) - - >>> l1_loss(pred, target) - tensor(1.3333) - >>> l1_loss(pred, target, weight) - tensor(1.5000) - >>> l1_loss(pred, target, reduction='none') - tensor([1., 1., 2.]) - >>> l1_loss(pred, target, weight, reduction='sum') - tensor(3.) - """ - - @functools.wraps(loss_func) - def wrapper(pred, target, weight=None, reduction='mean', **kwargs): - # get element-wise loss - loss = loss_func(pred, target, **kwargs) - loss = weight_reduce_loss(loss, weight, reduction) - return loss - - return wrapper - - -def get_local_weights(residual, ksize): - """Get local weights for generating the artifact map of LDL. - - It is only called by the `get_refined_artifact_map` function. - - Args: - residual (Tensor): Residual between predicted and ground truth images. - ksize (Int): size of the local window. - - Returns: - Tensor: weight for each pixel to be discriminated as an artifact pixel - """ - - pad = (ksize - 1) // 2 - residual_pad = F.pad(residual, pad=[pad, pad, pad, pad], mode='reflect') - - unfolded_residual = residual_pad.unfold(2, ksize, 1).unfold(3, ksize, 1) - pixel_level_weight = torch.var(unfolded_residual, dim=(-1, -2), unbiased=True, keepdim=True).squeeze(-1).squeeze(-1) - - return pixel_level_weight - - -def get_refined_artifact_map(img_gt, img_output, img_ema, ksize): - """Calculate the artifact map of LDL - (Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution. In CVPR 2022) - - Args: - img_gt (Tensor): ground truth images. - img_output (Tensor): output images given by the optimizing model. - img_ema (Tensor): output images given by the ema model. - ksize (Int): size of the local window. - - Returns: - overall_weight: weight for each pixel to be discriminated as an artifact pixel - (calculated based on both local and global observations). - """ - - residual_ema = torch.sum(torch.abs(img_gt - img_ema), 1, keepdim=True) - residual_sr = torch.sum(torch.abs(img_gt - img_output), 1, keepdim=True) - - patch_level_weight = torch.var(residual_sr.clone(), dim=(-1, -2, -3), keepdim=True)**(1 / 5) - pixel_level_weight = get_local_weights(residual_sr.clone(), ksize) - overall_weight = patch_level_weight * pixel_level_weight - - overall_weight[residual_sr < residual_ema] = 0 - - return overall_weight diff --git a/basicsr/metrics/README.md b/basicsr/metrics/README.md deleted file mode 100644 index 98d0030..0000000 --- a/basicsr/metrics/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# Metrics - -[English](README.md) **|** [简体中文](README_CN.md) - -- [约定](#约定) -- [PSNR 和 SSIM](#psnr-和-ssim) - -## 约定 - -因为不同的输入类型会导致结果的不同，因此我们对输入做如下约定: - -- Numpy 类型 (一般是 cv2 的结果) - - UINT8: BGR, [0, 255], (h, w, c) - - float: BGR, [0, 1], (h, w, c). 一般作为中间结果 -- Tensor 类型 - - float: RGB, [0, 1], (n, c, h, w) - -其他约定: - -- 以 `_pt` 结尾的是 PyTorch 结果 -- PyTorch version 支持 batch 计算 -- 颜色转换在 float32 上做；metric计算在 float64 上做 - -## PSNR 和 SSIM - -PSNR 和 SSIM 的结果趋势是一致的，即一般 PSNR 高，则 SSIM 也高。 -在实现上, PSNR 的各种实现都很一致。SSIM 有各种各样的实现，我们这里和 MATLAB 最原始版本保持 (参考 [NTIRE17比赛](https://competitions.codalab.org/competitions/16306#participate) 的 [evaluation代码](https://competitions.codalab.org/my/datasets/download/ebe960d8-0ec8-4846-a1a2-7c4a586a7378)) - -下面列了各个实现的结果比对. -总结：PyTorch 实现和 MATLAB 实现基本一致，在 GPU 运行上会有稍许差异 - -- PSNR 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 20.419710 | 20.419710 | 20.419710 |20.419710 | -|baboon| Y | - |22.441898 | 22.441899 | 22.444916| -|comic | RGB | 20.239912 | 20.239912 | 20.239912 | 20.239912 | -|comic | Y | - | 21.720398 | 21.720398 | 21.721663| - -- SSIM 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 0.391853 | 0.391853 | 0.391853|0.391853 | -|baboon| Y | - |0.453097| 0.453097 | 0.453171| -|comic | RGB | 0.567738 | 0.567738 | 0.567738 | 0.567738| -|comic | Y | - | 0.585511 | 0.585511 | 0.585522 | diff --git a/basicsr/metrics/README_CN.md b/basicsr/metrics/README_CN.md deleted file mode 100644 index 98d0030..0000000 --- a/basicsr/metrics/README_CN.md +++ /dev/null @@ -1,48 +0,0 @@ -# Metrics - -[English](README.md) **|** [简体中文](README_CN.md) - -- [约定](#约定) -- [PSNR 和 SSIM](#psnr-和-ssim) - -## 约定 - -因为不同的输入类型会导致结果的不同，因此我们对输入做如下约定: - -- Numpy 类型 (一般是 cv2 的结果) - - UINT8: BGR, [0, 255], (h, w, c) - - float: BGR, [0, 1], (h, w, c). 一般作为中间结果 -- Tensor 类型 - - float: RGB, [0, 1], (n, c, h, w) - -其他约定: - -- 以 `_pt` 结尾的是 PyTorch 结果 -- PyTorch version 支持 batch 计算 -- 颜色转换在 float32 上做；metric计算在 float64 上做 - -## PSNR 和 SSIM - -PSNR 和 SSIM 的结果趋势是一致的，即一般 PSNR 高，则 SSIM 也高。 -在实现上, PSNR 的各种实现都很一致。SSIM 有各种各样的实现，我们这里和 MATLAB 最原始版本保持 (参考 [NTIRE17比赛](https://competitions.codalab.org/competitions/16306#participate) 的 [evaluation代码](https://competitions.codalab.org/my/datasets/download/ebe960d8-0ec8-4846-a1a2-7c4a586a7378)) - -下面列了各个实现的结果比对. -总结：PyTorch 实现和 MATLAB 实现基本一致，在 GPU 运行上会有稍许差异 - -- PSNR 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 20.419710 | 20.419710 | 20.419710 |20.419710 | -|baboon| Y | - |22.441898 | 22.441899 | 22.444916| -|comic | RGB | 20.239912 | 20.239912 | 20.239912 | 20.239912 | -|comic | Y | - | 21.720398 | 21.720398 | 21.721663| - -- SSIM 比对 - -|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | -|:---| :---: | :---: | :---: | :---: | :---: | -|baboon| RGB | 0.391853 | 0.391853 | 0.391853|0.391853 | -|baboon| Y | - |0.453097| 0.453097 | 0.453171| -|comic | RGB | 0.567738 | 0.567738 | 0.567738 | 0.567738| -|comic | Y | - | 0.585511 | 0.585511 | 0.585522 | diff --git a/basicsr/metrics/__init__.py b/basicsr/metrics/__init__.py deleted file mode 100644 index 4fb044a..0000000 --- a/basicsr/metrics/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from copy import deepcopy - -from basicsr.utils.registry import METRIC_REGISTRY -from .niqe import calculate_niqe -from .psnr_ssim import calculate_psnr, calculate_ssim - -__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe'] - - -def calculate_metric(data, opt): - """Calculate metric from data and options. - - Args: - opt (dict): Configuration. 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- - Args: - block (ndarray): 2D Image block. - - Returns: - list: Features with length of 18. - """ - feat = [] - alpha, beta_l, beta_r = estimate_aggd_param(block) - feat.extend([alpha, (beta_l + beta_r) / 2]) - - # distortions disturb the fairly regular structure of natural images. - # This deviation can be captured by analyzing the sample distribution of - # the products of pairs of adjacent coefficients computed along - # horizontal, vertical and diagonal orientations. - shifts = [[0, 1], [1, 0], [1, 1], [1, -1]] - for i in range(len(shifts)): - shifted_block = np.roll(block, shifts[i], axis=(0, 1)) - alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block) - # Eq. 8 - mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha)) - feat.extend([alpha, mean, beta_l, beta_r]) - return feat - - -def niqe(img, mu_pris_param, cov_pris_param, gaussian_window, block_size_h=96, block_size_w=96): - """Calculate NIQE (Natural Image Quality Evaluator) metric. - - ``Paper: Making a "Completely Blind" Image Quality Analyzer`` - - This implementation could produce almost the same results as the official - MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip - - Note that we do not include block overlap height and width, since they are - always 0 in the official implementation. - - For good performance, it is advisable by the official implementation to - divide the distorted image in to the same size patched as used for the - construction of multivariate Gaussian model. - - Args: - img (ndarray): Input image whose quality needs to be computed. The - image must be a gray or Y (of YCbCr) image with shape (h, w). - Range [0, 255] with float type. - mu_pris_param (ndarray): Mean of a pre-defined multivariate Gaussian - model calculated on the pristine dataset. - cov_pris_param (ndarray): Covariance of a pre-defined multivariate - Gaussian model calculated on the pristine dataset. - gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the - image. - block_size_h (int): Height of the blocks in to which image is divided. - Default: 96 (the official recommended value). - block_size_w (int): Width of the blocks in to which image is divided. - Default: 96 (the official recommended value). - """ - assert img.ndim == 2, ('Input image must be a gray or Y (of YCbCr) image with shape (h, w).') - # crop image - h, w = img.shape - num_block_h = math.floor(h / block_size_h) - num_block_w = math.floor(w / block_size_w) - img = img[0:num_block_h * block_size_h, 0:num_block_w * block_size_w] - - distparam = [] # dist param is actually the multiscale features - for scale in (1, 2): # perform on two scales (1, 2) - mu = convolve(img, gaussian_window, mode='nearest') - sigma = np.sqrt(np.abs(convolve(np.square(img), gaussian_window, mode='nearest') - np.square(mu))) - # normalize, as in Eq. 1 in the paper - img_nomalized = (img - mu) / (sigma + 1) - - feat = [] - for idx_w in range(num_block_w): - for idx_h in range(num_block_h): - # process ecah block - block = img_nomalized[idx_h * block_size_h // scale:(idx_h + 1) * block_size_h // scale, - idx_w * block_size_w // scale:(idx_w + 1) * block_size_w // scale] - feat.append(compute_feature(block)) - - distparam.append(np.array(feat)) - - if scale == 1: - img = imresize(img / 255., scale=0.5, antialiasing=True) - img = img * 255. - - distparam = np.concatenate(distparam, axis=1) - - # fit a MVG (multivariate Gaussian) model to distorted patch features - mu_distparam = np.nanmean(distparam, axis=0) - # use nancov. ref: https://ww2.mathworks.cn/help/stats/nancov.html - distparam_no_nan = distparam[~np.isnan(distparam).any(axis=1)] - cov_distparam = np.cov(distparam_no_nan, rowvar=False) - - # compute niqe quality, Eq. 10 in the paper - invcov_param = np.linalg.pinv((cov_pris_param + cov_distparam) / 2) - quality = np.matmul( - np.matmul((mu_pris_param - mu_distparam), invcov_param), np.transpose((mu_pris_param - mu_distparam))) - - quality = np.sqrt(quality) - quality = float(np.squeeze(quality)) - return quality - - -@METRIC_REGISTRY.register() -def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y', **kwargs): - """Calculate NIQE (Natural Image Quality Evaluator) metric. - - ``Paper: Making a "Completely Blind" Image Quality Analyzer`` - - This implementation could produce almost the same results as the official - MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip - - > MATLAB R2021a result for tests/data/baboon.png: 5.72957338 (5.7296) - > Our re-implementation result for tests/data/baboon.png: 5.7295763 (5.7296) - - We use the official params estimated from the pristine dataset. - We use the recommended block size (96, 96) without overlaps. - - Args: - img (ndarray): Input image whose quality needs to be computed. - The input image must be in range [0, 255] with float/int type. - The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order) - If the input order is 'HWC' or 'CHW', it will be converted to gray - or Y (of YCbCr) image according to the ``convert_to`` argument. - crop_border (int): Cropped pixels in each edge of an image. These - pixels are not involved in the metric calculation. - input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'. - Default: 'HWC'. - convert_to (str): Whether converted to 'y' (of MATLAB YCbCr) or 'gray'. - Default: 'y'. - - Returns: - float: NIQE result. - """ - ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) - # we use the official params estimated from the pristine dataset. - niqe_pris_params = np.load(os.path.join(ROOT_DIR, 'niqe_pris_params.npz')) - mu_pris_param = niqe_pris_params['mu_pris_param'] - cov_pris_param = niqe_pris_params['cov_pris_param'] - gaussian_window = niqe_pris_params['gaussian_window'] - - img = img.astype(np.float32) - if input_order != 'HW': - img = reorder_image(img, input_order=input_order) - if convert_to == 'y': - img = to_y_channel(img) - elif convert_to == 'gray': - img = cv2.cvtColor(img / 255., cv2.COLOR_BGR2GRAY) * 255. - img = np.squeeze(img) - - if crop_border != 0: - img = img[crop_border:-crop_border, crop_border:-crop_border] - - # round is necessary for being consistent with MATLAB's result - img = img.round() - - niqe_result = niqe(img, mu_pris_param, cov_pris_param, gaussian_window) - - return niqe_result diff --git a/basicsr/metrics/niqe_pris_params.npz b/basicsr/metrics/niqe_pris_params.npz deleted file mode 100644 index 204ddcee87c4cd39aca04a42b539f0a5bfccecc3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 11850 zcmbt)cUaH;+jr3=DyzX|m!hnqC9AVZMABZ`oA%y&X;D&HMUqmXgpjuqEi+{lC3KOH zvO@CYcVDlL`*+><^T+f2zK%G)KA+F`=s3UU`8r?Yt*tqhOOWHgULqW0eZL;d<>cV_ z{WFzAki*5t!rjx!%fj8t)5=BC)jfcdW6HlS{(j*1O}}61TKZeGjX61dIfBG(?YwL} z#a9c9ueDbYUn(qa@8;?4Y2|9+=4os9`~TNl?ewx6`F}4*D|fq*_Yy0X3d_hZS}GhO z{QvvnnI`=;G2{-HlBB`x_0d1xQg?O8QO%(qnsX|_U3TGla@yMO{j0i~EZ13=saQ19 zs$Ija!?JJad3blt(Dzf6o4IXBA@>?hy~l6iD$qc}-_4DDJ>QUV{rspT(fzE#WmAQm zXg&R`|LdE$!gbQGerum;`G!JI?(dQr%w|tgvUZDpipczx02yA zzneR2-cxJ-d%+jWdTD-fMhoBc3VJ4AyhiGHEy=i5$!~4ENn?ficteAG$>j5Eo?kM> z7DnTQ4dnDn}rvW|bKT@{wg)=4h9w>YDT)_R!DHJe^f&obA< z&S~zYiU8|hAwA=4Gn!hTB&h@&)bYvErN)GvQ)U*g1Q7XExe z`||h_;>fYBHaT{jE^V&f*>I(q1pUq&HXJ%l3bpR}?TtlL(%QMb;EOAj42_wmE_j5Z zzq$3!OU|crzZRu)brn%H*9C_gnx5>Nq>WIGaUR*m8H6hMy-ACe6_nob+v?2Z{d&fCCCX-~=>-`b8)5*cpIw-|{AK5+ssC)6O zHjDYp-|o(}hvYsxu5Ep{hf3y`n3})LpnXdgrfRSDVG~}QKY9FUIyv9nAbNXQ3eDdT zIVRaJgBt62+Gx2q>)e~!W0jXq^6zz8-qao^qUwx1Tz&JvfW@Muvu5_DrA+ zYE)2}t;}wYskAO|iKCrWaV^1W33Q;O^XJW1@pM%_F6o7lC7U$l;pF)>o|GbLoSyBA zqdQYSM>+?`Qq7c#7RfMI=Fy=zJT$b6Ottn~?b{tin;$<+N{opi<-v||%^}Y0WQC0` zx5X|};(RT-<9O6)zo0JXBUWs_Cz3{eb2sIOggVl?MWxC!<044-*h_UCi6klv5i7D< zn?@mbhP?H19w_q1U;cEyCWe;ry=+Tf5>Mqyp+|)>_fX`=-Q!)KTCrPxX}J=-2^24& z{?`oCXeys5D6X?@4-K{^j&HnR$r}E2-QB~VK)!tH7iN`3(U8*u{qgaIwENYZzhu6r zke8f_cJ)v!#hq5V6w0xZPI;7f&3GL~d0DesjV44<(Ljr4eM2k_E>PU);Ix(ro#^KH zaVwncn)j`Y?v0=)`?jd@B}UWxguH6?Jafj+OYOYJB8d&Nw5*z78WU5pslO?TWIh?M zTyx)q#ogkm;NB5Q8`bv9-Ea>cog?%M>T=gfan)XNciJMYQhO{+iP>0r{ygC9L`Hmj z+a zRO8@wT?r+YV>ZiD-`bLPzPR2uvE5)4Uoc1L7u4l@k}uE2i$V1Eokhd&+fXV!w(8E5 z;&7UoR;OH5CQV5j3vRBi3M9`ftM{dz3Z*p`vRPL@hS8hij1TuSELrmBl*BWf;WR6M zwab`ve;O0k*p{>;jGWJ>UD=~(#Rl$3HBS69oW8t|s+n2uN9&BObogQuX?#hyTgYGp z-HcW9zTo3Ucg#3)=O!AkU~awG)8*bYba|b0!5^N~JI!YHy&vv$@PNk8_0mF2!?p0i zj4BU0cEa&xy{H$}Z%)Z*H*}%bO=%|&>FBa^y4g7<_r2&@+P6tk(ymnPZ6Nh|)=m-= zOLe}Vs>_t-_8q)b=S4S~PKb(LcNxW9;0xvm{err*1(qwmS56>}{*TLZHIhjnFj&Fg zL7&}Q(#dmYNeuZVC?-k7M3a`7v&}q@M2d9LeyEsX%dD%$*>rx2rL2UWxus_!XhW#v z<(JbF$t?Ze_Q}5X?959+9{w$HRQ9qzTj)hNO}B3iwbnjNio+9lhh2Bk%JN5KW)Mz; zwUaHpG}TElS7=`Tl>kzEc6sRT^)PZ-6m2MSJ)CAASSOv|V#1V;@mqV{4JDDqA~O9+ zLG-|&D%yKZ2;I!w`qO`#1>=#oD-ul%BP-pD1FO{h$c`md8b}Az`PViF+qf*)(gtts z@AhHDUEC_8`ptKA9|3oPFPJ0r3+l4ly^M23VLH8OA5W7e=&;ig3l<-}8b@Qce=py< zHJ-RmnV9)mr%}P3v{K$$8x~jA@RaXk0%fjX3!TnHQ=7-td0z9+`S=m zyMni2+Wjaxr7>n1w|zV<ztwS z#*!sQI~K|Q8AUyF-I@gpg6Vyo`Z%$cXsWGK`1Qv-6Q(h3N~!qqNV0a85P$kOcyv!= z9|3oPFPJ0r3+gh?`a55WryFID^>?_t!H)e|74IRXrA|jWX1DY`Q)PquXKejxXh2gc zO+&dZ>#?(ACYx+j7iFn2PlmQ}sWa2unA1(oMr1Yq_A$PELzY;S^q{JXgRZFvL(Cgv--B0q3*xjU8QHJ)$^zKBG7MF|+1<1_|U38n1^(}d6 zK3>$0_UZ;icgwA3)9vE(g$GTj^zf7K-jz!!;dtT4#j94)W(NhX*6wu_KaaoUhVW+k zk$mEZkeL{r*c_s(OpD3aS*rZ#3>6xvJF6QIw1L21u&1$)fV;pK%n|wpbvfMQFme0M zC<^GYdnFeVPAv-S+>|0?NP#n~v_js2ndV#Z7R?{|e$1_F+9!n2pOPz&ipa*$?DB3$ zttrl|M|(zzcWyM9Z%Hy-xiN@*B>U_pf7wgk<_m>4eTbqBj*{Fbl>O+sLQ3bQ32M~y zre@;ubDm_kTKCHGbAGh?+`7Cqd;O`ua`vuC=dIb|{_ch6^aIG|M8(9mr=H|JQBtMx zPaj%dJ9mT0cr(@&-rBD>)t|zR%a%>K=uRg&jMg^0dDGezYnGayH(?)}CO^}@;78*^ zo4faQjO?esc^&u*_B8epa2NQ3IYPgnE(TZPxSpm(QK*2-`-)j{G}P2K5M*q@?hJTu zPRWa++3^B7Z*#(E+@l;{xx=xf!6UUpwA!34b`L6(IS@rrDmrZkuZ2?1nyMVdMf<78 zLvDNZqj;)Uy%Jhb6+#jxpYKZEs>bfiDn&@_3Z(3TF9Sk5f{1vkLZ|BllWz6B%AsI! 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z=XCch;XSV7e*8Z8eB9svaO&986R(UukL$P}&*Ss(`FI}haUJ*L_r>pz>-c=U$93HQ z&-&o={`vd-^L}kj&M|^x|8J+R|Ep(y{~i9X1J?iJ4E8@C|MwHU|NZe7BOmZT&-ecG k`G4=`|NgxA5|00^x3x9@829_Ou_J%3j{NJr?DxC>1Ij;2^Z)<= diff --git a/basicsr/metrics/psnr_ssim.py b/basicsr/metrics/psnr_ssim.py deleted file mode 100644 index ab03113..0000000 --- a/basicsr/metrics/psnr_ssim.py +++ /dev/null @@ -1,231 +0,0 @@ -import cv2 -import numpy as np -import torch -import torch.nn.functional as F - -from basicsr.metrics.metric_util import reorder_image, to_y_channel -from basicsr.utils.color_util import rgb2ycbcr_pt -from basicsr.utils.registry import METRIC_REGISTRY - - -@METRIC_REGISTRY.register() -def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): - """Calculate PSNR (Peak Signal-to-Noise Ratio). - - Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio - - Args: - img (ndarray): Images with range [0, 255]. - img2 (ndarray): Images with range [0, 255]. - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: PSNR result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - if input_order not in ['HWC', 'CHW']: - raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') - img = reorder_image(img, input_order=input_order) - img2 = reorder_image(img2, input_order=input_order) - - if crop_border != 0: - img = img[crop_border:-crop_border, crop_border:-crop_border, ...] - img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] - - if test_y_channel: - img = to_y_channel(img) - img2 = to_y_channel(img2) - - img = img.astype(np.float64) - img2 = img2.astype(np.float64) - - mse = np.mean((img - img2)**2) - if mse == 0: - return float('inf') - return 10. * np.log10(255. * 255. / mse) - - -@METRIC_REGISTRY.register() -def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs): - """Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). - - Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio - - Args: - img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: PSNR result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - - if crop_border != 0: - img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] - img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] - - if test_y_channel: - img = rgb2ycbcr_pt(img, y_only=True) - img2 = rgb2ycbcr_pt(img2, y_only=True) - - img = img.to(torch.float64) - img2 = img2.to(torch.float64) - - mse = torch.mean((img - img2)**2, dim=[1, 2, 3]) - return 10. * torch.log10(1. / (mse + 1e-8)) - - -@METRIC_REGISTRY.register() -def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): - """Calculate SSIM (structural similarity). - - ``Paper: Image quality assessment: From error visibility to structural similarity`` - - The results are the same as that of the official released MATLAB code in - https://ece.uwaterloo.ca/~z70wang/research/ssim/. - - For three-channel images, SSIM is calculated for each channel and then - averaged. - - Args: - img (ndarray): Images with range [0, 255]. - img2 (ndarray): Images with range [0, 255]. - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - input_order (str): Whether the input order is 'HWC' or 'CHW'. - Default: 'HWC'. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: SSIM result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - if input_order not in ['HWC', 'CHW']: - raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') - img = reorder_image(img, input_order=input_order) - img2 = reorder_image(img2, input_order=input_order) - - if crop_border != 0: - img = img[crop_border:-crop_border, crop_border:-crop_border, ...] - img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] - - if test_y_channel: - img = to_y_channel(img) - img2 = to_y_channel(img2) - - img = img.astype(np.float64) - img2 = img2.astype(np.float64) - - ssims = [] - for i in range(img.shape[2]): - ssims.append(_ssim(img[..., i], img2[..., i])) - return np.array(ssims).mean() - - -@METRIC_REGISTRY.register() -def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs): - """Calculate SSIM (structural similarity) (PyTorch version). - - ``Paper: Image quality assessment: From error visibility to structural similarity`` - - The results are the same as that of the official released MATLAB code in - https://ece.uwaterloo.ca/~z70wang/research/ssim/. - - For three-channel images, SSIM is calculated for each channel and then - averaged. - - Args: - img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. - test_y_channel (bool): Test on Y channel of YCbCr. Default: False. - - Returns: - float: SSIM result. - """ - - assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') - - if crop_border != 0: - img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] - img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] - - if test_y_channel: - img = rgb2ycbcr_pt(img, y_only=True) - img2 = rgb2ycbcr_pt(img2, y_only=True) - - img = img.to(torch.float64) - img2 = img2.to(torch.float64) - - ssim = _ssim_pth(img * 255., img2 * 255.) - return ssim - - -def _ssim(img, img2): - """Calculate SSIM (structural similarity) for one channel images. - - It is called by func:`calculate_ssim`. - - Args: - img (ndarray): Images with range [0, 255] with order 'HWC'. - img2 (ndarray): Images with range [0, 255] with order 'HWC'. - - Returns: - float: SSIM result. - """ - - c1 = (0.01 * 255)**2 - c2 = (0.03 * 255)**2 - kernel = cv2.getGaussianKernel(11, 1.5) - window = np.outer(kernel, kernel.transpose()) - - mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5] # valid mode for window size 11 - mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] - mu1_sq = mu1**2 - mu2_sq = mu2**2 - mu1_mu2 = mu1 * mu2 - sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq - sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq - sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 - - ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)) - return ssim_map.mean() - - -def _ssim_pth(img, img2): - """Calculate SSIM (structural similarity) (PyTorch version). - - It is called by func:`calculate_ssim_pt`. - - Args: - img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). - - Returns: - float: SSIM result. - """ - c1 = (0.01 * 255)**2 - c2 = (0.03 * 255)**2 - - kernel = cv2.getGaussianKernel(11, 1.5) - window = np.outer(kernel, kernel.transpose()) - window = torch.from_numpy(window).view(1, 1, 11, 11).expand(img.size(1), 1, 11, 11).to(img.dtype).to(img.device) - - mu1 = F.conv2d(img, window, stride=1, padding=0, groups=img.shape[1]) # valid mode - mu2 = F.conv2d(img2, window, stride=1, padding=0, groups=img2.shape[1]) # valid mode - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) - mu1_mu2 = mu1 * mu2 - sigma1_sq = F.conv2d(img * img, window, stride=1, padding=0, groups=img.shape[1]) - mu1_sq - sigma2_sq = F.conv2d(img2 * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu2_sq - sigma12 = F.conv2d(img * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu1_mu2 - - cs_map = (2 * sigma12 + c2) / (sigma1_sq + sigma2_sq + c2) - ssim_map = ((2 * mu1_mu2 + c1) / (mu1_sq + mu2_sq + c1)) * cs_map - return ssim_map.mean([1, 2, 3]) diff --git a/basicsr/metrics/test_metrics/test_psnr_ssim.py b/basicsr/metrics/test_metrics/test_psnr_ssim.py deleted file mode 100644 index 18b05a7..0000000 --- a/basicsr/metrics/test_metrics/test_psnr_ssim.py +++ /dev/null @@ -1,52 +0,0 @@ -import cv2 -import torch - -from basicsr.metrics import calculate_psnr, calculate_ssim -from basicsr.metrics.psnr_ssim import calculate_psnr_pt, calculate_ssim_pt -from basicsr.utils import img2tensor - - -def test(img_path, img_path2, crop_border, test_y_channel=False): - img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) - img2 = cv2.imread(img_path2, cv2.IMREAD_UNCHANGED) - - # --------------------- Numpy --------------------- - psnr = calculate_psnr(img, img2, crop_border=crop_border, input_order='HWC', test_y_channel=test_y_channel) - ssim = calculate_ssim(img, img2, crop_border=crop_border, input_order='HWC', test_y_channel=test_y_channel) - print(f'\tNumpy\tPSNR: {psnr:.6f} dB, \tSSIM: {ssim:.6f}') - - # --------------------- PyTorch (CPU) --------------------- - img = img2tensor(img / 255., bgr2rgb=True, float32=True).unsqueeze_(0) - img2 = img2tensor(img2 / 255., bgr2rgb=True, float32=True).unsqueeze_(0) - - psnr_pth = calculate_psnr_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) - ssim_pth = calculate_ssim_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) - print(f'\tTensor (CPU) \tPSNR: {psnr_pth[0]:.6f} dB, \tSSIM: {ssim_pth[0]:.6f}') - - # --------------------- PyTorch (GPU) --------------------- - img = img.cuda() - img2 = img2.cuda() - psnr_pth = calculate_psnr_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) - ssim_pth = calculate_ssim_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) - print(f'\tTensor (GPU) \tPSNR: {psnr_pth[0]:.6f} dB, \tSSIM: {ssim_pth[0]:.6f}') - - psnr_pth = calculate_psnr_pt( - torch.repeat_interleave(img, 2, dim=0), - torch.repeat_interleave(img2, 2, dim=0), - crop_border=crop_border, - test_y_channel=test_y_channel) - ssim_pth = calculate_ssim_pt( - torch.repeat_interleave(img, 2, dim=0), - torch.repeat_interleave(img2, 2, dim=0), - crop_border=crop_border, - test_y_channel=test_y_channel) - print(f'\tTensor (GPU batch) \tPSNR: {psnr_pth[0]:.6f}, {psnr_pth[1]:.6f} dB,' - f'\tSSIM: {ssim_pth[0]:.6f}, {ssim_pth[1]:.6f}') - - -if __name__ == '__main__': - test('tests/data/bic/baboon.png', 'tests/data/gt/baboon.png', crop_border=4, test_y_channel=False) - test('tests/data/bic/baboon.png', 'tests/data/gt/baboon.png', crop_border=4, test_y_channel=True) - - test('tests/data/bic/comic.png', 'tests/data/gt/comic.png', crop_border=4, test_y_channel=False) - test('tests/data/bic/comic.png', 'tests/data/gt/comic.png', crop_border=4, test_y_channel=True) diff --git a/basicsr/models/__init__.py b/basicsr/models/__init__.py deleted file mode 100644 index 85796de..0000000 --- a/basicsr/models/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -import importlib -from copy import deepcopy -from os import path as osp - -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import MODEL_REGISTRY - -__all__ = ['build_model'] - -# automatically scan and import model modules for registry -# scan all the files under the 'models' folder and collect files ending with '_model.py' -model_folder = osp.dirname(osp.abspath(__file__)) -model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')] -# import all the model modules -_model_modules = [importlib.import_module(f'basicsr.models.{file_name}') for file_name in model_filenames] - - -def build_model(opt): - """Build model from options. - - Args: - opt (dict): Configuration. 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a/basicsr/models/base_model.py +++ /dev/null @@ -1,392 +0,0 @@ -import os -import time -import torch -from collections import OrderedDict -from copy import deepcopy -from torch.nn.parallel import DataParallel, DistributedDataParallel - -from basicsr.models import lr_scheduler as lr_scheduler -from basicsr.utils import get_root_logger -from basicsr.utils.dist_util import master_only - - -class BaseModel(): - """Base model.""" - - def __init__(self, opt): - self.opt = opt - self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') - self.is_train = opt['is_train'] - self.schedulers = [] - self.optimizers = [] - - def feed_data(self, data): - pass - - def optimize_parameters(self): - pass - - def get_current_visuals(self): - pass - - def save(self, epoch, current_iter): - """Save networks and training state.""" - pass - - def validation(self, dataloader, current_iter, tb_logger, save_img=False): - """Validation function. - - Args: - dataloader (torch.utils.data.DataLoader): Validation dataloader. - current_iter (int): Current iteration. - tb_logger (tensorboard logger): Tensorboard logger. - save_img (bool): Whether to save images. Default: False. - """ - if self.opt['dist']: - self.dist_validation(dataloader, current_iter, tb_logger, save_img) - else: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def _initialize_best_metric_results(self, dataset_name): - """Initialize the best metric results dict for recording the best metric value and iteration.""" - if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results: - return - elif not hasattr(self, 'best_metric_results'): - self.best_metric_results = dict() - - # add a dataset record - record = dict() - for metric, content in self.opt['val']['metrics'].items(): - better = content.get('better', 'higher') - init_val = float('-inf') if better == 'higher' else float('inf') - record[metric] = dict(better=better, val=init_val, iter=-1) - self.best_metric_results[dataset_name] = record - - def _update_best_metric_result(self, dataset_name, metric, val, current_iter): - if self.best_metric_results[dataset_name][metric]['better'] == 'higher': - if val >= self.best_metric_results[dataset_name][metric]['val']: - self.best_metric_results[dataset_name][metric]['val'] = val - self.best_metric_results[dataset_name][metric]['iter'] = current_iter - else: - if val <= self.best_metric_results[dataset_name][metric]['val']: - self.best_metric_results[dataset_name][metric]['val'] = val - self.best_metric_results[dataset_name][metric]['iter'] = current_iter - - def model_ema(self, decay=0.999): - net_g = self.get_bare_model(self.net_g) - - net_g_params = dict(net_g.named_parameters()) - net_g_ema_params = dict(self.net_g_ema.named_parameters()) - - for k in net_g_ema_params.keys(): - net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay) - - def get_current_log(self): - return self.log_dict - - def model_to_device(self, net): - """Model to device. It also warps models with DistributedDataParallel - or DataParallel. - - Args: - net (nn.Module) - """ - net = net.to(self.device) - if self.opt['dist']: - find_unused_parameters = self.opt.get('find_unused_parameters', False) - net = DistributedDataParallel( - net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters) - elif self.opt['num_gpu'] > 1: - net = DataParallel(net) - return net - - def get_optimizer(self, optim_type, params, lr, **kwargs): - if optim_type == 'Adam': - optimizer = torch.optim.Adam(params, lr, **kwargs) - elif optim_type == 'AdamW': - optimizer = torch.optim.AdamW(params, lr, **kwargs) - elif optim_type == 'Adamax': - optimizer = torch.optim.Adamax(params, lr, **kwargs) - elif optim_type == 'SGD': - optimizer = torch.optim.SGD(params, lr, **kwargs) - elif optim_type == 'ASGD': - optimizer = torch.optim.ASGD(params, lr, **kwargs) - elif optim_type == 'RMSprop': - optimizer = torch.optim.RMSprop(params, lr, **kwargs) - elif optim_type == 'Rprop': - optimizer = torch.optim.Rprop(params, lr, **kwargs) - else: - raise NotImplementedError(f'optimizer {optim_type} is not supported yet.') - return optimizer - - def setup_schedulers(self): - """Set up schedulers.""" - train_opt = self.opt['train'] - scheduler_type = train_opt['scheduler'].pop('type') - if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']: - for optimizer in self.optimizers: - self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler'])) - elif scheduler_type == 'CosineAnnealingRestartLR': - for optimizer in self.optimizers: - self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler'])) - else: - raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.') - - def get_bare_model(self, net): - """Get bare model, especially under wrapping with - DistributedDataParallel or DataParallel. - """ - if isinstance(net, (DataParallel, DistributedDataParallel)): - net = net.module - return net - - @master_only - def print_network(self, net): - """Print the str and parameter number of a network. - - Args: - net (nn.Module) - """ - if isinstance(net, (DataParallel, DistributedDataParallel)): - net_cls_str = f'{net.__class__.__name__} - {net.module.__class__.__name__}' - else: - net_cls_str = f'{net.__class__.__name__}' - - net = self.get_bare_model(net) - net_str = str(net) - net_params = sum(map(lambda x: x.numel(), net.parameters())) - - logger = get_root_logger() - logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}') - logger.info(net_str) - - def _set_lr(self, lr_groups_l): - """Set learning rate for warm-up. - - Args: - lr_groups_l (list): List for lr_groups, each for an optimizer. - """ - for optimizer, lr_groups in zip(self.optimizers, lr_groups_l): - for param_group, lr in zip(optimizer.param_groups, lr_groups): - param_group['lr'] = lr - - def _get_init_lr(self): - """Get the initial lr, which is set by the scheduler. - """ - init_lr_groups_l = [] - for optimizer in self.optimizers: - init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups]) - return init_lr_groups_l - - def update_learning_rate(self, current_iter, warmup_iter=-1): - """Update learning rate. - - Args: - current_iter (int): Current iteration. - warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up. - Default： -1. - """ - if current_iter > 1: - for scheduler in self.schedulers: - scheduler.step() - # set up warm-up learning rate - if current_iter < warmup_iter: - # get initial lr for each group - init_lr_g_l = self._get_init_lr() - # modify warming-up learning rates - # currently only support linearly warm up - warm_up_lr_l = [] - for init_lr_g in init_lr_g_l: - warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g]) - # set learning rate - self._set_lr(warm_up_lr_l) - - def get_current_learning_rate(self): - return [param_group['lr'] for param_group in self.optimizers[0].param_groups] - - @master_only - def save_network(self, net, net_label, current_iter, param_key='params'): - """Save networks. - - Args: - net (nn.Module | list[nn.Module]): Network(s) to be saved. - net_label (str): Network label. - current_iter (int): Current iter number. - param_key (str | list[str]): The parameter key(s) to save network. - Default: 'params'. - """ - if current_iter == -1: - current_iter = 'latest' - save_filename = f'{net_label}_{current_iter}.pth' - save_path = os.path.join(self.opt['path']['models'], save_filename) - - net = net if isinstance(net, list) else [net] - param_key = param_key if isinstance(param_key, list) else [param_key] - assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.' - - save_dict = {} - for net_, param_key_ in zip(net, param_key): - net_ = self.get_bare_model(net_) - state_dict = net_.state_dict() - for key, param in state_dict.items(): - if key.startswith('module.'): # remove unnecessary 'module.' - key = key[7:] - state_dict[key] = param.cpu() - save_dict[param_key_] = state_dict - - # avoid occasional writing errors - retry = 3 - while retry > 0: - try: - torch.save(save_dict, save_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}') - time.sleep(1) - else: - break - finally: - retry -= 1 - if retry == 0: - logger.warning(f'Still cannot save {save_path}. Just ignore it.') - # raise IOError(f'Cannot save {save_path}.') - - def _print_different_keys_loading(self, crt_net, load_net, strict=True): - """Print keys with different name or different size when loading models. - - 1. Print keys with different names. - 2. If strict=False, print the same key but with different tensor size. - It also ignore these keys with different sizes (not load). - - Args: - crt_net (torch model): Current network. - load_net (dict): Loaded network. - strict (bool): Whether strictly loaded. Default: True. - """ - crt_net = self.get_bare_model(crt_net) - crt_net = crt_net.state_dict() - crt_net_keys = set(crt_net.keys()) - load_net_keys = set(load_net.keys()) - - logger = get_root_logger() - if crt_net_keys != load_net_keys: - logger.warning('Current net - loaded net:') - for v in sorted(list(crt_net_keys - load_net_keys)): - logger.warning(f' {v}') - logger.warning('Loaded net - current net:') - for v in sorted(list(load_net_keys - crt_net_keys)): - logger.warning(f' {v}') - - # check the size for the same keys - if not strict: - common_keys = crt_net_keys & load_net_keys - for k in common_keys: - if crt_net[k].size() != load_net[k].size(): - logger.warning(f'Size different, ignore [{k}]: crt_net: ' - f'{crt_net[k].shape}; load_net: {load_net[k].shape}') - load_net[k + '.ignore'] = load_net.pop(k) - - def load_network(self, net, load_path, strict=True, param_key='params'): - """Load network. - - Args: - load_path (str): The path of networks to be loaded. - net (nn.Module): Network. - strict (bool): Whether strictly loaded. - param_key (str): The parameter key of loaded network. If set to - None, use the root 'path'. - Default: 'params'. - """ - logger = get_root_logger() - net = self.get_bare_model(net) - load_net = torch.load(load_path, map_location=lambda storage, loc: storage) - if param_key is not None: - if param_key not in load_net and 'params' in load_net: - param_key = 'params' - logger.info('Loading: params_ema does not exist, use params.') - load_net = load_net[param_key] - logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].') - # remove unnecessary 'module.' - for k, v in deepcopy(load_net).items(): - if k.startswith('module.'): - load_net[k[7:]] = v - load_net.pop(k) - self._print_different_keys_loading(net, load_net, strict) - net.load_state_dict(load_net, strict=strict) - - @master_only - def save_training_state(self, epoch, current_iter): - """Save training states during training, which will be used for - resuming. - - Args: - epoch (int): Current epoch. - current_iter (int): Current iteration. - """ - if current_iter != -1: - state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []} - for o in self.optimizers: - state['optimizers'].append(o.state_dict()) - for s in self.schedulers: - state['schedulers'].append(s.state_dict()) - save_filename = f'{current_iter}.state' - save_path = os.path.join(self.opt['path']['training_states'], save_filename) - - # avoid occasional writing errors - retry = 3 - while retry > 0: - try: - torch.save(state, save_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}') - time.sleep(1) - else: - break - finally: - retry -= 1 - if retry == 0: - logger.warning(f'Still cannot save {save_path}. Just ignore it.') - # raise IOError(f'Cannot save {save_path}.') - - def resume_training(self, resume_state): - """Reload the optimizers and schedulers for resumed training. - - Args: - resume_state (dict): Resume state. - """ - resume_optimizers = resume_state['optimizers'] - resume_schedulers = resume_state['schedulers'] - assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers' - assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers' - for i, o in enumerate(resume_optimizers): - self.optimizers[i].load_state_dict(o) - for i, s in enumerate(resume_schedulers): - self.schedulers[i].load_state_dict(s) - - def reduce_loss_dict(self, loss_dict): - """reduce loss dict. - - In distributed training, it averages the losses among different GPUs . - - Args: - loss_dict (OrderedDict): Loss dict. - """ - with torch.no_grad(): - if self.opt['dist']: - keys = [] - losses = [] - for name, value in loss_dict.items(): - keys.append(name) - losses.append(value) - losses = torch.stack(losses, 0) - torch.distributed.reduce(losses, dst=0) - if self.opt['rank'] == 0: - losses /= self.opt['world_size'] - loss_dict = {key: loss for key, loss in zip(keys, losses)} - - log_dict = OrderedDict() - for name, value in loss_dict.items(): - log_dict[name] = value.mean().item() - - return log_dict diff --git a/basicsr/models/edvr_model.py b/basicsr/models/edvr_model.py deleted file mode 100644 index 9bdbf7b..0000000 --- a/basicsr/models/edvr_model.py +++ /dev/null @@ -1,62 +0,0 @@ -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class EDVRModel(VideoBaseModel): - """EDVR Model. - - Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. # noqa: E501 - """ - - def __init__(self, opt): - super(EDVRModel, self).__init__(opt) - if self.is_train: - self.train_tsa_iter = opt['train'].get('tsa_iter') - - def setup_optimizers(self): - train_opt = self.opt['train'] - dcn_lr_mul = train_opt.get('dcn_lr_mul', 1) - logger = get_root_logger() - logger.info(f'Multiple the learning rate for dcn with {dcn_lr_mul}.') - if dcn_lr_mul == 1: - optim_params = self.net_g.parameters() - else: # separate dcn params and normal params for different lr - normal_params = [] - dcn_params = [] - for name, param in self.net_g.named_parameters(): - if 'dcn' in name: - dcn_params.append(param) - else: - normal_params.append(param) - optim_params = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': dcn_params, - 'lr': train_opt['optim_g']['lr'] * dcn_lr_mul - }, - ] - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def optimize_parameters(self, current_iter): - if self.train_tsa_iter: - if current_iter == 1: - logger = get_root_logger() - logger.info(f'Only train TSA module for {self.train_tsa_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'fusion' not in name: - param.requires_grad = False - elif current_iter == self.train_tsa_iter: - logger = get_root_logger() - logger.warning('Train all the parameters.') - for param in self.net_g.parameters(): - param.requires_grad = True - - super(EDVRModel, self).optimize_parameters(current_iter) diff --git a/basicsr/models/esrgan_model.py b/basicsr/models/esrgan_model.py deleted file mode 100644 index 3d746d0..0000000 --- a/basicsr/models/esrgan_model.py +++ /dev/null @@ -1,83 +0,0 @@ -import torch -from collections import OrderedDict - -from basicsr.utils.registry import MODEL_REGISTRY -from .srgan_model import SRGANModel - - -@MODEL_REGISTRY.register() -class ESRGANModel(SRGANModel): - """ESRGAN model for single image super-resolution.""" - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss (relativistic gan) - real_d_pred = self.net_d(self.gt).detach() - fake_g_pred = self.net_d(self.output) - l_g_real = self.cri_gan(real_d_pred - torch.mean(fake_g_pred), False, is_disc=False) - l_g_fake = self.cri_gan(fake_g_pred - torch.mean(real_d_pred), True, is_disc=False) - l_g_gan = (l_g_real + l_g_fake) / 2 - - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # gan loss (relativistic gan) - - # In order to avoid the error in distributed training: - # "Error detected in CudnnBatchNormBackward: RuntimeError: one of - # the variables needed for gradient computation has been modified by - # an inplace operation", - # we separate the backwards for real and fake, and also detach the - # tensor for calculating mean. - - # real - fake_d_pred = self.net_d(self.output).detach() - real_d_pred = self.net_d(self.gt) - l_d_real = self.cri_gan(real_d_pred - torch.mean(fake_d_pred), True, is_disc=True) * 0.5 - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach()) - l_d_fake = self.cri_gan(fake_d_pred - torch.mean(real_d_pred.detach()), False, is_disc=True) * 0.5 - l_d_fake.backward() - self.optimizer_d.step() - - loss_dict['l_d_real'] = l_d_real - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) diff --git a/basicsr/models/hifacegan_model.py b/basicsr/models/hifacegan_model.py deleted file mode 100644 index 435a2b1..0000000 --- a/basicsr/models/hifacegan_model.py +++ /dev/null @@ -1,288 +0,0 @@ -import torch -from collections import OrderedDict -from os import path as osp -from tqdm import tqdm - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.metrics import calculate_metric -from basicsr.utils import imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class HiFaceGANModel(SRModel): - """HiFaceGAN model for generic-purpose face restoration. - No prior modeling required, works for any degradations. - Currently doesn't support EMA for inference. - """ - - def init_training_settings(self): - - train_opt = self.opt['train'] - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - raise (NotImplementedError('HiFaceGAN does not support EMA now. Pass')) - - self.net_g.train() - - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # define losses - # HiFaceGAN does not use pixel loss by default - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if train_opt.get('feature_matching_opt'): - self.cri_feat = build_loss(train_opt['feature_matching_opt']).to(self.device) - else: - self.cri_feat = None - - if self.cri_pix is None and self.cri_perceptual is None: - raise ValueError('Both pixel and perceptual losses are None.') - - if train_opt.get('gan_opt'): - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - # optimizer g - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - # optimizer d - optim_type = train_opt['optim_d'].pop('type') - self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) - self.optimizers.append(self.optimizer_d) - - def discriminate(self, input_lq, output, ground_truth): - """ - This is a conditional (on the input) discriminator - In Batch Normalization, the fake and real images are - recommended to be in the same batch to avoid disparate - statistics in fake and real images. - So both fake and real images are fed to D all at once. - """ - h, w = output.shape[-2:] - if output.shape[-2:] != input_lq.shape[-2:]: - lq = torch.nn.functional.interpolate(input_lq, (h, w)) - real = torch.nn.functional.interpolate(ground_truth, (h, w)) - fake_concat = torch.cat([lq, output], dim=1) - real_concat = torch.cat([lq, real], dim=1) - else: - fake_concat = torch.cat([input_lq, output], dim=1) - real_concat = torch.cat([input_lq, ground_truth], dim=1) - - fake_and_real = torch.cat([fake_concat, real_concat], dim=0) - discriminator_out = self.net_d(fake_and_real) - pred_fake, pred_real = self._divide_pred(discriminator_out) - return pred_fake, pred_real - - @staticmethod - def _divide_pred(pred): - """ - Take the prediction of fake and real images from the combined batch. - The prediction contains the intermediate outputs of multiscale GAN, - so it's usually a list - """ - if type(pred) == list: - fake = [] - real = [] - for p in pred: - fake.append([tensor[:tensor.size(0) // 2] for tensor in p]) - real.append([tensor[tensor.size(0) // 2:] for tensor in p]) - else: - fake = pred[:pred.size(0) // 2] - real = pred[pred.size(0) // 2:] - - return fake, real - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - - # Requires real prediction for feature matching loss - pred_fake, pred_real = self.discriminate(self.lq, self.output, self.gt) - l_g_gan = self.cri_gan(pred_fake, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - # feature matching loss - if self.cri_feat: - l_g_feat = self.cri_feat(pred_fake, pred_real) - l_g_total += l_g_feat - loss_dict['l_g_feat'] = l_g_feat - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # TODO: Benchmark test between HiFaceGAN and SRGAN implementation: - # SRGAN use the same fake output for discriminator update - # while HiFaceGAN regenerate a new output using updated net_g - # This should not make too much difference though. Stick to SRGAN now. - # ------------------------------------------------------------------- - # ---------- Below are original HiFaceGAN code snippet -------------- - # ------------------------------------------------------------------- - # with torch.no_grad(): - # fake_image = self.net_g(self.lq) - # fake_image = fake_image.detach() - # fake_image.requires_grad_() - # pred_fake, pred_real = self.discriminate(self.lq, fake_image, self.gt) - - # real - pred_fake, pred_real = self.discriminate(self.lq, self.output.detach(), self.gt) - l_d_real = self.cri_gan(pred_real, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - # fake - l_d_fake = self.cri_gan(pred_fake, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - - l_d_total = (l_d_real + l_d_fake) / 2 - l_d_total.backward() - self.optimizer_d.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - print('HiFaceGAN does not support EMA now. pass') - - def validation(self, dataloader, current_iter, tb_logger, save_img=False): - """ - Warning: HiFaceGAN requires train() mode even for validation - For more info, see https://github.com/Lotayou/Face-Renovation/issues/31 - - Args: - dataloader (torch.utils.data.DataLoader): Validation dataloader. - current_iter (int): Current iteration. - tb_logger (tensorboard logger): Tensorboard logger. - save_img (bool): Whether to save images. Default: False. - """ - - if self.opt['network_g']['type'] in ('HiFaceGAN', 'SPADEGenerator'): - self.net_g.train() - - if self.opt['dist']: - self.dist_validation(dataloader, current_iter, tb_logger, save_img) - else: - print('In HiFaceGANModel: The new metrics package is under development.' + - 'Using super method now (Only PSNR & SSIM are supported)') - super().nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - """ - TODO: Validation using updated metric system - The metrics are now evaluated after all images have been tested - This allows batch processing, and also allows evaluation of - distributional metrics, such as: - - @ Frechet Inception Distance: FID - @ Maximum Mean Discrepancy: MMD - - Warning: - Need careful batch management for different inference settings. - - """ - dataset_name = dataloader.dataset.opt['name'] - with_metrics = self.opt['val'].get('metrics') is not None - if with_metrics: - self.metric_results = dict() # {metric: 0 for metric in self.opt['val']['metrics'].keys()} - sr_tensors = [] - gt_tensors = [] - - pbar = tqdm(total=len(dataloader), unit='image') - for val_data in dataloader: - img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] - self.feed_data(val_data) - self.test() - - visuals = self.get_current_visuals() # detached cpu tensor, non-squeeze - sr_tensors.append(visuals['result']) - if 'gt' in visuals: - gt_tensors.append(visuals['gt']) - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], img_name, - f'{img_name}_{current_iter}.png') - else: - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["name"]}.png') - - imwrite(tensor2img(visuals['result']), save_img_path) - - pbar.update(1) - pbar.set_description(f'Test {img_name}') - pbar.close() - - if with_metrics: - sr_pack = torch.cat(sr_tensors, dim=0) - gt_pack = torch.cat(gt_tensors, dim=0) - # calculate metrics - for name, opt_ in self.opt['val']['metrics'].items(): - # The new metric caller automatically returns mean value - # FIXME: ERROR: calculate_metric only supports two arguments. Now the codes cannot be successfully run - self.metric_results[name] = calculate_metric(dict(sr_pack=sr_pack, gt_pack=gt_pack), opt_) - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - print('HiFaceGAN does not support EMA now. Fallback to normal mode.') - - self.save_network(self.net_g, 'net_g', current_iter) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/lr_scheduler.py b/basicsr/models/lr_scheduler.py deleted file mode 100644 index 11e1c6c..0000000 --- a/basicsr/models/lr_scheduler.py +++ /dev/null @@ -1,96 +0,0 @@ -import math -from collections import Counter -from torch.optim.lr_scheduler import _LRScheduler - - -class MultiStepRestartLR(_LRScheduler): - """ MultiStep with restarts learning rate scheme. - - Args: - optimizer (torch.nn.optimizer): Torch optimizer. - milestones (list): Iterations that will decrease learning rate. - gamma (float): Decrease ratio. Default: 0.1. - restarts (list): Restart iterations. Default: [0]. - restart_weights (list): Restart weights at each restart iteration. - Default: [1]. - last_epoch (int): Used in _LRScheduler. Default: -1. - """ - - def __init__(self, optimizer, milestones, gamma=0.1, restarts=(0, ), restart_weights=(1, ), last_epoch=-1): - self.milestones = Counter(milestones) - self.gamma = gamma - self.restarts = restarts - self.restart_weights = restart_weights - assert len(self.restarts) == len(self.restart_weights), 'restarts and their weights do not match.' - super(MultiStepRestartLR, self).