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net2.py
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import torch.nn as nn
import torch
from function import calc_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
)
class Net(nn.Module):
def __init__(self, encoder):
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.mse_loss = nn.MSELoss()
# 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):
assert (input.size() == target.size())
assert (target.requires_grad is False)
bs, ch = input.size()[:2]
input = input.view(bs, ch, -1)
target = target.view(bs, ch, -1)
input_mean, input_std,input_p3 = feature_moments_caculation(input)
target_mean, target_std,target_p3 = feature_moments_caculation(target)
return self.mse_loss(input_mean, target_mean) + \
self.mse_loss(input_std, target_std)+ \
self.mse_loss(input_p3, target_p3)
def forward(self, content_images, style_images, stylized_images):
style_feats = self.encode_with_intermediate(style_images)#style_images[2, 3, 256, 256];4
content_feat = self.encode(content_images)#content_feat[2, 512, 32, 32]
stylized_feats = self.encode_with_intermediate(stylized_images)
loss_c = self.calc_content_loss(stylized_feats[-1], content_feat)
loss_s = self.calc_style_loss(stylized_feats[0], style_feats[0])
for i in range(1, 4):
loss_s += self.calc_style_loss(stylized_feats[i], style_feats[i])
return loss_c, loss_s
def feature_moments_caculation(feat, eps=1e-5):
size = feat.size()
assert (len(size) == 3)
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)
# the first order
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1)
# the second order
feat_size = 2
# N, C = size[:2]
feat_p2 = torch.abs(feat-feat_mean).pow(feat_size).view(N, C, -1)
N, C,L = feat_p2.shape
feat_p2 = feat_p2.sum(dim=2)/L
feat_p2 = feat_p2.pow(1/feat_size).view(N, C, 1)
# the third order
feat_size = 3
# N, C = size[:2]
feat_p3 = torch.abs(feat-feat_mean).pow(feat_size).view(N, C, -1)
# N, C,L = feat_p3.shape
feat_p3 = feat_p3.sum(dim=2)/L
feat_p3 = feat_p3.pow(1/feat_size).view(N, C, 1)
return feat_mean.view(N, C), feat_p2.view(N, C), feat_p3.view(N, C)