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swin_mae.py
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swin_mae.py
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from functools import partial
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from swin_unet import PatchEmbedding, BasicBlock, PatchExpanding, BasicBlockUp
from utils.pos_embed import get_2d_sincos_pos_embed
# +
class SwinMAE(nn.Module):
"""
Masked Auto Encoder with Swin Transformer backbone
"""
def __init__(self, img_size: int = 224, patch_size: int = 4, mask_ratio: float = 0.35, in_chans: int = 3,
decoder_embed_dim=512, norm_pix_loss=False,
depths: tuple = (2, 2, 6, 2), embed_dim: int = 96, num_heads: tuple = (3, 6, 12, 24),
window_size: int = 7, qkv_bias: bool = True, mlp_ratio: float = 4.,
drop_path_rate: float = 0.1, drop_rate: float = 0., attn_drop_rate: float = 0.,
norm_layer=None, patch_norm: bool = True):
super().__init__()
self.mask_ratio = mask_ratio
assert img_size % patch_size == 0
self.num_patches = (img_size // patch_size) ** 2
self.patch_size = patch_size
self.norm_pix_loss = norm_pix_loss
self.num_layers = len(depths)
self.depths = depths
self.embed_dim = embed_dim
self.num_heads = num_heads
self.drop_path = drop_path_rate
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.drop_rate = drop_rate
self.attn_drop_rate = attn_drop_rate
self.norm_layer = norm_layer
self.patch_embed = PatchEmbedding(patch_size=patch_size, in_c=in_chans, embed_dim=embed_dim,
norm_layer=norm_layer if patch_norm else None)
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches, embed_dim), requires_grad=False)
self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.layers = self.build_layers()
self.first_patch_expanding = PatchExpanding(dim=decoder_embed_dim, norm_layer=norm_layer)
self.layers_up = self.build_layers_up()
self.norm_up = norm_layer(embed_dim)
self.decoder_pred = nn.Linear(decoder_embed_dim // 8, patch_size ** 2 * in_chans, bias=True)
self.initialize_weights()
def initialize_weights(self):
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.num_patches ** .5), cls_token=False)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
torch.nn.init.normal_(self.mask_token, std=.02)
self.apply(self._init_weights)
@staticmethod
def _init_weights(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
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)
def patchify(self, imgs):
"""
imgs: (N, 3, H, W)
x: (N, L, patch_size**2 *3)
"""
p = self.patch_size
assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0
h = w = imgs.shape[2] // p
x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
x = torch.einsum('nchpwq->nhwpqc', x)
x = x.reshape(imgs.shape[0], h * w, p ** 2 * 3)
return x
def unpatchify(self, x):
"""
x: (N, L, patch_size**2 *3)
imgs: (N, 3, H, W)
"""
p = self.patch_size
h = w = int(x.shape[1] ** .5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(x.shape[0], 3, h * p, h * p)
return imgs
def window_masking(self, x: torch.Tensor, r: int = 4,
remove: bool = False, mask_len_sparse: bool = False):
"""
The new masking method, masking the adjacent r*r number of patches together
Optional whether to remove the mask patch,
if so, the return value returns one more sparse_restore for restoring the order to x
Optionally, the returned mask index is sparse length or original length,
which corresponds to the different size choices of the decoder when restoring the image
x: [N, L, D]
r: There are r*r patches in a window
remove: Whether to remove the mask patch
mask_len_sparse: Whether the returned mask length is a sparse short length
"""
x = rearrange(x, 'B H W C -> B (H W) C')
B, L, D = x.shape
assert int(L ** 0.5 / r) == L ** 0.5 / r
d = int(L ** 0.5 // r)
noise = torch.rand(B, d ** 2, device=x.device)
sparse_shuffle = torch.argsort(noise, dim=1)
sparse_restore = torch.argsort(sparse_shuffle, dim=1)
sparse_keep = sparse_shuffle[:, :int(d ** 2 * (1 - self.mask_ratio))]