__init__(optimizer, last_epoch) - - def get_lr(self): - if self.last_epoch in self.restarts: - weight = self.restart_weights[self.restarts.index(self.last_epoch)] - return [group['initial_lr'] * weight for group in self.optimizer.param_groups] - if self.last_epoch not in self.milestones: - return [group['lr'] for group in self.optimizer.param_groups] - return [group['lr'] * self.gamma**self.milestones[self.last_epoch] for group in self.optimizer.param_groups] - - -def get_position_from_periods(iteration, cumulative_period): - """Get the position from a period list. - - It will return the index of the right-closest number in the period list. - For example, the cumulative_period = [100, 200, 300, 400], - if iteration == 50, return 0; - if iteration == 210, return 2; - if iteration == 300, return 2. - - Args: - iteration (int): Current iteration. - cumulative_period (list[int]): Cumulative period list. - - Returns: - int: The position of the right-closest number in the period list. - """ - for i, period in enumerate(cumulative_period): - if iteration <= period: - return i - - -class CosineAnnealingRestartLR(_LRScheduler): - """ Cosine annealing with restarts learning rate scheme. - - An example of config: - periods = [10, 10, 10, 10] - restart_weights = [1, 0.5, 0.5, 0.5] - eta_min=1e-7 - - It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the - scheduler will restart with the weights in restart_weights. - - Args: - optimizer (torch.nn.optimizer): Torch optimizer. - periods (list): Period for each cosine anneling cycle. - restart_weights (list): Restart weights at each restart iteration. - Default: [1]. - eta_min (float): The minimum lr. Default: 0. - last_epoch (int): Used in _LRScheduler. Default: -1. - """ - - def __init__(self, optimizer, periods, restart_weights=(1, ), eta_min=0, last_epoch=-1): - self.periods = periods - self.restart_weights = restart_weights - self.eta_min = eta_min - assert (len(self.periods) == len( - self.restart_weights)), 'periods and restart_weights should have the same length.' - self.cumulative_period = [sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))] - super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch) - - def get_lr(self): - idx = get_position_from_periods(self.last_epoch, self.cumulative_period) - current_weight = self.restart_weights[idx] - nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1] - current_period = self.periods[idx] - - return [ - self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) * - (1 + math.cos(math.pi * ((self.last_epoch - nearest_restart) / current_period))) - for base_lr in self.base_lrs - ] diff --git a/basicsr/models/realesrgan_model.py b/basicsr/models/realesrgan_model.py deleted file mode 100644 index c74b28f..0000000 --- a/basicsr/models/realesrgan_model.py +++ /dev/null @@ -1,267 +0,0 @@ -import numpy as np -import random -import torch -from collections import OrderedDict -from torch.nn import functional as F - -from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt -from basicsr.data.transforms import paired_random_crop -from basicsr.losses.loss_util import get_refined_artifact_map -from basicsr.models.srgan_model import SRGANModel -from basicsr.utils import DiffJPEG, USMSharp -from basicsr.utils.img_process_util import filter2D -from basicsr.utils.registry import MODEL_REGISTRY - - -@MODEL_REGISTRY.register(suffix='basicsr') -class RealESRGANModel(SRGANModel): - """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It mainly performs: - 1. randomly synthesize LQ images in GPU tensors - 2. optimize the networks with GAN training. - """ - - def __init__(self, opt): - super(RealESRGANModel, self).__init__(opt) - self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts - self.usm_sharpener = USMSharp().cuda() # do usm sharpening - self.queue_size = opt.get('queue_size', 180) - - @torch.no_grad() - def _dequeue_and_enqueue(self): - """It is the training pair pool for increasing the diversity in a batch. - - Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a - batch could not have different resize scaling factors. Therefore, we employ this training pair pool - to increase the degradation diversity in a batch. - """ - # initialize - b, c, h, w = self.lq.size() - if not hasattr(self, 'queue_lr'): - assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' - self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() - _, c, h, w = self.gt.size() - self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() - self.queue_ptr = 0 - if self.queue_ptr == self.queue_size: # the pool is full - # do dequeue and enqueue - # shuffle - idx = torch.randperm(self.queue_size) - self.queue_lr = self.queue_lr[idx] - self.queue_gt = self.queue_gt[idx] - # get first b samples - lq_dequeue = self.queue_lr[0:b, :, :, :].clone() - gt_dequeue = self.queue_gt[0:b, :, :, :].clone() - # update the queue - self.queue_lr[0:b, :, :, :] = self.lq.clone() - self.queue_gt[0:b, :, :, :] = self.gt.clone() - - self.lq = lq_dequeue - self.gt = gt_dequeue - else: - # only do enqueue - self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() - self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() - self.queue_ptr = self.queue_ptr + b - - @torch.no_grad() - def feed_data(self, data): - """Accept data from dataloader, and then add two-order degradations to obtain LQ images. - """ - if self.is_train and self.opt.get('high_order_degradation', True): - # training data synthesis - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - self.kernel1 = data['kernel1'].to(self.device) - self.kernel2 = data['kernel2'].to(self.device) - self.sinc_kernel = data['sinc_kernel'].to(self.device) - - ori_h, ori_w = self.gt.size()[2:4] - - # ----------------------- The first degradation process ----------------------- # - # blur - out = filter2D(self.gt_usm, self.kernel1) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, scale_factor=scale, mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob'] - if np.random.uniform() < self.opt['gaussian_noise_prob']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) - out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts - out = self.jpeger(out, quality=jpeg_p) - - # ----------------------- The second degradation process ----------------------- # - # blur - if np.random.uniform() < self.opt['second_blur_prob']: - out = filter2D(out, self.kernel2) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range2'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range2'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate( - out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob2'] - if np.random.uniform() < self.opt['gaussian_noise_prob2']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range2'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - - # JPEG compression + the final sinc filter - # We also need to resize images to desired sizes. We group [resize back + sinc filter] together - # as one operation. - # We consider two orders: - # 1. [resize back + sinc filter] + JPEG compression - # 2. JPEG compression + [resize back + sinc filter] - # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. - if np.random.uniform() < 0.5: - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - else: - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - - # clamp and round - self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. - - # random crop - gt_size = self.opt['gt_size'] - (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size, - self.opt['scale']) - - # training pair pool - self._dequeue_and_enqueue() - # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue - self.gt_usm = self.usm_sharpener(self.gt) - self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract - else: - # for paired training or validation - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - # do not use the synthetic process during validation - self.is_train = False - super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) - self.is_train = True - - def optimize_parameters(self, current_iter): - # usm sharpening - l1_gt = self.gt_usm - percep_gt = self.gt_usm - gan_gt = self.gt_usm - if self.opt['l1_gt_usm'] is False: - l1_gt = self.gt - if self.opt['percep_gt_usm'] is False: - percep_gt = self.gt - if self.opt['gan_gt_usm'] is False: - gan_gt = self.gt - - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - if self.cri_ldl: - self.output_ema = self.net_g_ema(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, l1_gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - if self.cri_ldl: - pixel_weight = get_refined_artifact_map(self.gt, self.output, self.output_ema, 7) - l_g_ldl = self.cri_ldl(torch.mul(pixel_weight, self.output), torch.mul(pixel_weight, self.gt)) - l_g_total += l_g_ldl - loss_dict['l_g_ldl'] = l_g_ldl - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - real_d_pred = self.net_d(gan_gt) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach().clone()) # clone for pt1.9 - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - self.log_dict = self.reduce_loss_dict(loss_dict) diff --git a/basicsr/models/realesrnet_model.py b/basicsr/models/realesrnet_model.py deleted file mode 100644 index f579091..0000000 --- a/basicsr/models/realesrnet_model.py +++ /dev/null @@ -1,189 +0,0 @@ -import numpy as np -import random -import torch -from torch.nn import functional as F - -from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt -from basicsr.data.transforms import paired_random_crop -from basicsr.models.sr_model import SRModel -from basicsr.utils import DiffJPEG, USMSharp -from basicsr.utils.img_process_util import filter2D -from basicsr.utils.registry import MODEL_REGISTRY - - -@MODEL_REGISTRY.register(suffix='basicsr') -class RealESRNetModel(SRModel): - """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - It is trained without GAN losses. - It mainly performs: - 1. randomly synthesize LQ images in GPU tensors - 2. optimize the networks with GAN training. - """ - - def __init__(self, opt): - super(RealESRNetModel, self).__init__(opt) - self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts - self.usm_sharpener = USMSharp().cuda() # do usm sharpening - self.queue_size = opt.get('queue_size', 180) - - @torch.no_grad() - def _dequeue_and_enqueue(self): - """It is the training pair pool for increasing the diversity in a batch. - - Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a - batch could not have different resize scaling factors. Therefore, we employ this training pair pool - to increase the degradation diversity in a batch. - """ - # initialize - b, c, h, w = self.lq.size() - if not hasattr(self, 'queue_lr'): - assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' - self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() - _, c, h, w = self.gt.size() - self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() - self.queue_ptr = 0 - if self.queue_ptr == self.queue_size: # the pool is full - # do dequeue and enqueue - # shuffle - idx = torch.randperm(self.queue_size) - self.queue_lr = self.queue_lr[idx] - self.queue_gt = self.queue_gt[idx] - # get first b samples - lq_dequeue = self.queue_lr[0:b, :, :, :].clone() - gt_dequeue = self.queue_gt[0:b, :, :, :].clone() - # update the queue - self.queue_lr[0:b, :, :, :] = self.lq.clone() - self.queue_gt[0:b, :, :, :] = self.gt.clone() - - self.lq = lq_dequeue - self.gt = gt_dequeue - else: - # only do enqueue - self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() - self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() - self.queue_ptr = self.queue_ptr + b - - @torch.no_grad() - def feed_data(self, data): - """Accept data from dataloader, and then add two-order degradations to obtain LQ images. - """ - if self.is_train and self.opt.get('high_order_degradation', True): - # training data synthesis - self.gt = data['gt'].to(self.device) - # USM sharpen the GT images - if self.opt['gt_usm'] is True: - self.gt = self.usm_sharpener(self.gt) - - self.kernel1 = data['kernel1'].to(self.device) - self.kernel2 = data['kernel2'].to(self.device) - self.sinc_kernel = data['sinc_kernel'].to(self.device) - - ori_h, ori_w = self.gt.size()[2:4] - - # ----------------------- The first degradation process ----------------------- # - # blur - out = filter2D(self.gt, self.kernel1) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, scale_factor=scale, mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob'] - if np.random.uniform() < self.opt['gaussian_noise_prob']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) - out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts - out = self.jpeger(out, quality=jpeg_p) - - # ----------------------- The second degradation process ----------------------- # - # blur - if np.random.uniform() < self.opt['second_blur_prob']: - out = filter2D(out, self.kernel2) - # random resize - updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] - if updown_type == 'up': - scale = np.random.uniform(1, self.opt['resize_range2'][1]) - elif updown_type == 'down': - scale = np.random.uniform(self.opt['resize_range2'][0], 1) - else: - scale = 1 - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate( - out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) - # add noise - gray_noise_prob = self.opt['gray_noise_prob2'] - if np.random.uniform() < self.opt['gaussian_noise_prob2']: - out = random_add_gaussian_noise_pt( - out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) - else: - out = random_add_poisson_noise_pt( - out, - scale_range=self.opt['poisson_scale_range2'], - gray_prob=gray_noise_prob, - clip=True, - rounds=False) - - # JPEG compression + the final sinc filter - # We also need to resize images to desired sizes. We group [resize back + sinc filter] together - # as one operation. - # We consider two orders: - # 1. [resize back + sinc filter] + JPEG compression - # 2. JPEG compression + [resize back + sinc filter] - # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. - if np.random.uniform() < 0.5: - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - else: - # JPEG compression - jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) - out = torch.clamp(out, 0, 1) - out = self.jpeger(out, quality=jpeg_p) - # resize back + the final sinc filter - mode = random.choice(['area', 'bilinear', 'bicubic']) - out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) - out = filter2D(out, self.sinc_kernel) - - # clamp and round - self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. - - # random crop - gt_size = self.opt['gt_size'] - self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale']) - - # training pair pool - self._dequeue_and_enqueue() - self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract - else: - # for paired training or validation - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - self.gt_usm = self.usm_sharpener(self.gt) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - # do not use the synthetic process during validation - self.is_train = False - super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) - self.is_train = True diff --git a/basicsr/models/sr_model.py b/basicsr/models/sr_model.py deleted file mode 100644 index 787f1fd..0000000 --- a/basicsr/models/sr_model.py +++ /dev/null @@ -1,279 +0,0 @@ -import torch -from collections import OrderedDict -from os import path as osp -from tqdm import tqdm - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from .base_model import BaseModel - - -@MODEL_REGISTRY.register() -class SRModel(BaseModel): - """Base SR model for single image super-resolution.""" - - def __init__(self, opt): - super(SRModel, self).__init__(opt) - - # define network - self.net_g = build_network(opt['network_g']) - self.net_g = self.model_to_device(self.net_g) - self.print_network(self.net_g) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_g', 'params') - self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) - - if self.is_train: - self.init_training_settings() - - def init_training_settings(self): - self.net_g.train() - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema is used only for testing on one GPU and saving - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if self.cri_pix is None and self.cri_perceptual is None: - raise ValueError('Both pixel and perceptual losses are None.') - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - optim_params = [] - for k, v in self.net_g.named_parameters(): - if v.requires_grad: - optim_params.append(v) - else: - logger = get_root_logger() - logger.warning(f'Params {k} will not be optimized.') - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def feed_data(self, data): - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - - def optimize_parameters(self, current_iter): - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_total = 0 - loss_dict = OrderedDict() - # pixel loss - if self.cri_pix: - l_pix = self.cri_pix(self.output, self.gt) - l_total += l_pix - loss_dict['l_pix'] = l_pix - # perceptual loss - if self.cri_perceptual: - l_percep, l_style = self.cri_perceptual(self.output, self.gt) - if l_percep is not None: - l_total += l_percep - loss_dict['l_percep'] = l_percep - if l_style is not None: - l_total += l_style - loss_dict['l_style'] = l_style - - l_total.backward() - self.optimizer_g.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def test(self): - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - self.output = self.net_g_ema(self.lq) - else: - self.net_g.eval() - with torch.no_grad(): - self.output = self.net_g(self.lq) - self.net_g.train() - - def test_selfensemble(self): - # TODO: to be tested - # 8 augmentations - # modified from https://github.com/thstkdgus35/EDSR-PyTorch - - def _transform(v, op): - # if self.precision != 'single': v = v.float() - v2np = v.data.cpu().numpy() - if op == 'v': - tfnp = v2np[:, :, :, ::-1].copy() - elif op == 'h': - tfnp = v2np[:, :, ::-1, :].copy() - elif op == 't': - tfnp = v2np.transpose((0, 1, 3, 2)).copy() - - ret = torch.Tensor(tfnp).to(self.device) - # if self.precision == 'half': ret = ret.half() - - return ret - - # prepare augmented data - lq_list = [self.lq] - for tf in 'v', 'h', 't': - lq_list.extend([_transform(t, tf) for t in lq_list]) - - # inference - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - out_list = [self.net_g_ema(aug) for aug in lq_list] - else: - self.net_g.eval() - with torch.no_grad(): - out_list = [self.net_g_ema(aug) for aug in lq_list] - self.net_g.train() - - # merge results - for i in range(len(out_list)): - if i > 3: - out_list[i] = _transform(out_list[i], 't') - if i % 4 > 1: - out_list[i] = _transform(out_list[i], 'h') - if (i % 4) % 2 == 1: - out_list[i] = _transform(out_list[i], 'v') - output = torch.cat(out_list, dim=0) - - self.output = output.mean(dim=0, keepdim=True) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - if self.opt['rank'] == 0: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset_name = dataloader.dataset.opt['name'] - with_metrics = self.opt['val'].get('metrics') is not None - use_pbar = self.opt['val'].get('pbar', False) - - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} - # initialize the best metric results for each dataset_name (supporting multiple validation datasets) - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - if with_metrics: - self.metric_results = {metric: 0 for metric in self.metric_results} - - metric_data = dict() - if use_pbar: - pbar = tqdm(total=len(dataloader), unit='image') - - for idx, val_data in enumerate(dataloader): - img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] - self.feed_data(val_data) - self.test() - - visuals = self.get_current_visuals() - sr_img = tensor2img([visuals['result']]) - metric_data['img'] = sr_img - if 'gt' in visuals: - gt_img = tensor2img([visuals['gt']]) - metric_data['img2'] = gt_img - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], img_name, - f'{img_name}_{current_iter}.png') - else: - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["name"]}.png') - imwrite(sr_img, save_img_path) - - if with_metrics: - # calculate metrics - for name, opt_ in self.opt['val']['metrics'].items(): - self.metric_results[name] += calculate_metric(metric_data, opt_) - if use_pbar: - pbar.update(1) - pbar.set_description(f'Test {img_name}') - if use_pbar: - pbar.close() - - if with_metrics: - for metric in self.metric_results.keys(): - self.metric_results[metric] /= (idx + 1) - # update the best metric result - self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) - - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): - log_str = f'Validation {dataset_name}\n' - for metric, value in self.metric_results.items(): - log_str += f'\t # {metric}: {value:.4f}' - if hasattr(self, 'best_metric_results'): - log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' - f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') - log_str += '\n' - - logger = get_root_logger() - logger.info(log_str) - if tb_logger: - for metric, value in self.metric_results.items(): - tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) - - def get_current_visuals(self): - out_dict = OrderedDict() - out_dict['lq'] = self.lq.detach().cpu() - out_dict['result'] = self.output.detach().cpu() - if hasattr(self, 'gt'): - out_dict['gt'] = self.gt.detach().cpu() - return out_dict - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/srgan_model.py b/basicsr/models/srgan_model.py deleted file mode 100644 index 45387ca..0000000 --- a/basicsr/models/srgan_model.py +++ /dev/null @@ -1,149 +0,0 @@ -import torch -from collections import OrderedDict - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class SRGANModel(SRModel): - """SRGAN model for single image super-resolution.""" - - def init_training_settings(self): - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema is used only for testing on one GPU and saving - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define network net_d - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_d', 'params') - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) - - self.net_g.train() - self.net_d.train() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('ldl_opt'): - self.cri_ldl = build_loss(train_opt['ldl_opt']).to(self.device) - else: - self.cri_ldl = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if train_opt.get('gan_opt'): - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - # optimizer g - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - # optimizer d - optim_type = train_opt['optim_d'].pop('type') - self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) - self.optimizers.append(self.optimizer_d) - - def optimize_parameters(self, current_iter): - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - real_d_pred = self.net_d(self.gt) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - fake_d_pred = self.net_d(self.output.detach()) - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/stylegan2_model.py b/basicsr/models/stylegan2_model.py deleted file mode 100644 index d7da708..0000000 --- a/basicsr/models/stylegan2_model.py +++ /dev/null @@ -1,283 +0,0 @@ -import cv2 -import math -import numpy as np -import random -import torch -from collections import OrderedDict -from os import path as osp - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.losses.gan_loss import g_path_regularize, r1_penalty -from basicsr.utils import imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from .base_model import BaseModel - - -@MODEL_REGISTRY.register() -class StyleGAN2Model(BaseModel): - """StyleGAN2 model.""" - - def __init__(self, opt): - super(StyleGAN2Model, self).__init__(opt) - - # define network net_g - self.net_g = build_network(opt['network_g']) - self.net_g = self.model_to_device(self.net_g) - self.print_network(self.net_g) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_g', 'params') - self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) - - # latent dimension: self.num_style_feat - self.num_style_feat = opt['network_g']['num_style_feat'] - num_val_samples = self.opt['val'].get('num_val_samples', 16) - self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device) - - if self.is_train: - self.init_training_settings() - - def init_training_settings(self): - train_opt = self.opt['train'] - - # define network net_d - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_d', 'params') - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) - - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema only used for testing on one GPU and saving, do not need to - # wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - - self.net_g.train() - self.net_d.train() - self.net_g_ema.eval() - - # define losses - # gan loss (wgan) - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - # regularization weights - self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator - self.path_reg_weight = train_opt['path_reg_weight'] # for generator - - self.net_g_reg_every = train_opt['net_g_reg_every'] - self.net_d_reg_every = train_opt['net_d_reg_every'] - self.mixing_prob = train_opt['mixing_prob'] - - self.mean_path_length = 0 - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - # optimizer g - net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1) - if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC': - normal_params = [] - style_mlp_params = [] - modulation_conv_params = [] - for name, param in self.net_g.named_parameters(): - if 'modulation' in name: - normal_params.append(param) - elif 'style_mlp' in name: - style_mlp_params.append(param) - elif 'modulated_conv' in name: - modulation_conv_params.append(param) - else: - normal_params.append(param) - optim_params_g = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': style_mlp_params, - 'lr': train_opt['optim_g']['lr'] * 0.01 - }, - { - 'params': modulation_conv_params, - 'lr': train_opt['optim_g']['lr'] / 3 - } - ] - else: - normal_params = [] - for name, param in self.net_g.named_parameters(): - normal_params.append(param) - optim_params_g = [{ # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }] - - optim_type = train_opt['optim_g'].pop('type') - lr = train_opt['optim_g']['lr'] * net_g_reg_ratio - betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) - self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) - self.optimizers.append(self.optimizer_g) - - # optimizer d - net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) - if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC': - normal_params = [] - linear_params = [] - for name, param in self.net_d.named_parameters(): - if 'final_linear' in name: - linear_params.append(param) - else: - normal_params.append(param) - optim_params_d = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_d']['lr'] - }, - { - 'params': linear_params, - 'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512)) - } - ] - else: - normal_params = [] - for name, param in self.net_d.named_parameters(): - normal_params.append(param) - optim_params_d = [{ # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_d']['lr'] - }] - - optim_type = train_opt['optim_d'].pop('type') - lr = train_opt['optim_d']['lr'] * net_d_reg_ratio - betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) - self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) - self.optimizers.append(self.optimizer_d) - - def feed_data(self, data): - self.real_img = data['gt'].to(self.device) - - def make_noise(self, batch, num_noise): - if num_noise == 1: - noises = torch.randn(batch, self.num_style_feat, device=self.device) - else: - noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0) - return noises - - def mixing_noise(self, batch, prob): - if random.random() < prob: - return self.make_noise(batch, 2) - else: - return [self.make_noise(batch, 1)] - - def optimize_parameters(self, current_iter): - loss_dict = OrderedDict() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - self.optimizer_d.zero_grad() - - batch = self.real_img.size(0) - noise = self.mixing_noise(batch, self.mixing_prob) - fake_img, _ = self.net_g(noise) - fake_pred = self.net_d(fake_img.detach()) - - real_pred = self.net_d(self.real_img) - # wgan loss with softplus (logistic loss) for discriminator - l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True) - loss_dict['l_d'] = l_d - # In wgan, real_score should be positive and fake_score should be - # negative - loss_dict['real_score'] = real_pred.detach().mean() - loss_dict['fake_score'] = fake_pred.detach().mean() - l_d.backward() - - if current_iter % self.net_d_reg_every == 0: - self.real_img.requires_grad = True - real_pred = self.net_d(self.real_img) - l_d_r1 = r1_penalty(real_pred, self.real_img) - l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) - # TODO: why do we need to add 0 * real_pred, otherwise, a runtime - # error will arise: RuntimeError: Expected to have finished - # reduction in the prior iteration before starting a new one. - # This error indicates that your module has parameters that were - # not used in producing loss. - loss_dict['l_d_r1'] = l_d_r1.detach().mean() - l_d_r1.backward() - - self.optimizer_d.step() - - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - self.optimizer_g.zero_grad() - - noise = self.mixing_noise(batch, self.mixing_prob) - fake_img, _ = self.net_g(noise) - fake_pred = self.net_d(fake_img) - - # wgan loss with softplus (non-saturating loss) for generator - l_g = self.cri_gan(fake_pred, True, is_disc=False) - loss_dict['l_g'] = l_g - l_g.backward() - - if current_iter % self.net_g_reg_every == 0: - path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink']) - noise = self.mixing_noise(path_batch_size, self.mixing_prob) - fake_img, latents = self.net_g(noise, return_latents=True) - l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length) - - l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0]) - # TODO: why do we need to add 0 * fake_img[0, 0, 0, 0] - l_g_path.backward() - loss_dict['l_g_path'] = l_g_path.detach().mean() - loss_dict['path_length'] = path_lengths - - self.optimizer_g.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - # EMA - self.model_ema(decay=0.5**(32 / (10 * 1000))) - - def test(self): - with torch.no_grad(): - self.net_g_ema.eval() - self.output, _ = self.net_g_ema([self.fixed_sample]) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - if self.opt['rank'] == 0: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - assert dataloader is None, 'Validation dataloader should be None.' - self.test() - result = tensor2img(self.output, min_max=(-1, 1)) - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png') - imwrite(result, save_img_path) - # add sample images to tb_logger - result = (result / 255.).astype(np.float32) - result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) - if tb_logger is not None: - tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC') - - def save(self, epoch, current_iter): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/swinir_model.py b/basicsr/models/swinir_model.py deleted file mode 100644 index 5ac182f..0000000 --- a/basicsr/models/swinir_model.py +++ /dev/null @@ -1,33 +0,0 @@ -import torch -from torch.nn import functional as F - -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class SwinIRModel(SRModel): - - def test(self): - # pad to multiplication of window_size - window_size = self.opt['network_g']['window_size'] - scale = self.opt.get('scale', 1) - mod_pad_h, mod_pad_w = 0, 0 - _, _, h, w = self.lq.size() - if h % window_size != 0: - mod_pad_h = window_size - h % window_size - if w % window_size != 0: - mod_pad_w = window_size - w % window_size - img = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect') - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - self.output = self.net_g_ema(img) - else: - self.net_g.eval() - with torch.no_grad(): - self.output = self.net_g(img) - self.net_g.train() - - _, _, h, w = self.output.size() - self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale] diff --git a/basicsr/models/video_base_model.py b/basicsr/models/video_base_model.py deleted file mode 100644 index 9f7993a..0000000 --- a/basicsr/models/video_base_model.py +++ /dev/null @@ -1,160 +0,0 @@ -import torch -from collections import Counter -from os import path as osp -from torch import distributed as dist -from tqdm import tqdm - -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class VideoBaseModel(SRModel): - """Base video SR model.""" - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset = dataloader.dataset - dataset_name = dataset.opt['name'] - with_metrics = self.opt['val']['metrics'] is not None - # initialize self.metric_results - # It is a dict: { - # 'folder1': tensor (num_frame x len(metrics)), - # 'folder2': tensor (num_frame x len(metrics)) - # } - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {} - num_frame_each_folder = Counter(dataset.data_info['folder']) - for folder, num_frame in num_frame_each_folder.items(): - self.metric_results[folder] = torch.zeros( - num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') - # initialize the best metric results - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - rank, world_size = get_dist_info() - if with_metrics: - for _, tensor in self.metric_results.items(): - tensor.zero_() - - metric_data = dict() - # record all frames (border and center frames) - if rank == 0: - pbar = tqdm(total=len(dataset), unit='frame') - for idx in range(rank, len(dataset), world_size): - val_data = dataset[idx] - val_data['lq'].unsqueeze_(0) - val_data['gt'].unsqueeze_(0) - folder = val_data['folder'] - frame_idx, max_idx = val_data['idx'].split('/') - lq_path = val_data['lq_path'] - - self.feed_data(val_data) - self.test() - visuals = self.get_current_visuals() - result_img = tensor2img([visuals['result']]) - metric_data['img'] = result_img - if 'gt' in visuals: - gt_img = tensor2img([visuals['gt']]) - metric_data['img2'] = gt_img - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - raise NotImplementedError('saving image is not supported during training.') - else: - if 'vimeo' in dataset_name.lower(): # vimeo90k dataset - split_result = lq_path.split('/') - img_name = f'{split_result[-3]}_{split_result[-2]}_{split_result[-1].split(".")[0]}' - else: # other datasets, e.g., REDS, Vid4 - img_name = osp.splitext(osp.basename(lq_path))[0] - - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f'{img_name}_{self.opt["name"]}.png') - imwrite(result_img, save_img_path) - - if with_metrics: - # calculate metrics - for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): - result = calculate_metric(metric_data, opt_) - self.metric_results[folder][int(frame_idx), metric_idx] += result - - # progress bar - if rank == 0: - for _ in range(world_size): - pbar.update(1) - pbar.set_description(f'Test {folder}: {int(frame_idx) + world_size}/{max_idx}') - if rank == 0: - pbar.close() - - if with_metrics: - if self.opt['dist']: - # collect data among GPUs - for _, tensor in self.metric_results.items(): - dist.reduce(tensor, 0) - dist.barrier() - else: - pass # assume use one gpu in non-dist testing - - if rank == 0: - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - logger = get_root_logger() - logger.warning('nondist_validation is not implemented. Run dist_validation.') - self.dist_validation(dataloader, current_iter, tb_logger, save_img) - - def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): - # ----------------- calculate the average values for each folder, and for each metric ----------------- # - # average all frames for each sub-folder - # metric_results_avg is a dict:{ - # 'folder1': tensor (len(metrics)), - # 'folder2': tensor (len(metrics)) - # } - metric_results_avg = { - folder: torch.mean(tensor, dim=0).cpu() - for (folder, tensor) in self.metric_results.items() - } - # total_avg_results is a dict: { - # 'metric1': float, - # 'metric2': float - # } - total_avg_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} - for folder, tensor in metric_results_avg.items(): - for idx, metric in enumerate(total_avg_results.keys()): - total_avg_results[metric] += metric_results_avg[folder][idx].item() - # average among folders - for metric in total_avg_results.keys(): - total_avg_results[metric] /= len(metric_results_avg) - # update the best metric result - self._update_best_metric_result(dataset_name, metric, total_avg_results[metric], current_iter) - - # ------------------------------------------ log the metric ------------------------------------------ # - log_str = f'Validation {dataset_name}\n' - for metric_idx, (metric, value) in enumerate(total_avg_results.items()): - log_str += f'\t # {metric}: {value:.4f}' - for folder, tensor in metric_results_avg.items(): - log_str += f'\t # {folder}: {tensor[metric_idx].item():.4f}' - if hasattr(self, 'best_metric_results'): - log_str += (f'\n\t Best: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' - f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') - log_str += '\n' - - logger = get_root_logger() - logger.info(log_str) - if tb_logger: - for metric_idx, (metric, value) in enumerate(total_avg_results.items()): - tb_logger.add_scalar(f'metrics/{metric}', value, current_iter) - for folder, tensor in metric_results_avg.items(): - tb_logger.add_scalar(f'metrics/{metric}/{folder}', tensor[metric_idx].item(), current_iter) diff --git a/basicsr/models/video_gan_model.py b/basicsr/models/video_gan_model.py deleted file mode 100644 index a2adcde..0000000 --- a/basicsr/models/video_gan_model.py +++ /dev/null @@ -1,19 +0,0 @@ -from basicsr.utils.registry import MODEL_REGISTRY -from .srgan_model import SRGANModel -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class VideoGANModel(SRGANModel, VideoBaseModel): - """Video GAN model. - - Use multiple inheritance. - It will first use the functions of :class:`SRGANModel`: - - - :func:`init_training_settings` - - :func:`setup_optimizers` - - :func:`optimize_parameters` - - :func:`save` - - Then find functions in :class:`VideoBaseModel`. - """ diff --git a/basicsr/models/video_recurrent_gan_model.py b/basicsr/models/video_recurrent_gan_model.py deleted file mode 100644 index 74cf811..0000000 --- a/basicsr/models/video_recurrent_gan_model.py +++ /dev/null @@ -1,180 +0,0 @@ -import torch -from collections import OrderedDict - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.utils import get_root_logger -from basicsr.utils.registry import MODEL_REGISTRY -from .video_recurrent_model import VideoRecurrentModel - - -@MODEL_REGISTRY.register() -class VideoRecurrentGANModel(VideoRecurrentModel): - - def init_training_settings(self): - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # build network net_g with Exponential Moving Average (EMA) - # net_g_ema only used for testing on one GPU and saving. - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define network net_d - self.net_d = build_network(self.opt['network_d']) - self.net_d = self.model_to_device(self.net_d) - self.print_network(self.net_d) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_d', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_d', 'params') - self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) - - self.net_g.train() - self.net_d.train() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if train_opt.get('gan_opt'): - self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) - - self.net_d_iters = train_opt.get('net_d_iters', 1) - self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - if train_opt['fix_flow']: - normal_params = [] - flow_params = [] - for name, param in self.net_g.named_parameters(): - if 'spynet' in name: # The fix_flow now only works for spynet. - flow_params.append(param) - else: - normal_params.append(param) - - optim_params = [ - { # add flow params first - 'params': flow_params, - 'lr': train_opt['lr_flow'] - }, - { - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - ] - else: - optim_params = self.net_g.parameters() - - # optimizer g - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - # optimizer d - optim_type = train_opt['optim_d'].pop('type') - self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) - self.optimizers.append(self.optimizer_d) - - def optimize_parameters(self, current_iter): - logger = get_root_logger() - # optimize net_g - for p in self.net_d.parameters(): - p.requires_grad = False - - if self.fix_flow_iter: - if current_iter == 1: - logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'spynet' in name or 'edvr' in name: - param.requires_grad_(False) - elif current_iter == self.fix_flow_iter: - logger.warning('Train all the parameters.') - self.net_g.requires_grad_(True) - - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - _, _, c, h, w = self.output.size() - - l_g_total = 0 - loss_dict = OrderedDict() - if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): - # pixel loss - if self.cri_pix: - l_g_pix = self.cri_pix(self.output, self.gt) - l_g_total += l_g_pix - loss_dict['l_g_pix'] = l_g_pix - # perceptual loss - if self.cri_perceptual: - l_g_percep, l_g_style = self.cri_perceptual(self.output.view(-1, c, h, w), self.gt.view(-1, c, h, w)) - if l_g_percep is not None: - l_g_total += l_g_percep - loss_dict['l_g_percep'] = l_g_percep - if l_g_style is not None: - l_g_total += l_g_style - loss_dict['l_g_style'] = l_g_style - # gan loss - fake_g_pred = self.net_d(self.output.view(-1, c, h, w)) - l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) - l_g_total += l_g_gan - loss_dict['l_g_gan'] = l_g_gan - - l_g_total.backward() - self.optimizer_g.step() - - # optimize net_d - for p in self.net_d.parameters(): - p.requires_grad = True - - self.optimizer_d.zero_grad() - # real - # reshape to (b*n, c, h, w) - real_d_pred = self.net_d(self.gt.view(-1, c, h, w)) - l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) - loss_dict['l_d_real'] = l_d_real - loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) - l_d_real.backward() - # fake - # reshape to (b*n, c, h, w) - fake_d_pred = self.net_d(self.output.view(-1, c, h, w).detach()) - l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) - loss_dict['l_d_fake'] = l_d_fake - loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) - l_d_fake.backward() - self.optimizer_d.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def save(self, epoch, current_iter): - if self.ema_decay > 0: - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_network(self.net_d, 'net_d', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/video_recurrent_model.py b/basicsr/models/video_recurrent_model.py deleted file mode 100644 index 796ee57..0000000 --- a/basicsr/models/video_recurrent_model.py +++ /dev/null @@ -1,197 +0,0 @@ -import torch -from collections import Counter -from os import path as osp -from torch import distributed as dist -from tqdm import tqdm - -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import MODEL_REGISTRY -from .video_base_model import VideoBaseModel - - -@MODEL_REGISTRY.register() -class VideoRecurrentModel(VideoBaseModel): - - def __init__(self, opt): - super(VideoRecurrentModel, self).__init__(opt) - if self.is_train: - self.fix_flow_iter = opt['train'].get('fix_flow') - - def setup_optimizers(self): - train_opt = self.opt['train'] - flow_lr_mul = train_opt.get('flow_lr_mul', 1) - logger = get_root_logger() - logger.info(f'Multiple the learning rate for flow network with {flow_lr_mul}.') - if flow_lr_mul == 1: - optim_params = self.net_g.parameters() - else: # separate flow params and normal params for different lr - normal_params = [] - flow_params = [] - for name, param in self.net_g.named_parameters(): - if 'spynet' in name: - flow_params.append(param) - else: - normal_params.append(param) - optim_params = [ - { # add normal params first - 'params': normal_params, - 'lr': train_opt['optim_g']['lr'] - }, - { - 'params': flow_params, - 'lr': train_opt['optim_g']['lr'] * flow_lr_mul - }, - ] - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def optimize_parameters(self, current_iter): - if self.fix_flow_iter: - logger = get_root_logger() - if current_iter == 1: - logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') - for name, param in self.net_g.named_parameters(): - if 'spynet' in name or 'edvr' in name: - param.requires_grad_(False) - elif current_iter == self.fix_flow_iter: - logger.warning('Train all the parameters.') - self.net_g.requires_grad_(True) - - super(VideoRecurrentModel, self).optimize_parameters(current_iter) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset = dataloader.dataset - dataset_name = dataset.opt['name'] - with_metrics = self.opt['val']['metrics'] is not None - # initialize self.metric_results - # It is a dict: { - # 'folder1': tensor (num_frame x len(metrics)), - # 'folder2': tensor (num_frame x len(metrics)) - # } - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {} - num_frame_each_folder = Counter(dataset.data_info['folder']) - for folder, num_frame in num_frame_each_folder.items(): - self.metric_results[folder] = torch.zeros( - num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') - # initialize the best metric results - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - rank, world_size = get_dist_info() - if with_metrics: - for _, tensor in self.metric_results.items(): - tensor.zero_() - - metric_data = dict() - num_folders = len(dataset) - num_pad = (world_size - (num_folders % world_size)) % world_size - if rank == 0: - pbar = tqdm(total=len(dataset), unit='folder') - # Will evaluate (num_folders + num_pad) times, but only the first num_folders results will be recorded. - # (To avoid wait-dead) - for i in range(rank, num_folders + num_pad, world_size): - idx = min(i, num_folders - 1) - val_data = dataset[idx] - folder = val_data['folder'] - - # compute outputs - val_data['lq'].unsqueeze_(0) - val_data['gt'].unsqueeze_(0) - self.feed_data(val_data) - val_data['lq'].squeeze_(0) - val_data['gt'].squeeze_(0) - - self.test() - visuals = self.get_current_visuals() - - # tentative for out of GPU memory - del self.lq - del self.output - if 'gt' in visuals: - del self.gt - torch.cuda.empty_cache() - - if self.center_frame_only: - visuals['result'] = visuals['result'].unsqueeze(1) - if 'gt' in visuals: - visuals['gt'] = visuals['gt'].unsqueeze(1) - - # evaluate - if i < num_folders: - for idx in range(visuals['result'].size(1)): - result = visuals['result'][0, idx, :, :, :] - result_img = tensor2img([result]) # uint8, bgr - metric_data['img'] = result_img - if 'gt' in visuals: - gt = visuals['gt'][0, idx, :, :, :] - gt_img = tensor2img([gt]) # uint8, bgr - metric_data['img2'] = gt_img - - if save_img: - if self.opt['is_train']: - raise NotImplementedError('saving image is not supported during training.') - else: - if self.center_frame_only: # vimeo-90k - clip_ = val_data['lq_path'].split('/')[-3] - seq_ = val_data['lq_path'].split('/')[-2] - name_ = f'{clip_}_{seq_}' - img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f"{name_}_{self.opt['name']}.png") - else: # others - img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, - f"{idx:08d}_{self.opt['name']}.png") - # image name only for REDS dataset - imwrite(result_img, img_path) - - # calculate metrics - if with_metrics: - for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): - result = calculate_metric(metric_data, opt_) - self.metric_results[folder][idx, metric_idx] += result - - # progress bar - if rank == 0: - for _ in range(world_size): - pbar.update(1) - pbar.set_description(f'Folder: {folder}') - - if rank == 0: - pbar.close() - - if with_metrics: - if self.opt['dist']: - # collect data among GPUs - for _, tensor in self.metric_results.items(): - dist.reduce(tensor, 0) - dist.barrier() - - if rank == 0: - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def test(self): - n = self.lq.size(1) - self.net_g.eval() - - flip_seq = self.opt['val'].get('flip_seq', False) - self.center_frame_only = self.opt['val'].get('center_frame_only', False) - - if flip_seq: - self.lq = torch.cat([self.lq, self.lq.flip(1)], dim=1) - - with torch.no_grad(): - self.output = self.net_g(self.lq) - - if flip_seq: - output_1 = self.output[:, :n, :, :, :] - output_2 = self.output[:, n:, :, :, :].flip(1) - self.output = 0.5 * (output_1 + output_2) - - if self.center_frame_only: - self.output = self.output[:, n // 2, :, :, :] - - self.net_g.train() diff --git a/basicsr/ops/__init__.py b/basicsr/ops/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/basicsr/ops/__pycache__/__init__.cpython-310.pyc b/basicsr/ops/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 065d85512e320f0aa8fa07ddacf25ef463fd0988..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 142 zcmd1j<>g`kf?Fq&(m?cM5P=LBfgA@QE@lA|DGb33nv8xc8Hzx{2;!Hai&czoWk6AW 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zi|-Bl#P6aH7<*yB^0j>Jw6GP#L6WE=jrwM%`#Kn}S)hNzSp{bvxKRRSk;g3Q8{0f&?C!%YriXO1oB9$;z@@Z~69 zRe!