PyTorch 教程-在PyTorch中进行风格转移的图像加载和转换

图像加载

我们必须将内容图像和风格图像加载到内存中，以便我们可以对其进行操作。加载过程在风格转移过程中起着至关重要的作用。在加载过程之前，我们需要内存中的图像，否则风格转移过程将无法进行。

#defining a method with three parameters i.e. image location, maximum size and shape   def load_image(img_path,max_size=400,shape=None):  # Open the image, convert it into RGB and store in a variable       image=Image.open(img_path).convert('RGB')      # comparing image size with the maximum size       if max(image.size)>max_size:        size=max_size      else:        size=max(image.size)      # checking for the image shape      if shape is not None:         size=shape      #Applying appropriate transformation to our image such as Resize, ToTensor and Normalization      in_transform=transforms.Compose([          transforms.Resize(size),          transforms.ToTensor(),          transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])      #Calling in_transform with our image       image=in_transform(image).unsqueeze(0) #unsqueeze(0) is used to add extra layer of dimensionality to the image      #Returning image       return image  #Calling load_image() with our image and add it to our device  content=load_image('ab.jpg').to(device)  style=load_image('abc.jpg',shape=content.shape[-2:]).to(device)

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图像转换

def im_convert(tensor):    image=tensor.cpu().clone().detach().numpy()       image=image.transpose(1,2,0)    image=image*np.array((0.5,0.5,0.5))+np.array((0.5,0.5,0.5))    image=image.clip(0,1)    return image

image=image.squeeze()

绘制图像

fig, (ax1,ax2)=plt.subplots(1,2,figsize=(20,10))  ax1.imshow(im_convert(content))  ax1.axis('off')  ax2.imshow(im_convert(style))  ax2.axis('off')

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