pytorch书法风格分析代码

以下是一个简单的PyTorch书法风格分析代码，它使用VGG模型来提取图像特征，然后使用Gram矩阵来计算图像的样式：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image

# 定义图像处理函数
def image_loader(image_name, imsize):
    loader = transforms.Compose([
        transforms.Resize(imsize),  # 调整图像大小
        transforms.ToTensor()  # 将图像转换为Tensor
    ])
    image = Image.open(image_name)
    image = loader(image).unsqueeze(0)
    return image.to(torch.float)

# 定义样式损失函数
class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super(StyleLoss, self).__init__()
        self.target = self.gram_matrix(target_feature).detach()
        
    def gram_matrix(self, input):
        batch_size, channel, height, width = input.size()
        features = input.view(batch_size * channel, height * width)
        G = torch.mm(features, features.t())
        return G.div(batch_size * channel * height * width)
    
    def forward(self, input):
        G = self.gram_matrix(input)
        loss = nn.functional.mse_loss(G, self.target)
        return loss

# 定义主函数
def main(style_img_path, content_img_path, output_img_path, num_epochs, style_weight, content_weight):
    # 加载图像
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    style_img = image_loader(style_img_path, imsize)
    content_img = image_loader(content_img_path, imsize).to(device)
    input_img = content_img.clone()
    
    # 加载模型
    cnn = models.vgg19(pretrained=True).features.to(device).eval()
    
    # 提取特征
    content_layers = ['conv_4']
    style_layers = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
    content_losses = []
    style_losses = []
    model = nn.Sequential()
    gram = None
    i = 0
    for layer in cnn.children():
        if isinstance(layer, nn.Conv2d):
            i += 1
            name = f'conv_{i}'
        elif isinstance(layer, nn.ReLU):
            name = f'relu_{i}'
            layer = nn.ReLU(inplace=False)
        elif isinstance(layer, nn.MaxPool2d):
            name = f'pool_{i}'
        elif isinstance(layer, nn.BatchNorm2d):
            name = f'bn_{i}'
        else:
            raise RuntimeError(f'Unknown layer: {layer.__class__.__name__}')
        
        model.add_module(name, layer)
        
        if name in content_layers:
            target = model(content_img).detach()
            content_loss = nn.functional.mse_loss(target, target)
            content_losses.append(content_loss)
            
        if name in style_layers:
            target_feature = model(style_img).detach()
            style_loss = StyleLoss(target_feature)
            style_losses.append(style_loss)
            
    # 训练模型
    optimizer = optim.Adam([input_img.requires_grad_()], lr=0.01)
    steps = 0
    while steps < num_epochs:
        input_img.data.clamp_(0, 1)
        
        optimizer.zero_grad()
        model(input_img)
        
        style_loss = 0
        content_loss = 0
        for sl in style_losses:
            style_loss += sl(input_img)
        for cl in content_losses:
            content_loss += cl(input_img)
            
        loss = style_weight * style_loss + content_weight * content_loss
        loss.backward()
        
        if steps % 50 == 0:
            print(f"Step {steps}: Style Loss: {style_loss.item()}, Content Loss: {content_loss.item()}")
            
        optimizer.step()
        steps += 1
        
    # 保存图像
    input_img.data.clamp_(0, 1)
    output_image = transforms.ToPILImage()(input_img.squeeze(0))
    output_image.save(output_img_path)
    
if __name__ == '__main__':
    style_img_path = 'style.jpg'
    content_img_path = 'content.jpg'
    output_img_path = 'output.jpg'
    num_epochs = 500
    imsize = 512
    style_weight = 1000000
    content_weight = 1
    main(style_img_path, content_img_path, output_img_path, num_epochs, style_weight, content_weight)

这个代码使用了VGG19模型来提取图像特征，然后使用Gram矩阵来计算图像的样式。它还使用了PyTorch的自动微分功能来计算图像的梯度，并使用Adam优化器来更新图像的像素值。这个代码可以根据您的需要进行修改，以实现不同的书法风格分析任务。