`M{4pbu=P!UJ6o)tY{Arq6nOkraOKOj+E-v_lIO9*S@oN9)Vk!QF!Ov-Zm1KX- zk-ud7rgpHs-MO8uJ~;8IBl8f6Ung5Z5@BV4rl#{L-dkgR_l4|<{HIcjFCua^uJkJT uiJdJY%W-3(ia!ymy76=O7e;Y`8=t!I*@C<9edlFIUoscIy;NIy`hNh+06^CO diff --git a/basicsr/ops/dcn/deform_conv.py b/basicsr/ops/dcn/deform_conv.py deleted file mode 100644 index 6268ca8..0000000 --- a/basicsr/ops/dcn/deform_conv.py +++ /dev/null @@ -1,379 +0,0 @@ -import math -import os -import torch -from torch import nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn import functional as F -from torch.nn.modules.utils import _pair, _single - -BASICSR_JIT = os.getenv('BASICSR_JIT') -if BASICSR_JIT == 'True': - from torch.utils.cpp_extension import load - module_path = os.path.dirname(__file__) - deform_conv_ext = load( - 'deform_conv', - sources=[ - os.path.join(module_path, 'src', 'deform_conv_ext.cpp'), - os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'), - os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'), - ], - ) -else: - try: - from . import deform_conv_ext - except ImportError: - pass - # avoid annoying print output - # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' - # '1. compile with BASICSR_EXT=True. or\n ' - # '2. set BASICSR_JIT=True during running') - - -class DeformConvFunction(Function): - - @staticmethod - def forward(ctx, - input, - offset, - weight, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - im2col_step=64): - if input is not None and input.dim() != 4: - raise ValueError(f'Expected 4D tensor as input, got {input.dim()}D tensor instead.') - ctx.stride = _pair(stride) - ctx.padding = _pair(padding) - ctx.dilation = _pair(dilation) - ctx.groups = groups - ctx.deformable_groups = deformable_groups - ctx.im2col_step = im2col_step - - ctx.save_for_backward(input, offset, weight) - - output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride)) - - ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones - - if not input.is_cuda: - raise NotImplementedError - else: - cur_im2col_step = min(ctx.im2col_step, input.shape[0]) - assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' - deform_conv_ext.deform_conv_forward(input, weight, - offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3), - weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], - ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, - ctx.deformable_groups, cur_im2col_step) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, offset, weight = ctx.saved_tensors - - grad_input = grad_offset = grad_weight = None - - if not grad_output.is_cuda: - raise NotImplementedError - else: - cur_im2col_step = min(ctx.im2col_step, input.shape[0]) - assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' - - if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input, - grad_offset, weight, ctx.bufs_[0], weight.size(3), - weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], - ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, - ctx.deformable_groups, cur_im2col_step) - - if ctx.needs_input_grad[2]: - grad_weight = torch.zeros_like(weight) - deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight, - ctx.bufs_[0], ctx.bufs_[1], weight.size(3), - weight.size(2), ctx.stride[1], ctx.stride[0], - ctx.padding[1], ctx.padding[0], ctx.dilation[1], - ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1, - cur_im2col_step) - - return (grad_input, grad_offset, grad_weight, None, None, None, None, None) - - @staticmethod - def _output_size(input, weight, padding, dilation, stride): - channels = weight.size(0) - output_size = (input.size(0), channels) - for d in range(input.dim() - 2): - in_size = input.size(d + 2) - pad = padding[d] - kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 - stride_ = stride[d] - output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) - if not all(map(lambda s: s > 0, output_size)): - raise ValueError(f'convolution input is too small (output would be {"x".join(map(str, output_size))})') - return output_size - - -class ModulatedDeformConvFunction(Function): - - @staticmethod - def forward(ctx, - input, - offset, - mask, - weight, - bias=None, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1): - ctx.stride = stride - ctx.padding = padding - ctx.dilation = dilation - ctx.groups = groups - ctx.deformable_groups = deformable_groups - ctx.with_bias = bias is not None - if not ctx.with_bias: - bias = input.new_empty(1) # fake tensor - if not input.is_cuda: - raise NotImplementedError - if weight.requires_grad or mask.requires_grad or offset.requires_grad or input.requires_grad: - ctx.save_for_backward(input, offset, mask, weight, bias) - output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight)) - ctx._bufs = [input.new_empty(0), input.new_empty(0)] - deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output, - ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride, - ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, - ctx.groups, ctx.deformable_groups, ctx.with_bias) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - if not grad_output.is_cuda: - raise NotImplementedError - input, offset, mask, weight, bias = ctx.saved_tensors - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - grad_mask = torch.zeros_like(mask) - grad_weight = torch.zeros_like(weight) - grad_bias = torch.zeros_like(bias) - deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1], - grad_input, grad_weight, grad_bias, grad_offset, grad_mask, - grad_output, weight.shape[2], weight.shape[3], ctx.stride, - ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, - ctx.groups, ctx.deformable_groups, ctx.with_bias) - if not ctx.with_bias: - grad_bias = None - - return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None) - - @staticmethod - def _infer_shape(ctx, input, weight): - n = input.size(0) - channels_out = weight.size(0) - height, width = input.shape[2:4] - kernel_h, kernel_w = weight.shape[2:4] - height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1 - width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1 - return n, channels_out, height_out, width_out - - -deform_conv = DeformConvFunction.apply -modulated_deform_conv = ModulatedDeformConvFunction.apply - - -class DeformConv(nn.Module): - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - bias=False): - super(DeformConv, self).__init__() - - assert not bias - assert in_channels % groups == 0, f'in_channels {in_channels} is not divisible by groups {groups}' - assert out_channels % groups == 0, f'out_channels {out_channels} is not divisible by groups {groups}' - - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = _pair(stride) - self.padding = _pair(padding) - self.dilation = _pair(dilation) - self.groups = groups - self.deformable_groups = deformable_groups - # enable compatibility with nn.Conv2d - self.transposed = False - self.output_padding = _single(0) - - self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size)) - - self.reset_parameters() - - def reset_parameters(self): - n = self.in_channels - for k in self.kernel_size: - n *= k - stdv = 1. / math.sqrt(n) - self.weight.data.uniform_(-stdv, stdv) - - def forward(self, x, offset): - # To fix an assert error in deform_conv_cuda.cpp:128 - # input image is smaller than kernel - input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1]) - if input_pad: - pad_h = max(self.kernel_size[0] - x.size(2), 0) - pad_w = max(self.kernel_size[1] - x.size(3), 0) - x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() - offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() - out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, - self.deformable_groups) - if input_pad: - out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous() - return out - - -class DeformConvPack(DeformConv): - """A Deformable Conv Encapsulation that acts as normal Conv layers. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int or tuple[int]): Same as nn.Conv2d. - padding (int or tuple[int]): Same as nn.Conv2d. - dilation (int or tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - """ - - _version = 2 - - def __init__(self, *args, **kwargs): - super(DeformConvPack, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Conv2d( - self.in_channels, - self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1], - kernel_size=self.kernel_size, - stride=_pair(self.stride), - padding=_pair(self.padding), - dilation=_pair(self.dilation), - bias=True) - self.init_offset() - - def init_offset(self): - self.conv_offset.weight.data.zero_() - self.conv_offset.bias.data.zero_() - - def forward(self, x): - offset = self.conv_offset(x) - return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, - self.deformable_groups) - - -class ModulatedDeformConv(nn.Module): - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deformable_groups=1, - bias=True): - super(ModulatedDeformConv, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = stride - self.padding = padding - self.dilation = dilation - self.groups = groups - self.deformable_groups = deformable_groups - self.with_bias = bias - # enable compatibility with nn.Conv2d - self.transposed = False - self.output_padding = _single(0) - - self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) - if bias: - self.bias = nn.Parameter(torch.Tensor(out_channels)) - else: - self.register_parameter('bias', None) - self.init_weights() - - def init_weights(self): - n = self.in_channels - for k in self.kernel_size: - n *= k - stdv = 1. / math.sqrt(n) - self.weight.data.uniform_(-stdv, stdv) - if self.bias is not None: - self.bias.data.zero_() - - def forward(self, x, offset, mask): - return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, - self.groups, self.deformable_groups) - - -class ModulatedDeformConvPack(ModulatedDeformConv): - """A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int or tuple[int]): Same as nn.Conv2d. - padding (int or tuple[int]): Same as nn.Conv2d. - dilation (int or tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - """ - - _version = 2 - - def __init__(self, *args, **kwargs): - super(ModulatedDeformConvPack, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Conv2d( - self.in_channels, - self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1], - kernel_size=self.kernel_size, - stride=_pair(self.stride), - padding=_pair(self.padding), - dilation=_pair(self.dilation), - bias=True) - self.init_weights() - - def init_weights(self): - super(ModulatedDeformConvPack, self).init_weights() - if hasattr(self, 'conv_offset'): - self.conv_offset.weight.data.zero_() - self.conv_offset.bias.data.zero_() - - def forward(self, x): - out = self.conv_offset(x) - o1, o2, mask = torch.chunk(out, 3, dim=1) - offset = torch.cat((o1, o2), dim=1) - mask = torch.sigmoid(mask) - return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, - self.groups, self.deformable_groups) diff --git a/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/basicsr/ops/dcn/src/deform_conv_cuda.cpp deleted file mode 100644 index b465c49..0000000 --- a/basicsr/ops/dcn/src/deform_conv_cuda.cpp +++ /dev/null @@ -1,685 +0,0 @@ -// modify from -// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c - -#include -#include - -#include -#include - -void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset, - const int channels, const int height, const int width, - const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - at::Tensor data_col); - -void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset, - const int channels, const int height, const int width, - const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - at::Tensor grad_im); - -void deformable_col2im_coord( - const at::Tensor data_col, const at::Tensor data_im, - const at::Tensor data_offset, const int channels, const int height, - const int width, const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int parallel_imgs, - const int deformable_group, at::Tensor grad_offset); - -void modulated_deformable_im2col_cuda( - const at::Tensor data_im, const at::Tensor data_offset, - const at::Tensor data_mask, const int batch_size, const int channels, - const int height_im, const int width_im, const int height_col, - const int width_col, const int kernel_h, const int kenerl_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int deformable_group, - at::Tensor data_col); - -void modulated_deformable_col2im_cuda( - const at::Tensor data_col, const at::Tensor data_offset, - const at::Tensor data_mask, const int batch_size, const int channels, - const int height_im, const int width_im, const int height_col, - const int width_col, const int kernel_h, const int kenerl_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int deformable_group, - at::Tensor grad_im); - -void modulated_deformable_col2im_coord_cuda( - const at::Tensor data_col, const at::Tensor data_im, - const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, - const int width_im, const int height_col, const int width_col, - const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, const int dilation_h, - const int dilation_w, const int deformable_group, at::Tensor grad_offset, - at::Tensor grad_mask); - -void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput, - at::Tensor weight, int kH, int kW, int dH, int dW, int padH, - int padW, int dilationH, int dilationW, int group, - int deformable_group) { - TORCH_CHECK(weight.ndimension() == 4, - "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " - "but got: %s", - weight.ndimension()); - - TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); - - TORCH_CHECK(kW > 0 && kH > 0, - "kernel size should be greater than zero, but got kH: %d kW: %d", kH, - kW); - - TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW), - "kernel size should be consistent with weight, ", - "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH, - kW, weight.size(2), weight.size(3)); - - TORCH_CHECK(dW > 0 && dH > 0, - "stride should be greater than zero, but got dH: %d dW: %d", dH, dW); - - TORCH_CHECK( - dilationW > 0 && dilationH > 0, - "dilation should be greater than 0, but got dilationH: %d dilationW: %d", - dilationH, dilationW); - - int ndim = input.ndimension(); - int dimf = 0; - int dimh = 1; - int dimw = 2; - - if (ndim == 4) { - dimf++; - dimh++; - dimw++; - } - - TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s", - ndim); - - long nInputPlane = weight.size(1) * group; - long inputHeight = input.size(dimh); - long inputWidth = input.size(dimw); - long nOutputPlane = weight.size(0); - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - - TORCH_CHECK(nInputPlane % deformable_group == 0, - "input channels must divide deformable group size"); - - if (outputWidth < 1 || outputHeight < 1) - AT_ERROR( - "Given input size: (%ld x %ld x %ld). " - "Calculated output size: (%ld x %ld x %ld). Output size is too small", - nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight, - outputWidth); - - TORCH_CHECK(input.size(1) == nInputPlane, - "invalid number of input planes, expected: %d, but got: %d", - nInputPlane, input.size(1)); - - TORCH_CHECK((inputHeight >= kH && inputWidth >= kW), - "input image is smaller than kernel"); - - TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth), - "invalid spatial size of offset, expected height: %d width: %d, but " - "got height: %d width: %d", - outputHeight, outputWidth, offset.size(2), offset.size(3)); - - TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW), - "invalid number of channels of offset"); - - if (gradOutput != NULL) { - TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane, - "invalid number of gradOutput planes, expected: %d, but got: %d", - nOutputPlane, gradOutput->size(dimf)); - - TORCH_CHECK((gradOutput->size(dimh) == outputHeight && - gradOutput->size(dimw) == outputWidth), - "invalid size of gradOutput, expected height: %d width: %d , but " - "got height: %d width: %d", - outputHeight, outputWidth, gradOutput->size(dimh), - gradOutput->size(dimw)); - } -} - -int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, - at::Tensor offset, at::Tensor output, - at::Tensor columns, at::Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - // todo: resize columns to include im2col: done - // todo: add im2col_step as input - // todo: add new output buffer and transpose it to output (or directly - // transpose output) todo: possibly change data indexing because of - // parallel_imgs - - shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW, - dilationH, dilationW, group, deformable_group); - at::DeviceGuard guard(input.device()); - - input = input.contiguous(); - offset = offset.contiguous(); - weight = weight.contiguous(); - - int batch = 1; - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input.unsqueeze_(0); - offset.unsqueeze_(0); - } - - // todo: assert batchsize dividable by im2col_step - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = weight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); - - output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, - outputHeight, outputWidth}); - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < outputHeight * outputWidth) { - ones = at::ones({outputHeight, outputWidth}, input.options()); - } - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - at::Tensor output_buffer = - at::zeros({batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth}, - output.options()); - - output_buffer = output_buffer.view( - {output_buffer.size(0), group, output_buffer.size(1) / group, - output_buffer.size(2), output_buffer.size(3)}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns); - - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - - for (int g = 0; g < group; g++) { - output_buffer[elt][g] = output_buffer[elt][g] - .flatten(1) - .addmm_(weight[g].flatten(1), columns[g]) - .view_as(output_buffer[elt][g]); - } - } - - output_buffer = output_buffer.view( - {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2), - output_buffer.size(3), output_buffer.size(4)}); - - output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step, outputHeight, outputWidth}); - output_buffer.transpose_(1, 2); - output.copy_(output_buffer); - output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - output = output.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); - } - - return 1; -} - -int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, - at::Tensor gradOutput, at::Tensor gradInput, - at::Tensor gradOffset, at::Tensor weight, - at::Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step) { - shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW, - dilationH, dilationW, group, deformable_group); - at::DeviceGuard guard(input.device()); - - input = input.contiguous(); - offset = offset.contiguous(); - gradOutput = gradOutput.contiguous(); - weight = weight.contiguous(); - - int batch = 1; - - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input = input.view({1, input.size(0), input.size(1), input.size(2)}); - offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); - gradOutput = gradOutput.view( - {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); - } - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = weight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - // change order of grad output - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput.transpose_(1, 2); - - gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, - outputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - // divide into groups - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - gradOutput = gradOutput.view( - {gradOutput.size(0), group, gradOutput.size(1) / group, - gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)}); - - for (int g = 0; g < group; g++) { - columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), - gradOutput[elt][g].flatten(1), 0.0f, 1.0f); - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - gradOutput = gradOutput.view( - {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2), - gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)}); - - deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, - inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, - dilationH, dilationW, im2col_step, deformable_group, - gradOffset[elt]); - - deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, gradInput[elt]); - } - - gradOutput.transpose_(1, 2); - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - gradOffset = gradOffset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); - gradOffset = - gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); - } - - return 1; -} - -int deform_conv_backward_parameters_cuda( - at::Tensor input, at::Tensor offset, at::Tensor gradOutput, - at::Tensor gradWeight, // at::Tensor gradBias, - at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step) { - // todo: transpose and reshape outGrad - // todo: reshape columns - // todo: add im2col_step as input - - shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH, - padW, dilationH, dilationW, group, deformable_group); - at::DeviceGuard guard(input.device()); - - input = input.contiguous(); - offset = offset.contiguous(); - gradOutput = gradOutput.contiguous(); - - int batch = 1; - - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input = input.view( - at::IntList({1, input.size(0), input.size(1), input.size(2)})); - gradOutput = gradOutput.view( - {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); - } - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = gradWeight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); - - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput.transpose_(1, 2); - - at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); - gradOutputBuffer = - gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, - outputHeight, outputWidth}); - gradOutputBuffer.copy_(gradOutput); - gradOutputBuffer = - gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth}); - - gradOutput.transpose_(1, 2); - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns); - - // divide into group - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group, - gradOutputBuffer.size(2), gradOutputBuffer.size(3)}); - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - gradWeight = - gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1), - gradWeight.size(2), gradWeight.size(3)}); - - for (int g = 0; g < group; g++) { - gradWeight[g] = gradWeight[g] - .flatten(1) - .addmm_(gradOutputBuffer[elt][g].flatten(1), - columns[g].transpose(1, 0), 1.0, scale) - .view_as(gradWeight[g]); - } - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.size(0), - gradOutputBuffer.size(1) * gradOutputBuffer.size(2), - gradOutputBuffer.size(3), gradOutputBuffer.size(4)}); - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), - gradWeight.size(2), gradWeight.size(3), - gradWeight.size(4)}); - } - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - } - - return 1; -} - -void modulated_deform_conv_cuda_forward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias) { - TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); - TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); - at::DeviceGuard guard(input.device()); - - const int batch = input.size(0); - const int channels = input.size(1); - const int height = input.size(2); - const int width = input.size(3); - - const int channels_out = weight.size(0); - const int channels_kernel = weight.size(1); - const int kernel_h_ = weight.size(2); - const int kernel_w_ = weight.size(3); - - if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) - AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", - kernel_h_, kernel_w, kernel_h_, kernel_w_); - if (channels != channels_kernel * group) - AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", - channels, channels_kernel * group); - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = at::ones({height_out, width_out}, input.options()); - } - - // resize output - output = output.view({batch, channels_out, height_out, width_out}).zero_(); - // resize temporary columns - columns = - at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, - input.options()); - - output = output.view({output.size(0), group, output.size(1) / group, - output.size(2), output.size(3)}); - - for (int b = 0; b < batch; b++) { - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns); - - // divide into group - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - - for (int g = 0; g < group; g++) { - output[b][g] = output[b][g] - .flatten(1) - .addmm_(weight[g].flatten(1), columns[g]) - .view_as(output[b][g]); - } - - weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), - weight.size(3), weight.size(4)}); - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - } - - output = output.view({output.size(0), output.size(1) * output.size(2), - output.size(3), output.size(4)}); - - if (with_bias) { - output += bias.view({1, bias.size(0), 1, 1}); - } -} - -void modulated_deform_conv_cuda_backward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor columns, - at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, - at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias) { - TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); - TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); - at::DeviceGuard guard(input.device()); - - const int batch = input.size(0); - const int channels = input.size(1); - const int height = input.size(2); - const int width = input.size(3); - - const int channels_kernel = weight.size(1); - const int kernel_h_ = weight.size(2); - const int kernel_w_ = weight.size(3); - if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) - AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", - kernel_h_, kernel_w, kernel_h_, kernel_w_); - if (channels != channels_kernel * group) - AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", - channels, channels_kernel * group); - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = at::ones({height_out, width_out}, input.options()); - } - - grad_input = grad_input.view({batch, channels, height, width}); - columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out}, - input.options()); - - grad_output = - grad_output.view({grad_output.size(0), group, grad_output.size(1) / group, - grad_output.size(2), grad_output.size(3)}); - - for (int b = 0; b < batch; b++) { - // divide int group - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - - for (int g = 0; g < group; g++) { - columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), - grad_output[b][g].flatten(1), 0.0f, 1.0f); - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), - weight.size(3), weight.size(4)}); - - // gradient w.r.t. input coordinate data - modulated_deformable_col2im_coord_cuda( - columns, input[b], offset[b], mask[b], 1, channels, height, width, - height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, - stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], - grad_mask[b]); - // gradient w.r.t. input data - modulated_deformable_col2im_cuda( - columns, offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, grad_input[b]); - - // gradient w.r.t. weight, dWeight should accumulate across the batch and - // group - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns); - - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - grad_weight = grad_weight.view({group, grad_weight.size(0) / group, - grad_weight.size(1), grad_weight.size(2), - grad_weight.size(3)}); - if (with_bias) - grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); - - for (int g = 0; g < group; g++) { - grad_weight[g] = - grad_weight[g] - .flatten(1) - .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) - .view_as(grad_weight[g]); - if (with_bias) { - grad_bias[g] = - grad_bias[g] - .view({-1, 1}) - .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) - .view(-1); - } - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), - grad_weight.size(2), grad_weight.size(3), - grad_weight.size(4)}); - if (with_bias) - grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); - } - grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), - grad_output.size(2), grad_output.size(3), - grad_output.size(4)}); -} diff --git a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu b/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu deleted file mode 100644 index 98752dc..0000000 --- a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu +++ /dev/null @@ -1,867 +0,0 @@ -/*! - ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** - * - * COPYRIGHT - * - * All contributions by the University of California: - * Copyright (c) 2014-2017 The Regents of the University of California (Regents) - * All rights reserved. - * - * All other contributions: - * Copyright (c) 2014-2017, the respective contributors - * All rights reserved. - * - * Caffe uses a shared copyright model: each contributor holds copyright over - * their contributions to Caffe. The project versioning records all such - * contribution and copyright details. If a contributor wants to further mark - * their specific copyright on a particular contribution, they should indicate - * their copyright solely in the commit message of the change when it is - * committed. - * - * LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * 1. Redistributions of source code must retain the above copyright notice, this - * list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND - * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - * - * CONTRIBUTION AGREEMENT - * - * By contributing to the BVLC/caffe repository through pull-request, comment, - * or otherwise, the contributor releases their content to the - * license and copyright terms herein. - * - ***************** END Caffe Copyright Notice and Disclaimer ******************** - * - * Copyright (c) 2018 Microsoft - * Licensed under The MIT License [see LICENSE for details] - * \file modulated_deformable_im2col.cuh - * \brief Function definitions of converting an image to - * column matrix based on kernel, padding, dilation, and offset. - * These functions are mainly used in deformable convolution operators. - * \ref: https://arxiv.org/abs/1703.06211 - * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng - */ - -// modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu - -#include -#include -#include -#include -#include -#include - -using namespace at; - -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ - i += blockDim.x * gridDim.x) - -const int CUDA_NUM_THREADS = 1024; -const int kMaxGridNum = 65535; - -inline int GET_BLOCKS(const int N) -{ - return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); -} - -template -__device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width, - const int height, const int width, scalar_t h, scalar_t w) -{ - - int h_low = floor(h); - int w_low = floor(w); - int h_high = h_low + 1; - int w_high = w_low + 1; - - scalar_t lh = h - h_low; - scalar_t lw = w - w_low; - scalar_t hh = 1 - lh, hw = 1 - lw; - - scalar_t v1 = 0; - if (h_low >= 0 && w_low >= 0) - v1 = bottom_data[h_low * data_width + w_low]; - scalar_t v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - v2 = bottom_data[h_low * data_width + w_high]; - scalar_t v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - v3 = bottom_data[h_high * data_width + w_low]; - scalar_t v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - v4 = bottom_data[h_high * data_width + w_high]; - - scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - - scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - return val; -} - -template -__device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, - const int h, const int w, const int height, const int width) -{ - - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - if (h == argmax_h_low && w == argmax_w_low) - weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); - if (h == argmax_h_low && w == argmax_w_high) - weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); - if (h == argmax_h_high && w == argmax_w_low) - weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); - if (h == argmax_h_high && w == argmax_w_high) - weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); - return weight; -} - -template -__device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, - const int height, const int width, const scalar_t *im_data, - const int data_width, const int bp_dir) -{ - - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - - if (bp_dir == 0) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - else if (bp_dir == 1) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - - return weight; -} - -template -__global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset, - const int height, const int width, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int channel_per_deformable_group, - const int batch_size, const int num_channels, const int deformable_group, - const int height_col, const int width_col, - scalar_t *data_col) -{ - CUDA_KERNEL_LOOP(index, n) - { - // index index of output matrix - const int w_col = index % width_col; - const int h_col = (index / width_col) % height_col; - const int b_col = (index / width_col / height_col) % batch_size; - const int c_im = (index / width_col / height_col) / batch_size; - const int c_col = c_im * kernel_h * kernel_w; - - // compute deformable group index - const int deformable_group_index = c_im / channel_per_deformable_group; - - const int h_in = h_col * stride_h - pad_h; - const int w_in = w_col * stride_w - pad_w; - scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; - //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; - const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; - const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - - for (int i = 0; i < kernel_h; ++i) - { - for (int j = 0; j < kernel_w; ++j) - { - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - scalar_t val = static_cast(0); - const scalar_t h_im = h_in + i * dilation_h + offset_h; - const scalar_t w_im = w_in + j * dilation_w + offset_w; - if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) - { - //const scalar_t map_h = i * dilation_h + offset_h; - //const scalar_t map_w = j * dilation_w + offset_w; - //const int cur_height = height - h_in; - //const int cur_width = width - w_in; - //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); - val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); - } - *data_col_ptr = val; - data_col_ptr += batch_size * height_col * width_col; - } - } - } -} - -void deformable_im2col( - const at::Tensor data_im, const at::Tensor data_offset, const int channels, - const int height, const int width, const int ksize_h, const int ksize_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int parallel_imgs, - const int deformable_group, at::Tensor data_col) -{ - // num_axes should be smaller than block size - // todo: check parallel_imgs is correctly passed in - int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = channels * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.scalar_type(), "deformable_im2col_gpu", ([&] { - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *data_col_ = data_col.data_ptr(); - - deformable_im2col_gpu_kernel<<>>( - num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w, - pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, parallel_imgs, channels, deformable_group, - height_col, width_col, data_col_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); - } -} - -template -__global__ void deformable_col2im_gpu_kernel( - const int n, const scalar_t *data_col, const scalar_t *data_offset, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int deformable_group, - const int height_col, const int width_col, - scalar_t *grad_im) -{ - CUDA_KERNEL_LOOP(index, n) - { - const int j = (index / width_col / height_col / batch_size) % kernel_w; - const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; - const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; - // compute the start and end of the output - - const int deformable_group_index = c / channel_per_deformable_group; - - int w_out = index % width_col; - int h_out = (index / width_col) % height_col; - int b = (index / width_col / height_col) % batch_size; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * - 2 * kernel_h * kernel_w * height_col * width_col; - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; - const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; - - const scalar_t cur_top_grad = data_col[index]; - const int cur_h = (int)cur_inv_h_data; - const int cur_w = (int)cur_inv_w_data; - for (int dy = -2; dy <= 2; dy++) - { - for (int dx = -2; dx <= 2; dx++) - { - if (cur_h + dy >= 0 && cur_h + dy < height && - cur_w + dx >= 0 && cur_w + dx < width && - abs(cur_inv_h_data - (cur_h + dy)) < 1 && - abs(cur_inv_w_data - (cur_w + dx)) < 1) - { - int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; - scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); - atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); - } - } - } - } -} - -void deformable_col2im( - const at::Tensor data_col, const at::Tensor data_offset, const int channels, - const int height, const int width, const int ksize_h, - const int ksize_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - at::Tensor grad_im) -{ - - // todo: make sure parallel_imgs is passed in correctly - int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "deformable_col2im_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *grad_im_ = grad_im.data_ptr(); - - deformable_col2im_gpu_kernel<<>>( - num_kernels, data_col_, data_offset_, channels, height, width, ksize_h, - ksize_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - parallel_imgs, deformable_group, height_col, width_col, grad_im_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); - } -} - -template -__global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col, - const scalar_t *data_im, const scalar_t *data_offset, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int offset_channels, const int deformable_group, - const int height_col, const int width_col, scalar_t *grad_offset) -{ - CUDA_KERNEL_LOOP(index, n) - { - scalar_t val = 0; - int w = index % width_col; - int h = (index / width_col) % height_col; - int c = (index / width_col / height_col) % offset_channels; - int b = (index / width_col / height_col) / offset_channels; - // compute the start and end of the output - - const int deformable_group_index = c / (2 * kernel_h * kernel_w); - const int col_step = kernel_h * kernel_w; - int cnt = 0; - const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * - batch_size * width_col * height_col; - const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * - channel_per_deformable_group / kernel_h / kernel_w * height * width; - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - - const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; - - for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) - { - const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; - const int bp_dir = offset_c % 2; - - int j = (col_pos / width_col / height_col / batch_size) % kernel_w; - int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; - int w_out = col_pos % width_col; - int h_out = (col_pos / width_col) % height_col; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); - const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - scalar_t inv_h = h_in + i * dilation_h + offset_h; - scalar_t inv_w = w_in + j * dilation_w + offset_w; - if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) - { - inv_h = inv_w = -2; - } - const scalar_t weight = get_coordinate_weight( - inv_h, inv_w, - height, width, data_im_ptr + cnt * height * width, width, bp_dir); - val += weight * data_col_ptr[col_pos]; - cnt += 1; - } - - grad_offset[index] = val; - } -} - -void deformable_col2im_coord( - const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, - const int channels, const int height, const int width, const int ksize_h, - const int ksize_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, at::Tensor grad_offset) -{ - - int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs; - int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *grad_offset_ = grad_offset.data_ptr(); - - deformable_col2im_coord_gpu_kernel<<>>( - num_kernels, data_col_, data_im_, data_offset_, channels, height, width, - ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group, - height_col, width_col, grad_offset_); - })); -} - -template -__device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width, - const int height, const int width, scalar_t h, scalar_t w) -{ - int h_low = floor(h); - int w_low = floor(w); - int h_high = h_low + 1; - int w_high = w_low + 1; - - scalar_t lh = h - h_low; - scalar_t lw = w - w_low; - scalar_t hh = 1 - lh, hw = 1 - lw; - - scalar_t v1 = 0; - if (h_low >= 0 && w_low >= 0) - v1 = bottom_data[h_low * data_width + w_low]; - scalar_t v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - v2 = bottom_data[h_low * data_width + w_high]; - scalar_t v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - v3 = bottom_data[h_high * data_width + w_low]; - scalar_t v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - v4 = bottom_data[h_high * data_width + w_high]; - - scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - - scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - return val; -} - -template -__device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, - const int h, const int w, const int height, const int width) -{ - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - if (h == argmax_h_low && w == argmax_w_low) - weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); - if (h == argmax_h_low && w == argmax_w_high) - weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); - if (h == argmax_h_high && w == argmax_w_low) - weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); - if (h == argmax_h_high && w == argmax_w_high) - weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); - return weight; -} - -template -__device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, - const int height, const int width, const scalar_t *im_data, - const int data_width, const int bp_dir) -{ - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) - { - //empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - scalar_t weight = 0; - - if (bp_dir == 0) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - else if (bp_dir == 1) - { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; - } - - return weight; -} - -template -__global__ void modulated_deformable_im2col_gpu_kernel(const int n, - const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask, - const int height, const int width, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int num_channels, const int deformable_group, - const int height_col, const int width_col, - scalar_t *data_col) -{ - CUDA_KERNEL_LOOP(index, n) - { - // index index of output matrix - const int w_col = index % width_col; - const int h_col = (index / width_col) % height_col; - const int b_col = (index / width_col / height_col) % batch_size; - const int c_im = (index / width_col / height_col) / batch_size; - const int c_col = c_im * kernel_h * kernel_w; - - // compute deformable group index - const int deformable_group_index = c_im / channel_per_deformable_group; - - const int h_in = h_col * stride_h - pad_h; - const int w_in = w_col * stride_w - pad_w; - - scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; - //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; - const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; - const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - - const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; - - for (int i = 0; i < kernel_h; ++i) - { - for (int j = 0; j < kernel_w; ++j) - { - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; - const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; - scalar_t val = static_cast(0); - const scalar_t h_im = h_in + i * dilation_h + offset_h; - const scalar_t w_im = w_in + j * dilation_w + offset_w; - //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { - if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) - { - //const float map_h = i * dilation_h + offset_h; - //const float map_w = j * dilation_w + offset_w; - //const int cur_height = height - h_in; - //const int cur_width = width - w_in; - //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); - val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); - } - *data_col_ptr = val * mask; - data_col_ptr += batch_size * height_col * width_col; - //data_col_ptr += height_col * width_col; - } - } - } -} - -template -__global__ void modulated_deformable_col2im_gpu_kernel(const int n, - const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int deformable_group, - const int height_col, const int width_col, - scalar_t *grad_im) -{ - CUDA_KERNEL_LOOP(index, n) - { - const int j = (index / width_col / height_col / batch_size) % kernel_w; - const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; - const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; - // compute the start and end of the output - - const int deformable_group_index = c / channel_per_deformable_group; - - int w_out = index % width_col; - int h_out = (index / width_col) % height_col; - int b = (index / width_col / height_col) % batch_size; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; - const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; - const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; - const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; - const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; - const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; - - const scalar_t cur_top_grad = data_col[index] * mask; - const int cur_h = (int)cur_inv_h_data; - const int cur_w = (int)cur_inv_w_data; - for (int dy = -2; dy <= 2; dy++) - { - for (int dx = -2; dx <= 2; dx++) - { - if (cur_h + dy >= 0 && cur_h + dy < height && - cur_w + dx >= 0 && cur_w + dx < width && - abs(cur_inv_h_data - (cur_h + dy)) < 1 && - abs(cur_inv_w_data - (cur_w + dx)) < 1) - { - int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; - scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); - atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); - } - } - } - } -} - -template -__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, - const scalar_t *data_col, const scalar_t *data_im, - const scalar_t *data_offset, const scalar_t *data_mask, - const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, - const int batch_size, const int offset_channels, const int deformable_group, - const int height_col, const int width_col, - scalar_t *grad_offset, scalar_t *grad_mask) -{ - CUDA_KERNEL_LOOP(index, n) - { - scalar_t val = 0, mval = 0; - int w = index % width_col; - int h = (index / width_col) % height_col; - int c = (index / width_col / height_col) % offset_channels; - int b = (index / width_col / height_col) / offset_channels; - // compute the start and end of the output - - const int deformable_group_index = c / (2 * kernel_h * kernel_w); - const int col_step = kernel_h * kernel_w; - int cnt = 0; - const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; - const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; - const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; - const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; - - const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; - - for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) - { - const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; - const int bp_dir = offset_c % 2; - - int j = (col_pos / width_col / height_col / batch_size) % kernel_w; - int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; - int w_out = col_pos % width_col; - int h_out = (col_pos / width_col) % height_col; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); - const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); - const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); - const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; - const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; - const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; - scalar_t inv_h = h_in + i * dilation_h + offset_h; - scalar_t inv_w = w_in + j * dilation_w + offset_w; - if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) - { - inv_h = inv_w = -2; - } - else - { - mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); - } - const scalar_t weight = dmcn_get_coordinate_weight( - inv_h, inv_w, - height, width, data_im_ptr + cnt * height * width, width, bp_dir); - val += weight * data_col_ptr[col_pos] * mask; - cnt += 1; - } - // KERNEL_ASSIGN(grad_offset[index], offset_req, val); - grad_offset[index] = val; - if (offset_c % 2 == 0) - // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); - grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; - } -} - -void modulated_deformable_im2col_cuda( - const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, const int width_im, - const int height_col, const int width_col, const int kernel_h, const int kenerl_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int deformable_group, at::Tensor data_col) -{ - // num_axes should be smaller than block size - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = channels * batch_size * height_col * width_col; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *data_col_ = data_col.data_ptr(); - - modulated_deformable_im2col_gpu_kernel<<>>( - num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w, - pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, - batch_size, channels, deformable_group, height_col, width_col, data_col_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); - } -} - -void modulated_deformable_col2im_cuda( - const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, const int width_im, - const int height_col, const int width_col, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int deformable_group, at::Tensor grad_im) -{ - - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *grad_im_ = grad_im.data_ptr(); - - modulated_deformable_col2im_gpu_kernel<<>>( - num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im, - kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - batch_size, deformable_group, height_col, width_col, grad_im_); - })); - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); - } -} - -void modulated_deformable_col2im_coord_cuda( - const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, - const int batch_size, const int channels, const int height_im, const int width_im, - const int height_col, const int width_col, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int deformable_group, - at::Tensor grad_offset, at::Tensor grad_mask) -{ - const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; - const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *grad_offset_ = grad_offset.data_ptr(); - scalar_t *grad_mask_ = grad_mask.data_ptr(); - - modulated_deformable_col2im_coord_gpu_kernel<<>>( - num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im, - kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, - batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, - grad_offset_, grad_mask_); - })); - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) - { - printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); - } -} diff --git a/basicsr/ops/dcn/src/deform_conv_ext.cpp b/basicsr/ops/dcn/src/deform_conv_ext.cpp deleted file mode 100644 index 41c6df6..0000000 --- a/basicsr/ops/dcn/src/deform_conv_ext.cpp +++ /dev/null @@ -1,164 +0,0 @@ -// modify from -// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c - -#include -#include - -#include -#include - -#define WITH_CUDA // always use cuda -#ifdef WITH_CUDA -int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, - at::Tensor offset, at::Tensor output, - at::Tensor columns, at::Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step); - -int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, - at::Tensor gradOutput, at::Tensor gradInput, - at::Tensor gradOffset, at::Tensor weight, - at::Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step); - -int deform_conv_backward_parameters_cuda( - at::Tensor input, at::Tensor offset, at::Tensor gradOutput, - at::Tensor gradWeight, // at::Tensor gradBias, - at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step); - -void modulated_deform_conv_cuda_forward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias); - -void modulated_deform_conv_cuda_backward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor columns, - at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, - at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias); -#endif - -int deform_conv_forward(at::Tensor input, at::Tensor weight, - at::Tensor offset, at::Tensor output, - at::Tensor columns, at::Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return deform_conv_forward_cuda(input, weight, offset, output, columns, - ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, - deformable_group, im2col_step); -#else - AT_ERROR("deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("deform conv is not implemented on CPU"); -} - -int deform_conv_backward_input(at::Tensor input, at::Tensor offset, - at::Tensor gradOutput, at::Tensor gradInput, - at::Tensor gradOffset, at::Tensor weight, - at::Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return deform_conv_backward_input_cuda(input, offset, gradOutput, - gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH, - dilationW, dilationH, group, deformable_group, im2col_step); -#else - AT_ERROR("deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("deform conv is not implemented on CPU"); -} - -int deform_conv_backward_parameters( - at::Tensor input, at::Tensor offset, at::Tensor gradOutput, - at::Tensor gradWeight, // at::Tensor gradBias, - at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return deform_conv_backward_parameters_cuda(input, offset, gradOutput, - gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW, - dilationH, group, deformable_group, scale, im2col_step); -#else - AT_ERROR("deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("deform conv is not implemented on CPU"); -} - -void modulated_deform_conv_forward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return modulated_deform_conv_cuda_forward(input, weight, bias, ones, - offset, mask, output, columns, kernel_h, kernel_w, stride_h, - stride_w, pad_h, pad_w, dilation_h, dilation_w, group, - deformable_group, with_bias); -#else - AT_ERROR("modulated deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("modulated deform conv is not implemented on CPU"); -} - -void modulated_deform_conv_backward( - at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, - at::Tensor offset, at::Tensor mask, at::Tensor columns, - at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, - at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias) { - if (input.device().is_cuda()) { -#ifdef WITH_CUDA - return modulated_deform_conv_cuda_backward(input, weight, bias, ones, - offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset, - grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w, - pad_h, pad_w, dilation_h, dilation_w, group, deformable_group, - with_bias); -#else - AT_ERROR("modulated deform conv is not compiled with GPU support"); -#endif - } - AT_ERROR("modulated deform conv is not implemented on CPU"); -} - - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("deform_conv_forward", &deform_conv_forward, - "deform forward"); - m.def("deform_conv_backward_input", &deform_conv_backward_input, - "deform_conv_backward_input"); - m.def("deform_conv_backward_parameters", - &deform_conv_backward_parameters, - "deform_conv_backward_parameters"); - m.def("modulated_deform_conv_forward", - &modulated_deform_conv_forward, - "modulated deform conv forward"); - m.def("modulated_deform_conv_backward", - &modulated_deform_conv_backward, - "modulated deform conv backward"); -} diff --git a/basicsr/ops/fused_act/__init__.py b/basicsr/ops/fused_act/__init__.py deleted file mode 100644 index 241dc07..0000000 --- a/basicsr/ops/fused_act/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .fused_act import FusedLeakyReLU, fused_leaky_relu - -__all__ = ['FusedLeakyReLU', 'fused_leaky_relu'] diff --git a/basicsr/ops/fused_act/__pycache__/__init__.cpython-310.pyc b/basicsr/ops/fused_act/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 774231c913ae592463871ae7c100a461ba17e1ab..