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style_transfer.py
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from PIL import Image
import asyncio
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms
import copy
CNN = ''
class ContentLoss(nn.Module):
def __init__(self, target,):
super(ContentLoss, self).__init__()
self.target = target.detach()
self.loss = F.mse_loss(self.target, self.target )
def forward(self, input):
self.loss = F.mse_loss(input, self.target)
return input
class StyleLossForSingleImage(nn.Module):
def __init__(self, target_feature):
super(StyleLossForSingleImage, self).__init__()
self.target = self.gram_matrix(target_feature).detach()
self.loss = F.mse_loss(self.target, self.target)
def forward(self, input):
G = self.gram_matrix(input)
self.loss = F.mse_loss(G, self.target)
return input
def gram_matrix(self, input):
batch_size , h, w, f_map_num = input.size()
features = input.view(batch_size * h, w * f_map_num)
G = torch.mm(features, features.t())
return G.div(batch_size * h * w * f_map_num)
class StyleLossForDoubleImage(nn.Module):
def __init__(self, target1, target2):
super(StyleLossForDoubleImage, self).__init__()
self.center = target1.shape[3]//2
self.g_target1 = self.gram_matrix( target1[:,:,:,:self.center] ).detach()
self.g_target2 = self.gram_matrix( target2[:,:,:,self.center:] ).detach()
self.loss1 = F.mse_loss(self.g_target1, self.g_target1) # init loss 1
self.loss2 = F.mse_loss(self.g_target2, self.g_target2) # init loss 2
self.loss = self.loss1 + self.loss2
def forward(self, x):
g_x1 = self.gram_matrix( x[:,:,:,:self.center] )
g_x2 = self.gram_matrix( x[:,:,:,self.center:] )
self.loss1 = F.mse_loss(g_x1, self.g_target1)
self.loss2 = F.mse_loss(g_x2, self.g_target2)
self.loss = self.loss1 + self.loss2
return x
def gram_matrix(self, x):
batch_size , h, w, f_map_num = x.size()
features = x.reshape(batch_size * h, w * f_map_num)
G = torch.mm(features, features.t())
return G.div(batch_size * h * w * f_map_num)
class Normalization(nn.Module):
def __init__(self, device):
super(Normalization, self).__init__()
mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
std = torch.tensor([0.229, 0.224, 0.225]).to(device)
self.mean = mean.clone().detach().view(-1, 1, 1)
self.std = std.clone().detach().view(-1, 1, 1)
def forward(self, img):
return (img - self.mean) / self.std
class Simple_style_transfer:
def __init__(self, style_img, content_img, imsize = 1024, num_steps=500,
style_weight=100000, content_weight=1):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.imsize = imsize
self.style_img = self.image_loader(style_img)
self.content_img = self.image_loader(content_img)
self.input_img = self.content_img.clone()
self.content_layers = ['conv_4']
self.style_layers = ['conv_2','conv_3', 'conv_4', 'conv_5']
self.num_steps = num_steps
self.style_weight = style_weight
self.content_weight = content_weight
def image_loader(self, image_name):
loader = transforms.Compose([
transforms.Resize(self.imsize),
transforms.CenterCrop(self.imsize),
transforms.ToTensor()])
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(self.device, torch.float)
def get_style_model_and_losses(self):
cnn = copy.deepcopy(CNN)
normalization = Normalization(self.device).to(self.device)
content_losses = []
style_losses = []
model = nn.Sequential(normalization)
i = 0
for layer in cnn.children():
if isinstance(layer, nn.Conv2d):
i += 1
name = 'conv_{}'.format(i)
elif isinstance(layer, nn.ReLU):
name = 'relu_{}'.format(i)
layer = nn.ReLU(inplace=False)
elif isinstance(layer, nn.MaxPool2d):
name = 'pool_{}'.format(i)
elif isinstance(layer, nn.BatchNorm2d):
name = 'bn_{}'.format(i)
else:
raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
model.add_module(name, layer)
if name in self.content_layers:
target = model(self.content_img).detach()
content_loss = ContentLoss(target)
model.add_module("content_loss_{}".format(i), content_loss)
content_losses.append(content_loss)
if name in self.style_layers:
target_feature = model(self.style_img).detach()
style_loss = StyleLossForSingleImage(target_feature)
model.add_module("style_loss_{}".format(i), style_loss)
style_losses.append(style_loss)
