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net.py
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net.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Aug 1 11:35:21 2020
@author: ZJU
"""
import torch
import torch.nn as nn
def calc_mean_std(feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
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 mean_variance_norm(feat):
size = feat.size()
mean, std = calc_mean_std(feat)
normalized_feat = (feat - mean.expand(size)) / std.expand(size)
return normalized_feat
def _calc_feat_flatten_mean_std(feat):
# takes 3D feat (C, H, W), return mean and std of array within channels
assert (feat.size()[0] == 3)
assert (isinstance(feat, torch.FloatTensor))
feat_flatten = feat.view(3, -1)
mean = feat_flatten.mean(dim=-1, keepdim=True)
std = feat_flatten.std(dim=-1, keepdim=True)
return feat_flatten, mean, std
decoder = nn.Sequential(
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 256, (3, 3)),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 128, (3, 3)),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 64, (3, 3)),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 3, (3, 3)),
)
vgg = nn.Sequential(
nn.Conv2d(3, 3, (1, 1)),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(3, 64, (3, 3)),
nn.ReLU(), # relu1-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(), # relu1-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 128, (3, 3)),
nn.ReLU(), # relu2-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(), # relu2-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 256, (3, 3)),
nn.ReLU(), # relu3-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 512, (3, 3)),
nn.ReLU(), # relu4-1, this is the last layer used
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU() # relu5-4
)
projection_style = nn.Sequential(
nn.Linear(in_features=256, out_features=128),
nn.ReLU(),
nn.Linear(in_features=128, out_features=128)
)
projection_content = nn.Sequential(
nn.Linear(in_features=512, out_features=256),
nn.ReLU(),
nn.Linear(in_features=256, out_features=128)
)
class MultiDiscriminator(nn.Module):
def __init__(self, in_channels=3):
super(MultiDiscriminator, self).__init__()
def discriminator_block(in_filters, out_filters, normalize=True):
"""Returns downsampling layers of each discriminator block"""
layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]
if normalize:
layers.append(nn.InstanceNorm2d(out_filters))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
# Extracts three discriminator models
self.models = nn.ModuleList()
for i in range(3):
self.models.add_module(
"disc_%d" % i,
nn.Sequential(
*discriminator_block(in_channels, 64, normalize=False),
*discriminator_block(64, 128),
*discriminator_block(128, 256),
*discriminator_block(256, 512),
nn.Conv2d(512, 1, 3, padding=1)
),
)
self.downsample = nn.AvgPool2d(in_channels, stride=2, padding=[1, 1], count_include_pad=False)
def compute_loss(self, x, gt):
"""Computes the MSE between model output and scalar gt"""
loss = sum([torch.mean((out - gt) ** 2) for out in self.forward(x)])
return loss
def forward(self, x):
outputs = []
for m in self.models:
outputs.append(m(x))
x = self.downsample(x)
return outputs
class SANet(nn.Module):
def __init__(self, in_planes):
super(SANet, self).__init__()
self.f = nn.Conv2d(in_planes, in_planes, (1, 1))
self.g = nn.Conv2d(in_planes, in_planes, (1, 1))
self.h = nn.Conv2d(in_planes, in_planes, (1, 1))
self.sm = nn.Softmax(dim = -1)
self.out_conv = nn.Conv2d(in_planes, in_planes, (1, 1))
def forward(self, content, style):
F = self.f(mean_variance_norm(content))
G = self.g(mean_variance_norm(style))
H = self.h(style)
b, c, h, w = F.size()
F = F.view(b, -1, w * h).permute(0, 2, 1)
b, c, h, w = G.size()
G = G.view(b, -1, w * h)
S = torch.bmm(F, G)
S = self.sm(S)
b, c, h, w = H.size()
H = H.view(b, -1, w * h)
O = torch.bmm(H, S.permute(0, 2, 1))
b, c, h, w = content.size()
O = O.view(b, c, h, w)
O = self.out_conv(O)
O += content
return O
class Transform(nn.Module):
def __init__(self, in_planes):
super(Transform, self).__init__()
self.sanet4_1 = SANet(in_planes = in_planes)
self.sanet5_1 = SANet(in_planes = in_planes)
#self.upsample5_1 = nn.Upsample(scale_factor=2, mode='nearest')