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 256 zcmd1j<>g`kf?Fq&(sF_HV-N=!FabFZKwK;XBvKes7;_kM8KW3;nWC5&8B&;1nA4e} zm{VAS88lg6G6I!oGTq{HD=kh<@kve0t_(``3B4td2I9x(fCS=;Qgcd+K!z4E0|`G( zmMBh$bYgM|Li`qce0*X~PJH}Ih9XvwVi57m+Qllyw=$q8KPxr4B*rZZ{SAc|<1zDZ~6ha6I1cH(UvLM80#b`3!-u3Qy#&nMf zYt4$oM&iT|fCI9R`%mD&A82tv;(&5S;=m!Hs%LHQh5&7KxvRUUr>efMswZnSY6P13 z@p|`{1wwv9WqY|Yx>NkTX zC7+VC%86`keLxBRJ|ii;LEa^zDGuEv;*jJ!POu1iODuxEX!SPeZE+a%!;)Tx0f=8Y?aS6@4BBRZ%Yx2q` zQ;#l0@#c+4iP(jsrmYGC>=&Wy0f>T(Nx>$RlENuSNDB^<6?6>moX~AI=VP~^g0@sr3|~73a{^3ZknlFi}D^!>m(aU(}YQWVQ;Qs#M{DdD^H_}?p-twq(Ubp;@Y3EPF~)S-5cL_- zOP~~FgRIhRC#Pe;w_t^370d$e8;B||IAk&Wx*TwFmQ@p;vJiD#(?E1Yn9EdWO53{4h+Tz6?WC3&VaUh6#@AVfe)`O7>RjItPZueVMOkLY+cOo7o&M-Rz3gjVFWSfUm)Lh6z zI?Y4KUZ?P8*$W6l_PT{v;z#lI2x7-mhjEPAQli$v(Twh$iiOU&CDXYDYPPB##?6S^ zzNpxMV7Sgf*XSc~mezo~9_4KGFEwe#R0a=B8#2{{ei6EU7Q`M?*^H@Ej`CHu?Ep`q z3Qc*sOP7IoSS>9K#*GGpWNV*mB`ei~xWoE@2&zX>%rUm!Anxx$*JzB-jd_%g_*86JU2v>MFNx;7$ zoCH|;ebYQ>-S6LjhwY~~fB*UQue(2t|9mr^o{MMW7-Nmhv~kYK4jI!4+^#trJ7uMx z0Na2=9@GaCR_vYN3$j8KmJq0%t{x7g!qn=ey*vyRB5A50MhY@5hZPrUU%YPIkyM#h zr(ty;7~F6zldh@6>ro1KeRn zE`YA_jYsI{u2i3rewV2i@aY#pKt?`={+PyI9AcRy(^%MmU`0?TQ9KXg zBj_bnNt3lIc5eQHe;_~0Fiia%a4#?#a0^UkQDByv>N(iZ;iabs@w?k`r1$4&20GrN z3U&1ch|V#?0R2}`$2HTmtXPVMd4{E5AqS1O28>C+R7lL={&L4K?KkEs=W%z zfLW90g 2: - dim += list(range(2, grad_input.ndim)) - - grad_bias = grad_input.sum(dim).detach() - - return grad_input, grad_bias - - @staticmethod - def backward(ctx, gradgrad_input, gradgrad_bias): - out, = ctx.saved_tensors - gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, - ctx.scale) - - return gradgrad_out, None, None, None - - -class FusedLeakyReLUFunction(Function): - - @staticmethod - def forward(ctx, input, bias, negative_slope, scale): - empty = input.new_empty(0) - out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - return out - - @staticmethod - def backward(ctx, grad_output): - out, = ctx.saved_tensors - - grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale) - - return grad_input, grad_bias, None, None - - -class FusedLeakyReLU(nn.Module): - - def __init__(self, channel, negative_slope=0.2, scale=2**0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): - return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/basicsr/ops/fused_act/src/fused_bias_act.cpp b/basicsr/ops/fused_act/src/fused_bias_act.cpp deleted file mode 100644 index 85ed0a7..0000000 --- a/basicsr/ops/fused_act/src/fused_bias_act.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act.cpp -#include - - -torch::Tensor fused_bias_act_op(const torch::Tensor& input, - const torch::Tensor& bias, - const torch::Tensor& refer, - int act, int grad, float alpha, float scale); - -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -torch::Tensor fused_bias_act(const torch::Tensor& input, - const torch::Tensor& bias, - const torch::Tensor& refer, - int act, int grad, float alpha, float scale) { - CHECK_CUDA(input); - CHECK_CUDA(bias); - - return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); -} diff --git a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu b/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu deleted file mode 100644 index 54c7ff5..0000000 --- a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu +++ /dev/null @@ -1,100 +0,0 @@ -// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act_kernel.cu -// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. -// -// This work is made available under the Nvidia Source Code License-NC. -// To view a copy of this license, visit -// https://nvlabs.github.io/stylegan2/license.html - -#include - -#include -#include -#include -#include - -#include -#include - - -template -static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, - int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { - int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; - - scalar_t zero = 0.0; - - for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { - scalar_t x = p_x[xi]; - - if (use_bias) { - x += p_b[(xi / step_b) % size_b]; - } - - scalar_t ref = use_ref ? p_ref[xi] : zero; - - scalar_t y; - - switch (act * 10 + grad) { - default: - case 10: y = x; break; - case 11: y = x; break; - case 12: y = 0.0; break; - - case 30: y = (x > 0.0) ? x : x * alpha; break; - case 31: y = (ref > 0.0) ? x : x * alpha; break; - case 32: y = 0.0; break; - } - - out[xi] = y * scale; - } -} - - -torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, - int act, int grad, float alpha, float scale) { - int curDevice = -1; - cudaGetDevice(&curDevice); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); - - auto x = input.contiguous(); - auto b = bias.contiguous(); - auto ref = refer.contiguous(); - - int use_bias = b.numel() ? 1 : 0; - int use_ref = ref.numel() ? 1 : 0; - - int size_x = x.numel(); - int size_b = b.numel(); - int step_b = 1; - - for (int i = 1 + 1; i < x.dim(); i++) { - step_b *= x.size(i); - } - - int loop_x = 4; - int block_size = 4 * 32; - int grid_size = (size_x - 1) / (loop_x * block_size) + 1; - - auto y = torch::empty_like(x); - - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { - fused_bias_act_kernel<<>>( - y.data_ptr(), - x.data_ptr(), - b.data_ptr(), - ref.data_ptr(), - act, - grad, - alpha, - scale, - loop_x, - size_x, - step_b, - size_b, - use_bias, - use_ref - ); - }); - - return y; -} diff --git a/basicsr/ops/upfirdn2d/__init__.py b/basicsr/ops/upfirdn2d/__init__.py deleted file mode 100644 index 397e85b..0000000 --- a/basicsr/ops/upfirdn2d/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .upfirdn2d import upfirdn2d - -__all__ = ['upfirdn2d'] diff --git a/basicsr/ops/upfirdn2d/__pycache__/__init__.cpython-310.pyc b/basicsr/ops/upfirdn2d/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index dd8c8135cbc8ee10548923043b854a3bf674c31b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 206 zcmd1j<>g`kf?Fq&(rkhBV-N=!FabFZKwK;aBvKes7;_kM8KM{%8B& diff --git a/basicsr/ops/upfirdn2d/__pycache__/upfirdn2d.cpython-310.pyc b/basicsr/ops/upfirdn2d/__pycache__/upfirdn2d.cpython-310.pyc deleted file mode 100644 index ad5e7c944752c68bf6ab2fd77029cb0502e613b3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4226 zcma)9&vP3|6`t<-q0wkGmSp)?HVbA6>?(jO$%SPJA)90aS&Bey0u(A&O(~j@Ek%|@ z&&ZBlJtrsJ;LgqBmVboeFW}JEJ>^W*Qbkc&zSlFBWjj=%seawBd-`?H>#x7}G_F=X z13&fmXT4YJhVdmSi$532U6kY>5RnnFq0!E3)@IQ8knfpolTn{JwD#<_!;IKCDMi+d zwcXf_?B`tE!KgDV?|E&HMhBG>Kdxdth`p$!^`$t7+^9S=7c_5X?(nGcC*H1&8b@yA zN7Z3{uhyew7p)@?hIAs-ltC<-GA~$`1zx! z%6clMFDF3_$5gX+BHjmASMf|04%-R;(W@PEM9ohIMN7a8bdDs^Z zu(5lc?(>6=h`RcyVS#>YC_c)4l;m#^(%R;kmsO;V+Rv)8v~6WUwv^Sfx@0FfdRdP6 zh5>1A;IL;_#!_~|C6lJ~WmS5zA_KW3YqCD`vW9G&n9|iVT$+KlrKP=LU~WSiCzj+h z#ti9TrRA)dtz@gPVuUs<8KDEKMp%MnBXs35WLY*LJ-Gr|k*kotbRny<3>nA9qo{A@^{& ziq1me(}}WZJ!N7QW$%Qj#8ukx`y`qqR!FRppo3Jdeu;K;+PZn#!mf%w?90=u_eWS< z;JO=PgTxgQ??Jpa#P?AB`}TuRwtso}YcU?g-E{k5ryG9}C+S$kV*6Pq>30*cJ)R`n zb8MfrwkC(F1UC}iHeo8c4dI$D^VvGLIA@n1>#I%Q<34w|!!AA+Kias2@AaH7!0+&^SUlgYCC>e`H z)M-ec_#CJ2I`(%Rp=i)t0!qsg-$11TI`f5d-=t1|-U%q`L(j$cY0y2r13!nB(EBdG zTAZH6E<9g-6s+>2E0<5{)X3x4j&j3r)Y*%}P%^A8U&MF#3=g=kAxlj|`fG-)q6F*U*DDCCYev=t=Q$Yj&|g7W!kAl(t$+zvdcZAM z(vc~K50p(jznfPF;=8o!M zDc;Am#0HE`}?u}B=H$` z+j7K@QRV)=0Q(0taKZDG0sRF<`^ab4nPuI2Yw>fLBBdI0muw>Dl1mnt(9rP}Y@?a( zY!o?U7HD?lltqF8i4pyX)0;AwIayu1s182F16~9}6>Tu&y0PQVIC9T=K2Mt9!m8wu z0k~vgZfZ*>GLJdxQc)x0)H-t2W4`0$b_mh0=qp-M>2 z_oitqenc;QLgP+%Jlek%wN@68{eLpJAm$T4C2^O;rx42C>yO4lP+ls2LW1svVh7?L zDMXkTFUjZV`zuOv10rBGu&~FP{2E(lH~3R<^>r|_kGzO-+f_W)e`?M(M*Lmlf@ZhO zJloR_c}N{=ijNchl`7CtZmTm*$CQ)_+%TkIdFN;(OsL`))aUHPND%i0rMm5n{Do=S zA13rqlm0g%d&GZNK^&&6NhjS^rKm3`y`luH*Vjo`rAWW@xHTB}M>>KkLscH_O~xYq zObGagGOV0eh$Hk@Ecj*4vdX=k+q*+lnox+y;}}IUif6v|N`R=w-@0$PTgyH5b - - -torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, - int up_x, int up_y, int down_x, int down_y, - int pad_x0, int pad_x1, int pad_y0, int pad_y1); - -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, - int up_x, int up_y, int down_x, int down_y, - int pad_x0, int pad_x1, int pad_y0, int pad_y1) { - CHECK_CUDA(input); - CHECK_CUDA(kernel); - - return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); -} diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu b/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu deleted file mode 100644 index 8870063..0000000 --- a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu +++ /dev/null @@ -1,370 +0,0 @@ -// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu -// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. -// -// This work is made available under the Nvidia Source Code License-NC. -// To view a copy of this license, visit -// https://nvlabs.github.io/stylegan2/license.html - -#include - -#include -#include -#include -#include - -#include -#include - -static __host__ __device__ __forceinline__ int floor_div(int a, int b) { - int c = a / b; - - if (c * b > a) { - c--; - } - - return c; -} - -struct UpFirDn2DKernelParams { - int up_x; - int up_y; - int down_x; - int down_y; - int pad_x0; - int pad_x1; - int pad_y0; - int pad_y1; - - int major_dim; - int in_h; - int in_w; - int minor_dim; - int kernel_h; - int kernel_w; - int out_h; - int out_w; - int loop_major; - int loop_x; -}; - -template -__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, - const scalar_t *kernel, - const UpFirDn2DKernelParams p) { - int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; - int out_y = minor_idx / p.minor_dim; - minor_idx -= out_y * p.minor_dim; - int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; - int major_idx_base = blockIdx.z * p.loop_major; - - if (out_x_base >= p.out_w || out_y >= p.out_h || - major_idx_base >= p.major_dim) { - return; - } - - int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; - int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); - int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; - int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; - - for (int loop_major = 0, major_idx = major_idx_base; - loop_major < p.loop_major && major_idx < p.major_dim; - loop_major++, major_idx++) { - for (int loop_x = 0, out_x = out_x_base; - loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { - int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; - int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); - int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; - int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; - - const scalar_t *x_p = - &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + - minor_idx]; - const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; - int x_px = p.minor_dim; - int k_px = -p.up_x; - int x_py = p.in_w * p.minor_dim; - int k_py = -p.up_y * p.kernel_w; - - scalar_t v = 0.0f; - - for (int y = 0; y < h; y++) { - for (int x = 0; x < w; x++) { - v += static_cast(*x_p) * static_cast(*k_p); - x_p += x_px; - k_p += k_px; - } - - x_p += x_py - w * x_px; - k_p += k_py - w * k_px; - } - - out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + - minor_idx] = v; - } - } -} - -template -__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, - const scalar_t *kernel, - const UpFirDn2DKernelParams p) { - const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; - const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; - - __shared__ volatile float sk[kernel_h][kernel_w]; - __shared__ volatile float sx[tile_in_h][tile_in_w]; - - int minor_idx = blockIdx.x; - int tile_out_y = minor_idx / p.minor_dim; - minor_idx -= tile_out_y * p.minor_dim; - tile_out_y *= tile_out_h; - int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; - int major_idx_base = blockIdx.z * p.loop_major; - - if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | - major_idx_base >= p.major_dim) { - return; - } - - for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; - tap_idx += blockDim.x) { - int ky = tap_idx / kernel_w; - int kx = tap_idx - ky * kernel_w; - scalar_t v = 0.0; - - if (kx < p.kernel_w & ky < p.kernel_h) { - v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; - } - - sk[ky][kx] = v; - } - - for (int loop_major = 0, major_idx = major_idx_base; - loop_major < p.loop_major & major_idx < p.major_dim; - loop_major++, major_idx++) { - for (int loop_x = 0, tile_out_x = tile_out_x_base; - loop_x < p.loop_x & tile_out_x < p.out_w; - loop_x++, tile_out_x += tile_out_w) { - int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; - int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; - int tile_in_x = floor_div(tile_mid_x, up_x); - int tile_in_y = floor_div(tile_mid_y, up_y); - - __syncthreads(); - - for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; - in_idx += blockDim.x) { - int rel_in_y = in_idx / tile_in_w; - int rel_in_x = in_idx - rel_in_y * tile_in_w; - int in_x = rel_in_x + tile_in_x; - int in_y = rel_in_y + tile_in_y; - - scalar_t v = 0.0; - - if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { - v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * - p.minor_dim + - minor_idx]; - } - - sx[rel_in_y][rel_in_x] = v; - } - - __syncthreads(); - for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; - out_idx += blockDim.x) { - int rel_out_y = out_idx / tile_out_w; - int rel_out_x = out_idx - rel_out_y * tile_out_w; - int out_x = rel_out_x + tile_out_x; - int out_y = rel_out_y + tile_out_y; - - int mid_x = tile_mid_x + rel_out_x * down_x; - int mid_y = tile_mid_y + rel_out_y * down_y; - int in_x = floor_div(mid_x, up_x); - int in_y = floor_div(mid_y, up_y); - int rel_in_x = in_x - tile_in_x; - int rel_in_y = in_y - tile_in_y; - int kernel_x = (in_x + 1) * up_x - mid_x - 1; - int kernel_y = (in_y + 1) * up_y - mid_y - 1; - - scalar_t v = 0.0; - -#pragma unroll - for (int y = 0; y < kernel_h / up_y; y++) -#pragma unroll - for (int x = 0; x < kernel_w / up_x; x++) - v += sx[rel_in_y + y][rel_in_x + x] * - sk[kernel_y + y * up_y][kernel_x + x * up_x]; - - if (out_x < p.out_w & out_y < p.out_h) { - out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + - minor_idx] = v; - } - } - } - } -} - -torch::Tensor upfirdn2d_op(const torch::Tensor &input, - const torch::Tensor &kernel, int up_x, int up_y, - int down_x, int down_y, int pad_x0, int pad_x1, - int pad_y0, int pad_y1) { - int curDevice = -1; - cudaGetDevice(&curDevice); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); - - UpFirDn2DKernelParams p; - - auto x = input.contiguous(); - auto k = kernel.contiguous(); - - p.major_dim = x.size(0); - p.in_h = x.size(1); - p.in_w = x.size(2); - p.minor_dim = x.size(3); - p.kernel_h = k.size(0); - p.kernel_w = k.size(1); - p.up_x = up_x; - p.up_y = up_y; - p.down_x = down_x; - p.down_y = down_y; - p.pad_x0 = pad_x0; - p.pad_x1 = pad_x1; - p.pad_y0 = pad_y0; - p.pad_y1 = pad_y1; - - p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / - p.down_y; - p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / - p.down_x; - - auto out = - at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); - - int mode = -1; - - int tile_out_h = -1; - int tile_out_w = -1; - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 4 && p.kernel_w <= 4) { - mode = 1; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 3 && p.kernel_w <= 3) { - mode = 2; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 4 && p.kernel_w <= 4) { - mode = 3; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && - p.kernel_h <= 2 && p.kernel_w <= 2) { - mode = 4; - tile_out_h = 16; - tile_out_w = 64; - } - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && - p.kernel_h <= 4 && p.kernel_w <= 4) { - mode = 5; - tile_out_h = 8; - tile_out_w = 32; - } - - if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && - p.kernel_h <= 2 && p.kernel_w <= 2) { - mode = 6; - tile_out_h = 8; - tile_out_w = 32; - } - - dim3 block_size; - dim3 grid_size; - - if (tile_out_h > 0 && tile_out_w > 0) { - p.loop_major = (p.major_dim - 1) / 16384 + 1; - p.loop_x = 1; - block_size = dim3(32 * 8, 1, 1); - grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, - (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, - (p.major_dim - 1) / p.loop_major + 1); - } else { - p.loop_major = (p.major_dim - 1) / 16384 + 1; - p.loop_x = 4; - block_size = dim3(4, 32, 1); - grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, - (p.out_w - 1) / (p.loop_x * block_size.y) + 1, - (p.major_dim - 1) / p.loop_major + 1); - } - - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { - switch (mode) { - case 1: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 2: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 3: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 4: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 5: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - case 6: - upfirdn2d_kernel - <<>>(out.data_ptr(), - x.data_ptr(), - k.data_ptr(), p); - - break; - - default: - upfirdn2d_kernel_large<<>>( - out.data_ptr(), x.data_ptr(), - k.data_ptr(), p); - } - }); - - return out; -} diff --git a/basicsr/ops/upfirdn2d/upfirdn2d.py b/basicsr/ops/upfirdn2d/upfirdn2d.py deleted file mode 100644 index d6122d5..0000000 --- a/basicsr/ops/upfirdn2d/upfirdn2d.py +++ /dev/null @@ -1,192 +0,0 @@ -# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 - -import os -import torch -from torch.autograd import Function -from torch.nn import functional as F - -BASICSR_JIT = os.getenv('BASICSR_JIT') -if BASICSR_JIT == 'True': - from torch.utils.cpp_extension import load - module_path = os.path.dirname(__file__) - upfirdn2d_ext = load( - 'upfirdn2d', - sources=[ - os.path.join(module_path, 'src', 'upfirdn2d.cpp'), - os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'), - ], - ) -else: - try: - from . import upfirdn2d_ext - except ImportError: - pass - # avoid annoying print output - # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' - # '1. compile with BASICSR_EXT=True. or\n ' - # '2. set BASICSR_JIT=True during running') - - -class UpFirDn2dBackward(Function): - - @staticmethod - def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size): - - up_x, up_y = up - down_x, down_y = down - g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad - - grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) - - grad_input = upfirdn2d_ext.upfirdn2d( - grad_output, - grad_kernel, - down_x, - down_y, - up_x, - up_y, - g_pad_x0, - g_pad_x1, - g_pad_y0, - g_pad_y1, - ) - grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) - - ctx.save_for_backward(kernel) - - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - ctx.up_x = up_x - ctx.up_y = up_y - ctx.down_x = down_x - ctx.down_y = down_y - ctx.pad_x0 = pad_x0 - ctx.pad_x1 = pad_x1 - ctx.pad_y0 = pad_y0 - ctx.pad_y1 = pad_y1 - ctx.in_size = in_size - ctx.out_size = out_size - - return grad_input - - @staticmethod - def backward(ctx, gradgrad_input): - kernel, = ctx.saved_tensors - - gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) - - gradgrad_out = upfirdn2d_ext.upfirdn2d( - gradgrad_input, - kernel, - ctx.up_x, - ctx.up_y, - ctx.down_x, - ctx.down_y, - ctx.pad_x0, - ctx.pad_x1, - ctx.pad_y0, - ctx.pad_y1, - ) - # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], - # ctx.out_size[1], ctx.in_size[3]) - gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]) - - return gradgrad_out, None, None, None, None, None, None, None, None - - -class UpFirDn2d(Function): - - @staticmethod - def forward(ctx, input, kernel, up, down, pad): - up_x, up_y = up - down_x, down_y = down - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - kernel_h, kernel_w = kernel.shape - _, channel, in_h, in_w = input.shape - ctx.in_size = input.shape - - input = input.reshape(-1, in_h, in_w, 1) - - ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - ctx.out_size = (out_h, out_w) - - ctx.up = (up_x, up_y) - ctx.down = (down_x, down_y) - ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) - - g_pad_x0 = kernel_w - pad_x0 - 1 - g_pad_y0 = kernel_h - pad_y0 - 1 - g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 - g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 - - ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) - - out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1) - # out = out.view(major, out_h, out_w, minor) - out = out.view(-1, channel, out_h, out_w) - - return out - - @staticmethod - def backward(ctx, grad_output): - kernel, grad_kernel = ctx.saved_tensors - - grad_input = UpFirDn2dBackward.apply( - grad_output, - kernel, - grad_kernel, - ctx.up, - ctx.down, - ctx.pad, - ctx.g_pad, - ctx.in_size, - ctx.out_size, - ) - - return grad_input, None, None, None, None - - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - if input.device.type == 'cpu': - out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) - else: - out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])) - - return out - - -def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): - _, channel, in_h, in_w = input.shape - input = input.reshape(-1, in_h, in_w, 1) - - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) - out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - out = out[:, ::down_y, ::down_x, :] - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - - return out.view(-1, channel, out_h, out_w) diff --git a/basicsr/test.py b/basicsr/test.py deleted file mode 100644 index 53cb3b7..0000000 --- a/basicsr/test.py +++ /dev/null @@ -1,45 +0,0 @@ -import logging -import torch -from os import path as osp - -from basicsr.data import build_dataloader, build_dataset -from basicsr.models import build_model -from basicsr.utils import get_env_info, get_root_logger, get_time_str, make_exp_dirs -from basicsr.utils.options import dict2str, parse_options - - -def test_pipeline(root_path): - # parse options, set distributed setting, set ramdom seed - opt, _ = parse_options(root_path, is_train=False) - - torch.backends.cudnn.benchmark = True - # torch.backends.cudnn.deterministic = True - - # mkdir and initialize loggers - make_exp_dirs(opt) - log_file = osp.join(opt['path']['log'], f"test_{opt['name']}_{get_time_str()}.log") - logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) - logger.info(get_env_info()) - logger.info(dict2str(opt)) - - # create test dataset and dataloader - test_loaders = [] - for _, dataset_opt in sorted(opt['datasets'].items()): - test_set = build_dataset(dataset_opt) - test_loader = build_dataloader( - test_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed']) - logger.info(f"Number of test images in {dataset_opt['name']}: {len(test_set)}") - test_loaders.append(test_loader) - - # create model - model = build_model(opt) - - for test_loader in test_loaders: - test_set_name = test_loader.dataset.opt['name'] - logger.info(f'Testing {test_set_name}...') - model.validation(test_loader, current_iter=opt['name'], tb_logger=None, save_img=opt['val']['save_img']) - - -if __name__ == '__main__': - root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) - test_pipeline(root_path) diff --git a/basicsr/train.py b/basicsr/train.py deleted file mode 100644 index e02d98f..0000000 --- a/basicsr/train.py +++ /dev/null @@ -1,215 +0,0 @@ -import datetime -import logging -import math -import time -import torch -from os import path as osp - -from basicsr.data import build_dataloader, build_dataset -from basicsr.data.data_sampler import EnlargedSampler -from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher -from basicsr.models import build_model -from basicsr.utils import (AvgTimer, MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, - init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, scandir) -from basicsr.utils.options import copy_opt_file, dict2str, parse_options - - -def init_tb_loggers(opt): - # initialize wandb logger before tensorboard logger to allow proper sync - if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') - is not None) and ('debug' not in opt['name']): - assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb') - init_wandb_logger(opt) - tb_logger = None - if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']: - tb_logger = init_tb_logger(log_dir=osp.join(opt['root_path'], 'tb_logger', opt['name'])) - return tb_logger - - -def create_train_val_dataloader(opt, logger): - # create train and val dataloaders - train_loader, val_loaders = None, [] - for phase, dataset_opt in opt['datasets'].items(): - if phase == 'train': - dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1) - train_set = build_dataset(dataset_opt) - train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio) - train_loader = build_dataloader( - train_set, - dataset_opt, - num_gpu=opt['num_gpu'], - dist=opt['dist'], - sampler=train_sampler, - seed=opt['manual_seed']) - - num_iter_per_epoch = math.ceil( - len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size'])) - total_iters = int(opt['train']['total_iter']) - total_epochs = math.ceil(total_iters / (num_iter_per_epoch)) - logger.info('Training statistics:' - f'\n\tNumber of train images: {len(train_set)}' - f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}' - f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}' - f'\n\tWorld size (gpu number): {opt["world_size"]}' - f'\n\tRequire iter number per epoch: {num_iter_per_epoch}' - f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.') - elif phase.split('_')[0] == 'val': - val_set = build_dataset(dataset_opt) - val_loader = build_dataloader( - val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed']) - logger.info(f'Number of val images/folders in {dataset_opt["name"]}: {len(val_set)}') - val_loaders.append(val_loader) - else: - raise ValueError(f'Dataset phase {phase} is not recognized.') - - return train_loader, train_sampler, val_loaders, total_epochs, total_iters - - -def load_resume_state(opt): - resume_state_path = None - if opt['auto_resume']: - state_path = osp.join('experiments', opt['name'], 'training_states') - if osp.isdir(state_path): - states = list(scandir(state_path, suffix='state', recursive=False, full_path=False)) - if len(states) != 0: - states = [float(v.split('.state')[0]) for v in states] - resume_state_path = osp.join(state_path, f'{max(states):.0f}.state') - opt['path']['resume_state'] = resume_state_path - else: - if opt['path'].get('resume_state'): - resume_state_path = opt['path']['resume_state'] - - if resume_state_path is None: - resume_state = None - else: - device_id = torch.cuda.current_device() - resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id)) - check_resume(opt, resume_state['iter']) - return resume_state - - -def train_pipeline(root_path): - # parse options, set distributed setting, set random seed - opt, args = parse_options(root_path, is_train=True) - opt['root_path'] = root_path - - torch.backends.cudnn.benchmark = True - # torch.backends.cudnn.deterministic = True - - # load resume states if necessary - resume_state = load_resume_state(opt) - # mkdir for experiments and logger - if resume_state is None: - make_exp_dirs(opt) - if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0: - mkdir_and_rename(osp.join(opt['root_path'], 'tb_logger', opt['name'])) - - # copy the yml file to the experiment root - copy_opt_file(args.opt, opt['path']['experiments_root']) - - # WARNING: should not use get_root_logger in the above codes, including the called functions - # Otherwise the logger will not be properly initialized - log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log") - logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) - logger.info(get_env_info()) - logger.info(dict2str(opt)) - # initialize wandb and tb loggers - tb_logger = init_tb_loggers(opt) - - # create train and validation dataloaders - result = create_train_val_dataloader(opt, logger) - train_loader, train_sampler, val_loaders, total_epochs, total_iters = result - - # create model - model = build_model(opt) - if resume_state: # resume training - model.resume_training(resume_state) # handle optimizers and schedulers - logger.info(f"Resuming training from epoch: {resume_state['epoch']}, iter: {resume_state['iter']}.") - start_epoch = resume_state['epoch'] - current_iter = resume_state['iter'] - else: - start_epoch = 0 - current_iter = 0 - - # create message logger (formatted outputs) - msg_logger = MessageLogger(opt, current_iter, tb_logger) - - # dataloader prefetcher - prefetch_mode = opt['datasets']['train'].get('prefetch_mode') - if prefetch_mode is None or prefetch_mode == 'cpu': - prefetcher = CPUPrefetcher(train_loader) - elif prefetch_mode == 'cuda': - prefetcher = CUDAPrefetcher(train_loader, opt) - logger.info(f'Use {prefetch_mode} prefetch dataloader') - if opt['datasets']['train'].get('pin_memory') is not True: - raise ValueError('Please set pin_memory=True for CUDAPrefetcher.') - else: - raise ValueError(f"Wrong prefetch_mode {prefetch_mode}. Supported ones are: None, 'cuda', 'cpu'.") - - # training - logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}') - data_timer, iter_timer = AvgTimer(), AvgTimer() - start_time = time.time() - - for epoch in range(start_epoch, total_epochs + 1): - train_sampler.set_epoch(epoch) - prefetcher.reset() - train_data = prefetcher.next() - - while train_data is not None: - data_timer.record() - - current_iter += 1 - if current_iter > total_iters: - break - # update learning rate - model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1)) - # training - model.feed_data(train_data) - model.optimize_parameters(current_iter) - iter_timer.record() - if current_iter == 1: - # reset start time in msg_logger for more accurate eta_time - # not work in resume mode - msg_logger.reset_start_time() - # log - if current_iter % opt['logger']['print_freq'] == 0: - log_vars = {'epoch': epoch, 'iter': current_iter} - log_vars.update({'lrs': model.get_current_learning_rate()}) - log_vars.update({'time': iter_timer.get_avg_time(), 'data_time': data_timer.get_avg_time()}) - log_vars.update(model.get_current_log()) - msg_logger(log_vars) - - # save models and training states - if current_iter % opt['logger']['save_checkpoint_freq'] == 0: - logger.info('Saving models and training states.') - model.save(epoch, current_iter) - - # validation - if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0): - if len(val_loaders) > 1: - logger.warning('Multiple validation datasets are *only* supported by SRModel.') - for val_loader in val_loaders: - model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) - - data_timer.start() - iter_timer.start() - train_data = prefetcher.next() - # end of iter - - # end of epoch - - consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time))) - logger.info(f'End of training. Time consumed: {consumed_time}') - logger.info('Save the latest model.') - model.save(epoch=-1, current_iter=-1) # -1 stands for the latest - if opt.get('val') is not None: - for val_loader in val_loaders: - model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) - if tb_logger: - tb_logger.close() - - -if __name__ == '__main__': - root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) - train_pipeline(root_path) diff --git a/basicsr/utils/__init__.py b/basicsr/utils/__init__.py deleted file mode 100644 index 9569c50..0000000 --- a/basicsr/utils/__init__.py +++ /dev/null @@ -1,47 +0,0 @@ -from .color_util import bgr2ycbcr, rgb2ycbcr, rgb2ycbcr_pt, ycbcr2bgr, ycbcr2rgb -from .diffjpeg import DiffJPEG -from .file_client import FileClient -from .img_process_util import USMSharp, usm_sharp -from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img -from .logger import AvgTimer, MessageLogger, get_env_info, get_root_logger, init_tb_logger, init_wandb_logger -from .misc import check_resume, get_time_str, make_exp_dirs, mkdir_and_rename, scandir, set_random_seed, sizeof_fmt -from .options import yaml_load - -__all__ = [ - # color_util.py - 'bgr2ycbcr', - 'rgb2ycbcr', - 'rgb2ycbcr_pt', - 'ycbcr2bgr', - 'ycbcr2rgb', - # file_client.py - 'FileClient', - # img_util.py - 'img2tensor', - 'tensor2img', - 'imfrombytes', - 'imwrite', - 'crop_border', - # logger.py - 'MessageLogger', - 'AvgTimer', - 'init_tb_logger', - 'init_wandb_logger', - 'get_root_logger', - 'get_env_info', - # misc.py - 'set_random_seed', - 'get_time_str', - 'mkdir_and_rename', - 'make_exp_dirs', - 'scandir', - 'check_resume', - 'sizeof_fmt', - # diffjpeg - 'DiffJPEG', - # img_process_util - 'USMSharp', - 'usm_sharp', - # options - 'yaml_load' -] diff --git a/basicsr/utils/__pycache__/__init__.cpython-310.pyc b/basicsr/utils/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 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zj0RNwN7X#Y$%Q0(W~+q=E@Hber-;FE*>%(3AFfLQ{9)jAQ@+L}7d6v&U3G>;5_{EW z)KFs8RcfwNLrP~dF|~$5N@>!>NXM|5bGhu`?}Bs2u_~6L{kSB&$kRZY?J0jhF2EMq zjqPrzbzD3Issg{&C?;py=kBj;uWYSt=RYjs>dNNocloqkTi>|9_H;V0KHb>bnoiHI iuWfHWTFn>!&Fa{>ordEMnU{PdYL@fN0dgIDEdBw4gp$7i diff --git a/basicsr/utils/color_util.py b/basicsr/utils/color_util.py deleted file mode 100644 index 4740d5c..0000000 --- a/basicsr/utils/color_util.py +++ /dev/null @@ -1,208 +0,0 @@ -import numpy as np -import torch - - -def rgb2ycbcr(img, y_only=False): - """Convert a RGB image to YCbCr image. - - This function produces the same results as Matlab's `rgb2ycbcr` function. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - ndarray: The converted YCbCr image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) - if y_only: - out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0 - else: - out_img = np.matmul( - img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def bgr2ycbcr(img, y_only=False): - """Convert a BGR image to YCbCr image. - - The bgr version of rgb2ycbcr. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - ndarray: The converted YCbCr image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) - if y_only: - out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 - else: - out_img = np.matmul( - img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def ycbcr2rgb(img): - """Convert a YCbCr image to RGB image. - - This function produces the same results as Matlab's ycbcr2rgb function. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - ndarray: The converted RGB image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], - [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] # noqa: E126 - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def ycbcr2bgr(img): - """Convert a YCbCr image to BGR image. - - The bgr version of ycbcr2rgb. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - ndarray: The converted BGR image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0], - [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921] # noqa: E126 - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def _convert_input_type_range(img): - """Convert the type and range of the input image. - - It converts the input image to np.float32 type and range of [0, 1]. - It is mainly used for pre-processing the input image in colorspace - conversion functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - (ndarray): The converted image with type of np.float32 and range of - [0, 1]. - """ - img_type = img.dtype - img = img.astype(np.float32) - if img_type == np.float32: - pass - elif img_type == np.uint8: - img /= 255. - else: - raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}') - return img - - -def _convert_output_type_range(img, dst_type): - """Convert the type and range of the image according to dst_type. - - It converts the image to desired type and range. If `dst_type` is np.uint8, - images will be converted to np.uint8 type with range [0, 255]. If - `dst_type` is np.float32, it converts the image to np.float32 type with - range [0, 1]. - It is mainly used for post-processing images in colorspace conversion - functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The image to be converted with np.float32 type and - range [0, 255]. - dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it - converts the image to np.uint8 type with range [0, 255]. If - dst_type is np.float32, it converts the image to np.float32 type - with range [0, 1]. - - Returns: - (ndarray): The converted image with desired type and range. - """ - if dst_type not in (np.uint8, np.float32): - raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}') - if dst_type == np.uint8: - img = img.round() - else: - img /= 255. - return img.astype(dst_type) - - -def rgb2ycbcr_pt(img, y_only=False): - """Convert RGB images to YCbCr images (PyTorch version). - - It implements the ITU-R BT.601 conversion for standard-definition television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - Args: - img (Tensor): Images with shape (n, 3, h, w), the range [0, 1], float, RGB format. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - (Tensor): converted images with the shape (n, 3/1, h, w), the range [0, 1], float. - """ - if y_only: - weight = torch.tensor([[65.481], [128.553], [24.966]]).to(img) - out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + 16.0 - else: - weight = torch.tensor([[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]).to(img) - bias = torch.tensor([16, 128, 128]).view(1, 3, 1, 1).to(img) - out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + bias - - out_img = out_img / 255. - return out_img diff --git a/basicsr/utils/diffjpeg.py b/basicsr/utils/diffjpeg.py deleted file mode 100644 index 65f96b4..0000000 --- a/basicsr/utils/diffjpeg.py +++ /dev/null @@ -1,515 +0,0 @@ -""" -Modified from https://github.com/mlomnitz/DiffJPEG - -For images not divisible by 8 -https://dsp.stackexchange.com/questions/35339/jpeg-dct-padding/35343#35343 -""" -import itertools -import numpy as np -import torch -import torch.nn as nn -from torch.nn import functional as F - -# ------------------------ utils ------------------------# -y_table = np.array( - [[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], - [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], - [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], - dtype=np.float32).T -y_table = nn.Parameter(torch.from_numpy(y_table)) -c_table = np.empty((8, 8), dtype=np.float32) -c_table.fill(99) -c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]).T -c_table = nn.Parameter(torch.from_numpy(c_table)) - - -def diff_round(x): - """ Differentiable rounding function - """ - return torch.round(x) + (x - torch.round(x))**3 - - -def quality_to_factor(quality): - """ Calculate factor corresponding to quality - - Args: - quality(float): Quality for jpeg compression. - - Returns: - float: Compression factor. - """ - if quality < 50: - quality = 5000. / quality - else: - quality = 200. - quality * 2 - return quality / 100. - - -# ------------------------ compression ------------------------# -class RGB2YCbCrJpeg(nn.Module): - """ Converts RGB image to YCbCr - """ - - def __init__(self): - super(RGB2YCbCrJpeg, self).__init__() - matrix = np.array([[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], [0.5, -0.418688, -0.081312]], - dtype=np.float32).T - self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) - self.matrix = nn.Parameter(torch.from_numpy(matrix)) - - def forward(self, image): - """ - Args: - image(Tensor): batch x 3 x height x width - - Returns: - Tensor: batch x height x width x 3 - """ - image = image.permute(0, 2, 3, 1) - result = torch.tensordot(image, self.matrix, dims=1) + self.shift - return result.view(image.shape) - - -class ChromaSubsampling(nn.Module): - """ Chroma subsampling on CbCr channels - """ - - def __init__(self): - super(ChromaSubsampling, self).__init__() - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width x 3 - - Returns: - y(tensor): batch x height x width - cb(tensor): batch x height/2 x width/2 - cr(tensor): batch x height/2 x width/2 - """ - image_2 = image.permute(0, 3, 1, 2).clone() - cb = F.avg_pool2d(image_2[:, 1, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) - cr = F.avg_pool2d(image_2[:, 2, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) - cb = cb.permute(0, 2, 3, 1) - cr = cr.permute(0, 2, 3, 1) - return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) - - -class BlockSplitting(nn.Module): - """ Splitting image into patches - """ - - def __init__(self): - super(BlockSplitting, self).__init__() - self.k = 8 - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x h*w/64 x h x w - """ - height, _ = image.shape[1:3] - batch_size = image.shape[0] - image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) - image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) - return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) - - -class DCT8x8(nn.Module): - """ Discrete Cosine Transformation - """ - - def __init__(self): - super(DCT8x8, self).__init__() - tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) - for x, y, u, v in itertools.product(range(8), repeat=4): - tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos((2 * y + 1) * v * np.pi / 16) - alpha = np.array([1. / np.sqrt(2)] + [1] * 7) - self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) - self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float()) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - image = image - 128 - result = self.scale * torch.tensordot(image, self.tensor, dims=2) - result.view(image.shape) - return result - - -class YQuantize(nn.Module): - """ JPEG Quantization for Y channel - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding): - super(YQuantize, self).__init__() - self.rounding = rounding - self.y_table = y_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - image = image.float() / (self.y_table * factor) - else: - b = factor.size(0) - table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - image = image.float() / table - image = self.rounding(image) - return image - - -class CQuantize(nn.Module): - """ JPEG Quantization for CbCr channels - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding): - super(CQuantize, self).__init__() - self.rounding = rounding - self.c_table = c_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - image = image.float() / (self.c_table * factor) - else: - b = factor.size(0) - table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - image = image.float() / table - image = self.rounding(image) - return image - - -class CompressJpeg(nn.Module): - """Full JPEG compression algorithm - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding=torch.round): - super(CompressJpeg, self).__init__() - self.l1 = nn.Sequential(RGB2YCbCrJpeg(), ChromaSubsampling()) - self.l2 = nn.Sequential(BlockSplitting(), DCT8x8()) - self.c_quantize = CQuantize(rounding=rounding) - self.y_quantize = YQuantize(rounding=rounding) - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x 3 x height x width - - Returns: - dict(tensor): Compressed tensor with batch x h*w/64 x 8 x 8. - """ - y, cb, cr = self.l1(image * 255) - components = {'y': y, 'cb': cb, 'cr': cr} - for k in components.keys(): - comp = self.l2(components[k]) - if k in ('cb', 'cr'): - comp = self.c_quantize(comp, factor=factor) - else: - comp = self.y_quantize(comp, factor=factor) - - components[k] = comp - - return components['y'], components['cb'], components['cr'] - - -# ------------------------ decompression ------------------------# - - -class YDequantize(nn.Module): - """Dequantize Y channel - """ - - def __init__(self): - super(YDequantize, self).__init__() - self.y_table = y_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - out = image * (self.y_table * factor) - else: - b = factor.size(0) - table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - out = image * table - return out - - -class CDequantize(nn.Module): - """Dequantize CbCr channel - """ - - def __init__(self): - super(CDequantize, self).__init__() - self.c_table = c_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - out = image * (self.c_table * factor) - else: - b = factor.size(0) - table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - out = image * table - return out - - -class iDCT8x8(nn.Module): - """Inverse discrete Cosine Transformation - """ - - def __init__(self): - super(iDCT8x8, self).__init__() - alpha = np.array([1. / np.sqrt(2)] + [1] * 7) - self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) - tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) - for x, y, u, v in itertools.product(range(8), repeat=4): - tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos((2 * v + 1) * y * np.pi / 16) - self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - image = image * self.alpha - result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 - result.view(image.shape) - return result - - -class BlockMerging(nn.Module): - """Merge patches into image - """ - - def __init__(self): - super(BlockMerging, self).__init__() - - def forward(self, patches, height, width): - """ - Args: - patches(tensor) batch x height*width/64, height x width - height(int) - width(int) - - Returns: - Tensor: batch x height x width - """ - k = 8 - batch_size = patches.shape[0] - image_reshaped = patches.view(batch_size, height // k, width // k, k, k) - image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) - return image_transposed.contiguous().view(batch_size, height, width) - - -class ChromaUpsampling(nn.Module): - """Upsample chroma layers - """ - - def __init__(self): - super(ChromaUpsampling, self).__init__() - - def forward(self, y, cb, cr): - """ - Args: - y(tensor): y channel image - cb(tensor): cb channel - cr(tensor): cr channel - - Returns: - Tensor: batch x height x width x 3 - """ - - def repeat(x, k=2): - height, width = x.shape[1:3] - x = x.unsqueeze(-1) - x = x.repeat(1, 1, k, k) - x = x.view(-1, height * k, width * k) - return x - - cb = repeat(cb) - cr = repeat(cr) - return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) - - -class YCbCr2RGBJpeg(nn.Module): - """Converts YCbCr image to RGB JPEG - """ - - def __init__(self): - super(YCbCr2RGBJpeg, self).__init__() - - matrix = np.array([[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], dtype=np.float32).T - self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) - self.matrix = nn.Parameter(torch.from_numpy(matrix)) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width x 3 - - Returns: - Tensor: batch x 3 x height x width - """ - result = torch.tensordot(image + self.shift, self.matrix, dims=1) - return result.view(image.shape).permute(0, 3, 1, 2) - - -class DeCompressJpeg(nn.Module): - """Full JPEG decompression algorithm - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding=torch.round): - super(DeCompressJpeg, self).__init__() - self.c_dequantize = CDequantize() - self.y_dequantize = YDequantize() - self.idct = iDCT8x8() - self.merging = BlockMerging() - self.chroma = ChromaUpsampling() - self.colors = YCbCr2RGBJpeg() - - def forward(self, y, cb, cr, imgh, imgw, factor=1): - """ - Args: - compressed(dict(tensor)): batch x h*w/64 x 8 x 8 - imgh(int) - imgw(int) - factor(float) - - Returns: - Tensor: batch x 3 x height x width - """ - components = {'y': y, 'cb': cb, 'cr': cr} - for k in components.keys(): - if k in ('cb', 'cr'): - comp = self.c_dequantize(components[k], factor=factor) - height, width = int(imgh / 2), int(imgw / 2) - else: - comp = self.y_dequantize(components[k], factor=factor) - height, width = imgh, imgw - comp = self.idct(comp) - components[k] = self.merging(comp, height, width) - # - image = self.chroma(components['y'], components['cb'], components['cr']) - image = self.colors(image) - - image = torch.min(255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)) - return image / 255 - - -# ------------------------ main DiffJPEG ------------------------ # - - -class DiffJPEG(nn.Module): - """This JPEG algorithm result is slightly different from cv2. - DiffJPEG supports batch processing. - - Args: - differentiable(bool): If True, uses custom differentiable rounding function, if False, uses standard torch.round - """ - - def __init__(self, differentiable=True): - super(DiffJPEG, self).