[人工智能]逐句讲解：在PyTorch下使用gpu进行图像风格迁移

1.优化的目标是提供内容信息的图片的复制。

2.损失函数由内容损失和风格损失两方面构成。

上代码：

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np

import torch
import torch.optim as optim
from torchvision import transforms, models


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#  使用预训练的VGG19，features表示只提取不包括全连接层的部分
vgg = models.vgg19(pretrained=True).features.to(device)
#  不要求训练VGG的参数
for i in vgg.parameters():
    i.requires_grad_(False)


#  定义一个读入与处理图片的函数
def load_img(path, max_size=400, shape=None):
    img = Image.open(path).convert('RGB')

    if (max(img.size)) > max_size:  # 规定图像的最大尺寸
        size = max_size
    else:
        size = max(img.size)

    if shape is not None:
        size = shape
    transform = transforms.Compose([
        transforms.Resize(size),
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406),
                             (0.229, 0.224, 0.225))
    ])
    '''删除alpha通道(jpg)， 转为png，补足另一个维度-batch'''
    img = transform(img)[:3, :, :].unsqueeze(0)
    return img.to(device)


#  载入图像
content = load_img('./images/tomb_sail.jpg')
style = load_img('./images/cloudy_field.jpg', shape=content.shape[-2:])  # 让两张图尺寸一样


#  用于plt显示图片
def im_convert(tensor):
    img = tensor.clone().detach()
    img = img.cpu().numpy().squeeze()
    img = img.transpose(1, 2, 0)
    img = img * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
    img = img.clip(0, 1)
    return img


#  将通过以下网络层的输出存入feature数组中，并返回
def get_features(img, model, layers=None):

    if layers is None:
        layers = {
            '0': 'conv1_1',
            '5': 'conv2_1',
            '10': 'conv3_1',
            '19': 'conv4_1',
            '21': 'conv4_2',  # content层所需
            '28': 'conv5_1'
        }

    features = {}
    x = img
    for name, layer in model._modules.items():
        x = layer(x)
        if name in layers:
            features[layers[name]] = x

    return features


#  输出Gram matrix
def gram_matrix(tensor):
    _, d, h, w = tensor.size()  # 第一个是batch_size

    tensor = tensor.view(d, h * w)

    gram = torch.mm(tensor, tensor.t())

    return gram


content_features = get_features(content, vgg)
style_features = get_features(style, vgg)

style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}

#  定义优化目标
target = content.clone().requires_grad_(True)

'''定义不同层的权重'''
style_weights = {
    'conv1_1': 1,
    'conv2_1': 0.8,
    'conv3_1': 0.5,
    'conv4_1': 0.3,
    'conv5_1': 0.1,
}
'''定义2种损失对应的权重'''
content_weight = 1
style_weight = 1e4


#  开始训练
show_every = 2000
optimizer = optim.Adam([target], lr=0.003)
steps = 20000

for ii in range(steps):
    target_features = get_features(target, vgg)

    content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2']) ** 2)
    style_loss = 0
    '''加上每一层的gram_matrix矩阵的损失'''
    for layer in style_weights:
        target_feature = target_features[layer]
        target_gram = gram_matrix(target_feature)
        _, d, h, w = target_feature.shape
        style_gram = style_grams[layer]
        layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram) ** 2)
        style_loss += layer_style_loss / (d * h * w)  # 加到总的style_loss里，除以大小

    total_loss = content_weight * content_loss + style_weight * style_loss

    optimizer.zero_grad()
    total_loss.backward()
    optimizer.step()

    if ii % show_every == 0:
        print('Total Loss:', total_loss.item())
        plt.imshow(im_convert(target))
        plt.show()