神经网络空间插值的代码

以下是神经网络空间插值的代码示例：

import torch
from torchvision import models
from torch.autograd import Variable
from torch.nn import functional as F
import numpy as np
from PIL import Image

def load_image(filename, size=None, scale=None):
    img = Image.open(filename)
    if size is not None:
        img = img.resize(size=size)
    elif scale is not None:
        img = img.resize(size=(int(img.size[0]/scale), int(img.size[1]/scale)))
    return np.array(img)

def preprocess(image, size=None, scale=None):
    image = Image.fromarray(np.uint8(image))
    if size is not None:
        image = image.resize(size)
    elif scale is not None:
        image = image.resize((int(image.size[0]/scale), int(image.size[1]/scale)))
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    image = np.array(image) / 255.0
    image = (image - mean) / std
    image = np.transpose(image, (2, 0, 1))
    image = np.expand_dims(image, axis=0)
    return torch.from_numpy(image).float()

def deprocess(image):
    image = image.cpu().numpy()[0]
    image = np.transpose(image, (1, 2, 0))
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    image = (image * std) + mean
    image = np.clip(image, 0, 1)
    image = (255 * image).astype(np.uint8)
    return image

def get_features(image, model, layers=None):
    if layers is None:
        layers = {'0': 'conv1_1', '5': 'conv2_1', '10': 'conv3_1', '19': 'conv4_1', '21': 'conv4_2', '28': 'conv5_1'}
    features = {}
    x = image
    for name, layer in enumerate(model.features):
        x = layer(x)
        if str(name) in layers:
            features[layers[str(name)]] = x
    return features

def gram_matrix(tensor):
    b, c, h, w = tensor.size()
    tensor = tensor.view(b * c, h * w)
    gram = torch.mm(tensor, tensor.t())
    return gram

class StyleTransfer():
    def __init__(self, content_image, style_image, size=None, scale=None, alpha=1, beta=1000, num_epochs=300, device='cpu'):
        self.device = device
        self.alpha = alpha
        self.beta = beta
        self.num_epochs = num_epochs
        self.content_image = preprocess(load_image(content_image), size=size, scale=scale).to(device)
        self.style_image = preprocess(load_image(style_image), size=size, scale=scale).to(device)
        self.model = models.vgg19(pretrained=True).features.to(device).eval()
        self.content_targets = get_features(self.content_image, self.model)
        self.style_targets = get_features(self.style_image, self.model)
        self.optimizer = torch.optim.Adam([self.target], lr=0.01)
        self.target = Variable(self.content_image.clone(), requires_grad=True).to(device)

    def train(self):
        for epoch in range(self.num_epochs):
            self.optimizer.zero_grad()
            target_features