index_keep_part = torch.div(sparse_keep, d, rounding_mode='floor') * d * r ** 2 + sparse_keep % d * r
index_keep = index_keep_part
for i in range(r):
for j in range(r):
if i == 0 and j == 0:
continue
index_keep = torch.cat([index_keep, index_keep_part + int(L ** 0.5) * i + j], dim=1)
index_all = np.expand_dims(range(L), axis=0).repeat(B, axis=0)
index_mask = np.zeros([B, int(L - index_keep.shape[-1])], dtype=np.int)
for i in range(B):
index_mask[i] = np.setdiff1d(index_all[i], index_keep.cpu().numpy()[i], assume_unique=True)
index_mask = torch.tensor(index_mask, device=x.device)
index_shuffle = torch.cat([index_keep, index_mask], dim=1)
index_restore = torch.argsort(index_shuffle, dim=1)
if mask_len_sparse:
mask = torch.ones([B, d ** 2], device=x.device)
mask[:, :sparse_keep.shape[-1]] = 0
mask = torch.gather(mask, dim=1, index=sparse_restore)
else:
mask = torch.ones([B, L], device=x.device)
mask[:, :index_keep.shape[-1]] = 0
mask = torch.gather(mask, dim=1, index=index_restore)
if remove:
x_masked = torch.gather(x, dim=1, index=index_keep.unsqueeze(-1).repeat(1, 1, D))
x_masked = rearrange(x_masked, 'B (H W) C -> B H W C', H=int(x_masked.shape[1] ** 0.5))
return x_masked, mask, sparse_restore
else:
x_masked = torch.clone(x)
for i in range(B):
x_masked[i, index_mask.cpu().numpy()[i, :], :] = self.mask_token
x_masked = rearrange(x_masked, 'B (H W) C -> B H W C', H=int(x_masked.shape[1] ** 0.5))
return x_masked, mask
def window_masking_(self, x: torch.Tensor, r: int = 4,
remove: bool = False, mask_len_sparse: bool = False):
"""
The new masking method, masking the adjacent r*r number of patches together
Optional whether to remove the mask patch,
if so, the return value returns one more sparse_restore for restoring the order to x
Optionally, the returned mask index is sparse length or original length,
which corresponds to the different size choices of the decoder when restoring the image
x: [N, L, D]
r: There are r*r patches in a window
remove: Whether to remove the mask patch
mask_len_sparse: Whether the returned mask length is a sparse short length
"""
x = rearrange(x, 'B H W C -> B (H W) C')
B, L, D = x.shape
assert int(L ** 0.5 / r) == L ** 0.5 / r
d = int(L ** 0.5 // r)
noise = torch.rand(B, d ** 2, device=x.device)
sparse_shuffle = torch.argsort(noise, dim=1)
sparse_restore = torch.argsort(sparse_shuffle, dim=1)
# noise = torch.tensor(list(range(0,d**2))).unsqueeze(dim=0)
# sparse_restore = noise.to(x.device)
# print('B',B)
# print('d',d)
#print(sparse_restore)
# sparse_keep = sparse_shuffle[:, :int(d ** 2 * (1 - self.mask_ratio))] #commented by Rashmi
#added by rashmi
sparse_keep = sparse_shuffle[:, :int(d ** 2 * (1 - self.mask_ratio))]
#*******************Bottom left*************************************************
# sorted_val, indices = torch.sort(sparse_restore) #added by Rashmi
# sparse_keep = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15,
# 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27,28, 29, 30, 31, 32, 33,34,35,36,
# 37, 38, 39, 40, 41,42, 43,44, 45,46, 47,48, 49,50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62,
# 63, 64, 65, 66, 67, 68, 69,70,71,72,73,74,75,76,77,78,79,80, 81, 82, 83, 84, 85,86,87,88,89,90,91,92, 93, 94, 95, 96, 97,105, 106,
# 107, 108,109, 110,111, 119, 120, 121, 122,
# 123, 124,125,133,134, 135,136, 137, 138,139, 147, 148, 149, 150, 151,152, 153, 161, 162, 163, 164, 165,166,167,176, 177, 178, 179,180, 181, 182, 190,
# 191, 192, 193, 194,195]]) #Bottom left
#***********************Bottom right**************************************************