__init__() - if differentiable: - rounding = diff_round - else: - rounding = torch.round - - self.compress = CompressJpeg(rounding=rounding) - self.decompress = DeCompressJpeg(rounding=rounding) - - def forward(self, x, quality): - """ - Args: - x (Tensor): Input image, bchw, rgb, [0, 1] - quality(float): Quality factor for jpeg compression scheme. - """ - factor = quality - if isinstance(factor, (int, float)): - factor = quality_to_factor(factor) - else: - for i in range(factor.size(0)): - factor[i] = quality_to_factor(factor[i]) - h, w = x.size()[-2:] - h_pad, w_pad = 0, 0 - # why should use 16 - if h % 16 != 0: - h_pad = 16 - h % 16 - if w % 16 != 0: - w_pad = 16 - w % 16 - x = F.pad(x, (0, w_pad, 0, h_pad), mode='constant', value=0) - - y, cb, cr = self.compress(x, factor=factor) - recovered = self.decompress(y, cb, cr, (h + h_pad), (w + w_pad), factor=factor) - recovered = recovered[:, :, 0:h, 0:w] - return recovered - - -if __name__ == '__main__': - import cv2 - - from basicsr.utils import img2tensor, tensor2img - - img_gt = cv2.imread('test.png') / 255. - - # -------------- cv2 -------------- # - encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 20] - _, encimg = cv2.imencode('.jpg', img_gt * 255., encode_param) - img_lq = np.float32(cv2.imdecode(encimg, 1)) - cv2.imwrite('cv2_JPEG_20.png', img_lq) - - # -------------- DiffJPEG -------------- # - jpeger = DiffJPEG(differentiable=False).cuda() - img_gt = img2tensor(img_gt) - img_gt = torch.stack([img_gt, img_gt]).cuda() - quality = img_gt.new_tensor([20, 40]) - out = jpeger(img_gt, quality=quality) - - cv2.imwrite('pt_JPEG_20.png', tensor2img(out[0])) - cv2.imwrite('pt_JPEG_40.png', tensor2img(out[1])) diff --git a/basicsr/utils/dist_util.py b/basicsr/utils/dist_util.py deleted file mode 100644 index 0fab887..0000000 --- a/basicsr/utils/dist_util.py +++ /dev/null @@ -1,82 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501 -import functools -import os -import subprocess -import torch -import torch.distributed as dist -import torch.multiprocessing as mp - - -def init_dist(launcher, backend='nccl', **kwargs): - if mp.get_start_method(allow_none=True) is None: - mp.set_start_method('spawn') - if launcher == 'pytorch': - _init_dist_pytorch(backend, **kwargs) - elif launcher == 'slurm': - _init_dist_slurm(backend, **kwargs) - else: - raise ValueError(f'Invalid launcher type: {launcher}') - - -def _init_dist_pytorch(backend, **kwargs): - rank = int(os.environ['RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_slurm(backend, port=None): - """Initialize slurm distributed training environment. - - If argument ``port`` is not specified, then the master port will be system - environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system - environment variable, then a default port ``29500`` will be used. - - Args: - backend (str): Backend of torch.distributed. - port (int, optional): Master port. Defaults to None. - """ - proc_id = int(os.environ['SLURM_PROCID']) - ntasks = int(os.environ['SLURM_NTASKS']) - node_list = os.environ['SLURM_NODELIST'] - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(proc_id % num_gpus) - addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1') - # specify master port - if port is not None: - os.environ['MASTER_PORT'] = str(port) - elif 'MASTER_PORT' in os.environ: - pass # use MASTER_PORT in the environment variable - else: - # 29500 is torch.distributed default port - os.environ['MASTER_PORT'] = '29500' - os.environ['MASTER_ADDR'] = addr - os.environ['WORLD_SIZE'] = str(ntasks) - os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) - os.environ['RANK'] = str(proc_id) - dist.init_process_group(backend=backend) - - -def get_dist_info(): - if dist.is_available(): - initialized = dist.is_initialized() - else: - initialized = False - if initialized: - rank = dist.get_rank() - world_size = dist.get_world_size() - else: - rank = 0 - world_size = 1 - return rank, world_size - - -def master_only(func): - - @functools.wraps(func) - def wrapper(*args, **kwargs): - rank, _ = get_dist_info() - if rank == 0: - return func(*args, **kwargs) - - return wrapper diff --git a/basicsr/utils/download_util.py b/basicsr/utils/download_util.py deleted file mode 100644 index f73abd0..0000000 --- a/basicsr/utils/download_util.py +++ /dev/null @@ -1,98 +0,0 @@ -import math -import os -import requests -from torch.hub import download_url_to_file, get_dir -from tqdm import tqdm -from urllib.parse import urlparse - -from .misc import sizeof_fmt - - -def download_file_from_google_drive(file_id, save_path): - """Download files from google drive. - - Reference: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive - - Args: - file_id (str): File id. - save_path (str): Save path. - """ - - session = requests.Session() - URL = 'https://docs.google.com/uc?export=download' - params = {'id': file_id} - - response = session.get(URL, params=params, stream=True) - token = get_confirm_token(response) - if token: - params['confirm'] = token - response = session.get(URL, params=params, stream=True) - - # get file size - response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'}) - if 'Content-Range' in response_file_size.headers: - file_size = int(response_file_size.headers['Content-Range'].split('/')[1]) - else: - file_size = None - - save_response_content(response, save_path, file_size) - - -def get_confirm_token(response): - for key, value in response.cookies.items(): - if key.startswith('download_warning'): - return value - return None - - -def save_response_content(response, destination, file_size=None, chunk_size=32768): - if file_size is not None: - pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk') - - readable_file_size = sizeof_fmt(file_size) - else: - pbar = None - - with open(destination, 'wb') as f: - downloaded_size = 0 - for chunk in response.iter_content(chunk_size): - downloaded_size += chunk_size - if pbar is not None: - pbar.update(1) - pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}') - if chunk: # filter out keep-alive new chunks - f.write(chunk) - if pbar is not None: - pbar.close() - - -def load_file_from_url(url, model_dir=None, progress=True, file_name=None): - """Load file form http url, will download models if necessary. - - Reference: https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py - - Args: - url (str): URL to be downloaded. - model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. - Default: None. - progress (bool): Whether to show the download progress. Default: True. - file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. - - Returns: - str: The path to the downloaded file. - """ - if model_dir is None: # use the pytorch hub_dir - hub_dir = get_dir() - model_dir = os.path.join(hub_dir, 'checkpoints') - - os.makedirs(model_dir, exist_ok=True) - - parts = urlparse(url) - filename = os.path.basename(parts.path) - if file_name is not None: - filename = file_name - cached_file = os.path.abspath(os.path.join(model_dir, filename)) - if not os.path.exists(cached_file): - print(f'Downloading: "{url}" to {cached_file}\n') - download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) - return cached_file diff --git a/basicsr/utils/file_client.py b/basicsr/utils/file_client.py deleted file mode 100644 index 89d83ab..0000000 --- a/basicsr/utils/file_client.py +++ /dev/null @@ -1,167 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py # noqa: E501 -from abc import ABCMeta, abstractmethod - - -class BaseStorageBackend(metaclass=ABCMeta): - """Abstract class of storage backends. - - All backends need to implement two apis: ``get()`` and ``get_text()``. - ``get()`` reads the file as a byte stream and ``get_text()`` reads the file - as texts. - """ - - @abstractmethod - def get(self, filepath): - pass - - @abstractmethod - def get_text(self, filepath): - pass - - -class MemcachedBackend(BaseStorageBackend): - """Memcached storage backend. - - Attributes: - server_list_cfg (str): Config file for memcached server list. - client_cfg (str): Config file for memcached client. - sys_path (str | None): Additional path to be appended to `sys.path`. - Default: None. - """ - - def __init__(self, server_list_cfg, client_cfg, sys_path=None): - if sys_path is not None: - import sys - sys.path.append(sys_path) - try: - import mc - except ImportError: - raise ImportError('Please install memcached to enable MemcachedBackend.') - - self.server_list_cfg = server_list_cfg - self.client_cfg = client_cfg - self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, self.client_cfg) - # mc.pyvector servers as a point which points to a memory cache - self._mc_buffer = mc.pyvector() - - def get(self, filepath): - filepath = str(filepath) - import mc - self._client.Get(filepath, self._mc_buffer) - value_buf = mc.ConvertBuffer(self._mc_buffer) - return value_buf - - def get_text(self, filepath): - raise NotImplementedError - - -class HardDiskBackend(BaseStorageBackend): - """Raw hard disks storage backend.""" - - def get(self, filepath): - filepath = str(filepath) - with open(filepath, 'rb') as f: - value_buf = f.read() - return value_buf - - def get_text(self, filepath): - filepath = str(filepath) - with open(filepath, 'r') as f: - value_buf = f.read() - return value_buf - - -class LmdbBackend(BaseStorageBackend): - """Lmdb storage backend. - - Args: - db_paths (str | list[str]): Lmdb database paths. - client_keys (str | list[str]): Lmdb client keys. Default: 'default'. - readonly (bool, optional): Lmdb environment parameter. If True, - disallow any write operations. Default: True. - lock (bool, optional): Lmdb environment parameter. If False, when - concurrent access occurs, do not lock the database. Default: False. - readahead (bool, optional): Lmdb environment parameter. If False, - disable the OS filesystem readahead mechanism, which may improve - random read performance when a database is larger than RAM. - Default: False. - - Attributes: - db_paths (list): Lmdb database path. - _client (list): A list of several lmdb envs. - """ - - def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs): - try: - import lmdb - except ImportError: - raise ImportError('Please install lmdb to enable LmdbBackend.') - - if isinstance(client_keys, str): - client_keys = [client_keys] - - if isinstance(db_paths, list): - self.db_paths = [str(v) for v in db_paths] - elif isinstance(db_paths, str): - self.db_paths = [str(db_paths)] - assert len(client_keys) == len(self.db_paths), ('client_keys and db_paths should have the same length, ' - f'but received {len(client_keys)} and {len(self.db_paths)}.') - - self._client = {} - for client, path in zip(client_keys, self.db_paths): - self._client[client] = lmdb.open(path, readonly=readonly, lock=lock, readahead=readahead, **kwargs) - - def get(self, filepath, client_key): - """Get values according to the filepath from one lmdb named client_key. - - Args: - filepath (str | obj:`Path`): Here, filepath is the lmdb key. - client_key (str): Used for distinguishing different lmdb envs. - """ - filepath = str(filepath) - assert client_key in self._client, (f'client_key {client_key} is not in lmdb clients.') - client = self._client[client_key] - with client.begin(write=False) as txn: - value_buf = txn.get(filepath.encode('ascii')) - return value_buf - - def get_text(self, filepath): - raise NotImplementedError - - -class FileClient(object): - """A general file client to access files in different backend. - - The client loads a file or text in a specified backend from its path - and return it as a binary file. it can also register other backend - accessor with a given name and backend class. - - Attributes: - backend (str): The storage backend type. Options are "disk", - "memcached" and "lmdb". - client (:obj:`BaseStorageBackend`): The backend object. - """ - - _backends = { - 'disk': HardDiskBackend, - 'memcached': MemcachedBackend, - 'lmdb': LmdbBackend, - } - - def __init__(self, backend='disk', **kwargs): - if backend not in self._backends: - raise ValueError(f'Backend {backend} is not supported. Currently supported ones' - f' are {list(self._backends.keys())}') - self.backend = backend - self.client = self._backends[backend](**kwargs) - - def get(self, filepath, client_key='default'): - # client_key is used only for lmdb, where different fileclients have - # different lmdb environments. - if self.backend == 'lmdb': - return self.client.get(filepath, client_key) - else: - return self.client.get(filepath) - - def get_text(self, filepath): - return self.client.get_text(filepath) diff --git a/basicsr/utils/flow_util.py b/basicsr/utils/flow_util.py deleted file mode 100644 index 3d7180b..0000000 --- a/basicsr/utils/flow_util.py +++ /dev/null @@ -1,170 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py # noqa: E501 -import cv2 -import numpy as np -import os - - -def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs): - """Read an optical flow map. - - Args: - flow_path (ndarray or str): Flow path. - quantize (bool): whether to read quantized pair, if set to True, - remaining args will be passed to :func:`dequantize_flow`. - concat_axis (int): The axis that dx and dy are concatenated, - can be either 0 or 1. Ignored if quantize is False. - - Returns: - ndarray: Optical flow represented as a (h, w, 2) numpy array - """ - if quantize: - assert concat_axis in [0, 1] - cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED) - if cat_flow.ndim != 2: - raise IOError(f'{flow_path} is not a valid quantized flow file, its dimension is {cat_flow.ndim}.') - assert cat_flow.shape[concat_axis] % 2 == 0 - dx, dy = np.split(cat_flow, 2, axis=concat_axis) - flow = dequantize_flow(dx, dy, *args, **kwargs) - else: - with open(flow_path, 'rb') as f: - try: - header = f.read(4).decode('utf-8') - except Exception: - raise IOError(f'Invalid flow file: {flow_path}') - else: - if header != 'PIEH': - raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH') - - w = np.fromfile(f, np.int32, 1).squeeze() - h = np.fromfile(f, np.int32, 1).squeeze() - flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2)) - - return flow.astype(np.float32) - - -def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs): - """Write optical flow to file. - - If the flow is not quantized, it will be saved as a .flo file losslessly, - otherwise a jpeg image which is lossy but of much smaller size. (dx and dy - will be concatenated horizontally into a single image if quantize is True.) - - Args: - flow (ndarray): (h, w, 2) array of optical flow. - filename (str): Output filepath. - quantize (bool): Whether to quantize the flow and save it to 2 jpeg - images. If set to True, remaining args will be passed to - :func:`quantize_flow`. - concat_axis (int): The axis that dx and dy are concatenated, - can be either 0 or 1. Ignored if quantize is False. - """ - if not quantize: - with open(filename, 'wb') as f: - f.write('PIEH'.encode('utf-8')) - np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) - flow = flow.astype(np.float32) - flow.tofile(f) - f.flush() - else: - assert concat_axis in [0, 1] - dx, dy = quantize_flow(flow, *args, **kwargs) - dxdy = np.concatenate((dx, dy), axis=concat_axis) - os.makedirs(os.path.dirname(filename), exist_ok=True) - cv2.imwrite(filename, dxdy) - - -def quantize_flow(flow, max_val=0.02, norm=True): - """Quantize flow to [0, 255]. - - After this step, the size of flow will be much smaller, and can be - dumped as jpeg images. - - Args: - flow (ndarray): (h, w, 2) array of optical flow. - max_val (float): Maximum value of flow, values beyond - [-max_val, max_val] will be truncated. - norm (bool): Whether to divide flow values by image width/height. - - Returns: - tuple[ndarray]: Quantized dx and dy. - """ - h, w, _ = flow.shape - dx = flow[..., 0] - dy = flow[..., 1] - if norm: - dx = dx / w # avoid inplace operations - dy = dy / h - # use 255 levels instead of 256 to make sure 0 is 0 after dequantization. - flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]] - return tuple(flow_comps) - - -def dequantize_flow(dx, dy, max_val=0.02, denorm=True): - """Recover from quantized flow. - - Args: - dx (ndarray): Quantized dx. - dy (ndarray): Quantized dy. - max_val (float): Maximum value used when quantizing. - denorm (bool): Whether to multiply flow values with width/height. - - Returns: - ndarray: Dequantized flow. - """ - assert dx.shape == dy.shape - assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1) - - dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]] - - if denorm: - dx *= dx.shape[1] - dy *= dx.shape[0] - flow = np.dstack((dx, dy)) - return flow - - -def quantize(arr, min_val, max_val, levels, dtype=np.int64): - """Quantize an array of (-inf, inf) to [0, levels-1]. - - Args: - arr (ndarray): Input array. - min_val (scalar): Minimum value to be clipped. - max_val (scalar): Maximum value to be clipped. - levels (int): Quantization levels. - dtype (np.type): The type of the quantized array. - - Returns: - tuple: Quantized array. - """ - if not (isinstance(levels, int) and levels > 1): - raise ValueError(f'levels must be a positive integer, but got {levels}') - if min_val >= max_val: - raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') - - arr = np.clip(arr, min_val, max_val) - min_val - quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1) - - return quantized_arr - - -def dequantize(arr, min_val, max_val, levels, dtype=np.float64): - """Dequantize an array. - - Args: - arr (ndarray): Input array. - min_val (scalar): Minimum value to be clipped. - max_val (scalar): Maximum value to be clipped. - levels (int): Quantization levels. - dtype (np.type): The type of the dequantized array. - - Returns: - tuple: Dequantized array. - """ - if not (isinstance(levels, int) and levels > 1): - raise ValueError(f'levels must be a positive integer, but got {levels}') - if min_val >= max_val: - raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') - - dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val - - return dequantized_arr diff --git a/basicsr/utils/img_process_util.py b/basicsr/utils/img_process_util.py deleted file mode 100644 index 52e02f0..0000000 --- a/basicsr/utils/img_process_util.py +++ /dev/null @@ -1,83 +0,0 @@ -import cv2 -import numpy as np -import torch -from torch.nn import functional as F - - -def filter2D(img, kernel): - """PyTorch version of cv2.filter2D - - Args: - img (Tensor): (b, c, h, w) - kernel (Tensor): (b, k, k) - """ - k = kernel.size(-1) - b, c, h, w = img.size() - if k % 2 == 1: - img = F.pad(img, (k // 2, k // 2, k // 2, k // 2), mode='reflect') - else: - raise ValueError('Wrong kernel size') - - ph, pw = img.size()[-2:] - - if kernel.size(0) == 1: - # apply the same kernel to all batch images - img = img.view(b * c, 1, ph, pw) - kernel = kernel.view(1, 1, k, k) - return F.conv2d(img, kernel, padding=0).view(b, c, h, w) - else: - img = img.view(1, b * c, ph, pw) - kernel = kernel.view(b, 1, k, k).repeat(1, c, 1, 1).view(b * c, 1, k, k) - return F.conv2d(img, kernel, groups=b * c).view(b, c, h, w) - - -def usm_sharp(img, weight=0.5, radius=50, threshold=10): - """USM sharpening. - - Input image: I; Blurry image: B. - 1. sharp = I + weight * (I - B) - 2. Mask = 1 if abs(I - B) > threshold, else: 0 - 3. Blur mask: - 4. Out = Mask * sharp + (1 - Mask) * I - - - Args: - img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. - weight (float): Sharp weight. Default: 1. - radius (float): Kernel size of Gaussian blur. Default: 50. - threshold (int): - """ - if radius % 2 == 0: - radius += 1 - blur = cv2.GaussianBlur(img, (radius, radius), 0) - residual = img - blur - mask = np.abs(residual) * 255 > threshold - mask = mask.astype('float32') - soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) - - sharp = img + weight * residual - sharp = np.clip(sharp, 0, 1) - return soft_mask * sharp + (1 - soft_mask) * img - - -class USMSharp(torch.nn.Module): - - def __init__(self, radius=50, sigma=0): - super(USMSharp, self).__init__() - if radius % 2 == 0: - radius += 1 - self.radius = radius - kernel = cv2.getGaussianKernel(radius, sigma) - kernel = torch.FloatTensor(np.dot(kernel, kernel.transpose())).unsqueeze_(0) - self.register_buffer('kernel', kernel) - - def forward(self, img, weight=0.5, threshold=10): - blur = filter2D(img, self.kernel) - residual = img - blur - - mask = torch.abs(residual) * 255 > threshold - mask = mask.float() - soft_mask = filter2D(mask, self.kernel) - sharp = img + weight * residual - sharp = torch.clip(sharp, 0, 1) - return soft_mask * sharp + (1 - soft_mask) * img diff --git a/basicsr/utils/img_util.py b/basicsr/utils/img_util.py deleted file mode 100644 index 3a5f1da..0000000 --- a/basicsr/utils/img_util.py +++ /dev/null @@ -1,172 +0,0 @@ -import cv2 -import math -import numpy as np -import os -import torch -from torchvision.utils import make_grid - - -def img2tensor(imgs, bgr2rgb=True, float32=True): - """Numpy array to tensor. - - Args: - imgs (list[ndarray] | ndarray): Input images. - bgr2rgb (bool): Whether to change bgr to rgb. - float32 (bool): Whether to change to float32. - - Returns: - list[tensor] | tensor: Tensor images. If returned results only have - one element, just return tensor. - """ - - def _totensor(img, bgr2rgb, float32): - if img.shape[2] == 3 and bgr2rgb: - if img.dtype == 'float64': - img = img.astype('float32') - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - img = torch.from_numpy(img.transpose(2, 0, 1)) - if float32: - img = img.float() - return img - - if isinstance(imgs, list): - return [_totensor(img, bgr2rgb, float32) for img in imgs] - else: - return _totensor(imgs, bgr2rgb, float32) - - -def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): - """Convert torch Tensors into image numpy arrays. - - After clamping to [min, max], values will be normalized to [0, 1]. - - Args: - tensor (Tensor or list[Tensor]): Accept shapes: - 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); - 2) 3D Tensor of shape (3/1 x H x W); - 3) 2D Tensor of shape (H x W). - Tensor channel should be in RGB order. - rgb2bgr (bool): Whether to change rgb to bgr. - out_type (numpy type): output types. If ``np.uint8``, transform outputs - to uint8 type with range [0, 255]; otherwise, float type with - range [0, 1]. Default: ``np.uint8``. - min_max (tuple[int]): min and max values for clamp. - - Returns: - (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of - shape (H x W). The channel order is BGR. - """ - if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): - raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}') - - if torch.is_tensor(tensor): - tensor = [tensor] - result = [] - for _tensor in tensor: - _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max) - _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0]) - - n_dim = _tensor.dim() - if n_dim == 4: - img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy() - img_np = img_np.transpose(1, 2, 0) - if rgb2bgr: - img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) - elif n_dim == 3: - img_np = _tensor.numpy() - img_np = img_np.transpose(1, 2, 0) - if img_np.shape[2] == 1: # gray image - img_np = np.squeeze(img_np, axis=2) - else: - if rgb2bgr: - img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) - elif n_dim == 2: - img_np = _tensor.numpy() - else: - raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}') - if out_type == np.uint8: - # Unlike MATLAB, numpy.unit8() WILL NOT round by default. - img_np = (img_np * 255.0).round() - img_np = img_np.astype(out_type) - result.append(img_np) - if len(result) == 1: - result = result[0] - return result - - -def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)): - """This implementation is slightly faster than tensor2img. - It now only supports torch tensor with shape (1, c, h, w). - - Args: - tensor (Tensor): Now only support torch tensor with (1, c, h, w). - rgb2bgr (bool): Whether to change rgb to bgr. Default: True. - min_max (tuple[int]): min and max values for clamp. - """ - output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0) - output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255 - output = output.type(torch.uint8).cpu().numpy() - if rgb2bgr: - output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) - return output - - -def imfrombytes(content, flag='color', float32=False): - """Read an image from bytes. - - Args: - content (bytes): Image bytes got from files or other streams. - flag (str): Flags specifying the color type of a loaded image, - candidates are `color`, `grayscale` and `unchanged`. - float32 (bool): Whether to change to float32., If True, will also norm - to [0, 1]. Default: False. - - Returns: - ndarray: Loaded image array. - """ - img_np = np.frombuffer(content, np.uint8) - imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED} - img = cv2.imdecode(img_np, imread_flags[flag]) - if float32: - img = img.astype(np.float32) / 255. - return img - - -def imwrite(img, file_path, params=None, auto_mkdir=True): - """Write image to file. - - Args: - img (ndarray): Image array to be written. - file_path (str): Image file path. - params (None or list): Same as opencv's :func:`imwrite` interface. - auto_mkdir (bool): If the parent folder of `file_path` does not exist, - whether to create it automatically. - - Returns: - bool: Successful or not. - """ - if auto_mkdir: - dir_name = os.path.abspath(os.path.dirname(file_path)) - os.makedirs(dir_name, exist_ok=True) - ok = cv2.imwrite(file_path, img, params) - if not ok: - raise IOError('Failed in writing images.') - - -def crop_border(imgs, crop_border): - """Crop borders of images. - - Args: - imgs (list[ndarray] | ndarray): Images with shape (h, w, c). - crop_border (int): Crop border for each end of height and weight. - - Returns: - list[ndarray]: Cropped images. - """ - if crop_border == 0: - return imgs - else: - if isinstance(imgs, list): - return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs] - else: - return imgs[crop_border:-crop_border, crop_border:-crop_border, ...] diff --git a/basicsr/utils/lmdb_util.py b/basicsr/utils/lmdb_util.py deleted file mode 100644 index a2b45ce..0000000 --- a/basicsr/utils/lmdb_util.py +++ /dev/null @@ -1,199 +0,0 @@ -import cv2 -import lmdb -import sys -from multiprocessing import Pool -from os import path as osp -from tqdm import tqdm - - -def make_lmdb_from_imgs(data_path, - lmdb_path, - img_path_list, - keys, - batch=5000, - compress_level=1, - multiprocessing_read=False, - n_thread=40, - map_size=None): - """Make lmdb from images. - - Contents of lmdb. The file structure is: - - :: - - example.lmdb - ├── data.mdb - ├── lock.mdb - ├── meta_info.txt - - The data.mdb and lock.mdb are standard lmdb files and you can refer to - https://lmdb.readthedocs.io/en/release/ for more details. - - The meta_info.txt is a specified txt file to record the meta information - of our datasets. It will be automatically created when preparing - datasets by our provided dataset tools. - Each line in the txt file records 1)image name (with extension), - 2)image shape, and 3)compression level, separated by a white space. - - For example, the meta information could be: - `000_00000000.png (720,1280,3) 1`, which means: - 1) image name (with extension): 000_00000000.png; - 2) image shape: (720,1280,3); - 3) compression level: 1 - - We use the image name without extension as the lmdb key. - - If `multiprocessing_read` is True, it will read all the images to memory - using multiprocessing. Thus, your server needs to have enough memory. - - Args: - data_path (str): Data path for reading images. - lmdb_path (str): Lmdb save path. - img_path_list (str): Image path list. - keys (str): Used for lmdb keys. - batch (int): After processing batch images, lmdb commits. - Default: 5000. - compress_level (int): Compress level when encoding images. Default: 1. - multiprocessing_read (bool): Whether use multiprocessing to read all - the images to memory. Default: False. - n_thread (int): For multiprocessing. - map_size (int | None): Map size for lmdb env. If None, use the - estimated size from images. Default: None - """ - - assert len(img_path_list) == len(keys), ('img_path_list and keys should have the same length, ' - f'but got {len(img_path_list)} and {len(keys)}') - print(f'Create lmdb for {data_path}, save to {lmdb_path}...') - print(f'Totoal images: {len(img_path_list)}') - if not lmdb_path.endswith('.lmdb'): - raise ValueError("lmdb_path must end with '.lmdb'.") - if osp.exists(lmdb_path): - print(f'Folder {lmdb_path} already exists. Exit.') - sys.exit(1) - - if multiprocessing_read: - # read all the images to memory (multiprocessing) - dataset = {} # use dict to keep the order for multiprocessing - shapes = {} - print(f'Read images with multiprocessing, #thread: {n_thread} ...') - pbar = tqdm(total=len(img_path_list), unit='image') - - def callback(arg): - """get the image data and update pbar.""" - key, dataset[key], shapes[key] = arg - pbar.update(1) - pbar.set_description(f'Read {key}') - - pool = Pool(n_thread) - for path, key in zip(img_path_list, keys): - pool.apply_async(read_img_worker, args=(osp.join(data_path, path), key, compress_level), callback=callback) - pool.close() - pool.join() - pbar.close() - print(f'Finish reading {len(img_path_list)} images.') - - # create lmdb environment - if map_size is None: - # obtain data size for one image - img = cv2.imread(osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED) - _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) - data_size_per_img = img_byte.nbytes - print('Data size per image is: ', data_size_per_img) - data_size = data_size_per_img * len(img_path_list) - map_size = data_size * 10 - - env = lmdb.open(lmdb_path, map_size=map_size) - - # write data to lmdb - pbar = tqdm(total=len(img_path_list), unit='chunk') - txn = env.begin(write=True) - txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w') - for idx, (path, key) in enumerate(zip(img_path_list, keys)): - pbar.update(1) - pbar.set_description(f'Write {key}') - key_byte = key.encode('ascii') - if multiprocessing_read: - img_byte = dataset[key] - h, w, c = shapes[key] - else: - _, img_byte, img_shape = read_img_worker(osp.join(data_path, path), key, compress_level) - h, w, c = img_shape - - txn.put(key_byte, img_byte) - # write meta information - txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n') - if idx % batch == 0: - txn.commit() - txn = env.begin(write=True) - pbar.close() - txn.commit() - env.close() - txt_file.close() - print('\nFinish writing lmdb.') - - -def read_img_worker(path, key, compress_level): - """Read image worker. - - Args: - path (str): Image path. - key (str): Image key. - compress_level (int): Compress level when encoding images. - - Returns: - str: Image key. - byte: Image byte. - tuple[int]: Image shape. - """ - - img = cv2.imread(path, cv2.IMREAD_UNCHANGED) - if img.ndim == 2: - h, w = img.shape - c = 1 - else: - h, w, c = img.shape - _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) - return (key, img_byte, (h, w, c)) - - -class LmdbMaker(): - """LMDB Maker. - - Args: - lmdb_path (str): Lmdb save path. - map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB. - batch (int): After processing batch images, lmdb commits. - Default: 5000. - compress_level (int): Compress level when encoding images. Default: 1. - """ - - def __init__(self, lmdb_path, map_size=1024**4, batch=5000, compress_level=1): - if not lmdb_path.endswith('.lmdb'): - raise ValueError("lmdb_path must end with '.lmdb'.") - if osp.exists(lmdb_path): - print(f'Folder {lmdb_path} already exists. Exit.') - sys.exit(1) - - self.lmdb_path = lmdb_path - self.batch = batch - self.compress_level = compress_level - self.env = lmdb.open(lmdb_path, map_size=map_size) - self.txn = self.env.begin(write=True) - self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w') - self.counter = 0 - - def put(self, img_byte, key, img_shape): - self.counter += 1 - key_byte = key.encode('ascii') - self.txn.put(key_byte, img_byte) - # write meta information - h, w, c = img_shape - self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n') - if self.counter % self.batch == 0: - self.txn.commit() - self.txn = self.env.begin(write=True) - - def close(self): - self.txn.commit() - self.env.close() - self.txt_file.close() diff --git a/basicsr/utils/logger.py b/basicsr/utils/logger.py deleted file mode 100644 index 73553dc..0000000 --- a/basicsr/utils/logger.py +++ /dev/null @@ -1,213 +0,0 @@ -import datetime -import logging -import time - -from .dist_util import get_dist_info, master_only - -initialized_logger = {} - - -class AvgTimer(): - - def __init__(self, window=200): - self.window = window # average window - self.current_time = 0 - self.total_time = 0 - self.count = 0 - self.avg_time = 0 - self.start() - - def start(self): - self.start_time = self.tic = time.time() - - def record(self): - self.count += 1 - self.toc = time.time() - self.current_time = self.toc - self.tic - self.total_time += self.current_time - # calculate average time - self.avg_time = self.total_time / self.count - - # reset - if self.count > self.window: - self.count = 0 - self.total_time = 0 - - self.tic = time.time() - - def get_current_time(self): - return self.current_time - - def get_avg_time(self): - return self.avg_time - - -class MessageLogger(): - """Message logger for printing. - - Args: - opt (dict): Config. It contains the following keys: - name (str): Exp name. - logger (dict): Contains 'print_freq' (str) for logger interval. - train (dict): Contains 'total_iter' (int) for total iters. - use_tb_logger (bool): Use tensorboard logger. - start_iter (int): Start iter. Default: 1. - tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None. - """ - - def __init__(self, opt, start_iter=1, tb_logger=None): - self.exp_name = opt['name'] - self.interval = opt['logger']['print_freq'] - self.start_iter = start_iter - self.max_iters = opt['train']['total_iter'] - self.use_tb_logger = opt['logger']['use_tb_logger'] - self.tb_logger = tb_logger - self.start_time = time.time() - self.logger = get_root_logger() - - def reset_start_time(self): - self.start_time = time.time() - - @master_only - def __call__(self, log_vars): - """Format logging message. - - Args: - log_vars (dict): It contains the following keys: - epoch (int): Epoch number. - iter (int): Current iter. - lrs (list): List for learning rates. - - time (float): Iter time. - data_time (float): Data time for each iter. - """ - # epoch, iter, learning rates - epoch = log_vars.pop('epoch') - current_iter = log_vars.pop('iter') - lrs = log_vars.pop('lrs') - - message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, iter:{current_iter:8,d}, lr:(') - for v in lrs: - message += f'{v:.3e},' - message += ')] ' - - # time and estimated time - if 'time' in log_vars.keys(): - iter_time = log_vars.pop('time') - data_time = log_vars.pop('data_time') - - total_time = time.time() - self.start_time - time_sec_avg = total_time / (current_iter - self.start_iter + 1) - eta_sec = time_sec_avg * (self.max_iters - current_iter - 1) - eta_str = str(datetime.timedelta(seconds=int(eta_sec))) - message += f'[eta: {eta_str}, ' - message += f'time (data): {iter_time:.3f} ({data_time:.3f})] ' - - # other items, especially losses - for k, v in log_vars.items(): - message += f'{k}: {v:.4e} ' - # tensorboard logger - if self.use_tb_logger and 'debug' not in self.exp_name: - if k.startswith('l_'): - self.tb_logger.add_scalar(f'losses/{k}', v, current_iter) - else: - self.tb_logger.add_scalar(k, v, current_iter) - self.logger.info(message) - - -@master_only -def init_tb_logger(log_dir): - from torch.utils.tensorboard import SummaryWriter - tb_logger = SummaryWriter(log_dir=log_dir) - return tb_logger - - -@master_only -def init_wandb_logger(opt): - """We now only use wandb to sync tensorboard log.""" - import wandb - logger = get_root_logger() - - project = opt['logger']['wandb']['project'] - resume_id = opt['logger']['wandb'].get('resume_id') - if resume_id: - wandb_id = resume_id - resume = 'allow' - logger.warning(f'Resume wandb logger with id={wandb_id}.') - else: - wandb_id = wandb.util.generate_id() - resume = 'never' - - wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True) - - logger.info(f'Use wandb logger with id={wandb_id}; project={project}.') - - -def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None): - """Get the root logger. - - The logger will be initialized if it has not been initialized. By default a - StreamHandler will be added. If `log_file` is specified, a FileHandler will - also be added. - - Args: - logger_name (str): root logger name. Default: 'basicsr'. - log_file (str | None): The log filename. If specified, a FileHandler - will be added to the root logger. - log_level (int): The root logger level. Note that only the process of - rank 0 is affected, while other processes will set the level to - "Error" and be silent most of the time. - - Returns: - logging.Logger: The root logger. - """ - logger = logging.getLogger(logger_name) - # if the logger has been initialized, just return it - if logger_name in initialized_logger: - return logger - - format_str = '%(asctime)s %(levelname)s: %(message)s' - stream_handler = logging.StreamHandler() - stream_handler.setFormatter(logging.Formatter(format_str)) - logger.addHandler(stream_handler) - logger.propagate = False - rank, _ = get_dist_info() - if rank != 0: - logger.setLevel('ERROR') - elif log_file is not None: - logger.setLevel(log_level) - # add file handler - file_handler = logging.FileHandler(log_file, 'w') - file_handler.setFormatter(logging.Formatter(format_str)) - file_handler.setLevel(log_level) - logger.addHandler(file_handler) - initialized_logger[logger_name] = True - return logger - - -def get_env_info(): - """Get environment information. - - Currently, only log the software version. - """ - import torch - import torchvision - - from basicsr.version import __version__ - msg = r""" - ____ _ _____ ____ - / __ ) ____ _ _____ (_)_____/ ___/ / __ \ - / __ |/ __ `// ___// // ___/\__ \ / /_/ / - / /_/ // /_/ /(__ )/ // /__ ___/ // _, _/ - /_____/ \__,_//____//_/ \___//____//_/ |_| - ______ __ __ __ __ - / ____/____ ____ ____/ / / / __ __ _____ / /__ / / - / / __ / __ \ / __ \ / __ / / / / / / // ___// //_/ / / - / /_/ // /_/ // /_/ // /_/ / / /___/ /_/ // /__ / /< /_/ - \____/ \____/ \____/ \____/ /_____/\____/ \___//_/|_| (_) - """ - msg += ('\nVersion Information: ' - f'\n\tBasicSR: {__version__}' - f'\n\tPyTorch: {torch.__version__}' - f'\n\tTorchVision: {torchvision.__version__}') - return msg diff --git a/basicsr/utils/matlab_functions.py b/basicsr/utils/matlab_functions.py deleted file mode 100644 index a201f79..0000000 --- a/basicsr/utils/matlab_functions.py +++ /dev/null @@ -1,178 +0,0 @@ -import math -import numpy as np -import torch - - -def cubic(x): - """cubic function used for calculate_weights_indices.""" - absx = torch.abs(x) - absx2 = absx**2 - absx3 = absx**3 - return (1.5 * absx3 - 2.5 * absx2 + 1) * ( - (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (((absx > 1) * - (absx <= 2)).type_as(absx)) - - -def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): - """Calculate weights and indices, used for imresize function. - - Args: - in_length (int): Input length. - out_length (int): Output length. - scale (float): Scale factor. - kernel_width (int): Kernel width. - antialisaing (bool): Whether to apply anti-aliasing when downsampling. - """ - - if (scale < 1) and antialiasing: - # Use a modified kernel (larger kernel width) to simultaneously - # interpolate and antialias - kernel_width = kernel_width / scale - - # Output-space coordinates - x = torch.linspace(1, out_length, out_length) - - # Input-space coordinates. Calculate the inverse mapping such that 0.5 - # in output space maps to 0.5 in input space, and 0.5 + scale in output - # space maps to 1.5 in input space. - u = x / scale + 0.5 * (1 - 1 / scale) - - # What is the left-most pixel that can be involved in the computation? - left = torch.floor(u - kernel_width / 2) - - # What is the maximum number of pixels that can be involved in the - # computation? Note: it's OK to use an extra pixel here; if the - # corresponding weights are all zero, it will be eliminated at the end - # of this function. - p = math.ceil(kernel_width) + 2 - - # The indices of the input pixels involved in computing the k-th output - # pixel are in row k of the indices matrix. - indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand( - out_length, p) - - # The weights used to compute the k-th output pixel are in row k of the - # weights matrix. - distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices - - # apply cubic kernel - if (scale < 1) and antialiasing: - weights = scale * cubic(distance_to_center * scale) - else: - weights = cubic(distance_to_center) - - # Normalize the weights matrix so that each row sums to 1. - weights_sum = torch.sum(weights, 1).view(out_length, 1) - weights = weights / weights_sum.expand(out_length, p) - - # If a column in weights is all zero, get rid of it. only consider the - # first and last column. - weights_zero_tmp = torch.sum((weights == 0), 0) - if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): - indices = indices.narrow(1, 1, p - 2) - weights = weights.narrow(1, 1, p - 2) - if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): - indices = indices.narrow(1, 0, p - 2) - weights = weights.narrow(1, 0, p - 2) - weights = weights.contiguous() - indices = indices.contiguous() - sym_len_s = -indices.min() + 1 - sym_len_e = indices.max() - in_length - indices = indices + sym_len_s - 1 - return weights, indices, int(sym_len_s), int(sym_len_e) - - -@torch.no_grad() -def imresize(img, scale, antialiasing=True): - """imresize function same as MATLAB. - - It now only supports bicubic. - The same scale applies for both height and width. - - Args: - img (Tensor | Numpy array): - Tensor: Input image with shape (c, h, w), [0, 1] range. - Numpy: Input image with shape (h, w, c), [0, 1] range. - scale (float): Scale factor. The same scale applies for both height - and width. - antialisaing (bool): Whether to apply anti-aliasing when downsampling. - Default: True. - - Returns: - Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round. - """ - squeeze_flag = False - if type(img).__module__ == np.__name__: # numpy type - numpy_type = True - if img.ndim == 2: - img = img[:, :, None] - squeeze_flag = True - img = torch.from_numpy(img.transpose(2, 0, 1)).float() - else: - numpy_type = False - if img.ndim == 2: - img = img.unsqueeze(0) - squeeze_flag = True - - in_c, in_h, in_w = img.size() - out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale) - kernel_width = 4 - kernel = 'cubic' - - # get weights and indices - weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(in_h, out_h, scale, kernel, kernel_width, - antialiasing) - weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(in_w, out_w, scale, kernel, kernel_width, - antialiasing) - # process H dimension - # symmetric copying - img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w) - img_aug.narrow(1, sym_len_hs, in_h).copy_(img) - - sym_patch = img[:, :sym_len_hs, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv) - - sym_patch = img[:, -sym_len_he:, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv) - - out_1 = torch.FloatTensor(in_c, out_h, in_w) - kernel_width = weights_h.size(1) - for i in range(out_h): - idx = int(indices_h[i][0]) - for j in range(in_c): - out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i]) - - # process W dimension - # symmetric copying - out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we) - out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1) - - sym_patch = out_1[:, :, :sym_len_ws] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv) - - sym_patch = out_1[:, :, -sym_len_we:] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv) - - out_2 = torch.FloatTensor(in_c, out_h, out_w) - kernel_width = weights_w.size(1) - for i in range(out_w): - idx = int(indices_w[i][0]) - for j in range(in_c): - out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i]) - - if squeeze_flag: - out_2 = out_2.squeeze(0) - if numpy_type: - out_2 = out_2.numpy() - if not squeeze_flag: - out_2 = out_2.transpose(1, 2, 0) - - return out_2 diff --git a/basicsr/utils/misc.py b/basicsr/utils/misc.py deleted file mode 100644 index c8d4a14..0000000 --- a/basicsr/utils/misc.py +++ /dev/null @@ -1,141 +0,0 @@ -import numpy as np -import os -import random -import time -import torch -from os import path as osp - -from .dist_util import master_only - - -def set_random_seed(seed): - """Set random seeds.""" - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - - -def get_time_str(): - return time.strftime('%Y%m%d_%H%M%S', time.localtime()) - - -def mkdir_and_rename(path): - """mkdirs. If path exists, rename it with timestamp and create a new one. - - Args: - path (str): Folder path. - """ - if osp.exists(path): - new_name = path + '_archived_' + get_time_str() - print(f'Path already exists. Rename it to {new_name}', flush=True) - os.rename(path, new_name) - os.makedirs(path, exist_ok=True) - - -@master_only -def make_exp_dirs(opt): - """Make dirs for experiments.""" - path_opt = opt['path'].copy() - if opt['is_train']: - mkdir_and_rename(path_opt.pop('experiments_root')) - else: - mkdir_and_rename(path_opt.pop('results_root')) - for key, path in path_opt.items(): - if ('strict_load' in key) or ('pretrain_network' in key) or ('resume' in key) or ('param_key' in key): - continue - else: - os.makedirs(path, exist_ok=True) - - -def scandir(dir_path, suffix=None, recursive=False, full_path=False): - """Scan a directory to find the interested files. - - Args: - dir_path (str): Path of the directory. - suffix (str | tuple(str), optional): File suffix that we are - interested in. Default: None. - recursive (bool, optional): If set to True, recursively scan the - directory. Default: False. - full_path (bool, optional): If set to True, include the dir_path. - Default: False. - - Returns: - A generator for all the interested files with relative paths. - """ - - if (suffix is not None) and not isinstance(suffix, (str, tuple)): - raise TypeError('"suffix" must be a string or tuple of strings') - - root = dir_path - - def _scandir(dir_path, suffix, recursive): - for entry in os.scandir(dir_path): - if not entry.name.startswith('.') and entry.is_file(): - if full_path: - return_path = entry.path - else: - return_path = osp.relpath(entry.path, root) - - if suffix is None: - yield return_path - elif return_path.endswith(suffix): - yield return_path - else: - if recursive: - yield from _scandir(entry.path, suffix=suffix, recursive=recursive) - else: - continue - - return _scandir(dir_path, suffix=suffix, recursive=recursive) - - -def check_resume(opt, resume_iter): - """Check resume states and pretrain_network paths. - - Args: - opt (dict): Options. - resume_iter (int): Resume iteration. - """ - if opt['path']['resume_state']: - # get all the networks - networks = [key for key in opt.keys() if key.startswith('network_')] - flag_pretrain = False - for network in networks: - if opt['path'].get(f'pretrain_{network}') is not None: - flag_pretrain = True - if flag_pretrain: - print('pretrain_network path will be ignored during resuming.') - # set pretrained model paths - for network in networks: - name = f'pretrain_{network}' - basename = network.replace('network_', '') - if opt['path'].get('ignore_resume_networks') is None or (network - not in opt['path']['ignore_resume_networks']): - opt['path'][name] = osp.join(opt['path']['models'], f'net_{basename}_{resume_iter}.pth') - print(f"Set {name} to {opt['path'][name]}") - - # change param_key to params in resume - param_keys = [key for key in opt['path'].keys() if key.startswith('param_key')] - for param_key in param_keys: - if opt['path'][param_key] == 'params_ema': - opt['path'][param_key] = 'params' - print(f'Set {param_key} to params') - - -def sizeof_fmt(size, suffix='B'): - """Get human readable file size. - - Args: - size (int): File size. - suffix (str): Suffix. Default: 'B'. - - Return: - str: Formatted file size. - """ - for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']: - if abs(size) < 1024.0: - return f'{size:3.1f} {unit}{suffix}' - size /= 1024.0 - return f'{size:3.1f} Y{suffix}' diff --git a/basicsr/utils/options.py b/basicsr/utils/options.py deleted file mode 100644 index 5c7155e..0000000 --- a/basicsr/utils/options.py +++ /dev/null @@ -1,218 +0,0 @@ -import argparse -import os -import random -import torch -import yaml -from collections import OrderedDict -from os import path as osp - -from basicsr.utils import set_random_seed -from basicsr.utils.dist_util import get_dist_info, init_dist, master_only - - -def ordered_yaml(): - """Support OrderedDict for yaml. - - Returns: - tuple: yaml Loader and Dumper. - """ - try: - from yaml import CDumper as Dumper - from yaml import CLoader as Loader - except ImportError: - from yaml import Dumper, Loader - - _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG - - def dict_representer(dumper, data): - return dumper.represent_dict(data.items()) - - def dict_constructor(loader, node): - return OrderedDict(loader.construct_pairs(node)) - - Dumper.add_representer(OrderedDict, dict_representer) - Loader.add_constructor(_mapping_tag, dict_constructor) - return Loader, Dumper - - -def yaml_load(f): - """Load yaml file or string. - - Args: - f (str): File path or a python string. - - Returns: - dict: Loaded dict. - """ - if os.path.isfile(f): - with open(f, 'r') as f: - return yaml.load(f, Loader=ordered_yaml()[0]) - else: - return yaml.load(f, Loader=ordered_yaml()[0]) - - -def dict2str(opt, indent_level=1): - """dict to string for printing options. - - Args: - opt (dict): Option dict. - indent_level (int): Indent level. Default: 1. - - Return: - (str): Option string for printing. - """ - msg = '\n' - for k, v in opt.items(): - if isinstance(v, dict): - msg += ' ' * (indent_level * 2) + k + ':[' - msg += dict2str(v, indent_level + 1) - msg += ' ' * (indent_level * 2) + ']\n' - else: - msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n' - return msg - - -def _postprocess_yml_value(value): - # None - if value == '~' or value.lower() == 'none': - return None - # bool - if value.lower() == 'true': - return True - elif value.lower() == 'false': - return False - # !!float number - if value.startswith('!!float'): - return float(value.replace('!!float', '')) - # number - if value.isdigit(): - return int(value) - elif value.replace('.', '', 1).isdigit() and value.count('.') < 2: - return float(value) - # list - if value.startswith('['): - return eval(value) - # str - return value - - -def parse_options(root_path, is_train=True): - parser = argparse.ArgumentParser() - parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.') - parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher') - parser.add_argument('--auto_resume', action='store_true') - parser.add_argument('--debug', action='store_true') - parser.add_argument('--local_rank', type=int, default=0) - parser.add_argument( - '--force_yml', nargs='+', default=None, help='Force to update yml files. Examples: train:ema_decay=0.999') - args = parser.parse_args() - - # parse yml to dict - opt = yaml_load(args.opt) - - # distributed settings - if args.launcher == 'none': - opt['dist'] = False - print('Disable distributed.', flush=True) - else: - opt['dist'] = True - if args.launcher == 'slurm' and 'dist_params' in opt: - init_dist(args.launcher, **opt['dist_params']) - else: - init_dist(args.launcher) - opt['rank'], opt['world_size'] = get_dist_info() - - # random seed - seed = opt.get('manual_seed') - if seed is None: - seed = random.randint(1, 10000) - opt['manual_seed'] = seed - set_random_seed(seed + opt['rank']) - - # force to update yml options - if args.force_yml is not None: - for entry in args.force_yml: - # now do not support creating new keys - keys, value = entry.split('=') - keys, value = keys.strip(), value.strip() - value = _postprocess_yml_value(value) - eval_str = 'opt' - for key in keys.split(':'): - eval_str += f'["{key}"]' - eval_str += '=value' - # using exec function - exec(eval_str) - - opt['auto_resume'] = args.auto_resume - opt['is_train'] = is_train - - # debug setting - if args.debug and not opt['name'].startswith('debug'): - opt['name'] = 'debug_' + opt['name'] - - if opt['num_gpu'] == 'auto': - opt['num_gpu'] = torch.cuda.device_count() - - # datasets - for phase, dataset in opt['datasets'].items(): - # for multiple datasets, e.g., val_1, val_2; test_1, test_2 - phase = phase.split('_')[0] - dataset['phase'] = phase - if 'scale' in opt: - dataset['scale'] = opt['scale'] - if dataset.get('dataroot_gt') is not None: - dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt']) - if dataset.get('dataroot_lq') is not None: - dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq']) - - # paths - for key, val in opt['path'].items(): - if (val is not None) and ('resume_state' in key or 'pretrain_network' in key): - opt['path'][key] = osp.expanduser(val) - - if is_train: - experiments_root = opt['path'].get('experiments_root') - if experiments_root is None: - experiments_root = osp.join(root_path, 'experiments') - experiments_root = osp.join(experiments_root, opt['name']) - - opt['path']['experiments_root'] = experiments_root - opt['path']['models'] = osp.join(experiments_root, 'models') - opt['path']['training_states'] = osp.join(experiments_root, 'training_states') - opt['path']['log'] = experiments_root - opt['path']['visualization'] = osp.join(experiments_root, 'visualization') - - # change some options for debug mode - if 'debug' in opt['name']: - if 'val' in opt: - opt['val']['val_freq'] = 8 - opt['logger']['print_freq'] = 1 - opt['logger']['save_checkpoint_freq'] = 8 - else: # test - results_root = opt['path'].get('results_root') - if results_root is None: - results_root = osp.join(root_path, 'results') - results_root = osp.join(results_root, opt['name']) - - opt['path']['results_root'] = results_root - opt['path']['log'] = results_root - opt['path']['visualization'] = osp.join(results_root, 'visualization') - - return opt, args - - -@master_only -def copy_opt_file(opt_file, experiments_root): - # copy the yml file to the experiment root - import sys - import time - from shutil import copyfile - cmd = ' '.join(sys.argv) - filename = osp.join(experiments_root, osp.basename(opt_file)) - copyfile(opt_file, filename) - - with open(filename, 'r+') as f: - lines = f.readlines() - lines.insert(0, f'# GENERATE TIME: {time.asctime()}\n# CMD:\n# {cmd}\n\n') - f.seek(0) - f.writelines(lines) diff --git a/basicsr/utils/plot_util.py b/basicsr/utils/plot_util.py deleted file mode 100644 index 1e6da5b..0000000 --- a/basicsr/utils/plot_util.py +++ /dev/null @@ -1,83 +0,0 @@ -import re - - -def read_data_from_tensorboard(log_path, tag): - """Get raw data (steps and values) from tensorboard events. - - Args: - log_path (str): Path to the tensorboard log. - tag (str): tag to be read. - """ - from tensorboard.backend.event_processing.event_accumulator import EventAccumulator - - # tensorboard event - event_acc = EventAccumulator(log_path) - event_acc.Reload() - scalar_list = event_acc.Tags()['scalars'] - print('tag list: ', scalar_list) - steps = [int(s.step) for s in event_acc.Scalars(tag)] - values = [s.value for s in event_acc.Scalars(tag)] - return steps, values - - -def read_data_from_txt_2v(path, pattern, step_one=False): - """Read data from txt with 2 returned values (usually [step, value]). - - Args: - path (str): path to the txt file. - pattern (str): re (regular expression) pattern. - step_one (bool): add 1 to steps. Default: False. - """ - with open(path) as f: - lines = f.readlines() - lines = [line.strip() for line in lines] - steps = [] - values = [] - - pattern = re.compile(pattern) - for line in lines: - match = pattern.match(line) - if match: - steps.append(int(match.group(1))) - values.append(float(match.group(2))) - if step_one: - steps = [v + 1 for v in steps] - return steps, values - - -def read_data_from_txt_1v(path, pattern): - """Read data from txt with 1 returned values. - - Args: - path (str): path to the txt file. - pattern (str): re (regular expression) pattern. - """ - with open(path) as f: - lines = f.readlines() - lines = [line.strip() for line in lines] - data = [] - - pattern = re.compile(pattern) - for line in lines: - match = pattern.match(line) - if match: - data.append(float(match.group(1))) - return data - - -def smooth_data(values, smooth_weight): - """ Smooth data using 1st-order IIR low-pass filter (what tensorflow does). - - Reference: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 # noqa: E501 - - Args: - values (list): A list of values to be smoothed. - smooth_weight (float): Smooth weight. - """ - values_sm = [] - last_sm_value = values[0] - for value in values: - value_sm = last_sm_value * smooth_weight + (1 - smooth_weight) * value - values_sm.append(value_sm) - last_sm_value = value_sm - return values_sm diff --git a/basicsr/utils/registry.py b/basicsr/utils/registry.py deleted file mode 100644 index 5e72ef7..0000000 --- a/basicsr/utils/registry.py +++ /dev/null @@ -1,88 +0,0 @@ -# Modified from: https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/registry.py # noqa: E501 - - -class Registry(): - """ - The registry that provides name -> object mapping, to support third-party - users' custom modules. - - To create a registry (e.g. a backbone registry): - - .. code-block:: python - - BACKBONE_REGISTRY = Registry('BACKBONE') - - To register an object: - - .. code-block:: python - - @BACKBONE_REGISTRY.register() - class MyBackbone(): - ... - - Or: - - .. code-block:: python - - BACKBONE_REGISTRY.register(MyBackbone) - """ - - def __init__(self, name): - """ - Args: - name (str): the name of this registry - """ - self._name = name - self._obj_map = {} - - def _do_register(self, name, obj, suffix=None): - if isinstance(suffix, str): - name = name + '_' + suffix - - assert (name not in self._obj_map), (f"An object named '{name}' was already registered " - f"in '{self._name}' registry!") - self._obj_map[name] = obj - - def register(self, obj=None, suffix=None): - """ - Register the given object under the the name `obj.