深度学习出现这种维度问题

return torch.stack(batch, 0, out=out)

RuntimeError: stack expects each tensor to be equal size, but got [1, 312, 312] at entry 0 and [1, 512, 512] at entry 1

• 建议你看下这篇博客👉 ：RuntimeError: stack expects each tensor to be equal size, but got [3, ] at entry 0 and [1,]at entry1
• 除此之外, 这篇博客: stack expects each tensor to be equal size, but got [3, 224, 224] at entry 0 and [1,224,224] at entr中的  验证方法： 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  

• path_load = glob.glob(r'D:\BaiduNetdiskDownload\pytorch_learning\dataset\dataset2\*.jpg')
  #图片路径
  transform = transforms.Compose([
      transforms.Resize((224,224)),
      transforms.ToTensor()
  ])
  
  all_labels=[]
  species = ['cloudy', 'rain', 'shine', 'sunrise']
  #图片类别
  for img in path_load:
      for i,c in enumerate(species):
          if c in img:
              all_labels.append(i)
  #图片标签            
  species_to_idx = dict((c, i) for i, c in enumerate(species))          
  label_to_class =  dict((v,k) for k,v in species_to_idx.items())           
  class Mydataset(data.Dataset):
      def __init__(self,root,labels,transform):
          super(Mydataset,self).__init__()
          self.imgs_path = root
          self.labels = labels
          self.transform = transform
      def __getitem__(self,index):
          ig_path = self.imgs_path[index]
          label=self.labels[index]
          pil_image = Image.open(ig_path).convert('RGB')
          data = self.transform(pil_image)
          return data,label
      def __len__(self):
          return len(self.imgs_path)
  
  
  wheather_dataset = Mydataset(path_load,all_labels,transform)
  wheather_dl = data.DataLoader(wheather_dataset,
                                batch_size=16,
                               shuffle=True,
                               drop_last=True)
  
  
  
  plt.figure(figsize=(12,8))
  imgs_batch,labels_batch=next(iter(wheather_dl))
  for i,(img,label) in enumerate(zip(imgs_batch,labels_batch)):
          img = img.permute(1,2,0).numpy()
          plt.subplot(4,4,i+1)
          plt.title(label_to_class.get(label.item()))
          plt.imshow(img)

  result：