# sparse_keep = torch.tensor([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15,
# 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27,28, 29, 30, 31, 32, 33, 34,35,
# 36, 37, 38, 39, 40, 41, 42, 43, 44,45,46, 47, 48, 49, 50,51, 52,53, 54, 55,
# 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
# 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
# 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99,100, 101,102, 103, 104,110,111, 112, 113, 114, 115, 116, 117,118,124, 125, 126, 127, 128, 129, 130, 131,132, 138,139,140, 141,
# 142, 143, 144,145, 146,152,153, 154, 155, 156,
# 157, 158, 159, 160,166,167,168, 169, 170, 171, 172,
# 173,174,175,176,177,178,179,180,181, 182, 183, 184, 185, 186, 187,188,189,190,191,192,193,194,195]]) #Bottom right
# print("sparse keep indexes are:")
# #***********************Bottom right with checkerboard mask**************************************************
# sparse_keep = torch.tensor([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15,
# 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27,28, 29, 30, 31, 32, 33, 34,35,
# 36, 37, 38, 39, 40, 41, 42, 43, 44,45,46, 47, 48, 49, 50,51, 52,53, 54, 55,
# 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
# 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
# 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99,100, 101,102, 103, 104,105,107,109,110,111, 112, 113, 114, 115, 116, 117,118,120,122,124, 125, 126, 127, 128, 129, 130, 131,132,133,135,137, 138,139,140, 141,
# 142, 143, 144,145, 146,147,149,151,152,153, 154, 155, 156,
# 157, 158, 159, 160,162,164,166,167,168, 169, 170, 171, 172,
# 173,174,175,176,177,178,179,180,181, 182, 183, 184, 185, 186, 187,188,189,190,191,192,193,194,195]]) #Bottom right
# print("sparse keep indexes are:")
#***************************Top left*****************************************************
# sparse_keep = torch.tensor([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14,15,16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27,35,36,37, 38, 39, 40, 41,49,50,51, 52,53, 54, 55,63,64,65, 66, 67, 68, 69, 77, 78, 79, 80, 81, 82, 83, 91, 92, 93, 94, 95, 96, 97,98, 99,100, 101,102, 103, 104, 105, 106,
# 107, 108,109, 110,111, 112, 113, 114, 115, 116, 117,118, 119, 120, 121, 122,
# 123, 124,125, 126, 127, 128, 129, 130, 131,132,133,134, 135,136, 137, 138,139, 140, 141,
# 142, 143, 144,145, 146, 147, 148, 149, 150, 151,152, 153, 154, 155, 156,
# 157, 158, 159, 160, 161, 162, 163, 164, 165,166,167,168, 169, 170, 171, 172,
# 173,174,175, 176, 177, 178, 179,180, 181, 182, 183, 184, 185, 186, 187,188, 189,190,
# 191, 192, 193, 194,195]])
#***********************************************************
#**************************Top Right******************************************************
# sparse_keep = torch.tensor([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15,
# 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27,28, 29, 30, 31, 32, 33,
# 41,42,43, 44,45,46, 47,55,56,57, 58, 59, 60, 61,69,70, 71, 72,
# 73, 74, 75,83,84, 85, 86, 87,
# 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99,100, 101,102, 103, 104, 105, 106,
# 107, 108,109, 110,111, 112, 113, 114, 115, 116, 117,118, 119, 120, 121, 122,
# 123, 124,125, 126, 127, 128, 129, 130, 131,132,133,134, 135,136, 137, 138,139, 140, 141,
# 142, 143, 144,145, 146, 147, 148, 149, 150, 151,152, 153, 154, 155, 156,
# 157, 158, 159, 160, 161, 162, 163, 164, 165,166,167,168, 169, 170, 171, 172,
# 173,174,175, 176, 177, 178, 179,180, 181, 182, 183, 184, 185, 186, 187,188, 189,190,
# 191, 192, 193, 194,195]])
# #************************************Top RIght and small masking square*******************************###
# sparse_keep = torch.tensor([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15,
# 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27,28, 29, 30, 31, 32,33,34,
# 35,36,37,38,39,40,41,42,43, 44,45,48,49,50,51,52,53,54,55,56,57, 58, 59,62,63,64,65,66,67,68,69,70, 71, 72,
# 73, 74, 75,78,79,80,81,82,83,84, 85, 86, 87,
# 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99,100, 101,102, 103, 104, 105, 106,
# 107, 108,109, 110,111, 112, 113, 114, 115, 116, 117,118, 119, 120, 121, 122,
# 123, 124,125, 126, 127, 128, 129, 130, 131,132,133,134, 135,136, 137, 138,139, 140, 141,
# 142, 143, 144,145, 146, 147, 148, 149, 150, 151,152, 153, 154, 155, 156,
# 157, 158, 159, 160, 161, 162, 163, 164, 165,166,167,168, 169, 170, 171, 172,
# 173,174,175, 176, 177, 178, 179,180, 181, 182, 183, 184, 185, 186, 187,188, 189,190,
# 191, 192, 193, 194,195]])
#***********************************************************
