Pytorch Advanced(三) Neural Style Transfer-Toy模板网

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神经风格迁移在之前的博客中已经用keras实现过了，比较复杂，keras版本。

这里用pytorch重新实现一次，原理图如下：

Pytorch Advanced(三) Neural Style Transfer,deep learning,pytorch,人工智能,python

from __future__ import division
from torchvision import models
from torchvision import transforms
from PIL import Image
import argparse
import torch
import torchvision
import torch.nn as nn
import numpy as np

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

加载图像

def load_image(image_path, transform=None, max_size=None, shape=None):
    """Load an image and convert it to a torch tensor."""
    image = Image.open(image_path)
    
    if max_size:
        scale = max_size / max(image.size)
        size = np.array(image.size) * scale
        image = image.resize(size.astype(int), Image.ANTIALIAS)
    
    if shape:
        image = image.resize(shape, Image.LANCZOS)
    
    if transform:
        image = transform(image).unsqueeze(0)
    
    return image.to(device)

这里用的模型是 VGG-19，所要用的是网络中的5个卷积层

class VGGNet(nn.Module):
    def __init__(self):
        """Select conv1_1 ~ conv5_1 activation maps."""
        super(VGGNet, self).__init__()
        self.select = ['0', '5', '10', '19', '28'] 
        self.vgg = models.vgg19(pretrained=True).features
        
    def forward(self, x):
        """Extract multiple convolutional feature maps."""
        features = []
        for name, layer in self.vgg._modules.items():
            x = layer(x)
            if name in self.select:
                features.append(x)
        return features

模型结构如下，可以看到使用序列模型来写的VGG-NET，所以标号即层号，我们要保存的是['0', '5', '10', '19', '28'] 的输出结果。

VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (17): ReLU(inplace)
    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): ReLU(inplace)
    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (26): ReLU(inplace)
    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): ReLU(inplace)
    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (33): ReLU(inplace)
    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (35): ReLU(inplace)
    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)

训练：

接下来对训练过程进行解释：

1、加载风格图像和内容图像，我们在之前的博客中使用的一幅加噪图进行训练，这里是用的内容图像的拷贝。

2、我们需要优化的就是作为目标的内容图像拷贝，可以看到target需要求导。

3、VGGnet参数是不需要优化的，所以设置为验证状态。

4、将3幅图像输入网络，得到总共15个输出（每个图像有5层的输出）

5、内容损失：这里是遍历5个层的输出来计算损失，而在keras版本中只用了第4层的输出计算损失

6、风格损失：同样计算格拉姆风格矩阵，将每一层的风格损失叠加，得到总的风格损失，计算公式同样和keras版本有所不一样

7、反向传播

def main(config):
    
    # Image preprocessing
    # VGGNet was trained on ImageNet where images are normalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
    # We use the same normalization statistics here.
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(mean=(0.485, 0.456, 0.406), 
                             std=(0.229, 0.224, 0.225))])
    
    # Load content and style images
    # Make the style image same size as the content image
    content = load_image(config.content, transform, max_size=config.max_size)
    style = load_image(config.style, transform, shape=[content.size(2), content.size(3)])
    
    # Initialize a target image with the content image
    target = content.clone().requires_grad_(True)
    
    optimizer = torch.optim.Adam([target], lr=config.lr, betas=[0.5, 0.999])
    vgg = VGGNet().to(device).eval()
    
    for step in range(config.total_step):
        
        # Extract multiple(5) conv feature vectors
        target_features = vgg(target)
        content_features = vgg(content)
        style_features = vgg(style)

        style_loss = 0
        content_loss = 0
        for f1, f2, f3 in zip(target_features, content_features, style_features):
            # Compute content loss with target and content images
            content_loss += torch.mean((f1 - f2)**2)

            # Reshape convolutional feature maps
            _, c, h, w = f1.size()
            f1 = f1.view(c, h * w)
            f3 = f3.view(c, h * w)

            # Compute gram matrix
            f1 = torch.mm(f1, f1.t())
            f3 = torch.mm(f3, f3.t())

            # Compute style loss with target and style images
            style_loss += torch.mean((f1 - f3)**2) / (c * h * w) 
        
        # Compute total loss, backprop and optimize
        loss = content_loss + config.style_weight * style_loss 
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (step+1) % config.log_step == 0:
            print ('Step [{}/{}], Content Loss: {:.4f}, Style Loss: {:.4f}' 
                   .format(step+1, config.total_step, content_loss.item(), style_loss.item()))

        if (step+1) % config.sample_step == 0:
            # Save the generated image
            denorm = transforms.Normalize((-2.12, -2.04, -1.80), (4.37, 4.46, 4.44))
            img = target.clone().squeeze()
            img = denorm(img).clamp_(0, 1)
            torchvision.utils.save_image(img, 'output-{}.png'.format(step+1))

写在if __name__=="__main__"后面的语句只会在本脚本中才能被执行，被调用时是不会被执行的。

python的命令行工具：argparse，很优雅的添加参数

但是由于jupyter不支持添加外部参数，所以使用了外部博客的方法来支持（记住更改读取图片的位置）文章来源地址https://www.toymoban.com/news/detail-707929.html

import sys
if __name__ == "__main__":
    
    #解决方案来自于博客
    if '-f' in sys.argv:
        sys.argv.remove('-f')
    
    parser = argparse.ArgumentParser()
    parser.add_argument('--content', type=str, default='png/content.png')
    parser.add_argument('--style', type=str, default='png/style.png')
    parser.add_argument('--max_size', type=int, default=400)
    parser.add_argument('--total_step', type=int, default=2000)
    parser.add_argument('--log_step', type=int, default=10)
    parser.add_argument('--sample_step', type=int, default=500)
    parser.add_argument('--style_weight', type=float, default=100)
    parser.add_argument('--lr', type=float, default=0.003)
    #config = parser.parse_args()
    config = parser.parse_known_args()[0]   #参考博客 https://blog.csdn.net/ken_for_learning/article/details/89675904
    print(config)
    main(config)

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