__name__`. - Can be used as either a decorator or not. - See docstring of this class for usage. - """ - if obj is None: - # used as a decorator - def deco(func_or_class): - name = func_or_class.__name__ - self._do_register(name, func_or_class, suffix) - return func_or_class - - return deco - - # used as a function call - name = obj.__name__ - self._do_register(name, obj, suffix) - - def get(self, name, suffix='basicsr'): - ret = self._obj_map.get(name) - if ret is None: - ret = self._obj_map.get(name + '_' + suffix) - print(f'Name {name} is not found, use name: {name}_{suffix}!') - if ret is None: - raise KeyError(f"No object named '{name}' found in '{self._name}' registry!") - return ret - - def __contains__(self, name): - return name in self._obj_map - - def __iter__(self): - return iter(self._obj_map.items()) - - def keys(self): - return self._obj_map.keys() - - -DATASET_REGISTRY = Registry('dataset') -ARCH_REGISTRY = Registry('arch') -MODEL_REGISTRY = Registry('model') -LOSS_REGISTRY = Registry('loss') -METRIC_REGISTRY = Registry('metric') diff --git a/facexlib/__init__.py b/facexlib/__init__.py deleted file mode 100644 index 39181bf..0000000 --- a/facexlib/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# flake8: noqa -from .alignment import * -from .detection import * -from .recognition import * -from .tracking import * -from .utils import * -from .visualization import * diff --git a/facexlib/__pycache__/__init__.cpython-310.pyc b/facexlib/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index bbcf88f44a61512acd6e4838ce63aeb76dfdcacc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 271 zcmd1j<>g`kf{?RGX={P>V-N=!FabFZKwNABBvKes7;_jxAT%SCW`fepP?`lwvjSMqBs+CGSl;NQ}arqI8#zfQj<$E^Yfy(i&B&G)AKUH9FCHr z#N_PEy!2bFr6rj;#ZkOvnZ>0*eN~Ad`IQVsEI<>%#4kM;s~F$PfTH{?ph+=qiOH!! zsl_GvMX5zGX+TazPG(X}e0(O*;M1& diff --git a/facexlib/alignment/README.md b/facexlib/alignment/README.md deleted file mode 100644 index 754c03e..0000000 --- a/facexlib/alignment/README.md +++ /dev/null @@ -1,20 +0,0 @@ - -## Landmarks - -- 5 landmarks - -

- -

- -- 68 landmarks - -

- -

- -- 98 landmarks - -

- -

diff --git a/facexlib/alignment/__init__.py b/facexlib/alignment/__init__.py deleted file mode 100644 index e989ca7..0000000 --- a/facexlib/alignment/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -import torch - -from facexlib.utils import load_file_from_url -from .awing_arch import FAN -from .convert_98_to_68_landmarks import landmark_98_to_68 - -__all__ = ['FAN', 'landmark_98_to_68'] - - -def init_alignment_model(model_name, half=False, device='cuda', model_rootpath=None): - if model_name == 'awing_fan': - model = FAN(num_modules=4, num_landmarks=98, device=device) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/alignment_WFLW_4HG.pth' - else: - raise NotImplementedError(f'{model_name} is not implemented.') - - model_path = load_file_from_url( - url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) - model.load_state_dict(torch.load(model_path)['state_dict'], strict=True) - model.eval() - model = model.to(device) - return model diff --git a/facexlib/alignment/__pycache__/__init__.cpython-310.pyc b/facexlib/alignment/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 2d092ecbdca1c9cdab25f24ea96111c01d53c120..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1006 zcmYjQOK%e~5MFy9*`#TBv>@QrYc5HV5GoZyJOfA&A&OMVie$NKC)w7!j%+7=XnO&! z{RIfAJ@QZBFYJ|5{{j~nZ%JB9o*8>=&o|$U)$($~gYom%&T#B|-fy?KI$+Ox3e!9X z!99+oN1VnIWtd>(ai0g$&jJ!ub|}LvA`#dTuZ=LN@g{HZrLTy@9Bp~+<#Tw)>$g#P zQ!2*kFqMK1waVyJ%X0)iaQVGw?!*nra-K20M;~oatLVcG9l!w{g6Ne&GUY6B8}m^E z!_5~k%`S)n@a|#VvVfQS1?Itte;n}eILvDn72Y>M`5k{jpOM8y$ZI_2Ug7ilNq8I; z!LDzE)3Aun5O3V|z_)Y)`4+)xbmmpMR^T&q925cM)$Efx+fVZm9kTq~p*r6I(e}ww zKFw&R_*4o*mfd2WmMJ4HW@#dhw#L>@%x1ScO6_>M(@9j;JxKH1GSwZjL>$O;r>lh& z%m~xvYCm`6b!Y3H`gt}YKO!J@<|;)3Q2jYNj%YNq=hkMy(&=7GC^X)W}*5#%e!T0l}^TG;P%Tv%$O{(RqYySTtKP924g0Ny6zrm zU2JQm?1b4d39I!SxXa=HMd$tD=@q#2XXEAO;QiqTt#(CX2R&f(qc9Ly=)s(d!Ggg7 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calculate_points(heatmaps): - # change heatmaps to landmarks - B, N, H, W = heatmaps.shape - HW = H * W - BN_range = np.arange(B * N) - - heatline = heatmaps.reshape(B, N, HW) - indexes = np.argmax(heatline, axis=2) - - preds = np.stack((indexes % W, indexes // W), axis=2) - preds = preds.astype(np.float, copy=False) - - inr = indexes.ravel() - - heatline = heatline.reshape(B * N, HW) - x_up = heatline[BN_range, inr + 1] - x_down = heatline[BN_range, inr - 1] - # y_up = heatline[BN_range, inr + W] - - if any((inr + W) >= 4096): - y_up = heatline[BN_range, 4095] - else: - y_up = heatline[BN_range, inr + W] - if any((inr - W) <= 0): - y_down = heatline[BN_range, 0] - else: - y_down = heatline[BN_range, inr - W] - - think_diff = np.sign(np.stack((x_up - x_down, y_up - y_down), axis=1)) - think_diff *= .25 - - preds += think_diff.reshape(B, N, 2) - preds += .5 - return preds - - -class AddCoordsTh(nn.Module): - - def __init__(self, x_dim=64, y_dim=64, with_r=False, with_boundary=False): - super(AddCoordsTh, self).__init__() - self.x_dim = x_dim - self.y_dim = y_dim - self.with_r = with_r - self.with_boundary = with_boundary - - def forward(self, input_tensor, heatmap=None): - """ - input_tensor: (batch, c, x_dim, y_dim) - """ - batch_size_tensor = input_tensor.shape[0] - - xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32, device=input_tensor.device) - xx_ones = xx_ones.unsqueeze(-1) - - xx_range = torch.arange(self.x_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0) - xx_range = xx_range.unsqueeze(1) - - xx_channel = torch.matmul(xx_ones.float(), xx_range.float()) - xx_channel = xx_channel.unsqueeze(-1) - - yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32, device=input_tensor.device) - yy_ones = yy_ones.unsqueeze(1) - - yy_range = torch.arange(self.y_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0) - yy_range = yy_range.unsqueeze(-1) - - yy_channel = torch.matmul(yy_range.float(), yy_ones.float()) - yy_channel = yy_channel.unsqueeze(-1) - - xx_channel = xx_channel.permute(0, 3, 2, 1) - yy_channel = yy_channel.permute(0, 3, 2, 1) - - xx_channel = xx_channel / (self.x_dim - 1) - yy_channel = yy_channel / (self.y_dim - 1) - - xx_channel = xx_channel * 2 - 1 - yy_channel = yy_channel * 2 - 1 - - xx_channel = xx_channel.repeat(batch_size_tensor, 1, 1, 1) - yy_channel = yy_channel.repeat(batch_size_tensor, 1, 1, 1) - - if self.with_boundary and heatmap is not None: - boundary_channel = torch.clamp(heatmap[:, -1:, :, :], 0.0, 1.0) - - zero_tensor = torch.zeros_like(xx_channel) - xx_boundary_channel = torch.where(boundary_channel > 0.05, xx_channel, zero_tensor) - yy_boundary_channel = torch.where(boundary_channel > 0.05, yy_channel, zero_tensor) - if self.with_boundary and heatmap is not None: - xx_boundary_channel = xx_boundary_channel.to(input_tensor.device) - yy_boundary_channel = yy_boundary_channel.to(input_tensor.device) - ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1) - - if self.with_r: - rr = torch.sqrt(torch.pow(xx_channel, 2) + torch.pow(yy_channel, 2)) - rr = rr / torch.max(rr) - ret = torch.cat([ret, rr], dim=1) - - if self.with_boundary and heatmap is not None: - ret = torch.cat([ret, xx_boundary_channel, yy_boundary_channel], dim=1) - return ret - - -class CoordConvTh(nn.Module): - """CoordConv layer as in the paper.""" - - def __init__(self, x_dim, y_dim, with_r, with_boundary, in_channels, first_one=False, *args, **kwargs): - super(CoordConvTh, self).__init__() - self.addcoords = AddCoordsTh(x_dim=x_dim, y_dim=y_dim, with_r=with_r, with_boundary=with_boundary) - in_channels += 2 - if with_r: - in_channels += 1 - if with_boundary and not first_one: - in_channels += 2 - self.conv = nn.Conv2d(in_channels=in_channels, *args, **kwargs) - - def forward(self, input_tensor, heatmap=None): - ret = self.addcoords(input_tensor, heatmap) - last_channel = ret[:, -2:, :, :] - ret = self.conv(ret) - return ret, last_channel - - -def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False, dilation=1): - '3x3 convolution with padding' - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd, padding=padding, bias=bias, dilation=dilation) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - # self.bn1 = nn.BatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - # self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.relu(out) - - out = self.conv2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class ConvBlock(nn.Module): - - def __init__(self, in_planes, out_planes): - super(ConvBlock, self).__init__() - self.bn1 = nn.BatchNorm2d(in_planes) - self.conv1 = conv3x3(in_planes, int(out_planes / 2)) - self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) - self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4), padding=1, dilation=1) - self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) - self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4), padding=1, dilation=1) - - if in_planes != out_planes: - self.downsample = nn.Sequential( - nn.BatchNorm2d(in_planes), - nn.ReLU(True), - nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False), - ) - else: - self.downsample = None - - def forward(self, x): - residual = x - - out1 = self.bn1(x) - out1 = F.relu(out1, True) - out1 = self.conv1(out1) - - out2 = self.bn2(out1) - out2 = F.relu(out2, True) - out2 = self.conv2(out2) - - out3 = self.bn3(out2) - out3 = F.relu(out3, True) - out3 = self.conv3(out3) - - out3 = torch.cat((out1, out2, out3), 1) - - if self.downsample is not None: - residual = self.downsample(residual) - - out3 += residual - - return out3 - - -class HourGlass(nn.Module): - - def __init__(self, num_modules, depth, num_features, first_one=False): - super(HourGlass, self).__init__() - self.num_modules = num_modules - self.depth = depth - self.features = num_features - self.coordconv = CoordConvTh( - x_dim=64, - y_dim=64, - with_r=True, - with_boundary=True, - in_channels=256, - first_one=first_one, - out_channels=256, - kernel_size=1, - stride=1, - padding=0) - self._generate_network(self.depth) - - def _generate_network(self, level): - self.add_module('b1_' + str(level), ConvBlock(256, 256)) - - self.add_module('b2_' + str(level), ConvBlock(256, 256)) - - if level > 1: - self._generate_network(level - 1) - else: - self.add_module('b2_plus_' + str(level), ConvBlock(256, 256)) - - self.add_module('b3_' + str(level), ConvBlock(256, 256)) - - def _forward(self, level, inp): - # Upper branch - up1 = inp - up1 = self._modules['b1_' + str(level)](up1) - - # Lower branch - low1 = F.avg_pool2d(inp, 2, stride=2) - low1 = self._modules['b2_' + str(level)](low1) - - if level > 1: - low2 = self._forward(level - 1, low1) - else: - low2 = low1 - low2 = self._modules['b2_plus_' + str(level)](low2) - - low3 = low2 - low3 = self._modules['b3_' + str(level)](low3) - - up2 = F.interpolate(low3, scale_factor=2, mode='nearest') - - return up1 + up2 - - def forward(self, x, heatmap): - x, last_channel = self.coordconv(x, heatmap) - return self._forward(self.depth, x), last_channel - - -class FAN(nn.Module): - - def __init__(self, num_modules=1, end_relu=False, gray_scale=False, num_landmarks=68, device='cuda'): - super(FAN, self).__init__() - self.device = device - self.num_modules = num_modules - self.gray_scale = gray_scale - self.end_relu = end_relu - self.num_landmarks = num_landmarks - - # Base part - if self.gray_scale: - self.conv1 = CoordConvTh( - x_dim=256, - y_dim=256, - with_r=True, - with_boundary=False, - in_channels=3, - out_channels=64, - kernel_size=7, - stride=2, - padding=3) - else: - self.conv1 = CoordConvTh( - x_dim=256, - y_dim=256, - with_r=True, - with_boundary=False, - in_channels=3, - out_channels=64, - kernel_size=7, - stride=2, - padding=3) - self.bn1 = nn.BatchNorm2d(64) - self.conv2 = ConvBlock(64, 128) - self.conv3 = ConvBlock(128, 128) - self.conv4 = ConvBlock(128, 256) - - # Stacking part - for hg_module in range(self.num_modules): - if hg_module == 0: - first_one = True - else: - first_one = False - self.add_module('m' + str(hg_module), HourGlass(1, 4, 256, first_one)) - self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) - self.add_module('conv_last' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) - self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) - self.add_module('l' + str(hg_module), nn.Conv2d(256, num_landmarks + 1, kernel_size=1, stride=1, padding=0)) - - if hg_module < self.num_modules - 1: - self.add_module('bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) - self.add_module('al' + str(hg_module), - nn.Conv2d(num_landmarks + 1, 256, kernel_size=1, stride=1, padding=0)) - - def forward(self, x): - x, _ = self.conv1(x) - x = F.relu(self.bn1(x), True) - # x = F.relu(self.bn1(self.conv1(x)), True) - x = F.avg_pool2d(self.conv2(x), 2, stride=2) - x = self.conv3(x) - x = self.conv4(x) - - previous = x - - outputs = [] - boundary_channels = [] - tmp_out = None - for i in range(self.num_modules): - hg, boundary_channel = self._modules['m' + str(i)](previous, tmp_out) - - ll = hg - ll = self._modules['top_m_' + str(i)](ll) - - ll = F.relu(self._modules['bn_end' + str(i)](self._modules['conv_last' + str(i)](ll)), True) - - # Predict heatmaps - tmp_out = self._modules['l' + str(i)](ll) - if self.end_relu: - tmp_out = F.relu(tmp_out) # HACK: Added relu - outputs.append(tmp_out) - boundary_channels.append(boundary_channel) - - if i < self.num_modules - 1: - ll = self._modules['bl' + str(i)](ll) - tmp_out_ = self._modules['al' + str(i)](tmp_out) - previous = previous + ll + tmp_out_ - - return outputs, boundary_channels - - def get_landmarks(self, img): - H, W, _ = img.shape - offset = W / 64, H / 64, 0, 0 - - img = cv2.resize(img, (256, 256)) - inp = img[..., ::-1] - inp = torch.from_numpy(np.ascontiguousarray(inp.transpose((2, 0, 1)))).float() - inp = inp.to(self.device) - inp.div_(255.0).unsqueeze_(0) - - outputs, _ = self.forward(inp) - out = outputs[-1][:, :-1, :, :] - heatmaps = out.detach().cpu().numpy() - - pred = calculate_points(heatmaps).reshape(-1, 2) - - pred *= offset[:2] - pred += offset[-2:] - - return pred diff --git a/facexlib/alignment/convert_98_to_68_landmarks.py b/facexlib/alignment/convert_98_to_68_landmarks.py deleted file mode 100644 index 376f661..0000000 --- a/facexlib/alignment/convert_98_to_68_landmarks.py +++ /dev/null @@ -1,82 +0,0 @@ -import numpy as np - - -def load_txt_file(file_path): - """Load data or string from txt file.""" - - with open(file_path, 'r') as cfile: - content = cfile.readlines() - cfile.close() - content = [x.strip() for x in content] - num_lines = len(content) - return content, num_lines - - -def anno_parser(anno_path, num_pts, line_offset=0): - """Parse the annotation. - Args: - anno_path: path of anno file (suffix .txt) - num_pts: number of landmarks. - line_offset: first point starts, default: 0. - - Returns: - pts: num_pts x 2 (x, y) - """ - - data, _ = load_txt_file(anno_path) - n_points = num_pts - # read points coordinate. - pts = np.zeros((n_points, 2), dtype='float32') - for point_index in range(n_points): - try: - pts_list = data[point_index + line_offset].split(',') - pts[point_index, 0] = float(pts_list[0]) - pts[point_index, 1] = float(pts_list[1]) - except ValueError: - print(f'Error in loading points in {anno_path}') - return pts - - -def landmark_98_to_68(landmark_98): - """Transfer 98 landmark positions to 68 landmark positions. - Args: - landmark_98(numpy array): Polar coordinates of 98 landmarks, (98, 2) - Returns: - landmark_68(numpy array): Polar coordinates of 98 landmarks, (68, 2) - """ - - landmark_68 = np.zeros((68, 2), dtype='float32') - # cheek - for i in range(0, 33): - if i % 2 == 0: - landmark_68[int(i / 2), :] = landmark_98[i, :] - # nose - for i in range(51, 60): - landmark_68[i - 24, :] = landmark_98[i, :] - # mouth - for i in range(76, 96): - landmark_68[i - 28, :] = landmark_98[i, :] - # left eyebrow - landmark_68[17, :] = landmark_98[33, :] - landmark_68[18, :] = (landmark_98[34, :] + landmark_98[41, :]) / 2 - landmark_68[19, :] = (landmark_98[35, :] + landmark_98[40, :]) / 2 - landmark_68[20, :] = (landmark_98[36, :] + landmark_98[39, :]) / 2 - landmark_68[21, :] = (landmark_98[37, :] + landmark_98[38, :]) / 2 - # right eyebrow - landmark_68[22, :] = (landmark_98[42, :] + landmark_98[50, :]) / 2 - landmark_68[23, :] = (landmark_98[43, :] + landmark_98[49, :]) / 2 - landmark_68[24, :] = (landmark_98[44, :] + landmark_98[48, :]) / 2 - landmark_68[25, :] = (landmark_98[45, :] + landmark_98[47, :]) / 2 - landmark_68[26, :] = landmark_98[46, :] - # left eye - LUT_landmark_68_left_eye = [36, 37, 38, 39, 40, 41] - LUT_landmark_98_left_eye = [60, 61, 63, 64, 65, 67] - for idx, landmark_98_index in enumerate(LUT_landmark_98_left_eye): - landmark_68[LUT_landmark_68_left_eye[idx], :] = landmark_98[landmark_98_index, :] - # right eye - LUT_landmark_68_right_eye = [42, 43, 44, 45, 46, 47] - LUT_landmark_98_right_eye = [68, 69, 71, 72, 73, 75] - for idx, landmark_98_index in enumerate(LUT_landmark_98_right_eye): - landmark_68[LUT_landmark_68_right_eye[idx], :] = landmark_98[landmark_98_index, :] - - return landmark_68 diff --git a/facexlib/assessment/__init__.py b/facexlib/assessment/__init__.py deleted file mode 100644 index 8f6f3a4..0000000 --- a/facexlib/assessment/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -import torch - -from facexlib.utils import load_file_from_url -from .hyperiqa_net import HyperIQA - - -def init_assessment_model(model_name, half=False, device='cuda', model_rootpath=None): - if model_name == 'hypernet': - model = HyperIQA(16, 112, 224, 112, 56, 28, 14, 7) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/assessment_hyperIQA.pth' - else: - raise NotImplementedError(f'{model_name} is not implemented.') - - # load the pre-trained hypernet model - hypernet_model_path = load_file_from_url( - url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) - model.hypernet.load_state_dict((torch.load(hypernet_model_path, map_location=lambda storage, loc: storage))) - model = model.eval() - model = model.to(device) - return model diff --git a/facexlib/assessment/hyperiqa_net.py b/facexlib/assessment/hyperiqa_net.py deleted file mode 100644 index 216fbac..0000000 --- a/facexlib/assessment/hyperiqa_net.py +++ /dev/null @@ -1,298 +0,0 @@ -import torch as torch -import torch.nn as nn -from torch.nn import functional as F - - -class HyperIQA(nn.Module): - """ - Combine the hypernet and target network within a network. - """ - - def __init__(self, *args): - super(HyperIQA, self).__init__() - self.hypernet = HyperNet(*args) - - def forward(self, img): - net_params = self.hypernet(img) - # build the target network - target_net = TargetNet(net_params) - for param in target_net.parameters(): - param.requires_grad = False - # predict the face quality - pred = target_net(net_params['target_in_vec']) - return pred - - -class HyperNet(nn.Module): - """ - Hyper network for learning perceptual rules. - Args: - lda_out_channels: local distortion aware module output size. - hyper_in_channels: input feature channels for hyper network. - target_in_size: input vector size for target network. - target_fc(i)_size: fully connection layer size of target network. - feature_size: input feature map width/height for hyper network. - Note: - For size match, input args must satisfy: 'target_fc(i)_size * target_fc(i+1)_size' is divisible by 'feature_size ^ 2'. # noqa E501 - """ - - def __init__(self, lda_out_channels, hyper_in_channels, target_in_size, target_fc1_size, target_fc2_size, - target_fc3_size, target_fc4_size, feature_size): - super(HyperNet, self).__init__() - - self.hyperInChn = hyper_in_channels - self.target_in_size = target_in_size - self.f1 = target_fc1_size - self.f2 = target_fc2_size - self.f3 = target_fc3_size - self.f4 = target_fc4_size - self.feature_size = feature_size - - self.res = resnet50_backbone(lda_out_channels, target_in_size) - - self.pool = nn.AdaptiveAvgPool2d((1, 1)) - - # Conv layers for resnet output features - self.conv1 = nn.Sequential( - nn.Conv2d(2048, 1024, 1, padding=(0, 0)), nn.ReLU(inplace=True), nn.Conv2d(1024, 512, 1, padding=(0, 0)), - nn.ReLU(inplace=True), nn.Conv2d(512, self.hyperInChn, 1, padding=(0, 0)), nn.ReLU(inplace=True)) - - # Hyper network part, conv for generating target fc weights, fc for generating target fc biases - self.fc1w_conv = nn.Conv2d( - self.hyperInChn, int(self.target_in_size * self.f1 / feature_size**2), 3, padding=(1, 1)) - self.fc1b_fc = nn.Linear(self.hyperInChn, self.f1) - - self.fc2w_conv = nn.Conv2d(self.hyperInChn, int(self.f1 * self.f2 / feature_size**2), 3, padding=(1, 1)) - self.fc2b_fc = nn.Linear(self.hyperInChn, self.f2) - - self.fc3w_conv = nn.Conv2d(self.hyperInChn, int(self.f2 * self.f3 / feature_size**2), 3, padding=(1, 1)) - self.fc3b_fc = nn.Linear(self.hyperInChn, self.f3) - - self.fc4w_conv = nn.Conv2d(self.hyperInChn, int(self.f3 * self.f4 / feature_size**2), 3, padding=(1, 1)) - self.fc4b_fc = nn.Linear(self.hyperInChn, self.f4) - - self.fc5w_fc = nn.Linear(self.hyperInChn, self.f4) - self.fc5b_fc = nn.Linear(self.hyperInChn, 1) - - def forward(self, img): - feature_size = self.feature_size - - res_out = self.res(img) - - # input vector for target net - target_in_vec = res_out['target_in_vec'].view(-1, self.target_in_size, 1, 1) - - # input features for hyper net - hyper_in_feat = self.conv1(res_out['hyper_in_feat']).view(-1, self.hyperInChn, feature_size, feature_size) - - # generating target net weights & biases - target_fc1w = self.fc1w_conv(hyper_in_feat).view(-1, self.f1, self.target_in_size, 1, 1) - target_fc1b = self.fc1b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f1) - - target_fc2w = self.fc2w_conv(hyper_in_feat).view(-1, self.f2, self.f1, 1, 1) - target_fc2b = self.fc2b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f2) - - target_fc3w = self.fc3w_conv(hyper_in_feat).view(-1, self.f3, self.f2, 1, 1) - target_fc3b = self.fc3b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f3) - - target_fc4w = self.fc4w_conv(hyper_in_feat).view(-1, self.f4, self.f3, 1, 1) - target_fc4b = self.fc4b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f4) - - target_fc5w = self.fc5w_fc(self.pool(hyper_in_feat).squeeze()).view(-1, 1, self.f4, 1, 1) - target_fc5b = self.fc5b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, 1) - - out = {} - out['target_in_vec'] = target_in_vec - out['target_fc1w'] = target_fc1w - out['target_fc1b'] = target_fc1b - out['target_fc2w'] = target_fc2w - out['target_fc2b'] = target_fc2b - out['target_fc3w'] = target_fc3w - out['target_fc3b'] = target_fc3b - out['target_fc4w'] = target_fc4w - out['target_fc4b'] = target_fc4b - out['target_fc5w'] = target_fc5w - out['target_fc5b'] = target_fc5b - - return out - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * 4) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class ResNetBackbone(nn.Module): - - def __init__(self, lda_out_channels, in_chn, block, layers, num_classes=1000): - super(ResNetBackbone, self).__init__() - self.inplanes = 64 - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer(block, 128, layers[1], stride=2) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - - # local distortion aware module - self.lda1_pool = nn.Sequential( - nn.Conv2d(256, 16, kernel_size=1, stride=1, padding=0, bias=False), - nn.AvgPool2d(7, stride=7), - ) - self.lda1_fc = nn.Linear(16 * 64, lda_out_channels) - - self.lda2_pool = nn.Sequential( - nn.Conv2d(512, 32, kernel_size=1, stride=1, padding=0, bias=False), - nn.AvgPool2d(7, stride=7), - ) - self.lda2_fc = nn.Linear(32 * 16, lda_out_channels) - - self.lda3_pool = nn.Sequential( - nn.Conv2d(1024, 64, kernel_size=1, stride=1, padding=0, bias=False), - nn.AvgPool2d(7, stride=7), - ) - self.lda3_fc = nn.Linear(64 * 4, lda_out_channels) - - self.lda4_pool = nn.AvgPool2d(7, stride=7) - self.lda4_fc = nn.Linear(2048, in_chn - lda_out_channels * 3) - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes)) - - return nn.Sequential(*layers) - - def forward(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.maxpool(x) - x = self.layer1(x) - - # the same effect as lda operation in the paper, but save much more memory - lda_1 = self.lda1_fc(self.lda1_pool(x).view(x.size(0), -1)) - x = self.layer2(x) - lda_2 = self.lda2_fc(self.lda2_pool(x).view(x.size(0), -1)) - x = self.layer3(x) - lda_3 = self.lda3_fc(self.lda3_pool(x).view(x.size(0), -1)) - x = self.layer4(x) - lda_4 = self.lda4_fc(self.lda4_pool(x).view(x.size(0), -1)) - - vec = torch.cat((lda_1, lda_2, lda_3, lda_4), 1) - - out = {} - out['hyper_in_feat'] = x - out['target_in_vec'] = vec - - return out - - -def resnet50_backbone(lda_out_channels, in_chn, **kwargs): - """Constructs a ResNet-50 model_hyper.""" - model = ResNetBackbone(lda_out_channels, in_chn, Bottleneck, [3, 4, 6, 3], **kwargs) - return model - - -class TargetNet(nn.Module): - """ - Target network for quality prediction. - """ - - def __init__(self, paras): - super(TargetNet, self).__init__() - self.l1 = nn.Sequential( - TargetFC(paras['target_fc1w'], paras['target_fc1b']), - nn.Sigmoid(), - ) - self.l2 = nn.Sequential( - TargetFC(paras['target_fc2w'], paras['target_fc2b']), - nn.Sigmoid(), - ) - - self.l3 = nn.Sequential( - TargetFC(paras['target_fc3w'], paras['target_fc3b']), - nn.Sigmoid(), - ) - - self.l4 = nn.Sequential( - TargetFC(paras['target_fc4w'], paras['target_fc4b']), - nn.Sigmoid(), - TargetFC(paras['target_fc5w'], paras['target_fc5b']), - ) - - def forward(self, x): - q = self.l1(x) - # q = F.dropout(q) - q = self.l2(q) - q = self.l3(q) - q = self.l4(q).squeeze() - return q - - -class TargetFC(nn.Module): - """ - Fully connection operations for target net - Note: - Weights & biases are different for different images in a batch, - thus here we use group convolution for calculating images in a batch with individual weights & biases. - """ - - def __init__(self, weight, bias): - super(TargetFC, self).__init__() - self.weight = weight - self.bias = bias - - def forward(self, input_): - - input_re = input_.view(-1, input_.shape[0] * input_.shape[1], input_.shape[2], input_.shape[3]) - weight_re = self.weight.view(self.weight.shape[0] * self.weight.shape[1], self.weight.shape[2], - self.weight.shape[3], self.weight.shape[4]) - bias_re = self.bias.view(self.bias.shape[0] * self.bias.shape[1]) - out = F.conv2d(input=input_re, weight=weight_re, bias=bias_re, groups=self.weight.shape[0]) - - return out.view(input_.shape[0], self.weight.shape[1], input_.shape[2], input_.shape[3]) diff --git a/facexlib/detection/__init__.py b/facexlib/detection/__init__.py deleted file mode 100644 index 4a77a18..0000000 --- a/facexlib/detection/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -import torch -from copy import deepcopy - -from facexlib.utils import load_file_from_url -from .retinaface import RetinaFace - - -def init_detection_model(model_name, half=False, device='cuda', model_rootpath=None): - if model_name == 'retinaface_resnet50': - model = RetinaFace(network_name='resnet50', half=half, device=device) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth' - elif model_name == 'retinaface_mobile0.25': - model = RetinaFace(network_name='mobile0.25', half=half, device=device) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth' - else: - raise NotImplementedError(f'{model_name} is not implemented.') - - model_path = load_file_from_url( - url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) - - # TODO: clean pretrained model - load_net = torch.load(model_path, map_location=lambda storage, loc: storage) - # remove unnecessary 'module.' - for k, v in deepcopy(load_net).items(): - if k.startswith('module.'): - load_net[k[7:]] = v - load_net.pop(k) - model.load_state_dict(load_net, strict=True) - model.eval() - model = model.to(device) - return model diff --git a/facexlib/detection/__pycache__/__init__.cpython-310.pyc b/facexlib/detection/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index a400a7041c8b9c40e663454ff92a8a6fa353921b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1400 zcmb_cPj4eN6t_KpGfmUd?XE!L&=WA1Nf&7)RH_PzrHBI&>~dig7){1bGwsZHu$^=p zO(euF$9@8Y)ExOH%UAG~Q@;Wic%C$E4ji#$``JJHz31P1&wP73K+xX)IU0ZIAoQ16 zY&HuPpFx*LFi=Esfnqb00+%Ev*vM94m3C}{Y*VLjN;h^v4yaE%Z*c5U{1Sy*w{RZ~ zLacqtSe^0u3go?lr!*Po1xv<)m&sHVw;0X=6ue+6uhP>rW2f5ArZmlLqvEa^uRZ9W zLzi!1Xu!7%B6AXF<=g0?*ry;U>;ze!VUTjQU7=H8vlY7X&l-C z-%(@Ht!roDD(l>CT-v4EZxHQWlZB_8#(Rx#+$DOBet0q@3xCmR{2Pz%EV1~u=`>#B z(_M;wCGVkQ)Oa_3<1aBrKYiNR=YWFtcdmiC#=Z%bsGYYOvcwC!v4N?*fqo>~w~yiN 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[65.53179932, 51.50139999], [48.02519989, 71.73660278], - [33.54930115, 92.3655014], [62.72990036, 92.20410156]] - -DEFAULT_CROP_SIZE = (96, 112) - - -class FaceWarpException(Exception): - - def __str__(self): - return 'In File {}:{}'.format(__file__, super.__str__(self)) - - -def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False): - """ - Function: - ---------- - get reference 5 key points according to crop settings: - 0. Set default crop_size: - if default_square: - crop_size = (112, 112) - else: - crop_size = (96, 112) - 1. Pad the crop_size by inner_padding_factor in each side; - 2. Resize crop_size into (output_size - outer_padding*2), - pad into output_size with outer_padding; - 3. Output reference_5point; - Parameters: - ---------- - @output_size: (w, h) or None - size of aligned face image - @inner_padding_factor: (w_factor, h_factor) - padding factor for inner (w, h) - @outer_padding: (w_pad, h_pad) - each row is a pair of coordinates (x, y) - @default_square: True or False - if True: - default crop_size = (112, 112) - else: - default crop_size = (96, 112); - !!! make sure, if output_size is not None: - (output_size - outer_padding) - = some_scale * (default crop_size * (1.0 + - inner_padding_factor)) - Returns: - ---------- - @reference_5point: 5x2 np.array - each row is a pair of transformed coordinates (x, y) - """ - - tmp_5pts = np.array(REFERENCE_FACIAL_POINTS) - tmp_crop_size = np.array(DEFAULT_CROP_SIZE) - - # 0) make the inner region a square - if default_square: - size_diff = max(tmp_crop_size) - tmp_crop_size - tmp_5pts += size_diff / 2 - tmp_crop_size += size_diff - - if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]): - - return tmp_5pts - - if (inner_padding_factor == 0 and outer_padding == (0, 0)): - if output_size is None: - return tmp_5pts - else: - raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size)) - - # check output size - if not (0 <= inner_padding_factor <= 1.0): - raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)') - - if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None): - output_size = tmp_crop_size * \ - (1 + inner_padding_factor * 2).astype(np.int32) - output_size += np.array(outer_padding) - if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]): - raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])') - - # 1) pad the inner region according inner_padding_factor - if inner_padding_factor > 0: - size_diff = tmp_crop_size * inner_padding_factor * 2 - tmp_5pts += size_diff / 2 - tmp_crop_size += np.round(size_diff).astype(np.int32) - - # 2) resize the padded inner region - size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2 - - if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]: - raise FaceWarpException('Must have (output_size - outer_padding)' - '= some_scale * (crop_size * (1.0 + inner_padding_factor)') - - scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0] - tmp_5pts = tmp_5pts * scale_factor - # size_diff = tmp_crop_size * (scale_factor - min(scale_factor)) - # tmp_5pts = tmp_5pts + size_diff / 2 - tmp_crop_size = size_bf_outer_pad - - # 3) add outer_padding to make output_size - reference_5point = tmp_5pts + np.array(outer_padding) - tmp_crop_size = output_size - - return reference_5point - - -def get_affine_transform_matrix(src_pts, dst_pts): - """ - Function: - ---------- - get affine transform matrix 'tfm' from src_pts to dst_pts - Parameters: - ---------- - @src_pts: Kx2 np.array - source points matrix, each row is a pair of coordinates (x, y) - @dst_pts: Kx2 np.array - destination points matrix, each row is a pair of coordinates (x, y) - Returns: - ---------- - @tfm: 2x3 np.array - transform matrix from src_pts to dst_pts - """ - - tfm = np.float32([[1, 0, 0], [0, 1, 0]]) - n_pts = src_pts.shape[0] - ones = np.ones((n_pts, 1), src_pts.dtype) - src_pts_ = np.hstack([src_pts, ones]) - dst_pts_ = np.hstack([dst_pts, ones]) - - A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_) - - if rank == 3: - tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]]) - elif rank == 2: - tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]]) - - return tfm - - -def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'): - """ - Function: - ---------- - apply affine transform 'trans' to uv - Parameters: - ---------- - @src_img: 3x3 np.array - input image - @facial_pts: could be - 1)a list of K coordinates (x,y) - or - 2) Kx2 or 2xK np.array - each row or col is a pair of coordinates (x, y) - @reference_pts: could be - 1) a list of K coordinates (x,y) - or - 2) Kx2 or 2xK np.array - each row or col is a pair of coordinates (x, y) - or - 3) None - if None, use default reference facial points - @crop_size: (w, h) - output face image size - @align_type: transform type, could be one of - 1) 'similarity': use similarity transform - 2) 'cv2_affine': use the first 3 points to do affine transform, - by calling cv2.getAffineTransform() - 3) 'affine': use all points to do affine transform - Returns: - ---------- - @face_img: output face image with size (w, h) = @crop_size - """ - - if reference_pts is None: - if crop_size[0] == 96 and crop_size[1] == 112: - reference_pts = REFERENCE_FACIAL_POINTS - else: - default_square = False - inner_padding_factor = 0 - outer_padding = (0, 0) - output_size = crop_size - - reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding, - default_square) - - ref_pts = np.float32(reference_pts) - ref_pts_shp = ref_pts.shape - if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2: - raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2') - - if ref_pts_shp[0] == 2: - ref_pts = ref_pts.T - - src_pts = np.float32(facial_pts) - src_pts_shp = src_pts.shape - if max(src_pts_shp) < 3 or min(src_pts_shp) != 2: - raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2') - - if src_pts_shp[0] == 2: - src_pts = src_pts.T - - if src_pts.shape != ref_pts.shape: - raise FaceWarpException('facial_pts and reference_pts must have the same shape') - - if align_type == 'cv2_affine': - tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3]) - elif align_type == 'affine': - tfm = get_affine_transform_matrix(src_pts, ref_pts) - else: - tfm = get_similarity_transform_for_cv2(src_pts, ref_pts) - - face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1])) - - return face_img diff --git a/facexlib/detection/matlab_cp2tform.py b/facexlib/detection/matlab_cp2tform.py deleted file mode 100644 index b2a8b54..0000000 --- a/facexlib/detection/matlab_cp2tform.py +++ /dev/null @@ -1,317 +0,0 @@ -import numpy as np -from numpy.linalg import inv, lstsq -from numpy.linalg import matrix_rank as rank -from numpy.linalg import norm - - -class MatlabCp2tormException(Exception): - - def __str__(self): - return 'In File {}:{}'.format(__file__, super.__str__(self)) - - -def tformfwd(trans, uv): - """ - Function: - ---------- - apply affine transform 'trans' to uv - - Parameters: - ---------- - @trans: 3x3 np.array - transform matrix - @uv: Kx2 np.array - each row is a pair of coordinates (x, y) - - Returns: - ---------- - @xy: Kx2 np.array - each row is a pair of transformed coordinates (x, y) - """ - uv = np.hstack((uv, np.ones((uv.shape[0], 1)))) - xy = np.dot(uv, trans) - xy = xy[:, 0:-1] - return xy - - -def tforminv(trans, uv): - """ - Function: - ---------- - apply the inverse of affine transform 'trans' to uv - - Parameters: - ---------- - @trans: 3x3 np.array - transform matrix - @uv: Kx2 np.array - each row is a pair of coordinates (x, y) - - Returns: - ---------- - @xy: Kx2 np.array - each row is a pair of inverse-transformed coordinates (x, y) - """ - Tinv = inv(trans) - xy = tformfwd(Tinv, uv) - return xy - - -def findNonreflectiveSimilarity(uv, xy, options=None): - options = {'K': 2} - - K = options['K'] - M = xy.shape[0] - x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector - y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector - - tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1)))) - tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1)))) - X = np.vstack((tmp1, tmp2)) - - u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector - v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector - U = np.vstack((u, v)) - - # We know that X * r = U - if rank(X) >= 2 * K: - r, _, _, _ = lstsq(X, U, rcond=-1) - r = np.squeeze(r) - else: - raise Exception('cp2tform:twoUniquePointsReq') - sc = r[0] - ss = r[1] - tx = r[2] - ty = r[3] - - Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]]) - T = inv(Tinv) - T[:, 2] = np.array([0, 0, 1]) - - return T, Tinv - - -def findSimilarity(uv, xy, options=None): - options = {'K': 2} - - # uv = np.array(uv) - # xy = np.array(xy) - - # Solve for trans1 - trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options) - - # Solve for trans2 - - # manually reflect the xy data across the Y-axis - xyR = xy - xyR[:, 0] = -1 * xyR[:, 0] - - trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options) - - # manually reflect the tform to undo the reflection done on xyR - TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - trans2 = np.dot(trans2r, TreflectY) - - # Figure out if trans1 or trans2 is better - xy1 = tformfwd(trans1, uv) - norm1 = norm(xy1 - xy) - - xy2 = tformfwd(trans2, uv) - norm2 = norm(xy2 - xy) - - if norm1 <= norm2: - return trans1, trans1_inv - else: - trans2_inv = inv(trans2) - return trans2, trans2_inv - - -def get_similarity_transform(src_pts, dst_pts, reflective=True): - """ - Function: - ---------- - Find Similarity Transform Matrix 'trans': - u = src_pts[:, 0] - v = src_pts[:, 1] - x = dst_pts[:, 0] - y = dst_pts[:, 1] - [x, y, 1] = [u, v, 1] * trans - - Parameters: - ---------- - @src_pts: Kx2 np.array - source points, each row is a pair of coordinates (x, y) - @dst_pts: Kx2 np.array - destination points, each row is a pair of transformed - coordinates (x, y) - @reflective: True or False - if True: - use reflective similarity transform - else: - use non-reflective similarity transform - - Returns: - ---------- - @trans: 3x3 np.array - transform matrix from uv to xy - trans_inv: 3x3 np.array - inverse of trans, transform matrix from xy to uv - """ - - if reflective: - trans, trans_inv = findSimilarity(src_pts, dst_pts) - else: - trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts) - - return trans, trans_inv - - -def cvt_tform_mat_for_cv2(trans): - """ - Function: - ---------- - Convert Transform Matrix 'trans' into 'cv2_trans' which could be - directly used by cv2.warpAffine(): - u = src_pts[:, 0] - v = src_pts[:, 1] - x = dst_pts[:, 0] - y = dst_pts[:, 1] - [x, y].T = cv_trans * [u, v, 1].T - - Parameters: - ---------- - @trans: 3x3 np.array - transform matrix from uv to xy - - Returns: - ---------- - @cv2_trans: 2x3 np.array - transform matrix from src_pts to dst_pts, could be directly used - for cv2.warpAffine() - """ - cv2_trans = trans[:, 0:2].T - - return cv2_trans - - -def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True): - """ - Function: - ---------- - Find Similarity Transform Matrix 'cv2_trans' which could be - directly used by cv2.warpAffine(): - u = src_pts[:, 0] - v = src_pts[:, 1] - x = dst_pts[:, 0] - y = dst_pts[:, 1] - [x, y].T = cv_trans * [u, v, 1].T - - Parameters: - ---------- - @src_pts: Kx2 np.array - source points, each row is a pair of coordinates (x, y) - @dst_pts: Kx2 np.array - destination points, each row is a pair of transformed - coordinates (x, y) - reflective: True or False - if True: - use reflective similarity transform - else: - use non-reflective similarity transform - - Returns: - ---------- - @cv2_trans: 2x3 np.array - transform matrix from src_pts to dst_pts, could be directly used - for cv2.warpAffine() - """ - trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective) - cv2_trans = cvt_tform_mat_for_cv2(trans) - - return cv2_trans - - -if __name__ == '__main__': - """ - u = [0, 6, -2] - v = [0, 3, 5] - x = [-1, 0, 4] - y = [-1, -10, 4] - - # In Matlab, run: - # - # uv = [u'; v']; - # xy = [x'; y']; - # tform_sim=cp2tform(uv,xy,'similarity'); - # - # trans = tform_sim.tdata.T - # ans = - # -0.0764 -1.6190 0 - # 1.6190 -0.0764 0 - # -3.2156 0.0290 1.0000 - # trans_inv = tform_sim.tdata.Tinv - # ans = - # - # -0.0291 0.6163 0 - # -0.6163 -0.0291 0 - # -0.0756 1.9826 1.0000 - # xy_m=tformfwd(tform_sim, u,v) - # - # xy_m = - # - # -3.2156 0.0290 - # 1.1833 -9.9143 - # 5.0323 2.8853 - # uv_m=tforminv(tform_sim, x,y) - # - # uv_m = - # - # 0.5698 1.3953 - # 6.0872 2.2733 - # -2.6570 4.3314 - """ - u = [0, 6, -2] - v = [0, 3, 5] - x = [-1, 0, 4] - y = [-1, -10, 4] - - uv = np.array((u, v)).T - xy = np.array((x, y)).T - - print('\n--->uv:') - print(uv) - print('\n--->xy:') - print(xy) - - trans, trans_inv = get_similarity_transform(uv, xy) - - print('\n--->trans matrix:') - print(trans) - - print('\n--->trans_inv matrix:') - print(trans_inv) - - print('\n---> apply transform to uv') - print('\nxy_m = uv_augmented * trans') - uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1)))) - xy_m = np.dot(uv_aug, trans) - print(xy_m) - - print('\nxy_m = tformfwd(trans, uv)') - xy_m = tformfwd(trans, uv) - print(xy_m) - - print('\n---> apply inverse transform to xy') - print('\nuv_m = xy_augmented * trans_inv') - xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1)))) - uv_m = np.dot(xy_aug, trans_inv) - print(uv_m) - - print('\nuv_m = tformfwd(trans_inv, xy)') - uv_m = tformfwd(trans_inv, xy) - print(uv_m) - - uv_m = tforminv(trans, xy) - print('\nuv_m = tforminv(trans, xy)') - print(uv_m) diff --git a/facexlib/detection/retinaface.py b/facexlib/detection/retinaface.py deleted file mode 100644 index 127046f..0000000 --- a/facexlib/detection/retinaface.py +++ /dev/null @@ -1,366 +0,0 @@ -import cv2 -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from PIL import Image -from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter - -from facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face -from facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head -from facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm, - py_cpu_nms) - - -def generate_config(network_name): - - cfg_mnet = { - 'name': 'mobilenet0.25', - 'min_sizes': [[16, 32], [64, 128], [256, 512]], - 'steps': [8, 16, 32], - 'variance': [0.1, 0.2], - 'clip': False, - 'loc_weight': 2.0, - 'gpu_train': True, - 'batch_size': 32, - 'ngpu': 1, - 'epoch': 250, - 'decay1': 190, - 'decay2': 220, - 'image_size': 640, - 'return_layers': { - 'stage1': 1, - 'stage2': 2, - 'stage3': 3 - }, - 'in_channel': 32, - 'out_channel': 64 - } - - cfg_re50 = { - 'name': 'Resnet50', - 'min_sizes': [[16, 32], [64, 128], [256, 512]], - 'steps': [8, 16, 32], - 'variance': [0.1, 0.2], - 'clip': False, - 'loc_weight': 2.0, - 'gpu_train': True, - 'batch_size': 24, - 'ngpu': 4, - 'epoch': 100, - 'decay1': 70, - 'decay2': 90, - 'image_size': 840, - 'return_layers': { - 'layer2': 1, - 'layer3': 2, - 'layer4': 3 - }, - 'in_channel': 256, - 'out_channel': 256 - } - - if network_name == 'mobile0.25': - return cfg_mnet - elif network_name == 'resnet50': - return cfg_re50 - else: - raise NotImplementedError(f'network_name={network_name}') - - -class RetinaFace(nn.Module): - - def __init__(self, network_name='resnet50', half=False, phase='test', device=None): - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device - - super(RetinaFace, self).