#print(sparse_keep)
# print(len(sparse_keep))
index_keep_part = torch.div(sparse_keep, d, rounding_mode='floor') * d * r ** 2 + sparse_keep % d * r
index_keep = index_keep_part
for i in range(r):
for j in range(r):
if i == 0 and j == 0:
continue
index_keep = torch.cat([index_keep, index_keep_part + int(L ** 0.5) * i + j], dim=1)
index_all = np.expand_dims(range(L), axis=0).repeat(B, axis=0)
index_mask = np.zeros([B, int(L - index_keep.shape[-1])], dtype=np.int)
for i in range(B):
index_mask[i] = np.setdiff1d(index_all[i], index_keep.cpu().numpy()[i], assume_unique=True)
index_mask = torch.tensor(index_mask, device=x.device)
index_shuffle = torch.cat([index_keep, index_mask], dim=1)
index_restore = torch.argsort(index_shuffle, dim=1)
if mask_len_sparse:
mask = torch.ones([B, d ** 2], device=x.device)
mask[:, :sparse_keep.shape[-1]] = 0
mask = torch.gather(mask, dim=1, index=sparse_restore)
else:
mask = torch.ones([B, L], device=x.device)
mask[:, :index_keep.shape[-1]] = 0
mask = torch.gather(mask, dim=1, index=index_restore)
if remove:
x_masked = torch.gather(x, dim=1, index=index_keep.unsqueeze(-1).repeat(1, 1, D))
x_masked = rearrange(x_masked, 'B (H W) C -> B H W C', H=int(x_masked.shape[1] ** 0.5))
return x_masked, mask, sparse_restore
else:
x_masked = torch.clone(x)
for i in range(B):
x_masked[i, index_mask.cpu().numpy()[i, :], :] = self.mask_token
x_masked = rearrange(x_masked, 'B (H W) C -> B H W C', H=int(x_masked.shape[1] ** 0.5))
return x_masked, mask
def build_layers(self):
layers = nn.ModuleList()
for i in range(self.num_layers):
layer = BasicBlock(
index=i,
depths=self.depths,
embed_dim=self.embed_dim,
num_heads=self.num_heads,
drop_path=self.drop_path,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
drop_rate=self.drop_rate,
attn_drop_rate=self.attn_drop_rate,
norm_layer=self.norm_layer,
patch_merging=False if i == self.num_layers - 1 else True)
layers.append(layer)
return layers
def build_layers_up(self):
layers_up = nn.ModuleList()
for i in range(self.num_layers - 1):
layer = BasicBlockUp(
index=i,
depths=self.depths,
embed_dim=self.embed_dim,
num_heads=self.num_heads,
drop_path=self.drop_path,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
drop_rate=self.drop_rate,
attn_drop_rate=self.attn_drop_rate,
patch_expanding=True if i < self.num_layers - 2 else False,
norm_layer=self.norm_layer)
layers_up.append(layer)
return layers_up
def forward_encoder(self, x):
x = self.patch_embed(x)
x, mask = self.window_masking(x, remove=False, mask_len_sparse=False)
for layer in self.layers:
x = layer(x)
return x, mask
def forward_decoder(self, x):
x = self.first_patch_expanding(x)
for layer in self.layers_up:
x = layer(x)
x = self.norm_up(x)
x = rearrange(x, 'B H W C -> B (H W) C')
x = self.decoder_pred(x)
return x
def forward_loss(self, imgs, pred, mask):
"""
imgs: [N, 3, H, W]
pred: [N, L, p*p*3]
mask: [N, L], 0 is keep, 1 is remove,
"""
target = self.patchify(imgs)
if self.norm_pix_loss:
mean = target.mean(dim=-1, keepdim=True)
var = target.var(dim=-1, keepdim=True)
target = (target - mean) / (var + 1.e-6) ** .5
loss = (pred - target) ** 2
loss = loss.mean(dim=-1)
loss = (loss * mask).sum() / mask.sum()
return loss
def forward(self, x):
latent, mask = self.forward_encoder(x)
pred = self.forward_decoder(latent)
loss = self.forward_loss(x, pred, mask)
return loss, pred, mask
# -
def swin_mae(**kwargs):
model = SwinMAE(
img_size=224, patch_size=4, in_chans=3,
decoder_embed_dim=768,
depths=(2, 2, 2, 2), embed_dim=96, num_heads=(3, 6, 12, 24),
window_size=7, qkv_bias=True, mlp_ratio=4,
drop_path_rate=0.1, drop_rate=0, attn_drop_rate=0,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model