PyTorch 教程-针对风格转换的优化过程

我们现在已经有了三张图片，现在可以执行我们的优化过程。为了执行优化过程，我们需要按照以下步骤进行操作：

步骤1：

在第一步中，我们定义了一些基本参数，这些参数帮助我们可视化训练过程并简化训练过程。第一个参数在每次迭代时显示目标图像，以便我们可以检查优化过程。我们使用Adam优化器定义了目标图像，并设置了学习率。最后但同样重要的是，我们定义了训练过程应该进行的优化步骤数。

show_every=300  optimizer=optim.Adam([target],lr=0.003)  steps=2100  

步骤2：

height,width,channels=im_convert(target).shape  image_array=np.empty(shape=(300,height,width,channels))  

capture_frame=steps/300  counter=0  

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优化迭代过程

#Defining a loop statement from 1 to steps+1  for ii in range(1,steps+1): #To ensure that our loop runs for the defined number of steps     # Extracting feature for our current target image     target_features=get_features(target,vgg)    #Calculating the content loss for the iteration    content_loss=torch.mean((target_features['conv4_2']content_features['conv4_2'])**2)    #Initializing style loss     style_loss=0   #The style loss is the result of a combine loss from five different layer within our model.   #For this reason we iterate through the five style features to get the error at each layer.     for layer in style_weights:      #Collecting the target feature for the specific layer from the target feature variable       target_feature=target_features[layer]      #Applying gram matrix function to our target feature      target_gram=gram_matrix(target_feature)      #Getting style_gram value for our style image from the style grams variable      style_gram=style_grams[layer]      #Calculating the layer style loss as content loss      layer_style_loss=style_weights[layer]*torch.mean((target_gram-style_gram)**2)      #Obtaining feature dimensions        _,d,h,w=target_feature.shape          #Calculating total style loss      style_loss += layer_style_loss/(d*h*w)    #Calculating total loss    total_loss=content_weight*content_loss+style_weight*style_loss    #Using the optimizer to update parameters within our target image     optimizer.zero_grad()    total_loss.backward()    optimizer.step()    #Process  for visualization throughout the training process    #Comparing the iteration variable with our show every      if ii % show_every==0:       #Printing total loss      print('Total loss:',total_loss.item())      #Printing the iteration       print('Iteration',ii)      #Printting the target images       plt.imshow(im_convert(target))      #Removing the axis on the image       plt.axis('off')      # Showing image       plt.show()     #Comparing the iteration variable with our capture frame variable      if ii%capture_frame==0: # Capturing a frame at every 700 iteration       #Storing the target image into the image_array       image_array[counter]=im_convert(target)       # Increment in the counter variable        counter=counter+1  

当我们运行代码时，它将给出预期的输出：

绘制内容、样式和最终目标图像

#Making a grid arrangement with a single row and three columns for our three images  fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(20,10))  #Plotting content image   ax1.imshow(im_convert(content))  ax1.axis('off')  #Plotting style image  ax2.imshow(im_convert(style))  ax2.axis('off')  #Plotting target image  ax3.imshow(im_convert(target))  ax3.axis('off')

完整代码

#Required Libraries  import torch  import torch.optim as optim  from torchvision import transforms, models  from PIL import Image  import matplotlib.pyplot as plt  import numpy as np  #Creating Model  vgg=models.vgg19(pretrained=True).features  for param in vgg.parameters():      param.requires_grad_(False)  #Add model to device  device=torch.device("cuda" if torch.cuda.is_available() else "cpu")  vgg.to(device)  #Load Iamge  def load_image(img_path,max_size=400,shape=None):      image=Image.open(img_path).convert('RGB')      if max(image.size)>max_size:        size=max_size      else:        size=max(image.size)      if shape is not None:         size=shape      in_transform=transforms.Compose([          transforms.Resize(size),          transforms.ToTensor(),          transforms.Normalize((0.5,0.5,0.5),                              (0.5,0.5,0.5))      ])      image=in_transform(image).unsqueeze(0)      return image  content=load_image('ab.jpg').to(device)  style=load_image('abc.jpg',shape=content.shape[-2:]).to(device)  #Image Conversion  def im_convert(tensor):    image=tensor.cpu().clone().detach().numpy()    image=image.squeeze()    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  #Plotting Images  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')  #Getting Features  def get_features(image,model):    layers={'0':'conv1_1',            '5':'conv2_1',           '10':'conv3_1',           '19':'conv4_1',           '21':'conv4_2',           '28':'conv5_1',}    features={}    for name, layer in model._modules.items():      image=layer(image)      if name in layers:        features[layers[name]]=image    return features  #Making content and style features  content_features=get_features(content,vgg)  style_features=get_features(style, vgg)  #Creating gram matrix  def gram_matrix(tensor):    _,d,h,w=tensor.size()    tensor=tensor.view(d,h*w)    gram=torch.mm(tensor,tensor.t())    return gram  #Creating style grams  style_grams={layer:gram_matrix(style_features[layer]) for layer in style_features}  #Initializing style weights  style_weights={'conv1_1':1.,                 'conv2_1':0.75,                 'conv3_1':0.2,                 'conv4_1':0.2,                 'conv5_1':0.2}  content_weight=1  style_weight=1e6  target=content.clone().requires_grad_(True).to(device)  #Performing optimization  show_every=300  optimizer=optim.Adam([target],lr=0.003)  steps=2100  height,width,channels=im_convert(target).shape  image_array=np.empty(shape=(300,height,width,channels))  capture_frame=steps/300  counter=0  for ii in range(1,steps+1):    target_features=get_features(target,vgg)    content_loss=torch.mean((target_features['conv4_2']-content_features['conv4_2'])**2)    style_loss=0    for layer in style_weights:      target_feature=target_features[layer]      target_gram=gram_matrix(target_feature)      style_gram=style_grams[layer]      layer_style_loss=style_weights[layer]*torch.mean((target_gram-style_gram)**2)      _,d,h,w=target_feature.shape      style_loss += layer_style_loss/(d*h*w)  total_loss=content_weight*content_loss+style_weight*style_loss  optimizer.zero_grad()  total_loss.backward()  optimizer.step()  #Plotting output images  if ii % show_every==0:    print('Total loss:',total_loss.item())    print('Iteration',ii)    plt.imshow(im_convert(target))    plt.axis('off')    plt.show()  if ii%capture_frame==0:    image_array[counter]=im_convert(target)    counter=counter+1  #Plotting content, style and target images  fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(20,10))  ax1.imshow(im_convert(content))  ax1.axis('off')  ax2.imshow(im_convert(style))  ax2.axis('off')  ax3.imshow(im_convert(target))  ax3.axis('off')   

输出:

 1

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