__init__() - self.half_inference = half - cfg = generate_config(network_name) - self.backbone = cfg['name'] - - self.model_name = f'retinaface_{network_name}' - self.cfg = cfg - self.phase = phase - self.target_size, self.max_size = 1600, 2150 - self.resize, self.scale, self.scale1 = 1., None, None - self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]], device=self.device) - self.reference = get_reference_facial_points(default_square=True) - # Build network. - backbone = None - if cfg['name'] == 'mobilenet0.25': - backbone = MobileNetV1() - self.body = IntermediateLayerGetter(backbone, cfg['return_layers']) - elif cfg['name'] == 'Resnet50': - import torchvision.models as models - backbone = models.resnet50(weights=None) - self.body = IntermediateLayerGetter(backbone, cfg['return_layers']) - - in_channels_stage2 = cfg['in_channel'] - in_channels_list = [ - in_channels_stage2 * 2, - in_channels_stage2 * 4, - in_channels_stage2 * 8, - ] - - out_channels = cfg['out_channel'] - self.fpn = FPN(in_channels_list, out_channels) - self.ssh1 = SSH(out_channels, out_channels) - self.ssh2 = SSH(out_channels, out_channels) - self.ssh3 = SSH(out_channels, out_channels) - - self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel']) - self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel']) - self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel']) - - self.to(self.device) - self.eval() - if self.half_inference: - self.half() - - def forward(self, inputs): - out = self.body(inputs) - - if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50': - out = list(out.values()) - # FPN - fpn = self.fpn(out) - - # SSH - feature1 = self.ssh1(fpn[0]) - feature2 = self.ssh2(fpn[1]) - feature3 = self.ssh3(fpn[2]) - features = [feature1, feature2, feature3] - - bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1) - classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1) - tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)] - ldm_regressions = (torch.cat(tmp, dim=1)) - - if self.phase == 'train': - output = (bbox_regressions, classifications, ldm_regressions) - else: - output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions) - return output - - def __detect_faces(self, inputs): - # get scale - height, width = inputs.shape[2:] - self.scale = torch.tensor([width, height, width, height], dtype=torch.float32, device=self.device) - tmp = [width, height, width, height, width, height, width, height, width, height] - self.scale1 = torch.tensor(tmp, dtype=torch.float32, device=self.device) - - # forawrd - inputs = inputs.to(self.device) - if self.half_inference: - inputs = inputs.half() - loc, conf, landmarks = self(inputs) - - # get priorbox - priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:]) - priors = priorbox.forward().to(self.device) - - return loc, conf, landmarks, priors - - # single image detection - def transform(self, image, use_origin_size): - # convert to opencv format - if isinstance(image, Image.Image): - image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) - image = image.astype(np.float32) - - # testing scale - im_size_min = np.min(image.shape[0:2]) - im_size_max = np.max(image.shape[0:2]) - resize = float(self.target_size) / float(im_size_min) - - # prevent bigger axis from being more than max_size - if np.round(resize * im_size_max) > self.max_size: - resize = float(self.max_size) / float(im_size_max) - resize = 1 if use_origin_size else resize - - # resize - if resize != 1: - image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) - - # convert to torch.tensor format - # image -= (104, 117, 123) - image = image.transpose(2, 0, 1) - image = torch.from_numpy(image).unsqueeze(0) - - return image, resize - - def detect_faces( - self, - image, - conf_threshold=0.8, - nms_threshold=0.4, - use_origin_size=True, - ): - image, self.resize = self.transform(image, use_origin_size) - image = image.to(self.device) - if self.half_inference: - image = image.half() - image = image - self.mean_tensor - - loc, conf, landmarks, priors = self.__detect_faces(image) - - boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance']) - boxes = boxes * self.scale / self.resize - boxes = boxes.cpu().numpy() - - scores = conf.squeeze(0).data.cpu().numpy()[:, 1] - - landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance']) - landmarks = landmarks * self.scale1 / self.resize - landmarks = landmarks.cpu().numpy() - - # ignore low scores - inds = np.where(scores > conf_threshold)[0] - boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds] - - # sort - order = scores.argsort()[::-1] - boxes, landmarks, scores = boxes[order], landmarks[order], scores[order] - - # do NMS - bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) - keep = py_cpu_nms(bounding_boxes, nms_threshold) - bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep] - # self.t['forward_pass'].toc() - # print(self.t['forward_pass'].average_time) - # import sys - # sys.stdout.flush() - return np.concatenate((bounding_boxes, landmarks), axis=1) - - def __align_multi(self, image, boxes, landmarks, limit=None): - - if len(boxes) < 1: - return [], [] - - if limit: - boxes = boxes[:limit] - landmarks = landmarks[:limit] - - faces = [] - for landmark in landmarks: - facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)] - - warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112)) - faces.append(warped_face) - - return np.concatenate((boxes, landmarks), axis=1), faces - - def align_multi(self, img, conf_threshold=0.8, limit=None): - - rlt = self.detect_faces(img, conf_threshold=conf_threshold) - boxes, landmarks = rlt[:, 0:5], rlt[:, 5:] - - return self.__align_multi(img, boxes, landmarks, limit) - - # batched detection - def batched_transform(self, frames, use_origin_size): - """ - Arguments: - frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c], - type=np.float32, BGR format). - use_origin_size: whether to use origin size. - """ - from_PIL = True if isinstance(frames[0], Image.Image) else False - - # convert to opencv format - if from_PIL: - frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames] - frames = np.asarray(frames, dtype=np.float32) - - # testing scale - im_size_min = np.min(frames[0].shape[0:2]) - im_size_max = np.max(frames[0].shape[0:2]) - resize = float(self.target_size) / float(im_size_min) - - # prevent bigger axis from being more than max_size - if np.round(resize * im_size_max) > self.max_size: - resize = float(self.max_size) / float(im_size_max) - resize = 1 if use_origin_size else resize - - # resize - if resize != 1: - if not from_PIL: - frames = F.interpolate(frames, scale_factor=resize) - else: - frames = [ - cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) - for frame in frames - ] - - # convert to torch.tensor format - if not from_PIL: - frames = frames.transpose(1, 2).transpose(1, 3).contiguous() - else: - frames = frames.transpose((0, 3, 1, 2)) - frames = torch.from_numpy(frames) - - return frames, resize - - def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True): - """ - Arguments: - frames: a list of PIL.Image, or np.array(shape=[n, h, w, c], - type=np.uint8, BGR format). - conf_threshold: confidence threshold. - nms_threshold: nms threshold. - use_origin_size: whether to use origin size. - Returns: - final_bounding_boxes: list of np.array ([n_boxes, 5], - type=np.float32). - final_landmarks: list of np.array ([n_boxes, 10], type=np.float32). - """ - # self.t['forward_pass'].tic() - frames, self.resize = self.batched_transform(frames, use_origin_size) - frames = frames.to(self.device) - frames = frames - self.mean_tensor - - b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames) - - final_bounding_boxes, final_landmarks = [], [] - - # decode - priors = priors.unsqueeze(0) - b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize - b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize - b_conf = b_conf[:, :, 1] - - # index for selection - b_indice = b_conf > conf_threshold - - # concat - b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float() - - for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice): - - # ignore low scores - pred, landm = pred[inds, :], landm[inds, :] - if pred.shape[0] == 0: - final_bounding_boxes.append(np.array([], dtype=np.float32)) - final_landmarks.append(np.array([], dtype=np.float32)) - continue - - # sort - # order = score.argsort(descending=True) - # box, landm, score = box[order], landm[order], score[order] - - # to CPU - bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy() - - # NMS - keep = py_cpu_nms(bounding_boxes, nms_threshold) - bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep] - - # append - final_bounding_boxes.append(bounding_boxes) - final_landmarks.append(landmarks) - # self.t['forward_pass'].toc(average=True) - # self.batch_time += self.t['forward_pass'].diff - # self.total_frame += len(frames) - # print(self.batch_time / self.total_frame) - - return final_bounding_boxes, final_landmarks diff --git a/facexlib/detection/retinaface_net.py b/facexlib/detection/retinaface_net.py deleted file mode 100644 index ab6aa82..0000000 --- a/facexlib/detection/retinaface_net.py +++ /dev/null @@ -1,196 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def conv_bn(inp, oup, stride=1, leaky=0): - return nn.Sequential( - nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), - nn.LeakyReLU(negative_slope=leaky, inplace=True)) - - -def conv_bn_no_relu(inp, oup, stride): - return nn.Sequential( - nn.Conv2d(inp, oup, 3, stride, 1, bias=False), - nn.BatchNorm2d(oup), - ) - - -def conv_bn1X1(inp, oup, stride, leaky=0): - return nn.Sequential( - nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup), - nn.LeakyReLU(negative_slope=leaky, inplace=True)) - - -def conv_dw(inp, oup, stride, leaky=0.1): - return nn.Sequential( - nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), - nn.BatchNorm2d(inp), - nn.LeakyReLU(negative_slope=leaky, inplace=True), - nn.Conv2d(inp, oup, 1, 1, 0, bias=False), - nn.BatchNorm2d(oup), - nn.LeakyReLU(negative_slope=leaky, inplace=True), - ) - - -class SSH(nn.Module): - - def __init__(self, in_channel, out_channel): - super(SSH, self).__init__() - assert out_channel % 4 == 0 - leaky = 0 - if (out_channel <= 64): - leaky = 0.1 - self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1) - - self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky) - self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) - - self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky) - self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) - - def forward(self, input): - conv3X3 = self.conv3X3(input) - - conv5X5_1 = self.conv5X5_1(input) - conv5X5 = self.conv5X5_2(conv5X5_1) - - conv7X7_2 = self.conv7X7_2(conv5X5_1) - conv7X7 = self.conv7x7_3(conv7X7_2) - - out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1) - out = F.relu(out) - return out - - -class FPN(nn.Module): - - def __init__(self, in_channels_list, out_channels): - super(FPN, self).__init__() - leaky = 0 - if (out_channels <= 64): - leaky = 0.1 - self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky) - self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky) - self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky) - - self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky) - self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky) - - def forward(self, input): - # names = list(input.keys()) - # input = list(input.values()) - - output1 = self.output1(input[0]) - output2 = self.output2(input[1]) - output3 = self.output3(input[2]) - - up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest') - output2 = output2 + up3 - output2 = self.merge2(output2) - - up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest') - output1 = output1 + up2 - output1 = self.merge1(output1) - - out = [output1, output2, output3] - return out - - -class MobileNetV1(nn.Module): - - def __init__(self): - super(MobileNetV1, self).__init__() - self.stage1 = nn.Sequential( - conv_bn(3, 8, 2, leaky=0.1), # 3 - conv_dw(8, 16, 1), # 7 - conv_dw(16, 32, 2), # 11 - conv_dw(32, 32, 1), # 19 - conv_dw(32, 64, 2), # 27 - conv_dw(64, 64, 1), # 43 - ) - self.stage2 = nn.Sequential( - conv_dw(64, 128, 2), # 43 + 16 = 59 - conv_dw(128, 128, 1), # 59 + 32 = 91 - conv_dw(128, 128, 1), # 91 + 32 = 123 - conv_dw(128, 128, 1), # 123 + 32 = 155 - conv_dw(128, 128, 1), # 155 + 32 = 187 - conv_dw(128, 128, 1), # 187 + 32 = 219 - ) - self.stage3 = nn.Sequential( - conv_dw(128, 256, 2), # 219 +3 2 = 241 - conv_dw(256, 256, 1), # 241 + 64 = 301 - ) - self.avg = nn.AdaptiveAvgPool2d((1, 1)) - self.fc = nn.Linear(256, 1000) - - def forward(self, x): - x = self.stage1(x) - x = self.stage2(x) - x = self.stage3(x) - x = self.avg(x) - # x = self.model(x) - x = x.view(-1, 256) - x = self.fc(x) - return x - - -class ClassHead(nn.Module): - - def __init__(self, inchannels=512, num_anchors=3): - super(ClassHead, self).__init__() - self.num_anchors = num_anchors - self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0) - - def forward(self, x): - out = self.conv1x1(x) - out = out.permute(0, 2, 3, 1).contiguous() - - return out.view(out.shape[0], -1, 2) - - -class BboxHead(nn.Module): - - def __init__(self, inchannels=512, num_anchors=3): - super(BboxHead, self).__init__() - self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0) - - def forward(self, x): - out = self.conv1x1(x) - out = out.permute(0, 2, 3, 1).contiguous() - - return out.view(out.shape[0], -1, 4) - - -class LandmarkHead(nn.Module): - - def __init__(self, inchannels=512, num_anchors=3): - super(LandmarkHead, self).__init__() - self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0) - - def forward(self, x): - out = self.conv1x1(x) - out = out.permute(0, 2, 3, 1).contiguous() - - return out.view(out.shape[0], -1, 10) - - -def make_class_head(fpn_num=3, inchannels=64, anchor_num=2): - classhead = nn.ModuleList() - for i in range(fpn_num): - classhead.append(ClassHead(inchannels, anchor_num)) - return classhead - - -def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2): - bboxhead = nn.ModuleList() - for i in range(fpn_num): - bboxhead.append(BboxHead(inchannels, anchor_num)) - return bboxhead - - -def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2): - landmarkhead = nn.ModuleList() - for i in range(fpn_num): - landmarkhead.append(LandmarkHead(inchannels, anchor_num)) - return landmarkhead diff --git a/facexlib/detection/retinaface_utils.py b/facexlib/detection/retinaface_utils.py deleted file mode 100644 index 8c35775..0000000 --- a/facexlib/detection/retinaface_utils.py +++ /dev/null @@ -1,421 +0,0 @@ -import numpy as np -import torch -import torchvision -from itertools import product as product -from math import ceil - - -class PriorBox(object): - - def __init__(self, cfg, image_size=None, phase='train'): - super(PriorBox, self).__init__() - self.min_sizes = cfg['min_sizes'] - self.steps = cfg['steps'] - self.clip = cfg['clip'] - self.image_size = image_size - self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps] - self.name = 's' - - def forward(self): - anchors = [] - for k, f in enumerate(self.feature_maps): - min_sizes = self.min_sizes[k] - for i, j in product(range(f[0]), range(f[1])): - for min_size in min_sizes: - s_kx = min_size / self.image_size[1] - s_ky = min_size / self.image_size[0] - dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]] - dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]] - for cy, cx in product(dense_cy, dense_cx): - anchors += [cx, cy, s_kx, s_ky] - - # back to torch land - output = torch.Tensor(anchors).view(-1, 4) - if self.clip: - output.clamp_(max=1, min=0) - return output - - -def py_cpu_nms(dets, thresh): - """Pure Python NMS baseline.""" - keep = torchvision.ops.nms( - boxes=torch.Tensor(dets[:, :4]), - scores=torch.Tensor(dets[:, 4]), - iou_threshold=thresh, - ) - - return list(keep) - - -def point_form(boxes): - """ Convert prior_boxes to (xmin, ymin, xmax, ymax) - representation for comparison to point form ground truth data. - Args: - boxes: (tensor) center-size default boxes from priorbox layers. - Return: - boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. - """ - return torch.cat( - ( - boxes[:, :2] - boxes[:, 2:] / 2, # xmin, ymin - boxes[:, :2] + boxes[:, 2:] / 2), - 1) # xmax, ymax - - -def center_size(boxes): - """ Convert prior_boxes to (cx, cy, w, h) - representation for comparison to center-size form ground truth data. - Args: - boxes: (tensor) point_form boxes - Return: - boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. - """ - return torch.cat( - (boxes[:, 2:] + boxes[:, :2]) / 2, # cx, cy - boxes[:, 2:] - boxes[:, :2], - 1) # w, h - - -def intersect(box_a, box_b): - """ We resize both tensors to [A,B,2] without new malloc: - [A,2] -> [A,1,2] -> [A,B,2] - [B,2] -> [1,B,2] -> [A,B,2] - Then we compute the area of intersect between box_a and box_b. - Args: - box_a: (tensor) bounding boxes, Shape: [A,4]. - box_b: (tensor) bounding boxes, Shape: [B,4]. - Return: - (tensor) intersection area, Shape: [A,B]. - """ - A = box_a.size(0) - B = box_b.size(0) - max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) - min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2)) - inter = torch.clamp((max_xy - min_xy), min=0) - return inter[:, :, 0] * inter[:, :, 1] - - -def jaccard(box_a, box_b): - """Compute the jaccard overlap of two sets of boxes. The jaccard overlap - is simply the intersection over union of two boxes. Here we operate on - ground truth boxes and default boxes. - E.g.: - A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) - Args: - box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] - box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] - Return: - jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] - """ - inter = intersect(box_a, box_b) - area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] - area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] - union = area_a + area_b - inter - return inter / union # [A,B] - - -def matrix_iou(a, b): - """ - return iou of a and b, numpy version for data augenmentation - """ - lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) - rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) - - area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) - area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) - area_b = np.prod(b[:, 2:] - b[:, :2], axis=1) - return area_i / (area_a[:, np.newaxis] + area_b - area_i) - - -def matrix_iof(a, b): - """ - return iof of a and b, numpy version for data augenmentation - """ - lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) - rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) - - area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) - area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) - return area_i / np.maximum(area_a[:, np.newaxis], 1) - - -def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx): - """Match each prior box with the ground truth box of the highest jaccard - overlap, encode the bounding boxes, then return the matched indices - corresponding to both confidence and location preds. - Args: - threshold: (float) The overlap threshold used when matching boxes. - truths: (tensor) Ground truth boxes, Shape: [num_obj, 4]. - priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4]. - variances: (tensor) Variances corresponding to each prior coord, - Shape: [num_priors, 4]. - labels: (tensor) All the class labels for the image, Shape: [num_obj]. - landms: (tensor) Ground truth landms, Shape [num_obj, 10]. - loc_t: (tensor) Tensor to be filled w/ encoded location targets. - conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds. - landm_t: (tensor) Tensor to be filled w/ encoded landm targets. - idx: (int) current batch index - Return: - The matched indices corresponding to 1)location 2)confidence - 3)landm preds. - """ - # jaccard index - overlaps = jaccard(truths, point_form(priors)) - # (Bipartite Matching) - # [1,num_objects] best prior for each ground truth - best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True) - - # ignore hard gt - valid_gt_idx = best_prior_overlap[:, 0] >= 0.2 - best_prior_idx_filter = best_prior_idx[valid_gt_idx, :] - if best_prior_idx_filter.shape[0] <= 0: - loc_t[idx] = 0 - conf_t[idx] = 0 - return - - # [1,num_priors] best ground truth for each prior - best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True) - best_truth_idx.squeeze_(0) - best_truth_overlap.squeeze_(0) - best_prior_idx.squeeze_(1) - best_prior_idx_filter.squeeze_(1) - best_prior_overlap.squeeze_(1) - best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior - # TODO refactor: index best_prior_idx with long tensor - # ensure every gt matches with its prior of max overlap - for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes - best_truth_idx[best_prior_idx[j]] = j - matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来 - conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来 - conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本 - loc = encode(matches, priors, variances) - - matches_landm = landms[best_truth_idx] - landm = encode_landm(matches_landm, priors, variances) - loc_t[idx] = loc # [num_priors,4] encoded offsets to learn - conf_t[idx] = conf # [num_priors] top class label for each prior - landm_t[idx] = landm - - -def encode(matched, priors, variances): - """Encode the variances from the priorbox layers into the ground truth boxes - we have matched (based on jaccard overlap) with the prior boxes. - Args: - matched: (tensor) Coords of ground truth for each prior in point-form - Shape: [num_priors, 4]. - priors: (tensor) Prior boxes in center-offset form - Shape: [num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - encoded boxes (tensor), Shape: [num_priors, 4] - """ - - # dist b/t match center and prior's center - g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] - # encode variance - g_cxcy /= (variances[0] * priors[:, 2:]) - # match wh / prior wh - g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] - g_wh = torch.log(g_wh) / variances[1] - # return target for smooth_l1_loss - return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] - - -def encode_landm(matched, priors, variances): - """Encode the variances from the priorbox layers into the ground truth boxes - we have matched (based on jaccard overlap) with the prior boxes. - Args: - matched: (tensor) Coords of ground truth for each prior in point-form - Shape: [num_priors, 10]. - priors: (tensor) Prior boxes in center-offset form - Shape: [num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - encoded landm (tensor), Shape: [num_priors, 10] - """ - - # dist b/t match center and prior's center - matched = torch.reshape(matched, (matched.size(0), 5, 2)) - priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) - priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) - priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) - priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) - priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2) - g_cxcy = matched[:, :, :2] - priors[:, :, :2] - # encode variance - g_cxcy /= (variances[0] * priors[:, :, 2:]) - # g_cxcy /= priors[:, :, 2:] - g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1) - # return target for smooth_l1_loss - return g_cxcy - - -# Adapted from https://github.com/Hakuyume/chainer-ssd -def decode(loc, priors, variances): - """Decode locations from predictions using priors to undo - the encoding we did for offset regression at train time. - Args: - loc (tensor): location predictions for loc layers, - Shape: [num_priors,4] - priors (tensor): Prior boxes in center-offset form. - Shape: [num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - decoded bounding box predictions - """ - - boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], - priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) - boxes[:, :2] -= boxes[:, 2:] / 2 - boxes[:, 2:] += boxes[:, :2] - return boxes - - -def decode_landm(pre, priors, variances): - """Decode landm from predictions using priors to undo - the encoding we did for offset regression at train time. - Args: - pre (tensor): landm predictions for loc layers, - Shape: [num_priors,10] - priors (tensor): Prior boxes in center-offset form. - Shape: [num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - decoded landm predictions - """ - tmp = ( - priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:], - priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:], - priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:], - priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:], - priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:], - ) - landms = torch.cat(tmp, dim=1) - return landms - - -def batched_decode(b_loc, priors, variances): - """Decode locations from predictions using priors to undo - the encoding we did for offset regression at train time. - Args: - b_loc (tensor): location predictions for loc layers, - Shape: [num_batches,num_priors,4] - priors (tensor): Prior boxes in center-offset form. - Shape: [1,num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - decoded bounding box predictions - """ - boxes = ( - priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:], - priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]), - ) - boxes = torch.cat(boxes, dim=2) - - boxes[:, :, :2] -= boxes[:, :, 2:] / 2 - boxes[:, :, 2:] += boxes[:, :, :2] - return boxes - - -def batched_decode_landm(pre, priors, variances): - """Decode landm from predictions using priors to undo - the encoding we did for offset regression at train time. - Args: - pre (tensor): landm predictions for loc layers, - Shape: [num_batches,num_priors,10] - priors (tensor): Prior boxes in center-offset form. - Shape: [1,num_priors,4]. - variances: (list[float]) Variances of priorboxes - Return: - decoded landm predictions - """ - landms = ( - priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:], - priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:], - priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:], - priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:], - priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:], - ) - landms = torch.cat(landms, dim=2) - return landms - - -def log_sum_exp(x): - """Utility function for computing log_sum_exp while determining - This will be used to determine unaveraged confidence loss across - all examples in a batch. - Args: - x (Variable(tensor)): conf_preds from conf layers - """ - x_max = x.data.max() - return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max - - -# Original author: Francisco Massa: -# https://github.com/fmassa/object-detection.torch -# Ported to PyTorch by Max deGroot (02/01/2017) -def nms(boxes, scores, overlap=0.5, top_k=200): - """Apply non-maximum suppression at test time to avoid detecting too many - overlapping bounding boxes for a given object. - Args: - boxes: (tensor) The location preds for the img, Shape: [num_priors,4]. - scores: (tensor) The class predscores for the img, Shape:[num_priors]. - overlap: (float) The overlap thresh for suppressing unnecessary boxes. - top_k: (int) The Maximum number of box preds to consider. - Return: - The indices of the kept boxes with respect to num_priors. - """ - - keep = torch.Tensor(scores.size(0)).fill_(0).long() - if boxes.numel() == 0: - return keep - x1 = boxes[:, 0] - y1 = boxes[:, 1] - x2 = boxes[:, 2] - y2 = boxes[:, 3] - area = torch.mul(x2 - x1, y2 - y1) - v, idx = scores.sort(0) # sort in ascending order - # I = I[v >= 0.01] - idx = idx[-top_k:] # indices of the top-k largest vals - xx1 = boxes.new() - yy1 = boxes.new() - xx2 = boxes.new() - yy2 = boxes.new() - w = boxes.new() - h = boxes.new() - - # keep = torch.Tensor() - count = 0 - while idx.numel() > 0: - i = idx[-1] # index of current largest val - # keep.append(i) - keep[count] = i - count += 1 - if idx.size(0) == 1: - break - idx = idx[:-1] # remove kept element from view - # load bboxes of next highest vals - torch.index_select(x1, 0, idx, out=xx1) - torch.index_select(y1, 0, idx, out=yy1) - torch.index_select(x2, 0, idx, out=xx2) - torch.index_select(y2, 0, idx, out=yy2) - # store element-wise max with next highest score - xx1 = torch.clamp(xx1, min=x1[i]) - yy1 = torch.clamp(yy1, min=y1[i]) - xx2 = torch.clamp(xx2, max=x2[i]) - yy2 = torch.clamp(yy2, max=y2[i]) - w.resize_as_(xx2) - h.resize_as_(yy2) - w = xx2 - xx1 - h = yy2 - yy1 - # check sizes of xx1 and xx2.. after each iteration - w = torch.clamp(w, min=0.0) - h = torch.clamp(h, min=0.0) - inter = w * h - # IoU = i / (area(a) + area(b) - i) - rem_areas = torch.index_select(area, 0, idx) # load remaining areas) - union = (rem_areas - inter) + area[i] - IoU = inter / union # store result in iou - # keep only elements with an IoU <= overlap - idx = idx[IoU.le(overlap)] - return keep, count diff --git a/facexlib/headpose/__init__.py b/facexlib/headpose/__init__.py deleted file mode 100644 index c4de5da..0000000 --- a/facexlib/headpose/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -import torch - -from facexlib.utils import load_file_from_url -from .hopenet_arch import HopeNet - - -def init_headpose_model(model_name, half=False, device='cuda', model_rootpath=None): - if model_name == 'hopenet': - model = HopeNet('resnet', [3, 4, 6, 3], 66) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/headpose_hopenet.pth' - else: - raise NotImplementedError(f'{model_name} is not implemented.') - - model_path = load_file_from_url( - url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) - load_net = torch.load(model_path, map_location=lambda storage, loc: storage)['params'] - model.load_state_dict(load_net, strict=True) - model.eval() - model = model.to(device) - return model diff --git a/facexlib/headpose/hopenet_arch.py b/facexlib/headpose/hopenet_arch.py deleted file mode 100644 index b3a0141..0000000 --- a/facexlib/headpose/hopenet_arch.py +++ /dev/null @@ -1,72 +0,0 @@ -import torch -import torch.nn as nn -import torchvision - - -class HopeNet(nn.Module): - # Hopenet with 3 output layers for yaw, pitch and roll - # Predicts Euler angles by binning and regression with the expected value - def __init__(self, block, layers, num_bins): - super(HopeNet, self).__init__() - if block == 'resnet': - block = torchvision.models.resnet.Bottleneck - self.inplanes = 64 - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer(block, 128, layers[1], stride=2) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - self.avgpool = nn.AvgPool2d(7) - self.fc_yaw = nn.Linear(512 * block.expansion, num_bins) - self.fc_pitch = nn.Linear(512 * block.expansion, num_bins) - self.fc_roll = nn.Linear(512 * block.expansion, num_bins) - - self.idx_tensor = torch.arange(66).float() - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes)) - return nn.Sequential(*layers) - - @staticmethod - def softmax_temperature(tensor, temperature): - result = torch.exp(tensor / temperature) - result = torch.div(result, torch.sum(result, 1).unsqueeze(1).expand_as(result)) - return result - - def bin2degree(self, predict): - predict = self.softmax_temperature(predict, 1) - return torch.sum(predict * self.idx_tensor.type_as(predict), 1) * 3 - 99 - - def forward(self, x): - x = self.relu(self.bn1(self.conv1(x))) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - - x = self.avgpool(x) - x = x.view(x.size(0), -1) - pre_yaw = self.fc_yaw(x) - pre_pitch = self.fc_pitch(x) - pre_roll = self.fc_roll(x) - - yaw = self.bin2degree(pre_yaw) - pitch = self.bin2degree(pre_pitch) - roll = self.bin2degree(pre_roll) - return yaw, pitch, roll diff --git a/facexlib/matting/__init__.py b/facexlib/matting/__init__.py deleted file mode 100644 index 02a573b..0000000 --- a/facexlib/matting/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -import torch -from copy import deepcopy - -from facexlib.utils import load_file_from_url -from .modnet import MODNet - - -def init_matting_model(model_name='modnet', half=False, device='cuda', model_rootpath=None): - if model_name == 'modnet': - model = MODNet(backbone_pretrained=False) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/matting_modnet_portrait.pth' - else: - raise NotImplementedError(f'{model_name} is not implemented.') - - model_path = load_file_from_url( - url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) - # TODO: clean pretrained model - load_net = torch.load(model_path, map_location=lambda storage, loc: storage) - # remove unnecessary 'module.' - for k, v in deepcopy(load_net).items(): - if k.startswith('module.'): - load_net[k[7:]] = v - load_net.pop(k) - model.load_state_dict(load_net, strict=True) - model.eval() - model = model.to(device) - return model diff --git a/facexlib/matting/backbone.py b/facexlib/matting/backbone.py deleted file mode 100644 index 4cb295f..0000000 --- a/facexlib/matting/backbone.py +++ /dev/null @@ -1,80 +0,0 @@ -import os -import torch -import torch.nn as nn - -from .mobilenetv2 import MobileNetV2 - - -class BaseBackbone(nn.Module): - """ Superclass of Replaceable Backbone Model for Semantic Estimation - """ - - def __init__(self, in_channels): - super(BaseBackbone, self).__init__() - self.in_channels = in_channels - - self.model = None - self.enc_channels = [] - - def forward(self, x): - raise NotImplementedError - - def load_pretrained_ckpt(self): - raise NotImplementedError - - -class MobileNetV2Backbone(BaseBackbone): - """ MobileNetV2 Backbone - """ - - def __init__(self, in_channels): - super(MobileNetV2Backbone, self).__init__(in_channels) - - self.model = MobileNetV2(self.in_channels, alpha=1.0, expansion=6, num_classes=None) - self.enc_channels = [16, 24, 32, 96, 1280] - - def forward(self, x): - # x = reduce(lambda x, n: self.model.features[n](x), list(range(0, 2)), x) - x = self.model.features[0](x) - x = self.model.features[1](x) - enc2x = x - - # x = reduce(lambda x, n: self.model.features[n](x), list(range(2, 4)), x) - x = self.model.features[2](x) - x = self.model.features[3](x) - enc4x = x - - # x = reduce(lambda x, n: self.model.features[n](x), list(range(4, 7)), x) - x = self.model.features[4](x) - x = self.model.features[5](x) - x = self.model.features[6](x) - enc8x = x - - # x = reduce(lambda x, n: self.model.features[n](x), list(range(7, 14)), x) - x = self.model.features[7](x) - x = self.model.features[8](x) - x = self.model.features[9](x) - x = self.model.features[10](x) - x = self.model.features[11](x) - x = self.model.features[12](x) - x = self.model.features[13](x) - enc16x = x - - # x = reduce(lambda x, n: self.model.features[n](x), list(range(14, 19)), x) - x = self.model.features[14](x) - x = self.model.features[15](x) - x = self.model.features[16](x) - x = self.model.features[17](x) - x = self.model.features[18](x) - enc32x = x - return [enc2x, enc4x, enc8x, enc16x, enc32x] - - def load_pretrained_ckpt(self): - # the pre-trained model is provided by https://github.com/thuyngch/Human-Segmentation-PyTorch - ckpt_path = './pretrained/mobilenetv2_human_seg.ckpt' - if not os.path.exists(ckpt_path): - print('cannot find the pretrained mobilenetv2 backbone') - exit() - - ckpt = torch.load(ckpt_path) - self.model.load_state_dict(ckpt) diff --git a/facexlib/matting/mobilenetv2.py b/facexlib/matting/mobilenetv2.py deleted file mode 100644 index c649586..0000000 --- a/facexlib/matting/mobilenetv2.py +++ /dev/null @@ -1,192 +0,0 @@ -""" This file is adapted from https://github.com/thuyngch/Human-Segmentation-PyTorch""" - -import math -import torch -from torch import nn - -# ------------------------------------------------------------------------------ -# Useful functions -# ------------------------------------------------------------------------------ - - -def _make_divisible(v, divisor, min_value=None): - if min_value is None: - min_value = divisor - new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) - # Make sure that round down does not go down by more than 10%. - if new_v < 0.9 * v: - new_v += divisor - return new_v - - -def conv_bn(inp, oup, stride): - return nn.Sequential(nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True)) - - -def conv_1x1_bn(inp, oup): - return nn.Sequential(nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True)) - - -# ------------------------------------------------------------------------------ -# Class of Inverted Residual block -# ------------------------------------------------------------------------------ - - -class InvertedResidual(nn.Module): - - def __init__(self, inp, oup, stride, expansion, dilation=1): - super(InvertedResidual, self).__init__() - self.stride = stride - assert stride in [1, 2] - - hidden_dim = round(inp * expansion) - self.use_res_connect = self.stride == 1 and inp == oup - - if expansion == 1: - self.conv = nn.Sequential( - # dw - nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False), - nn.BatchNorm2d(hidden_dim), - nn.ReLU6(inplace=True), - # pw-linear - nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), - nn.BatchNorm2d(oup), - ) - else: - self.conv = nn.Sequential( - # pw - nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), - nn.BatchNorm2d(hidden_dim), - nn.ReLU6(inplace=True), - # dw - nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False), - nn.BatchNorm2d(hidden_dim), - nn.ReLU6(inplace=True), - # pw-linear - nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), - nn.BatchNorm2d(oup), - ) - - def forward(self, x): - if self.use_res_connect: - return x + self.conv(x) - else: - return self.conv(x) - - -# ------------------------------------------------------------------------------ -# Class of MobileNetV2 -# ------------------------------------------------------------------------------ - - -class MobileNetV2(nn.Module): - - def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000): - super(MobileNetV2, self).__init__() - self.in_channels = in_channels - self.num_classes = num_classes - input_channel = 32 - last_channel = 1280 - interverted_residual_setting = [ - # t, c, n, s - [1, 16, 1, 1], - [expansion, 24, 2, 2], - [expansion, 32, 3, 2], - [expansion, 64, 4, 2], - [expansion, 96, 3, 1], - [expansion, 160, 3, 2], - [expansion, 320, 1, 1], - ] - - # building first layer - input_channel = _make_divisible(input_channel * alpha, 8) - self.last_channel = _make_divisible(last_channel * alpha, 8) if alpha > 1.0 else last_channel - self.features = [conv_bn(self.in_channels, input_channel, 2)] - - # building inverted residual blocks - for t, c, n, s in interverted_residual_setting: - output_channel = _make_divisible(int(c * alpha), 8) - for i in range(n): - if i == 0: - self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t)) - else: - self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=t)) - input_channel = output_channel - - # building last several layers - self.features.append(conv_1x1_bn(input_channel, self.last_channel)) - - # make it nn.Sequential - self.features = nn.Sequential(*self.features) - - # building classifier - if self.num_classes is not None: - self.classifier = nn.Sequential( - nn.Dropout(0.2), - nn.Linear(self.last_channel, num_classes), - ) - - # Initialize weights - self._init_weights() - - def forward(self, x): - # Stage1 - x = self.features[0](x) - x = self.features[1](x) - # Stage2 - x = self.features[2](x) - x = self.features[3](x) - # Stage3 - x = self.features[4](x) - x = self.features[5](x) - x = self.features[6](x) - # Stage4 - x = self.features[7](x) - x = self.features[8](x) - x = self.features[9](x) - x = self.features[10](x) - x = self.features[11](x) - x = self.features[12](x) - x = self.features[13](x) - # Stage5 - x = self.features[14](x) - x = self.features[15](x) - x = self.features[16](x) - x = self.features[17](x) - x = self.features[18](x) - - # Classification - if self.num_classes is not None: - x = x.mean(dim=(2, 3)) - x = self.classifier(x) - - # Output - return x - - def _load_pretrained_model(self, pretrained_file): - pretrain_dict = torch.load(pretrained_file, map_location='cpu') - model_dict = {} - state_dict = self.state_dict() - print('[MobileNetV2] Loading pretrained model...') - for k, v in pretrain_dict.items(): - if k in state_dict: - model_dict[k] = v - else: - print(k, 'is ignored') - state_dict.update(model_dict) - self.load_state_dict(state_dict) - - def _init_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - m.weight.data.normal_(0, math.sqrt(2. / n)) - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - n = m.weight.size(1) - m.weight.data.normal_(0, 0.01) - m.bias.data.zero_() diff --git a/facexlib/matting/modnet.py b/facexlib/matting/modnet.py deleted file mode 100644 index cd23c38..0000000 --- a/facexlib/matting/modnet.py +++ /dev/null @@ -1,267 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .backbone import MobileNetV2Backbone - -# ------------------------------------------------------------------------------ -# MODNet Basic Modules -# ------------------------------------------------------------------------------ - - -class IBNorm(nn.Module): - """ Combine Instance Norm and Batch Norm into One Layer - """ - - def __init__(self, in_channels): - super(IBNorm, self).__init__() - in_channels = in_channels - self.bnorm_channels = int(in_channels / 2) - self.inorm_channels = in_channels - self.bnorm_channels - - self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True) - self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False) - - def forward(self, x): - bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous()) - in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous()) - - return torch.cat((bn_x, in_x), 1) - - -class Conv2dIBNormRelu(nn.Module): - """ Convolution + IBNorm + ReLu - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True, - with_ibn=True, - with_relu=True): - super(Conv2dIBNormRelu, self).__init__() - - layers = [ - nn.Conv2d( - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - bias=bias) - ] - - if with_ibn: - layers.append(IBNorm(out_channels)) - if with_relu: - layers.append(nn.ReLU(inplace=True)) - - self.layers = nn.Sequential(*layers) - - def forward(self, x): - return self.layers(x) - - -class SEBlock(nn.Module): - """ SE Block Proposed in https://arxiv.org/pdf/1709.01507.pdf - """ - - def __init__(self, in_channels, out_channels, reduction=1): - super(SEBlock, self).__init__() - self.pool = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Sequential( - nn.Linear(in_channels, int(in_channels // reduction), bias=False), nn.ReLU(inplace=True), - nn.Linear(int(in_channels // reduction), out_channels, bias=False), nn.Sigmoid()) - - def forward(self, x): - b, c, _, _ = x.size() - w = self.pool(x).view(b, c) - w = self.fc(w).view(b, c, 1, 1) - - return x * w.expand_as(x) - - -# ------------------------------------------------------------------------------ -# MODNet Branches -# ------------------------------------------------------------------------------ - - -class LRBranch(nn.Module): - """ Low Resolution Branch of MODNet - """ - - def __init__(self, backbone): - super(LRBranch, self).__init__() - - enc_channels = backbone.enc_channels - - self.backbone = backbone - self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4) - self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2) - self.conv_lr8x = Conv2dIBNormRelu(enc_channels[3], enc_channels[2], 5, stride=1, padding=2) - self.conv_lr = Conv2dIBNormRelu( - enc_channels[2], 1, kernel_size=3, stride=2, padding=1, with_ibn=False, with_relu=False) - - def forward(self, img, inference): - enc_features = self.backbone.forward(img) - enc2x, enc4x, enc32x = enc_features[0], enc_features[1], enc_features[4] - - enc32x = self.se_block(enc32x) - lr16x = F.interpolate(enc32x, scale_factor=2, mode='bilinear', align_corners=False) - lr16x = self.conv_lr16x(lr16x) - lr8x = F.interpolate(lr16x, scale_factor=2, mode='bilinear', align_corners=False) - lr8x = self.conv_lr8x(lr8x) - - pred_semantic = None - if not inference: - lr = self.conv_lr(lr8x) - pred_semantic = torch.sigmoid(lr) - - return pred_semantic, lr8x, [enc2x, enc4x] - - -class HRBranch(nn.Module): - """ High Resolution Branch of MODNet - """ - - def __init__(self, hr_channels, enc_channels): - super(HRBranch, self).__init__() - - self.tohr_enc2x = Conv2dIBNormRelu(enc_channels[0], hr_channels, 1, stride=1, padding=0) - self.conv_enc2x = Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=2, padding=1) - - self.tohr_enc4x = Conv2dIBNormRelu(enc_channels[1], hr_channels, 1, stride=1, padding=0) - self.conv_enc4x = Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1) - - self.conv_hr4x = nn.Sequential( - Conv2dIBNormRelu(3 * hr_channels + 3, 2 * hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), - ) - - self.conv_hr2x = nn.Sequential( - Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), - ) - - self.conv_hr = nn.Sequential( - Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(hr_channels, 1, kernel_size=1, stride=1, padding=0, with_ibn=False, with_relu=False), - ) - - def forward(self, img, enc2x, enc4x, lr8x, inference): - img2x = F.interpolate(img, scale_factor=1 / 2, mode='bilinear', align_corners=False) - img4x = F.interpolate(img, scale_factor=1 / 4, mode='bilinear', align_corners=False) - - enc2x = self.tohr_enc2x(enc2x) - hr4x = self.conv_enc2x(torch.cat((img2x, enc2x), dim=1)) - - enc4x = self.tohr_enc4x(enc4x) - hr4x = self.conv_enc4x(torch.cat((hr4x, enc4x), dim=1)) - - lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False) - hr4x = self.conv_hr4x(torch.cat((hr4x, lr4x, img4x), dim=1)) - - hr2x = F.interpolate(hr4x, scale_factor=2, mode='bilinear', align_corners=False) - hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1)) - - pred_detail = None - if not inference: - hr = F.interpolate(hr2x, scale_factor=2, mode='bilinear', align_corners=False) - hr = self.conv_hr(torch.cat((hr, img), dim=1)) - pred_detail = torch.sigmoid(hr) - - return pred_detail, hr2x - - -class FusionBranch(nn.Module): - """ Fusion Branch of MODNet - """ - - def __init__(self, hr_channels, enc_channels): - super(FusionBranch, self).__init__() - self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2) - - self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1) - self.conv_f = nn.Sequential( - Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1), - Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False), - ) - - def forward(self, img, lr8x, hr2x): - lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False) - lr4x = self.conv_lr4x(lr4x) - lr2x = F.interpolate(lr4x, scale_factor=2, mode='bilinear', align_corners=False) - - f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1)) - f = F.interpolate(f2x, scale_factor=2, mode='bilinear', align_corners=False) - f = self.conv_f(torch.cat((f, img), dim=1)) - pred_matte = torch.sigmoid(f) - - return pred_matte - - -# ------------------------------------------------------------------------------ -# MODNet -# ------------------------------------------------------------------------------ - - -class MODNet(nn.Module): - """ Architecture of MODNet - """ - - def __init__(self, in_channels=3, hr_channels=32, backbone_pretrained=True): - super(MODNet, self).__init__() - - self.in_channels = in_channels - self.hr_channels = hr_channels - self.backbone_pretrained = backbone_pretrained - - self.backbone = MobileNetV2Backbone(self.in_channels) - - self.lr_branch = LRBranch(self.backbone) - self.hr_branch = HRBranch(self.hr_channels, self.backbone.enc_channels) - self.f_branch = FusionBranch(self.hr_channels, self.backbone.enc_channels) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - self._init_conv(m) - elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.InstanceNorm2d): - self._init_norm(m) - - if self.backbone_pretrained: - self.backbone.load_pretrained_ckpt() - - def forward(self, img, inference): - pred_semantic, lr8x, [enc2x, enc4x] = self.lr_branch(img, inference) - pred_detail, hr2x = self.hr_branch(img, enc2x, enc4x, lr8x, inference) - pred_matte = self.f_branch(img, lr8x, hr2x) - - return pred_semantic, pred_detail, pred_matte - - def freeze_norm(self): - norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d] - for m in self.modules(): - for n in norm_types: - if isinstance(m, n): - m.eval() - continue - - def _init_conv(self, conv): - nn.init.kaiming_uniform_(conv.weight, a=0, mode='fan_in', nonlinearity='relu') - if conv.bias is not None: - nn.init.constant_(conv.bias, 0) - - def _init_norm(self, norm): - if norm.weight is not None: - nn.init.constant_(norm.weight, 1) - nn.init.constant_(norm.bias, 0) diff --git a/facexlib/parsing/__init__.py b/facexlib/parsing/__init__.py deleted file mode 100644 index 9be36a3..0000000 --- a/facexlib/parsing/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -import torch - -from facexlib.utils import load_file_from_url -from .bisenet import BiSeNet -from .parsenet import ParseNet - - -def init_parsing_model(model_name='bisenet', half=False, device='cuda', model_rootpath=None): - if model_name == 'bisenet': - model = BiSeNet(num_class=19) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth' - elif model_name == 'parsenet': - model = ParseNet(in_size=512, out_size=512, parsing_ch=19) - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth' - else: - raise NotImplementedError(f'{model_name} is not implemented.') - - model_path = load_file_from_url( - url=model_url, 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c&>@t2w}!ZTR7wTCBptTMJ#*3aZZ5U{0TnA5qW}N^ diff --git a/facexlib/parsing/bisenet.py b/facexlib/parsing/bisenet.py deleted file mode 100644 index 3898cab..0000000 --- a/facexlib/parsing/bisenet.py +++ /dev/null @@ -1,140 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .resnet import ResNet18 - - -class ConvBNReLU(nn.Module): - - def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1): - super(ConvBNReLU, self).__init__() - self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False) - self.bn = nn.BatchNorm2d(out_chan) - - def forward(self, x): - x = self.conv(x) - x = F.relu(self.bn(x)) - return x - - -class BiSeNetOutput(nn.Module): - - def __init__(self, in_chan, mid_chan, num_class): - super(BiSeNetOutput, self).__init__() - self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) - self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False) - - def forward(self, x): - feat = self.conv(x) - out = self.conv_out(feat) - return out, feat - - -class AttentionRefinementModule(nn.Module): - - def __init__(self, in_chan, out_chan): - super(AttentionRefinementModule, self).__init__() - self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) - self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False) - self.bn_atten = nn.BatchNorm2d(out_chan) - self.sigmoid_atten = nn.Sigmoid() - - def forward(self, x): - feat = self.conv(x) - atten = F.avg_pool2d(feat, feat.size()[2:]) - atten = self.conv_atten(atten) - atten = self.bn_atten(atten) - atten = self.sigmoid_atten(atten) - out = torch.mul(feat, atten) - return out - - -class ContextPath(nn.Module): - - def __init__(self): - super(ContextPath, self).