self.merge_conv_pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.merge_conv = nn.Conv2d(in_planes, in_planes, (3, 3))
def forward(self, content4_1, style4_1, content5_1, style5_1):
self.upsample5_1 = nn.Upsample(size=(content4_1.size()[2], content4_1.size()[3]), mode='nearest')
return self.merge_conv(self.merge_conv_pad(self.sanet4_1(content4_1, style4_1) + self.upsample5_1(self.sanet5_1(content5_1, style5_1))))
class Net(nn.Module):
def __init__(self, encoder, decoder, start_iter):
super(Net, self).__init__()
enc_layers = list(encoder.children())
self.enc_1 = nn.Sequential(*enc_layers[:4]) # input -> relu1_1
self.enc_2 = nn.Sequential(*enc_layers[4:11]) # relu1_1 -> relu2_1
self.enc_3 = nn.Sequential(*enc_layers[11:18]) # relu2_1 -> relu3_1
self.enc_4 = nn.Sequential(*enc_layers[18:31]) # relu3_1 -> relu4_1
self.enc_5 = nn.Sequential(*enc_layers[31:44]) # relu4_1 -> relu5_1
#projection
self.proj_style = projection_style
self.proj_content = projection_content
#transform
self.transform = Transform(in_planes = 512)
self.decoder = decoder
self.cross_entropy_loss = nn.CrossEntropyLoss()
if(start_iter > 0):
self.transform.load_state_dict(torch.load('transformer_iter_' + str(start_iter) + '.pth'))
self.decoder.load_state_dict(torch.load('decoder_iter_' + str(start_iter) + '.pth'))
self.mse_loss = nn.MSELoss()
# fix the encoder
for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4', 'enc_5']:
for param in getattr(self, name).parameters():
param.requires_grad = False
# extract relu1_1, relu2_1, relu3_1, relu4_1, relu5_1 from input image
def encode_with_intermediate(self, input):
results = [input]
for i in range(5):
func = getattr(self, 'enc_{:d}'.format(i + 1))
results.append(func(results[-1]))
return results[1:]
def calc_content_loss(self, input, target, norm = False):
if(norm == False):
return self.mse_loss(input, target)
else:
return self.mse_loss(mean_variance_norm(input), mean_variance_norm(target))
def calc_style_loss(self, input, target):
input_mean, input_std = calc_mean_std(input)
target_mean, target_std = calc_mean_std(target)
return self.mse_loss(input_mean, target_mean) + \
self.mse_loss(input_std, target_std)
def compute_contrastive_loss(self, feat_q, feat_k, tau, index):
out = torch.mm(feat_q, feat_k.transpose(1, 0)) / tau
#loss = self.cross_entropy_loss(out, torch.zeros(out.size(0), dtype=torch.long, device=feat_q.device))
loss = self.cross_entropy_loss(out, torch.tensor([index], dtype=torch.long, device=feat_q.device))
return loss
def style_feature_contrastive(self, input):
# out = self.enc_style(input)
out = torch.sum(input, dim=[2, 3])
out = self.proj_style(out)
out = out / torch.norm(out, p=2, dim=1, keepdim=True)
return out
def content_feature_contrastive(self, input):
#out = self.enc_content(input)
out = torch.sum(input, dim=[2, 3])
out = self.proj_content(out)
out = out / torch.norm(out, p=2, dim=1, keepdim=True)
return out
def forward(self, content, style, batch_size):
style_feats = self.encode_with_intermediate(style)
content_feats = self.encode_with_intermediate(content)
stylized = self.transform(content_feats[3], style_feats[3], content_feats[4], style_feats[4])
g_t = self.decoder(stylized)
g_t_feats = self.encode_with_intermediate(g_t)
loss_c = self.calc_content_loss(g_t_feats[3], content_feats[3], norm = True) + self.calc_content_loss(g_t_feats[4], content_feats[4], norm = True)
loss_s = self.calc_style_loss(g_t_feats[0], style_feats[0])
for i in range(1, 5):
loss_s += self.calc_style_loss(g_t_feats[i], style_feats[i])
"""IDENTITY LOSSES"""
Icc = self.decoder(self.transform(content_feats[3], content_feats[3], content_feats[4], content_feats[4]))
Iss = self.decoder(self.transform(style_feats[3], style_feats[3], style_feats[4], style_feats[4]))
l_identity1 = self.calc_content_loss(Icc, content) + self.calc_content_loss(Iss, style)
Fcc = self.encode_with_intermediate(Icc)
Fss = self.encode_with_intermediate(Iss)
l_identity2 = self.calc_content_loss(Fcc[0], content_feats[0]) + self.calc_content_loss(Fss[0], style_feats[0])
for i in range(1, 5):
l_identity2 += self.calc_content_loss(Fcc[i], content_feats[i]) + self.calc_content_loss(Fss[i], style_feats[i])
