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import torch.nn as nn
import torch
from function import adaptive_mean_normalization as adamean
from function import adaptive_std_normalization as adastd
from function import adaptive_instance_normalization as adain
from function import exact_feature_distribution_matching as efdm
from function import histogram_matching as hm
from function import calc_mean_std
# import ipdb
from skimage.exposure import match_histograms
import numpy as np
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
)
class Net(nn.Module):
def __init__(self, encoder, decoder, style):
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.decoder = decoder
self.mse_loss = nn.MSELoss()
self.style = style
# fix the encoder
for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4']:
for param in getattr(self, name).parameters():
param.requires_grad = False
# extract relu1_1, relu2_1, relu3_1, relu4_1 from input image
def encode_with_intermediate(self, input):
results = [input]
for i in range(4):
func = getattr(self, 'enc_{:d}'.format(i + 1))
results.append(func(results[-1]))
return results[1:]
# extract relu4_1 from input image
def encode(self, input):
for i in range(4):
input = getattr(self, 'enc_{:d}'.format(i + 1))(input)
return input
def calc_content_loss(self, input, target):
assert (input.size() == target.size())
assert (target.requires_grad is False)
return self.mse_loss(input, target)
def calc_style_loss(self, input, target):
# ipdb.set_trace()
assert (input.size() == target.size())
assert (target.requires_grad is False) ## first make sure which one require gradient and which one do not.
# print(input.requires_grad) ## True
input_mean, input_std = calc_mean_std(input)
target_mean, target_std = calc_mean_std(target)
if self.style == 'adain':
return self.mse_loss(input_mean, target_mean) + \
self.mse_loss(input_std, target_std)
elif self.style == 'adamean':
return self.mse_loss(input_mean, target_mean)
elif self.style == 'adastd':
return self.mse_loss(input_std, target_std)
elif self.style == 'efdm':
B, C, W, H = input.size(0), input.size(1), input.size(2), input.size(3)
value_content, index_content = torch.sort(input.view(B, C, -1))
value_style, index_style = torch.sort(target.view(B, C, -1))
inverse_index = index_content.argsort(-1)
return self.mse_loss(input.view(B,C,-1), value_style.gather(-1, inverse_index))
elif self.style == 'hm':
B, C, W, H = input.size(0), input.size(1), input.size(2), input.size(3)
x_view = input.view(-1, W, H)
image1_temp = match_histograms(np.array(x_view.detach().clone().cpu().float().transpose(0, 2)),
np.array(target.view(-1, W, H).detach().clone().cpu().float().transpose(0,2)),
multichannel=True)
image1_temp = torch.from_numpy(image1_temp).float().to(input.device).transpose(0, 2).view(B, C, W, H)
return self.mse_loss(input.reshape(B, C, -1), image1_temp.reshape(B, C, -1))
else:
raise NotImplementedError
def forward(self, content, style, alpha=1.0):
assert 0 <= alpha <= 1
# ipdb.set_trace()
style_feats = self.encode_with_intermediate(style)
content_feat = self.encode(content)
# print(content_feat.requires_grad) False
# print(style_feats[-1].requires_grad) False
if self.style == 'adain':
t = adain(content_feat, style_feats[-1])
elif self.style == 'adamean':
t = adamean(content_feat, style_feats[-1])
elif self.style == 'adastd':
t = adastd(content_feat, style_feats[-1])
elif self.style == 'efdm':
t = efdm(content_feat, style_feats[-1])
elif self.style == 'hm':
t = hm(content_feat, style_feats[-1])
else:
raise NotImplementedError
t = alpha * t + (1 - alpha) * content_feat
g_t = self.decoder(t)
g_t_feats = self.encode_with_intermediate(g_t)
loss_c = self.calc_content_loss(g_t_feats[-1], t) ### final feature should be the same.
loss_s = self.calc_style_loss(g_t_feats[0], style_feats[0])
for i in range(1, 4):
loss_s += self.calc_style_loss(g_t_feats[i], style_feats[i])
return loss_c, loss_s
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