for i in range(len(model) - 1, -1, -1):
if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLossForSingleImage):
break
model = model[:(i + 1)]
return model, style_losses, content_losses
def get_input_optimizer(self):
optimizer = optim.LBFGS([self.input_img.requires_grad_()])
return optimizer
async def test(self):
num = 0
while num < 20:
num += 1
print(num)
await asyncio.sleep(1)
return num
async def transfer(self):
global CNN
if CNN == '':
CNN = torch.load('my_models/vgg19.pth',).to(self.device).eval()
model, style_losses, content_losses = self.get_style_model_and_losses()
optimizer = self.get_input_optimizer()
run = [0]
while run[0] <= self.num_steps:
print(run[0])
await asyncio.sleep(0)
def closure():
self.input_img.data.clamp_(0, 1)
optimizer.zero_grad()
model(self.input_img)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.loss
for cl in content_losses:
content_score += cl.loss
style_score *= self.style_weight
content_score *= self.content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
return style_score + content_score
optimizer.step(closure)
self.input_img.data.clamp_(0, 1)
return self.input_img
class Double_style_transfer():
def __init__(self, style_1_img, style_2_img, content_img, imsize = 1024,
num_steps=500, style_weight=100000, content_weight=1):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.imsize = imsize
self.style_1_img = self.image_loader(style_1_img)
self.style_2_img = self.image_loader(style_2_img)
self.content_img = self.image_loader(content_img)
self.input_img = self.content_img.clone()
self.content_layers = ['conv_4']
self.style_layers = ['conv_2','conv_3', 'conv_4', 'conv_5']
self.num_steps = num_steps
self.style_weight = style_weight
self.content_weight = content_weight
def image_loader(self, image_name):
loader = transforms.Compose([
transforms.Resize(self.imsize),
transforms.CenterCrop(self.imsize),
transforms.ToTensor()])
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(self.device, torch.float)
def get_style_model_and_losses(self):
cnn = copy.deepcopy(CNN)
normalization = Normalization(self.device).to(self.device)
content_losses = []
style_losses = []
model = nn.Sequential(normalization)
i = 0
for layer in cnn.children():
if isinstance(layer, nn.Conv2d):
i += 1
name = 'conv_{}'.format(i)
elif isinstance(layer, nn.ReLU):
name = 'relu_{}'.format(i)
layer = nn.ReLU(inplace=False)
elif isinstance(layer, nn.MaxPool2d):
name = 'pool_{}'.format(i)
elif isinstance(layer, nn.BatchNorm2d):
name = 'bn_{}'.format(i)
else:
raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
model.add_module(name, layer)
if name in self.content_layers:
target = model(self.content_img).detach()
content_loss = ContentLoss(target)
model.add_module("content_loss_{}".format(i), content_loss)
content_losses.append(content_loss)
if name in self.style_layers:
target1 = model(self.style_1_img).detach()
target2 = model(self.style_2_img).detach()
style_loss = StyleLossForDoubleImage(target1, target2)
model.add_module("style_loss_{}".format(i), style_loss)
style_losses.append(style_loss)
for i in range(len(model) - 1, -1, -1):
if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLossForDoubleImage):
break
model = model[:(i + 1)]
return model, style_losses, content_losses
def get_input_optimizer(self):
optimizer = optim.LBFGS([self.input_img.requires_grad_()])
return optimizer
async def test(self):
num = 0
while num < 20:
num += 1
print(num)
await asyncio.sleep(1)
return num
async def transfer(self):
global CNN
if CNN == '':
CNN = torch.load('my_models/vgg19.pth').to(self.device).eval()
model, style_losses, content_losses = self.get_style_model_and_losses()
optimizer = self.get_input_optimizer()
run = [0]
while run[0] <= self.num_steps:
print(run[0])
await asyncio.sleep(0)
def closure():
self.input_img.data.clamp_(0, 1)
optimizer.zero_grad()
model(self.input_img)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.loss
for cl in content_losses:
content_score += cl.loss
style_score *= self.style_weight
content_score *= self.content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
return style_score + content_score
optimizer.step(closure)
self.input_img.data.clamp_(0, 1)
return self.input_img