# Contrastive learning.
half = int(batch_size / 2)
style_up = self.style_feature_contrastive(g_t_feats[2][0:half])
style_down = self.style_feature_contrastive(g_t_feats[2][half:])
content_up = self.content_feature_contrastive(g_t_feats[3][0:half])
content_down = self.content_feature_contrastive(g_t_feats[3][half:])
style_contrastive_loss = 0
for i in range(half):
reference_style = style_up[i:i+1]
if i ==0:
style_comparisons = torch.cat([style_down[0:half-1], style_up[1:]], 0)
elif i == 1:
style_comparisons = torch.cat([style_down[1:], style_up[0:1], style_up[2:]], 0)
elif i == (half-1):
style_comparisons = torch.cat([style_down[half-1:], style_down[0:half-2], style_up[0:half-1]], 0)
else:
style_comparisons = torch.cat([style_down[i:], style_down[0:i-1], style_up[0:i], style_up[i+1:]], 0)
style_contrastive_loss += self.compute_contrastive_loss(reference_style, style_comparisons, 0.2, 0)
for i in range(half):
reference_style = style_down[i:i+1]
if i ==0:
style_comparisons = torch.cat([style_up[0:1], style_up[2:], style_down[1:]], 0)
elif i == (half-2):
style_comparisons = torch.cat([style_up[half-2:half-1], style_up[0:half-2], style_down[0:half-2], style_down[half-1:]], 0)
elif i == (half-1):
style_comparisons = torch.cat([style_up[half-1:], style_up[1:half-1], style_down[0:half-1]], 0)
else:
style_comparisons = torch.cat([style_up[i:i+1], style_up[0:i], style_up[i+2:], style_down[0:i], style_down[i+1:]], 0)
style_contrastive_loss += self.compute_contrastive_loss(reference_style, style_comparisons, 0.2, 0)
content_contrastive_loss = 0
for i in range(half):
reference_content = content_up[i:i+1]
if i == 0:
content_comparisons = torch.cat([content_down[half-1:], content_down[1:half-1], content_up[1:]], 0)
elif i == 1:
content_comparisons = torch.cat([content_down[0:1], content_down[2:], content_up[0:1], content_up[2:]], 0)
elif i == (half-1):
content_comparisons = torch.cat([content_down[half-2:half-1], content_down[0:half-2], content_up[0:half-1]], 0)
else:
content_comparisons = torch.cat([content_down[i-1:i], content_down[0:i-1], content_down[i+1:], content_up[0:i], content_up[i+1:]], 0)
content_contrastive_loss += self.compute_contrastive_loss(reference_content, content_comparisons, 0.2, 0)
for i in range(half):
reference_content = content_down[i:i+1]
if i == 0:
content_comparisons = torch.cat([content_up[1:], content_down[1:]], 0)
elif i == (half-2):
content_comparisons = torch.cat([content_up[half-1:], content_up[0:half-2], content_down[0:half-2], content_down[half-1:]], 0)
elif i == (half-1):
content_comparisons = torch.cat([content_up[0:half-1], content_down[0:half-1]], 0)
else:
content_comparisons = torch.cat([content_up[i+1:i+2], content_up[0:i], content_up[i+2:], content_down[0:i], content_down[i+1:]], 0)
content_contrastive_loss += self.compute_contrastive_loss(reference_content, content_comparisons, 0.2, 0)
return g_t, loss_c, loss_s, l_identity1, l_identity2, content_contrastive_loss, style_contrastive_loss