221 lines
8.0 KiB
Python
221 lines
8.0 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import io
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from PIL import Image
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import matplotlib.pyplot as plt
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import torchvision.transforms as transforms
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from torchvision.models import vgg19, VGG19_Weights
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from torchvision import models
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import matplotlib.pyplot as plt
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import torchvision.utils as vutils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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torch.set_default_device(device)
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def image_loader(image):
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#image = Image.open(image_name)
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# fake batch dimension required to fit network's input dimensions
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imsize = 512 if torch.cuda.is_available() else 128 # use small size if no GPU
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loader = transforms.Compose([
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transforms.Resize(imsize), # scale imported image
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transforms.ToTensor()]) # transform it into a torch tensor
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image = loader(image).unsqueeze(0)
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return image.to(device, torch.float)
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def save_image(tensor, path):
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image = tensor.clone().detach()
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image = image.squeeze(0)
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image = transforms.ToPILImage()(image)
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image.save(path)
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class ContentLoss(nn.Module):
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def __init__(self, target,):
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super(ContentLoss, self).__init__()
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# we 'detach' the target content from the tree used
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# to dynamically compute the gradient: this is a stated value,
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# not a variable. Otherwise the forward method of the criterion
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# will throw an error.
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self.target = target.detach()
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def forward(self, input):
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self.loss = F.mse_loss(input, self.target)
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return input
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def gram_matrix(input):
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a, b, c, d = input.size() # a=batch size(=1)
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# b=number of feature maps
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# (c,d)=dimensions of a f. map (N=c*d)
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features = input.view(a * b, c * d) # resize F_XL into \hat F_XL
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G = torch.mm(features, features.t()) # compute the gram product
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# we 'normalize' the values of the gram matrix
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# by dividing by the number of element in each feature maps.
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return G.div(a * b * c * d)
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class StyleLoss(nn.Module):
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def __init__(self, target_feature):
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super(StyleLoss, self).__init__()
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self.target = gram_matrix(target_feature).detach()
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def forward(self, input):
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G = gram_matrix(input)
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self.loss = F.mse_loss(G, self.target)
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return input
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#cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.eval()
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#cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406])
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#cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225])
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# create a module to normalize input image so we can easily put it in a
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# ``nn.Sequential``
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class Normalization(nn.Module):
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def __init__(self, mean, std):
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super(Normalization, self).__init__()
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# .view the mean and std to make them [C x 1 x 1] so that they can
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# directly work with image Tensor of shape [B x C x H x W].
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# B is batch size. C is number of channels. H is height and W is width.
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self.mean = torch.tensor(mean).view(-1, 1, 1)
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self.std = torch.tensor(std).view(-1, 1, 1)
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def forward(self, img):
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# normalize ``img``
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return (img - self.mean) / self.std
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# desired depth layers to compute style/content losses :
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content_layers_default = ['conv_4']
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style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
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def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
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style_img, content_img,
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content_layers=content_layers_default,
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style_layers=style_layers_default):
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# normalization module
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normalization = Normalization(normalization_mean, normalization_std)
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# just in order to have an iterable access to or list of content/style
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# losses
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content_losses = []
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style_losses = []
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# assuming that ``cnn`` is a ``nn.Sequential``, so we make a new ``nn.Sequential``
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# to put in modules that are supposed to be activated sequentially
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model = nn.Sequential(normalization)
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i = 0 # increment every time we see a conv
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for layer in cnn.children():
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if isinstance(layer, nn.Conv2d):
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i += 1
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name = 'conv_{}'.format(i)
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elif isinstance(layer, nn.ReLU):
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name = 'relu_{}'.format(i)
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# The in-place version doesn't play very nicely with the ``ContentLoss``
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# and ``StyleLoss`` we insert below. So we replace with out-of-place
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# ones here.
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layer = nn.ReLU(inplace=False)
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elif isinstance(layer, nn.MaxPool2d):
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name = 'pool_{}'.format(i)
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elif isinstance(layer, nn.BatchNorm2d):
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name = 'bn_{}'.format(i)
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else:
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raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
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model.add_module(name, layer)
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if name in content_layers:
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# add content loss:
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target = model(content_img).detach()
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content_loss = ContentLoss(target)
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model.add_module("content_loss_{}".format(i), content_loss)
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content_losses.append(content_loss)
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if name in style_layers:
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# add style loss:
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target_feature = model(style_img).detach()
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style_loss = StyleLoss(target_feature)
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model.add_module("style_loss_{}".format(i), style_loss)
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style_losses.append(style_loss)
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# now we trim off the layers after the last content and style losses
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for i in range(len(model) - 1, -1, -1):
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if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
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break
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model = model[:(i + 1)]
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return model, style_losses, content_losses
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def get_input_optimizer(input_img):
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# this line to show that input is a parameter that requires a gradient
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optimizer = optim.LBFGS([input_img])
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return optimizer
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class StyleTransferModel:
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def __init__(self, content_img, style_img, num_steps=300, style_weight=1000000, content_weight=1):
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self.content_img = content_img
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self.style_img = style_img.resize(content_img.size)
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self.style_img = image_loader(self.style_img)
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self.content_img = image_loader(self.content_img)
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self.input_img = self.content_img.clone()
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self.num_steps = num_steps
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self.style_weight = style_weight
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self.content_weight = content_weight
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self.cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.to(device).eval()
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self.cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
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self.cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
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def run_style_transfer(self):
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print('Building the style transfer model..')
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model, style_losses, content_losses = get_style_model_and_losses(
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self.cnn, self.cnn_normalization_mean, self.cnn_normalization_std,
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self.style_img, self.content_img)
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self.input_img.requires_grad_(True)
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model.eval()
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model.requires_grad_(False)
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optimizer = get_input_optimizer(self.input_img)
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print('Optimizing..')
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run = [0]
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while run[0] <= self.num_steps:
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def closure():
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with torch.no_grad():
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self.input_img.clamp_(0, 1)
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optimizer.zero_grad()
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model(self.input_img)
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style_score = 0
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content_score = 0
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for sl in style_losses:
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style_score += sl.loss
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for cl in content_losses:
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content_score += cl.loss
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style_score *= self.style_weight
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content_score *= self.content_weight
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loss = style_score + content_score
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loss.backward()
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run[0] += 1
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if run[0] % 50 == 0:
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print(f"run {run[0]}:")
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print(f'Style Loss : {style_score.item():4f} Content Loss: {content_score.item():4f}')
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print()
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return style_score + content_score
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optimizer.step(closure)
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with torch.no_grad():
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self.input_img.clamp_(0, 1)
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return self.input_img
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