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models_img.py
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#!/usr/bin/env python3
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
def he_init(m):
s = np.sqrt(2./ m.in_features)
m.weight.data.normal_(0, s)
class GatedMaskedConv2d(nn.Module):
def __init__(self, in_dim, out_dim=None, kernel_size = 3, mask = 'B'):
super(GatedMaskedConv2d, self).__init__()
if out_dim is None:
out_dim = in_dim
self.dim = out_dim
self.size = kernel_size
self.mask = mask
pad = self.size // 2
#vertical stack
self.v_conv = nn.Conv2d(in_dim, 2*self.dim, kernel_size=(pad+1, self.size))
self.v_pad1 = nn.ConstantPad2d((pad, pad, pad, 0), 0)
self.v_pad2 = nn.ConstantPad2d((0, 0, 1, 0), 0)
self.vh_conv = nn.Conv2d(2*self.dim, 2*self.dim, kernel_size = 1)
#horizontal stack
self.h_conv = nn.Conv2d(in_dim, 2*self.dim, kernel_size=(1, pad+1))
self.h_pad1 = nn.ConstantPad2d((self.size // 2, 0, 0, 0), 0)
self.h_pad2 = nn.ConstantPad2d((1, 0, 0, 0), 0)
self.h_conv_res = nn.Conv2d(self.dim, self.dim, 1)
def forward(self, v_map, h_map):
v_out = self.v_pad2(self.v_conv(self.v_pad1(v_map)))[:, :, :-1, :]
v_map_out = F.tanh(v_out[:, :self.dim])*F.sigmoid(v_out[:, self.dim:])
vh = self.vh_conv(v_out)
h_out = self.h_conv(self.h_pad1(h_map))
if self.mask == 'A':
h_out = self.h_pad2(h_out)[:, :, :, :-1]
h_out = h_out + vh
h_out = F.tanh(h_out[:, :self.dim])*F.sigmoid(h_out[:, self.dim:])
h_map_out = self.h_conv_res(h_out)
if self.mask == 'B':
h_map_out = h_map_out + h_map
return v_map_out, h_map_out
class StackedGatedMaskedConv2d(nn.Module):
def __init__(self,
img_size = [1, 28, 28], layers = [64,64,64],
kernel_size = [7,7,7], latent_dim=64, latent_feature_map = 1):
super(StackedGatedMaskedConv2d, self).__init__()
input_dim = img_size[0]
self.conv_layers = []
if latent_feature_map > 0:
self.latent_feature_map = latent_feature_map
self.z_linear = nn.Linear(latent_dim, latent_feature_map*28*28)
for i in range(len(kernel_size)):
if i == 0:
self.conv_layers.append(GatedMaskedConv2d(input_dim+latent_feature_map,
layers[i], kernel_size[i], 'A'))
else:
self.conv_layers.append(GatedMaskedConv2d(layers[i-1], layers[i], kernel_size[i]))
self.modules = nn.ModuleList(self.conv_layers)
def forward(self, img, q_z=None):
if q_z is not None:
z_img = self.z_linear(q_z)
z_img = z_img.view(img.size(0), self.latent_feature_map, img.size(2), img.size(3))
for i in range(len(self.conv_layers)):
if i == 0:
if q_z is not None:
v_map = torch.cat([img, z_img], 1)
else:
v_map = img
h_map = v_map
v_map, h_map = self.conv_layers[i](v_map, h_map)
return h_map
class ResidualBlock(nn.Module):
def __init__(self, in_dim, out_dim=None, with_residual=True, with_batchnorm=True, mask=None,
kernel_size = 3, padding = 1):
if out_dim is None:
out_dim = in_dim
super(ResidualBlock, self).__init__()
if mask is None:
self.conv1 = nn.Conv2d(in_dim, out_dim, kernel_size=kernel_size, padding=padding)
self.conv2 = nn.Conv2d(out_dim, out_dim, kernel_size=kernel_size, padding=padding)
else:
self.conv1 = MaskedConv2d(mask, in_dim, out_dim, kernel_size=kernel_size, padding=padding)
self.conv2 = MaskedConv2d(mask, out_dim, out_dim, kernel_size=kernel_size, padding=padding)
self.with_batchnorm = with_batchnorm
if with_batchnorm:
self.bn1 = nn.BatchNorm2d(out_dim)
self.bn2 = nn.BatchNorm2d(out_dim)
self.with_residual = with_residual
if in_dim == out_dim or not with_residual:
self.proj = None
else:
self.proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)
def forward(self, x):
if self.with_batchnorm:
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
else:
out = self.conv2(F.relu(self.conv1(x)))
res = x if self.proj is None else self.proj(x)
if self.with_residual:
out = F.relu(res + out)
else:
out = F.relu(out)
return out
class MaskedConv2d(nn.Conv2d):
def __init__(self, include_center=False, *args, **kwargs):
super(MaskedConv2d, self).__init__(*args, **kwargs)
self.register_buffer('mask', self.weight.data.clone())
_, _, kH, kW = self.weight.size()
self.mask.fill_(1)
self.mask[:, :, kH // 2, kW // 2 + (include_center == True):] = 0
self.mask[:, :, kH // 2 + 1:] = 0
def forward(self, x):
self.weight.data *= self.mask.cuda()
return super(MaskedConv2d, self).forward(x)
class CNNVAE(nn.Module):
def __init__(self,
img_size = [1,28,28],
latent_dim = 32,
enc_layers = [64,64,64],
dec_kernel_size = [7,7,7],
dec_layers= [64,64,64],
latent_feature_map = 4):
super(CNNVAE, self).__init__()
enc_modules = []
img_h = img_size[1]
img_w = img_size[2]
for i in range(len(enc_layers)):
if i == 0:
input_dim = img_size[0]
else:
input_dim = enc_layers[i-1]
enc_modules.append(ResidualBlock(input_dim, enc_layers[i]))
enc_modules.append(nn.Conv2d(enc_layers[i], enc_layers[i], kernel_size=2, stride=2))
img_h //= 2
img_w //= 2
latent_in_dim = img_h*img_w*enc_layers[-1]
self.enc_cnn = nn.Sequential(*enc_modules)
self.latent_linear_mean = nn.Linear(latent_in_dim, latent_dim)
self.latent_linear_logvar = nn.Linear(latent_in_dim, latent_dim)
self.enc = nn.ModuleList([self.enc_cnn, self.latent_linear_mean, self.latent_linear_logvar])
self.dec_cnn = StackedGatedMaskedConv2d(img_size=img_size, layers = dec_layers,
latent_dim= latent_dim, kernel_size = dec_kernel_size,
latent_feature_map = latent_feature_map)
self.dec_linear = nn.Conv2d(dec_layers[-1], img_size[0], kernel_size = 1)
self.dec = nn.ModuleList([self.dec_cnn, self.dec_linear])
for m in self.modules():
if isinstance(m, nn.Linear):
he_init(m)
def _enc_forward(self, img):
img_code = self.enc_cnn(img)
img_code = img_code.view(img.size(0), -1)
self.img_code = img_code
mean = self.latent_linear_mean(img_code)
logvar = self.latent_linear_logvar(img_code)
return mean, logvar
def _reparameterize(self, mean, logvar, z = None):
self.std = logvar.mul(0.5).exp()
if z is None:
self.z = Variable(torch.FloatTensor(self.std.size()).normal_(0, 1).type_as(mean.data))
else:
self.z = z
self.q_z = self.z*self.std + mean
return self.q_z
def _dec_forward(self, img, q_z):
dec_cnn_output = self.dec_cnn(img, q_z)
pred = F.sigmoid(self.dec_linear(dec_cnn_output))
return pred