PyTorch 教程-针对风格转换的优化过程

整理：python架构师

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。

由于训练过程可能需要很长时间才能完成，因此我们需要在结果和时间效率之间保持平衡。因此，我们将定义我们的步骤，并在我们的情况下将步骤限制为2100。

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

步骤2：

现在，我们实现了一些用于数据可视化的代码行。我们定义了一个图像数组，该数组将在训练过程中存储目标图像。在训练过程结束后，我们可以通过这些图像创建一个视频，以了解样式和内容图像如何结合优化目标图像。我们将解开目标图像的形状。

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

我们将定义一个捕获帧，它帮助我们每次捕获一帧。最后，我们将定义一个计数器变量，它将跟踪数组索引。

capture_frame=steps/300  
counter=0  

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:

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