__init__() - self.resnet = ResNet18() - self.arm16 = AttentionRefinementModule(256, 128) - self.arm32 = AttentionRefinementModule(512, 128) - self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) - self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) - self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) - - def forward(self, x): - feat8, feat16, feat32 = self.resnet(x) - h8, w8 = feat8.size()[2:] - h16, w16 = feat16.size()[2:] - h32, w32 = feat32.size()[2:] - - avg = F.avg_pool2d(feat32, feat32.size()[2:]) - avg = self.conv_avg(avg) - avg_up = F.interpolate(avg, (h32, w32), mode='nearest') - - feat32_arm = self.arm32(feat32) - feat32_sum = feat32_arm + avg_up - feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest') - feat32_up = self.conv_head32(feat32_up) - - feat16_arm = self.arm16(feat16) - feat16_sum = feat16_arm + feat32_up - feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest') - feat16_up = self.conv_head16(feat16_up) - - return feat8, feat16_up, feat32_up # x8, x8, x16 - - -class FeatureFusionModule(nn.Module): - - def __init__(self, in_chan, out_chan): - super(FeatureFusionModule, self).__init__() - self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) - self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False) - self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False) - self.relu = nn.ReLU(inplace=True) - self.sigmoid = nn.Sigmoid() - - def forward(self, fsp, fcp): - fcat = torch.cat([fsp, fcp], dim=1) - feat = self.convblk(fcat) - atten = F.avg_pool2d(feat, feat.size()[2:]) - atten = self.conv1(atten) - atten = self.relu(atten) - atten = self.conv2(atten) - atten = self.sigmoid(atten) - feat_atten = torch.mul(feat, atten) - feat_out = feat_atten + feat - return feat_out - - -class BiSeNet(nn.Module): - - def __init__(self, num_class): - super(BiSeNet, self).__init__() - self.cp = ContextPath() - self.ffm = FeatureFusionModule(256, 256) - self.conv_out = BiSeNetOutput(256, 256, num_class) - self.conv_out16 = BiSeNetOutput(128, 64, num_class) - self.conv_out32 = BiSeNetOutput(128, 64, num_class) - - def forward(self, x, return_feat=False): - h, w = x.size()[2:] - feat_res8, feat_cp8, feat_cp16 = self.cp(x) # return res3b1 feature - feat_sp = feat_res8 # replace spatial path feature with res3b1 feature - feat_fuse = self.ffm(feat_sp, feat_cp8) - - out, feat = self.conv_out(feat_fuse) - out16, feat16 = self.conv_out16(feat_cp8) - out32, feat32 = self.conv_out32(feat_cp16) - - out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True) - out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True) - out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True) - - if return_feat: - feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True) - feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True) - feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True) - return out, out16, out32, feat, feat16, feat32 - else: - return out, out16, out32 diff --git a/facexlib/parsing/parsenet.py b/facexlib/parsing/parsenet.py deleted file mode 100644 index e178ebe..0000000 --- a/facexlib/parsing/parsenet.py +++ /dev/null @@ -1,194 +0,0 @@ -"""Modified from https://github.com/chaofengc/PSFRGAN -""" -import numpy as np -import torch.nn as nn -from torch.nn import functional as F - - -class NormLayer(nn.Module): - """Normalization Layers. - - Args: - channels: input channels, for batch norm and instance norm. - input_size: input shape without batch size, for layer norm. - """ - - def __init__(self, channels, normalize_shape=None, norm_type='bn'): - super(NormLayer, self).__init__() - norm_type = norm_type.lower() - self.norm_type = norm_type - if norm_type == 'bn': - self.norm = nn.BatchNorm2d(channels, affine=True) - elif norm_type == 'in': - self.norm = nn.InstanceNorm2d(channels, affine=False) - elif norm_type == 'gn': - self.norm = nn.GroupNorm(32, channels, affine=True) - elif norm_type == 'pixel': - self.norm = lambda x: F.normalize(x, p=2, dim=1) - elif norm_type == 'layer': - self.norm = nn.LayerNorm(normalize_shape) - elif norm_type == 'none': - self.norm = lambda x: x * 1.0 - else: - assert 1 == 0, f'Norm type {norm_type} not support.' - - def forward(self, x, ref=None): - if self.norm_type == 'spade': - return self.norm(x, ref) - else: - return self.norm(x) - - -class ReluLayer(nn.Module): - """Relu Layer. - - Args: - relu type: type of relu layer, candidates are - - ReLU - - LeakyReLU: default relu slope 0.2 - - PRelu - - SELU - - none: direct pass - """ - - def __init__(self, channels, relu_type='relu'): - super(ReluLayer, self).__init__() - relu_type = relu_type.lower() - if relu_type == 'relu': - self.func = nn.ReLU(True) - elif relu_type == 'leakyrelu': - self.func = nn.LeakyReLU(0.2, inplace=True) - elif relu_type == 'prelu': - self.func = nn.PReLU(channels) - elif relu_type == 'selu': - self.func = nn.SELU(True) - elif relu_type == 'none': - self.func = lambda x: x * 1.0 - else: - assert 1 == 0, f'Relu type {relu_type} not support.' - - def forward(self, x): - return self.func(x) - - -class ConvLayer(nn.Module): - - def __init__(self, - in_channels, - out_channels, - kernel_size=3, - scale='none', - norm_type='none', - relu_type='none', - use_pad=True, - bias=True): - super(ConvLayer, self).__init__() - self.use_pad = use_pad - self.norm_type = norm_type - if norm_type in ['bn']: - bias = False - - stride = 2 if scale == 'down' else 1 - - self.scale_func = lambda x: x - if scale == 'up': - self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest') - - self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2))) - self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias) - - self.relu = ReluLayer(out_channels, relu_type) - self.norm = NormLayer(out_channels, norm_type=norm_type) - - def forward(self, x): - out = self.scale_func(x) - if self.use_pad: - out = self.reflection_pad(out) - out = self.conv2d(out) - out = self.norm(out) - out = self.relu(out) - return out - - -class ResidualBlock(nn.Module): - """ - Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html - """ - - def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'): - super(ResidualBlock, self).__init__() - - if scale == 'none' and c_in == c_out: - self.shortcut_func = lambda x: x - else: - self.shortcut_func = ConvLayer(c_in, c_out, 3, scale) - - scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']} - scale_conf = scale_config_dict[scale] - - self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type) - self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none') - - def forward(self, x): - identity = self.shortcut_func(x) - - res = self.conv1(x) - res = self.conv2(res) - return identity + res - - -class ParseNet(nn.Module): - - def __init__(self, - in_size=128, - out_size=128, - min_feat_size=32, - base_ch=64, - parsing_ch=19, - res_depth=10, - relu_type='LeakyReLU', - norm_type='bn', - ch_range=[32, 256]): - super().__init__() - self.res_depth = res_depth - act_args = {'norm_type': norm_type, 'relu_type': relu_type} - min_ch, max_ch = ch_range - - ch_clip = lambda x: max(min_ch, min(x, max_ch)) # noqa: E731 - min_feat_size = min(in_size, min_feat_size) - - down_steps = int(np.log2(in_size // min_feat_size)) - up_steps = int(np.log2(out_size // min_feat_size)) - - # =============== define encoder-body-decoder ==================== - self.encoder = [] - self.encoder.append(ConvLayer(3, base_ch, 3, 1)) - head_ch = base_ch - for i in range(down_steps): - cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2) - self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args)) - head_ch = head_ch * 2 - - self.body = [] - for i in range(res_depth): - self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args)) - - self.decoder = [] - for i in range(up_steps): - cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) - self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args)) - head_ch = head_ch // 2 - - self.encoder = nn.Sequential(*self.encoder) - self.body = nn.Sequential(*self.body) - self.decoder = nn.Sequential(*self.decoder) - self.out_img_conv = ConvLayer(ch_clip(head_ch), 3) - self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch) - - def forward(self, x): - feat = self.encoder(x) - x = feat + self.body(feat) - x = self.decoder(x) - out_img = self.out_img_conv(x) - out_mask = self.out_mask_conv(x) - return out_mask, out_img diff --git a/facexlib/parsing/resnet.py b/facexlib/parsing/resnet.py deleted file mode 100644 index fec8e82..0000000 --- a/facexlib/parsing/resnet.py +++ /dev/null @@ -1,69 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F - - -def conv3x3(in_planes, out_planes, stride=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) - - -class BasicBlock(nn.Module): - - def __init__(self, in_chan, out_chan, stride=1): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(in_chan, out_chan, stride) - self.bn1 = nn.BatchNorm2d(out_chan) - self.conv2 = conv3x3(out_chan, out_chan) - self.bn2 = nn.BatchNorm2d(out_chan) - self.relu = nn.ReLU(inplace=True) - self.downsample = None - if in_chan != out_chan or stride != 1: - self.downsample = nn.Sequential( - nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(out_chan), - ) - - def forward(self, x): - residual = self.conv1(x) - residual = F.relu(self.bn1(residual)) - residual = self.conv2(residual) - residual = self.bn2(residual) - - shortcut = x - if self.downsample is not None: - shortcut = self.downsample(x) - - out = shortcut + residual - out = self.relu(out) - return out - - -def create_layer_basic(in_chan, out_chan, bnum, stride=1): - layers = [BasicBlock(in_chan, out_chan, stride=stride)] - for i in range(bnum - 1): - layers.append(BasicBlock(out_chan, out_chan, stride=1)) - return nn.Sequential(*layers) - - -class ResNet18(nn.Module): - - def __init__(self): - super(ResNet18, self).__init__() - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) - self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) - self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) - self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) - - def forward(self, x): - x = self.conv1(x) - x = F.relu(self.bn1(x)) - x = self.maxpool(x) - - x = self.layer1(x) - feat8 = self.layer2(x) # 1/8 - feat16 = self.layer3(feat8) # 1/16 - feat32 = self.layer4(feat16) # 1/32 - return feat8, feat16, feat32 diff --git a/facexlib/recognition/__init__.py b/facexlib/recognition/__init__.py deleted file mode 100644 index 1f65d2c..0000000 --- a/facexlib/recognition/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -import torch - -from facexlib.utils import load_file_from_url -from .arcface_arch import Backbone - - -def init_recognition_model(model_name, half=False, device='cuda', model_rootpath=None): - if model_name == 'arcface': - model = Backbone(num_layers=50, drop_ratio=0.6, mode='ir_se').to('cuda').eval() - model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/recognition_arcface_ir_se50.pth' - else: - raise NotImplementedError(f'{model_name} is not implemented.') - - model_path = load_file_from_url( - url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) - model.load_state_dict(torch.load(model_path), strict=True) - model.eval() - model = model.to(device) - return model diff --git a/facexlib/recognition/__pycache__/__init__.cpython-310.pyc b/facexlib/recognition/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 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padding=0, bias=False) - self.relu = ReLU(inplace=True) - self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) - self.sigmoid = Sigmoid() - - def forward(self, x): - module_input = x - x = self.avg_pool(x) - x = self.fc1(x) - x = self.relu(x) - x = self.fc2(x) - x = self.sigmoid(x) - return module_input * x - - -class bottleneck_IR(Module): - - def __init__(self, in_channel, depth, stride): - super(bottleneck_IR, self).__init__() - if in_channel == depth: - self.shortcut_layer = MaxPool2d(1, stride) - else: - self.shortcut_layer = Sequential(Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth)) - self.res_layer = Sequential( - BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), - Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)) - - def forward(self, x): - shortcut = self.shortcut_layer(x) - res = self.res_layer(x) - return res + shortcut - - -class bottleneck_IR_SE(Module): - - def __init__(self, in_channel, depth, stride): - super(bottleneck_IR_SE, self).__init__() - if in_channel == depth: - self.shortcut_layer = MaxPool2d(1, stride) - else: - self.shortcut_layer = Sequential(Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth)) - self.res_layer = Sequential( - BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), - Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth), SEModule(depth, 16)) - - def forward(self, x): - shortcut = self.shortcut_layer(x) - res = self.res_layer(x) - return res + shortcut - - -class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): - '''A named tuple describing a ResNet block.''' - - -def get_block(in_channel, depth, num_units, stride=2): - return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] - - -def get_blocks(num_layers): - if num_layers == 50: - blocks = [ - get_block(in_channel=64, depth=64, num_units=3), - get_block(in_channel=64, depth=128, num_units=4), - get_block(in_channel=128, depth=256, num_units=14), - get_block(in_channel=256, depth=512, num_units=3) - ] - elif num_layers == 100: - blocks = [ - get_block(in_channel=64, depth=64, num_units=3), - get_block(in_channel=64, depth=128, num_units=13), - get_block(in_channel=128, depth=256, num_units=30), - get_block(in_channel=256, depth=512, num_units=3) - ] - elif num_layers == 152: - blocks = [ - get_block(in_channel=64, depth=64, num_units=3), - get_block(in_channel=64, depth=128, num_units=8), - get_block(in_channel=128, depth=256, num_units=36), - get_block(in_channel=256, depth=512, num_units=3) - ] - return blocks - - -class Backbone(Module): - - def __init__(self, num_layers, drop_ratio, mode='ir'): - super(Backbone, self).__init__() - assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' - assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' - blocks = get_blocks(num_layers) - if mode == 'ir': - unit_module = bottleneck_IR - elif mode == 'ir_se': - unit_module = bottleneck_IR_SE - self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64)) - self.output_layer = Sequential( - BatchNorm2d(512), Dropout(drop_ratio), Flatten(), Linear(512 * 7 * 7, 512), BatchNorm1d(512)) - modules = [] - for block in blocks: - for bottleneck in block: - modules.append(unit_module(bottleneck.in_channel, bottleneck.depth, bottleneck.stride)) - self.body = Sequential(*modules) - - def forward(self, x): - x = self.input_layer(x) - x = self.body(x) - x = self.output_layer(x) - return l2_norm(x) - - -# MobileFaceNet - - -class Conv_block(Module): - - def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): - super(Conv_block, self).__init__() - self.conv = Conv2d( - in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False) - self.bn = BatchNorm2d(out_c) - self.prelu = PReLU(out_c) - - def forward(self, x): - x = self.conv(x) - x = self.bn(x) - x = self.prelu(x) - return x - - -class Linear_block(Module): - - def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): - super(Linear_block, self).__init__() - self.conv = Conv2d( - in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False) - self.bn = BatchNorm2d(out_c) - - def forward(self, x): - x = self.conv(x) - x = self.bn(x) - return x - - -class Depth_Wise(Module): - - def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1): - super(Depth_Wise, self).__init__() - self.conv = Conv_block(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)) - self.conv_dw = Conv_block(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride) - self.project = Linear_block(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)) - self.residual = residual - - def forward(self, x): - if self.residual: - short_cut = x - x = self.conv(x) - x = self.conv_dw(x) - x = self.project(x) - if self.residual: - output = short_cut + x - else: - output = x - return output - - -class Residual(Module): - - def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)): - super(Residual, self).__init__() - modules = [] - for _ in range(num_block): - modules.append( - Depth_Wise(c, c, residual=True, kernel=kernel, padding=padding, stride=stride, groups=groups)) - self.model = Sequential(*modules) - - def forward(self, x): - return self.model(x) - - -class MobileFaceNet(Module): - - def __init__(self, embedding_size): - super(MobileFaceNet, self).__init__() - self.conv1 = Conv_block(3, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1)) - self.conv2_dw = Conv_block(64, 64, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64) - self.conv_23 = Depth_Wise(64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128) - self.conv_3 = Residual(64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv_34 = Depth_Wise(64, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256) - self.conv_4 = Residual(128, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv_45 = Depth_Wise(128, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512) - self.conv_5 = Residual(128, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv_6_sep = Conv_block(128, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0)) - self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)) - self.conv_6_flatten = Flatten() - self.linear = Linear(512, embedding_size, bias=False) - self.bn = BatchNorm1d(embedding_size) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2_dw(out) - out = self.conv_23(out) - out = self.conv_3(out) - out = self.conv_34(out) - out = self.conv_4(out) - out = self.conv_45(out) - out = self.conv_5(out) - out = self.conv_6_sep(out) - out = self.conv_6_dw(out) - out = self.conv_6_flatten(out) - out = self.linear(out) - out = self.bn(out) - return l2_norm(out) diff --git a/facexlib/tracking/README.md b/facexlib/tracking/README.md deleted file mode 100644 index cd67a5d..0000000 --- a/facexlib/tracking/README.md +++ /dev/null @@ -1 +0,0 @@ -https://github.com/abewley/sort diff --git a/facexlib/tracking/__init__.py b/facexlib/tracking/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/facexlib/tracking/__pycache__/__init__.cpython-310.pyc b/facexlib/tracking/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 0c0560708ce4cbe988fa3cc9a841a1f3a2476d6c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 148 zcmd1j<>g`kf)gi`(m?cM5P=LBfgA@QE@lA|DGb33nv8xc8Hzx{2;!Hyi&czoWk6AW zR%&udj9X%IYEWu%Nq$jkQA`?;Q<0OI6jM@^n4F!NmmU)zpP83g5+AQuP 0: - self.hit_streak = 0 - self.time_since_update += 1 - self.history.append(convert_x_to_bbox(self.kf.x)) - return self.history[-1][0] - - def get_state(self): - """Returns the current bounding box estimate.""" - return convert_x_to_bbox(self.kf.x)[0] diff --git a/facexlib/tracking/sort.py b/facexlib/tracking/sort.py deleted file mode 100644 index 0fbad43..0000000 --- a/facexlib/tracking/sort.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np - -from facexlib.tracking.data_association import associate_detections_to_trackers -from facexlib.tracking.kalman_tracker import KalmanBoxTracker - - -class SORT(object): - """SORT: A Simple, Online and Realtime Tracker. - - Ref: https://github.com/abewley/sort - """ - - def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3): - self.max_age = max_age - self.min_hits = min_hits # 最小的连续命中, 只有满足的才会被返回 - self.iou_threshold = iou_threshold - self.trackers = [] - self.frame_count = 0 - - def update(self, dets, img_size, additional_attr, detect_interval): - """This method must be called once for each frame even with - empty detections. - NOTE:as in practical realtime MOT, the detector doesn't run on every - single frame. - - Args: - dets (Numpy array): detections in the format - [[x0,y0,x1,y1,score], [x0,y0,x1,y1,score], ...] - - Returns: - a similar array, where the last column is the object ID. - """ - self.frame_count += 1 - - # get predicted locations from existing trackers - trks = np.zeros((len(self.trackers), 5)) - to_del = [] # To be deleted - ret = [] - # predict tracker position using Kalman filter - for t, trk in enumerate(trks): - pos = self.trackers[t].predict() # Kalman predict ,very fast ,<1ms - trk[:] = [pos[0], pos[1], pos[2], pos[3], 0] - if np.any(np.isnan(pos)): - to_del.append(t) - trks = np.ma.compress_rows(np.ma.masked_invalid(trks)) - for t in reversed(to_del): - self.trackers.pop(t) - - if dets != []: - matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers( # noqa: E501 - dets, trks) - - # update matched trackers with assigned detections - for t, trk in enumerate(self.trackers): - if t not in unmatched_trks: - d = matched[np.where(matched[:, 1] == t)[0], 0] - trk.update(dets[d, :][0]) - trk.face_attributes.append(additional_attr[d[0]]) - - # create and initialize new trackers for unmatched detections - for i in unmatched_dets: - trk = KalmanBoxTracker(dets[i, :]) - trk.face_attributes.append(additional_attr[i]) - print(f'New tracker: {trk.id + 1}.') - self.trackers.append(trk) - - i = len(self.trackers) - for trk in reversed(self.trackers): - if dets == []: - trk.update([]) - - d = trk.get_state() - # get return tracklet - # 1) time_since_update < 1: detected - # 2) i) hit_streak >= min_hits: 最小的连续命中 - # ii) frame_count <= min_hits: 最开始的几帧 - if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits): - ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive - i -= 1 - - # remove dead tracklet - # 1) time_since_update >= max_age: 多久没有更新了 - # 2) predict_num: 连续预测的帧数 - # 3) out of image size - if (trk.time_since_update >= self.max_age) or (trk.predict_num >= detect_interval) or ( - d[2] < 0 or d[3] < 0 or d[0] > img_size[1] or d[1] > img_size[0]): - print(f'Remove tracker: {trk.id + 1}') - self.trackers.pop(i) - if len(ret) > 0: - return np.concatenate(ret) - else: - return np.empty((0, 5)) diff --git a/facexlib/utils/__init__.py b/facexlib/utils/__init__.py deleted file mode 100644 index 706e077..0000000 --- a/facexlib/utils/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back -from .misc import img2tensor, load_file_from_url, scandir - -__all__ = [ - 'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 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zEw7|mbse`>zeZD3P#4v9Yo??w)}wFS!fzi+t54#i@KlzWDFmzEV86g?%LD)V5P0Xg z&wT)KmGQ4VewK0W{decw_`D-HJf2a%!VyhRT_9~-_zN6?a;&Itf=+y{RHpFt7QRlk zR$tPPsIj_D4S}D!f#&ykHT|!7W*+s4pXBokcD~8iH=BHOv+1pGuCK$|YirF@ma2@6 zo9RVs7_y*RrG{!=t8VGaB8sUk!M201`lzn6?5DipHcmC(pFi_|)5aFb diff --git a/facexlib/utils/face_restoration_helper.py b/facexlib/utils/face_restoration_helper.py deleted file mode 100644 index 2c4c75a..0000000 --- a/facexlib/utils/face_restoration_helper.py +++ /dev/null @@ -1,374 +0,0 @@ -import cv2 -import numpy as np -import os -import torch -from torchvision.transforms.functional import normalize - -from facexlib.detection import init_detection_model -from facexlib.parsing import init_parsing_model -from facexlib.utils.misc import img2tensor, imwrite - - -def get_largest_face(det_faces, h, w): - - def get_location(val, length): - if val < 0: - return 0 - elif val > length: - return length - else: - return val - - face_areas = [] - for det_face in det_faces: - left = get_location(det_face[0], w) - right = get_location(det_face[2], w) - top = get_location(det_face[1], h) - bottom = get_location(det_face[3], h) - face_area = (right - left) * (bottom - top) - face_areas.append(face_area) - largest_idx = face_areas.index(max(face_areas)) - return det_faces[largest_idx], largest_idx - - -def get_center_face(det_faces, h=0, w=0, center=None): - if center is not None: - center = np.array(center) - else: - center = np.array([w / 2, h / 2]) - center_dist = [] - for det_face in det_faces: - face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2]) - dist = np.linalg.norm(face_center - center) - center_dist.append(dist) - center_idx = center_dist.index(min(center_dist)) - return det_faces[center_idx], center_idx - - -class FaceRestoreHelper(object): - """Helper for the face restoration pipeline (base class).""" - - def __init__(self, - upscale_factor, - face_size=512, - crop_ratio=(1, 1), - det_model='retinaface_resnet50', - save_ext='png', - template_3points=False, - pad_blur=False, - use_parse=False, - device=None, - model_rootpath=None): - self.template_3points = template_3points # improve robustness - self.upscale_factor = upscale_factor - # the cropped face ratio based on the square face - self.crop_ratio = crop_ratio # (h, w) - assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1' - self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0])) - - if self.template_3points: - self.face_template = np.array([[192, 240], [319, 240], [257, 371]]) - else: - # standard 5 landmarks for FFHQ faces with 512 x 512 - self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935], - [201.26117, 371.41043], [313.08905, 371.15118]]) - self.face_template = self.face_template * (face_size / 512.0) - if self.crop_ratio[0] > 1: - self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2 - if self.crop_ratio[1] > 1: - self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2 - self.save_ext = save_ext - self.pad_blur = pad_blur - if self.pad_blur is True: - self.template_3points = False - - self.all_landmarks_5 = [] - self.det_faces = [] - self.affine_matrices = [] - self.inverse_affine_matrices = [] - self.cropped_faces = [] - self.restored_faces = [] - self.pad_input_imgs = [] - - if device is None: - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - else: - self.device = device - - # init face detection model - self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath) - - # init face parsing model - self.use_parse = use_parse - self.face_parse = init_parsing_model(model_name='parsenet', device=self.device, model_rootpath=model_rootpath) - - def set_upscale_factor(self, upscale_factor): - self.upscale_factor = upscale_factor - - def read_image(self, img): - """img can be image path or cv2 loaded image.""" - # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255] - if isinstance(img, str): - img = cv2.imread(img) - - if np.max(img) > 256: # 16-bit image - img = img / 65535 * 255 - if len(img.shape) == 2: # gray image - img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - elif img.shape[2] == 4: # RGBA image with alpha channel - img = img[:, :, 0:3] - - self.input_img = img - - def get_face_landmarks_5(self, - only_keep_largest=False, - only_center_face=False, - resize=None, - blur_ratio=0.01, - eye_dist_threshold=None): - if resize is None: - scale = 1 - input_img = self.input_img - else: - h, w = self.input_img.shape[0:2] - scale = min(h, w) / resize - h, w = int(h / scale), int(w / scale) - input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4) - - with torch.no_grad(): - bboxes = self.face_det.detect_faces(input_img, 0.97) * scale - for bbox in bboxes: - # remove faces with too small eye distance: side faces or too small faces - eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]]) - if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold): - continue - - if self.template_3points: - landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)]) - else: - landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)]) - self.all_landmarks_5.append(landmark) - self.det_faces.append(bbox[0:5]) - if len(self.det_faces) == 0: - return 0 - if only_keep_largest: - h, w, _ = self.input_img.shape - self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w) - self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]] - elif only_center_face: - h, w, _ = self.input_img.shape - self.det_faces, center_idx = get_center_face(self.det_faces, h, w) - self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] - - # pad blurry images - if self.pad_blur: - self.pad_input_imgs = [] - for landmarks in self.all_landmarks_5: - # get landmarks - eye_left = landmarks[0, :] - eye_right = landmarks[1, :] - eye_avg = (eye_left + eye_right) * 0.5 - mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5 - eye_to_eye = eye_right - eye_left - eye_to_mouth = mouth_avg - eye_avg - - # Get the oriented crop rectangle - # x: half width of the oriented crop rectangle - x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] - # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise - # norm with the hypotenuse: get the direction - x /= np.hypot(*x) # get the hypotenuse of a right triangle - rect_scale = 1.5 - x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) - # y: half height of the oriented crop rectangle - y = np.flipud(x) * [-1, 1] - - # c: center - c = eye_avg + eye_to_mouth * 0.1 - # quad: (left_top, left_bottom, right_bottom, right_top) - quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) - # qsize: side length of the square - qsize = np.hypot(*x) * 2 - border = max(int(np.rint(qsize * 0.1)), 3) - - # get pad - # pad: (width_left, height_top, width_right, height_bottom) - pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), - int(np.ceil(max(quad[:, 1])))) - pad = [ - max(-pad[0] + border, 1), - max(-pad[1] + border, 1), - max(pad[2] - self.input_img.shape[0] + border, 1), - max(pad[3] - self.input_img.shape[1] + border, 1) - ] - - if max(pad) > 1: - # pad image - pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') - # modify landmark coords - landmarks[:, 0] += pad[0] - landmarks[:, 1] += pad[1] - # blur pad images - h, w, _ = pad_img.shape - y, x, _ = np.ogrid[:h, :w, :1] - mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], - np.float32(w - 1 - x) / pad[2]), - 1.0 - np.minimum(np.float32(y) / pad[1], - np.float32(h - 1 - y) / pad[3])) - blur = int(qsize * blur_ratio) - if blur % 2 == 0: - blur += 1 - blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur)) - # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0) - - pad_img = pad_img.astype('float32') - pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) - pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0) - pad_img = np.clip(pad_img, 0, 255) # float32, [0, 255] - self.pad_input_imgs.append(pad_img) - else: - self.pad_input_imgs.append(np.copy(self.input_img)) - - return len(self.all_landmarks_5) - - def align_warp_face(self, save_cropped_path=None, border_mode='constant'): - """Align and warp faces with face template. - """ - if self.pad_blur: - assert len(self.pad_input_imgs) == len( - self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}' - for idx, landmark in enumerate(self.all_landmarks_5): - # use 5 landmarks to get affine matrix - # use cv2.LMEDS method for the equivalence to skimage transform - # ref: https://blog.csdn.net/yichxi/article/details/115827338 - affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] - self.affine_matrices.append(affine_matrix) - # warp and crop faces - if border_mode == 'constant': - border_mode = cv2.BORDER_CONSTANT - elif border_mode == 'reflect101': - border_mode = cv2.BORDER_REFLECT101 - elif border_mode == 'reflect': - border_mode = cv2.BORDER_REFLECT - if self.pad_blur: - input_img = self.pad_input_imgs[idx] - else: - input_img = self.input_img - cropped_face = cv2.warpAffine( - input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) # gray - self.cropped_faces.append(cropped_face) - # save the cropped face - if save_cropped_path is not None: - path = os.path.splitext(save_cropped_path)[0] - save_path = f'{path}_{idx:02d}.{self.save_ext}' - imwrite(cropped_face, save_path) - - def get_inverse_affine(self, save_inverse_affine_path=None): - """Get inverse affine matrix.""" - for idx, affine_matrix in enumerate(self.affine_matrices): - inverse_affine = cv2.invertAffineTransform(affine_matrix) - inverse_affine *= self.upscale_factor - self.inverse_affine_matrices.append(inverse_affine) - # save inverse affine matrices - if save_inverse_affine_path is not None: - path, _ = os.path.splitext(save_inverse_affine_path) - save_path = f'{path}_{idx:02d}.pth' - torch.save(inverse_affine, save_path) - - def add_restored_face(self, face): - self.restored_faces.append(face) - - def paste_faces_to_input_image(self, save_path=None, upsample_img=None): - h, w, _ = self.input_img.shape - h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) - - if upsample_img is None: - # simply resize the background - upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) - else: - upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) - - assert len(self.restored_faces) == len( - self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.') - for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices): - # Add an offset to inverse affine matrix, for more precise back alignment - if self.upscale_factor > 1: - extra_offset = 0.5 * self.upscale_factor - else: - extra_offset = 0 - inverse_affine[:, 2] += extra_offset - inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up)) - - if self.use_parse: - # inference - face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR) - face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True) - normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) - face_input = torch.unsqueeze(face_input, 0).to(self.device) - with torch.no_grad(): - out = self.face_parse(face_input)[0] - out = out.argmax(dim=1).squeeze().cpu().numpy() - - mask = np.zeros(out.shape) - MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] - for idx, color in enumerate(MASK_COLORMAP): - mask[out == idx] = color - # blur the mask - mask = cv2.GaussianBlur(mask, (101, 101), 11) - mask = cv2.GaussianBlur(mask, (101, 101), 11) - # remove the black borders - thres = 10 - mask[:thres, :] = 0 - mask[-thres:, :] = 0 - mask[:, :thres] = 0 - mask[:, -thres:] = 0 - mask = mask / 255. - - mask = cv2.resize(mask, restored_face.shape[:2]) - mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3) - inv_soft_mask = mask[:, :, None] - pasted_face = inv_restored - - else: # use square parse maps - mask = np.ones(self.face_size, dtype=np.float32) - inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) - # remove the black borders - inv_mask_erosion = cv2.erode( - inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8)) - pasted_face = inv_mask_erosion[:, :, None] * inv_restored - total_face_area = np.sum(inv_mask_erosion) # // 3 - # compute the fusion edge based on the area of face - w_edge = int(total_face_area**0.5) // 20 - erosion_radius = w_edge * 2 - inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) - blur_size = w_edge * 2 - inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) - if len(upsample_img.shape) == 2: # upsample_img is gray image - upsample_img = upsample_img[:, :, None] - inv_soft_mask = inv_soft_mask[:, :, None] - - if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel - alpha = upsample_img[:, :, 3:] - upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3] - upsample_img = np.concatenate((upsample_img, alpha), axis=2) - else: - upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img - - if np.max(upsample_img) > 256: # 16-bit image - upsample_img = upsample_img.astype(np.uint16) - else: - upsample_img = upsample_img.astype(np.uint8) - if save_path is not None: - path = os.path.splitext(save_path)[0] - save_path = f'{path}.{self.save_ext}' - imwrite(upsample_img, save_path) - return upsample_img - - def clean_all(self): - self.all_landmarks_5 = [] - self.restored_faces = [] - self.affine_matrices = [] - self.cropped_faces = [] - self.inverse_affine_matrices = [] - self.det_faces = [] - self.pad_input_imgs = [] diff --git a/facexlib/utils/face_utils.py b/facexlib/utils/face_utils.py deleted file mode 100644 index 4602a63..0000000 --- a/facexlib/utils/face_utils.py +++ /dev/null @@ -1,250 +0,0 @@ -import cv2 -import numpy as np -import torch - - -def compute_increased_bbox(bbox, increase_area, preserve_aspect=True): - left, top, right, bot = bbox - width = right - left - height = bot - top - - if preserve_aspect: - width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width)) - height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height)) - else: - width_increase = height_increase = increase_area - left = int(left - width_increase * width) - top = int(top - height_increase * height) - right = int(right + width_increase * width) - bot = int(bot + height_increase * height) - return (left, top, right, bot) - - -def get_valid_bboxes(bboxes, h, w): - left = max(bboxes[0], 0) - top = max(bboxes[1], 0) - right = min(bboxes[2], w) - bottom = min(bboxes[3], h) - return (left, top, right, bottom) - - -def align_crop_face_landmarks(img, - landmarks, - output_size, - transform_size=None, - enable_padding=True, - return_inverse_affine=False, - shrink_ratio=(1, 1)): - """Align and crop face with landmarks. - - The output_size and transform_size are based on width. The height is - adjusted based on shrink_ratio_h/shring_ration_w. - - Modified from: - https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py - - Args: - img (Numpy array): Input image. - landmarks (Numpy array): 5 or 68 or 98 landmarks. - output_size (int): Output face size. - transform_size (ing): Transform size. Usually the four time of - output_size. - enable_padding (float): Default: True. - shrink_ratio (float | tuple[float] | list[float]): Shring the whole - face for height and width (crop larger area). Default: (1, 1). - - Returns: - (Numpy array): Cropped face. - """ - lm_type = 'retinaface_5' # Options: dlib_5, retinaface_5 - - if isinstance(shrink_ratio, (float, int)): - shrink_ratio = (shrink_ratio, shrink_ratio) - if transform_size is None: - transform_size = output_size * 4 - - # Parse landmarks - lm = np.array(landmarks) - if lm.shape[0] == 5 and lm_type == 'retinaface_5': - eye_left = lm[0] - eye_right = lm[1] - mouth_avg = (lm[3] + lm[4]) * 0.5 - elif lm.shape[0] == 5 and lm_type == 'dlib_5': - lm_eye_left = lm[2:4] - lm_eye_right = lm[0:2] - eye_left = np.mean(lm_eye_left, axis=0) - eye_right = np.mean(lm_eye_right, axis=0) - mouth_avg = lm[4] - elif lm.shape[0] == 68: - lm_eye_left = lm[36:42] - lm_eye_right = lm[42:48] - eye_left = np.mean(lm_eye_left, axis=0) - eye_right = np.mean(lm_eye_right, axis=0) - mouth_avg = (lm[48] + lm[54]) * 0.5 - elif lm.shape[0] == 98: - lm_eye_left = lm[60:68] - lm_eye_right = lm[68:76] - eye_left = np.mean(lm_eye_left, axis=0) - eye_right = np.mean(lm_eye_right, axis=0) - mouth_avg = (lm[76] + lm[82]) * 0.5 - - eye_avg = (eye_left + eye_right) * 0.5 - eye_to_eye = eye_right - eye_left - eye_to_mouth = mouth_avg - eye_avg - - # Get the oriented crop rectangle - # x: half width of the oriented crop rectangle - x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] - # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise - # norm with the hypotenuse: get the direction - x /= np.hypot(*x) # get the hypotenuse of a right triangle - rect_scale = 1 # TODO: you can edit it to get larger rect - x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) - # y: half height of the oriented crop rectangle - y = np.flipud(x) * [-1, 1] - - x *= shrink_ratio[1] # width - y *= shrink_ratio[0] # height - - # c: center - c = eye_avg + eye_to_mouth * 0.1 - # quad: (left_top, left_bottom, right_bottom, right_top) - quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) - # qsize: side length of the square - qsize = np.hypot(*x) * 2 - - quad_ori = np.copy(quad) - # Shrink, for large face - # TODO: do we really need shrink - shrink = int(np.floor(qsize / output_size * 0.5)) - if shrink > 1: - h, w = img.shape[0:2] - rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink))) - img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA) - quad /= shrink - qsize /= shrink - - # Crop - h, w = img.shape[0:2] - border = max(int(np.rint(qsize * 0.1)), 3) - crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), - int(np.ceil(max(quad[:, 1])))) - crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h)) - if crop[2] - crop[0] < w or crop[3] - crop[1] < h: - img = img[crop[1]:crop[3], crop[0]:crop[2], :] - quad -= crop[0:2] - - # Pad - # pad: (width_left, height_top, width_right, height_bottom) - h, w = img.shape[0:2] - pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), - int(np.ceil(max(quad[:, 1])))) - pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0)) - if enable_padding and max(pad) > border - 4: - pad = np.maximum(pad, int(np.rint(qsize * 0.3))) - img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') - h, w = img.shape[0:2] - y, x, _ = np.ogrid[:h, :w, :1] - mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], - np.float32(w - 1 - x) / pad[2]), - 1.0 - np.minimum(np.float32(y) / pad[1], - np.float32(h - 1 - y) / pad[3])) - blur = int(qsize * 0.02) - if blur % 2 == 0: - blur += 1 - blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur)) - - img = img.astype('float32') - img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) - img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) - img = np.clip(img, 0, 255) # float32, [0, 255] - quad += pad[:2] - - # Transform use cv2 - h_ratio = shrink_ratio[0] / shrink_ratio[1] - dst_h, dst_w = int(transform_size * h_ratio), transform_size - template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) - # use cv2.LMEDS method for the equivalence to skimage transform - # ref: https://blog.csdn.net/yichxi/article/details/115827338 - affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0] - cropped_face = cv2.warpAffine( - img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132)) # gray - - if output_size < transform_size: - cropped_face = cv2.resize( - cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR) - - if return_inverse_affine: - dst_h, dst_w = int(output_size * h_ratio), output_size - template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) - # use cv2.LMEDS method for the equivalence to skimage transform - # ref: https://blog.csdn.net/yichxi/article/details/115827338 - affine_matrix = cv2.estimateAffinePartial2D( - quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0] - inverse_affine = cv2.invertAffineTransform(affine_matrix) - else: - inverse_affine = None - return cropped_face, inverse_affine - - -def paste_face_back(img, face, inverse_affine): - h, w = img.shape[0:2] - face_h, face_w = face.shape[0:2] - inv_restored = cv2.warpAffine(face, inverse_affine, (w, h)) - mask = np.ones((face_h, face_w, 3), dtype=np.float32) - inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h)) - # remove the black borders - inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8)) - inv_restored_remove_border = inv_mask_erosion * inv_restored - total_face_area = np.sum(inv_mask_erosion) // 3 - # compute the fusion edge based on the area of face - w_edge = int(total_face_area**0.5) // 20 - erosion_radius = w_edge * 2 - inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) - blur_size = w_edge * 2 - inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) - img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img - # float32, [0, 255] - return img - - -if __name__ == '__main__': - import os - - from facexlib.detection import init_detection_model - from facexlib.utils.face_restoration_helper import get_largest_face - from facexlib.visualization import visualize_detection - - img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png' - img_name = os.splitext(os.path.basename(img_path))[0] - - # initialize model - det_net = init_detection_model('retinaface_resnet50', half=False) - img_ori = cv2.imread(img_path) - h, w = img_ori.shape[0:2] - # if larger than 800, scale it - scale = max(h / 800, w / 800) - if scale > 1: - img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR) - - with torch.no_grad(): - bboxes = det_net.detect_faces(img, 0.97) - if scale > 1: - bboxes *= scale # the score is incorrect - bboxes = get_largest_face(bboxes, h, w)[0] - visualize_detection(img_ori, [bboxes], f'tmp/{img_name}_det.png') - - landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)]) - - cropped_face, inverse_affine = align_crop_face_landmarks( - img_ori, - landmarks, - output_size=512, - transform_size=None, - enable_padding=True, - return_inverse_affine=True, - shrink_ratio=(1, 1)) - - cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face) - img = paste_face_back(img_ori, cropped_face, inverse_affine) - cv2.imwrite(f'tmp/{img_name}_back.png', img) diff --git a/facexlib/utils/misc.py b/facexlib/utils/misc.py deleted file mode 100644 index b1a597c..0000000 --- a/facexlib/utils/misc.py +++ /dev/null @@ -1,118 +0,0 @@ -import cv2 -import os -import os.path as osp -import torch -from torch.hub import download_url_to_file, get_dir -from urllib.parse import urlparse - -ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - - -def imwrite(img, file_path, params=None, auto_mkdir=True): - """Write image to file. - - Args: - img (ndarray): Image array to be written. - file_path (str): Image file path. - params (None or list): Same as opencv's :func:`imwrite` interface. - auto_mkdir (bool): If the parent folder of `file_path` does not exist, - whether to create it automatically. - - Returns: - bool: Successful or not. - """ - if auto_mkdir: - dir_name = os.path.abspath(os.path.dirname(file_path)) - os.makedirs(dir_name, exist_ok=True) - return cv2.imwrite(file_path, img, params) - - -def img2tensor(imgs, bgr2rgb=True, float32=True): - """Numpy array to tensor. - - Args: - imgs (list[ndarray] | ndarray): Input images. - bgr2rgb (bool): Whether to change bgr to rgb. - float32 (bool): Whether to change to float32. - - Returns: - list[tensor] | tensor: Tensor images. If returned results only have - one element, just return tensor. - """ - - def _totensor(img, bgr2rgb, float32): - if img.shape[2] == 3 and bgr2rgb: - if img.dtype == 'float64': - img = img.astype('float32') - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - img = torch.from_numpy(img.transpose(2, 0, 1)) - if float32: - img = img.float() - return img - - if isinstance(imgs, list): - return [_totensor(img, bgr2rgb, float32) for img in imgs] - else: - return _totensor(imgs, bgr2rgb, float32) - - -def load_file_from_url(url, model_dir=None, progress=True, file_name=None, save_dir=None): - """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py - """ - if model_dir is None: - hub_dir = get_dir() - model_dir = os.path.join(hub_dir, 'checkpoints') - - if save_dir is None: - save_dir = os.path.join(ROOT_DIR, model_dir) - os.makedirs(save_dir, exist_ok=True) - - parts = urlparse(url) - filename = os.path.basename(parts.path) - if file_name is not None: - filename = file_name - cached_file = os.path.abspath(os.path.join(save_dir, filename)) - if not os.path.exists(cached_file): - print(f'Downloading: "{url}" to {cached_file}\n') - download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) - return cached_file - - -def scandir(dir_path, suffix=None, recursive=False, full_path=False): - """Scan a directory to find the interested files. - Args: - dir_path (str): Path of the directory. - suffix (str | tuple(str), optional): File suffix that we are - interested in. Default: None. - recursive (bool, optional): If set to True, recursively scan the - directory. Default: False. - full_path (bool, optional): If set to True, include the dir_path. - Default: False. - Returns: - A generator for all the interested files with relative paths. - """ - - if (suffix is not None) and not isinstance(suffix, (str, tuple)): - raise TypeError('"suffix" must be a string or tuple of strings') - - root = dir_path - - def _scandir(dir_path, suffix, recursive): - for entry in os.scandir(dir_path): - if not entry.name.startswith('.') and entry.is_file(): - if full_path: - return_path = entry.path - else: - return_path = osp.relpath(entry.path, root) - - if suffix is None: - yield return_path - elif return_path.endswith(suffix): - yield return_path - else: - if recursive: - yield from _scandir(entry.path, suffix=suffix, recursive=recursive) - else: - continue - - return _scandir(dir_path, suffix=suffix, recursive=recursive) diff --git a/facexlib/visualization/__init__.py b/facexlib/visualization/__init__.py deleted file mode 100644 index 290fee7..0000000 --- a/facexlib/visualization/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .vis_alignment import visualize_alignment -from .vis_detection import visualize_detection -from .vis_headpose import visualize_headpose - -__all__ = ['visualize_detection', 'visualize_alignment', 'visualize_headpose'] diff --git a/facexlib/visualization/__pycache__/__init__.cpython-310.pyc b/facexlib/visualization/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index fa40eb81f2674c001ad414af47670c84ed45c349..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 358 zcmY*UyH3L}6t&|dtx&SSS7brVs6q^g4TNB`ELmxNTa4_uvRy>^95((0ejyVQ6TiUJ z>!8y1T0Xw7b9C(L`iNkBd`{Olb7Z2MCBFv(89k!2m+mFzyr3=t7&rr%I zKR!peZAQGty`A1L`|JIS4J2Nry{*!~?ne@dM#^{M%OsiLcXD+uZq|3+J)sqG-Ok`1 sLUbOym}Bu`^i*uEwaKfHQg=E^dAeNp%MIsgCw diff --git a/facexlib/visualization/__pycache__/vis_alignment.cpython-310.pyc b/facexlib/visualization/__pycache__/vis_alignment.cpython-310.pyc deleted file mode 100644 index 2271ef5433ff4d9dd3ebf87b08f49812bcc799a4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 663 zcmYjP&2G~`5Z>8cJ5Ep{1Qbz_5aNLFg+q@BA!w>pNRXoRW(7r#cau8xPrBZuO=X`7 zufVxE@+R;Kd*zf@;8JE?emd5CGqc}(*6e8WcDqH;zW*3c2qomFEp8ga;thK77(oyL z2??$v1XMVJ3H})ZCg70x?go2EzZY>E|5y{F^)7nx6+x3rD9IWsQj!@JAm|smO@_4M zm7|&FB`=)|D%c}(0hL=a^+Y?DZb`H=Ypf}%V2^xyST)K9#;wXj)I(H@5UyeX8sl>dbE$ zn#~Q5^7+a*#ZfqyhQ*mSEDg^Li82`(r=Xi1^%rLuqnMp1++icp1sHf>s@l2&Q?3#lx}>jW1k4(m;5 zs`S*p1()W?o4_mhgv80Oz@^M?LMd5mXJ_Wy8Lwx@K_-(%Fuwn&H%t|wU&6RP6fj=E z43|JqM6r*mqZL;P)u=+%b6iy@-bZF4hCQfUav;&w&_GMUEW!*+7$J@>@Bj_*DH;805uIVFso>S-!DppBWGCVNXr}$2hkAIAh7DvN(qamsXo*oj$BO1ol}xcOQr(F|Iv)3*)hE_L1!eL z9n+f=G$*MxrgIbYmZZ1GbbdrX&kP7%=wdd6^Gy#3)Qn(67xv{e7f*sWUf8vL@5D8z z%Uy?iLEB(%*!6i>NT&@LYpfZrN=;Z^t7+UXcUzslVKZj;%~j)NyVKg=+`#=ix-^}iw=c&=@l;>rJkA$kmt#Wfb3*|KxH!%k#;9i zouJc?6sNZw=}wOq13zF&sApC<#?LaF2PUz~@J~(r)>{+TN_}Z*RW0 zHeT=TY`%+B-wT1a>`s(oP<*@H^xY`oc+7z{>9xKvkGrM@DO%0yT)iF~yP;*bspW%c z+3Zsor9!*sS{<8zj1oMs>P;5m`ZvVxz;S=CZLHQ__4inC1lemP+i@#y$OGoGS_6z@ z->cVp;|{RJ-3!aQ>VyJ(WbQ9_|M^7~<s5pyt@|a}F1H43*uy_pc%wYAB-4it= r<&vqg9H^1j7LCJZL6=M(@|w6SHtA1W0qy$kGg0tRT&|9EoK^k+z*q)I diff --git a/facexlib/visualization/__pycache__/vis_headpose.cpython-310.pyc b/facexlib/visualization/__pycache__/vis_headpose.cpython-310.pyc deleted file mode 100644 index fd4126ca82dbdda170209710630c3af4ee745c4c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3142 zcmbtWPj4GV6rb5Y-nAY7Nn%KvLRm^dI8>pe5{C#u)mG^RQb>`IOpxVxHt`1AYt3#F z+uEE8$9@8+lpK5K18{_IfcAvM0a<(lF6H-TZL_x0N{C(Un|W{EpLsLyy?uV6kYjj$ z`E~!`uM%T_5R!g$Ab0QucL4+wyv=IK$ZHyKt*v*An$a<9CTC(n7{dIH*D|6YEMa42 ziK57fJZ84wk65)df*!V0)ndKr2eBS_-H`yLX%T(=U?Yt1d-#GMIb)F)vLjxB`K#1|UG0`yLUY%%K z0RLFK+DTzXv$8(6Sq#ndOvFMfGO$l8G9U8;4P#beIl|~lE^s+7;LIz|1;v?pDQ7`( z78NH>j&w#0S85L{LN-pFmfH|c&ktOu(f5Ll(NDO&oskCkOqaMsroiYqW+Mu#3Duq~ zfiRsb-e-2Kb$hYa^I|h-HF~ZjZIXl$J|~;8(e}D-tb5(CTEfsdjP*g|G&Xx)*lfjy z^xN%N4@IBAAT|PT=*F3r>m9bj*gW+_*ow9O2eCH5*Wbj5uYVik?PpA0hnc_c+~3;! zZ1B1CkKAUsx7}#EyKWHr(v^D$K>BTOfA7Q#jvH-n*a$tpOIV$bf=*(iH;A)zICVOb z3={^WwWybQk(ae4ZsT3mZ}Bzl7VtH`uC4QRZfNob*#5-F*w6cSW|E@C^p>n(euBDn~E$ z?|*7e-Og0gr5=d+Jx8n=4ce;0IsSnnQTl8cnh<*sERn1^g5jt(SrK` z#f+-td;#@*Nh|X*UqvNf-sw1!n>{!yumlQ@%)Caphj>DbkzDP6m2)j>&ZjT zPiy>dKtguTBX+^h&}ztHsGaNh>1#*Eg?5IfDB=Rm&AEAj>imO&{6NE#!p*0sE0Lb2 zJ=IY4GSGnLA<75~)!w3YVXZP`m5nz`ecKZYje`vyWg*d~Nc7aYDsN;*{w)-*NML>CIGaE6U({ryEH~s