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使用Google Colab在Pytorch中运行数据加载器

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我正在尝试使用Pytorch对猫和狗的图像数据集进行分类 . 在我的代码中，我到目前为止下载数据并进入文件夹列车，其中有两个文件夹，名为“cats”和“dogs” . 然后我尝试将这些数据加载到一个数据加载器并迭代批量，但是它给了我一些我在迭代步骤中无法理解的错误 .

由于它是Google Colabs，我在那里有代码用于下载数据和安装库 . 到目前为止，对我的代码的任何其他建议也将受到赞赏 .

!pip install torch
!pip install torchvision

from __future__ import print_function, division
import os
import torch
import pandas as pd
import numpy as np
# For showing and formatting images
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# For importing datasets into pytorch
import torchvision.datasets as dataset

# Used for dataloaders
import torch.utils.data as data

# For pretrained resnet34 model
import torchvision.models as models

# For optimisation function
import torch.nn as nn
import torch.optim as optim


!wget http://files.fast.ai/data/dogscats.zip
!unzip dogscats.zip    

batch_size = 256

train_raw = dataset.ImageFolder(PATH+"train", transform=transforms.ToTensor())
train_loader = data.DataLoader(train_raw, batch_size=batch_size, shuffle=True)

for batch_idx, (data, target) in enumerate(train_loader):
  print("Data: ", batch_idx)

错误出现在最后一行，如下所示：

RuntimeErrorTraceback (most recent call last)
<ipython-input-66-c32dd0c1b880> in <module>()
----> 1 for batch_idx, (data, target) in enumerate(train_loader):
      2   print("Data: ", batch_idx)
      3 

/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.pyc in __next__(self)
    257         if self.num_workers == 0:  # same-process loading
    258             indices = next(self.sample_iter)  # may raise StopIteration
--> 259             batch = self.collate_fn([self.dataset[i] for i in indices])
    260             if self.pin_memory:
    261                 batch = pin_memory_batch(batch)

/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.pyc in default_collate(batch)
    133     elif isinstance(batch[0], collections.Sequence):
    134         transposed = zip(*batch)
--> 135         return [default_collate(samples) for samples in transposed]
    136 
    137     raise TypeError((error_msg.format(type(batch[0]))))

/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.pyc in default_collate(batch)
    110             storage = batch[0].storage()._new_shared(numel)
    111             out = batch[0].new(storage)
--> 112         return torch.stack(batch, 0, out=out)
    113     elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
    114             and elem_type.__name__ != 'string_':

/usr/local/lib/python2.7/dist-packages/torch/functional.pyc in stack(sequence, dim, out)
     62     inputs = [t.unsqueeze(dim) for t in sequence]
     63     if out is None:
---> 64         return torch.cat(inputs, dim)
     65     else:
     66         return torch.cat(inputs, dim, out=out)

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 400 and 487 in dimension 2 at /pytorch/torch/lib/TH/generic/THTensorMath.c:2897

谢谢

python-3.x image deep-learning image-recognition pytorch

3 回答

  • 0

    我认为主要问题是图像尺寸不同 . 我可能以其他方式理解ImageFolder但是，如果目录结构在pytorch中指定，我认为你不需要图像标签，而pytorch会为你找出标签 . 我还会为您的转换添加更多内容，自动调整文件夹中的每个图像的大小，例如：

    normalize = transforms.Normalize(
                        mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225]
                        )
   transform = transforms.Compose(
        [transforms.ToTensor(),transforms.Resize((224,224)),
         normalize])

    您还可以使用其他技巧使您的DataLoader更快，例如添加batch_size和cpu worker的数量，例如：

    testloader = DataLoader(testset, batch_size=16,
                         shuffle=False, num_workers=4)

    我认为这会让你的管道更快 .

    回复于
  • 1

    我在您的代码中看到两个问题，首先您将导入torch.utils.data作为数据导入，然后再次替换数据加载器中的数据 . 请将导入的模块和变量名称保存在单独的命名空间中 . 我认为这个错误可能是因为dataloder（图像）和标签返回的数据大小不同 . 正如您所看到的，连接中存在错误，因为第一个维度即 . 文件夹中的标签大小和图像数量不匹配 . 希望这可以帮助 .

    回复于
  • 1

    我认为我对Manoj Acharya的评论错了，问题在于将batch_size放入dataloader . 我看了下面的来源，看来你不能用不同的尺寸批量图像：

    https://medium.com/@yvanscher/pytorch-tip-yielding-image-sizes-6a776eb4115b

    因此，在我更改数据变量后的代码中Manoj指出我将batch_size更改为1并且程序停止失败 . 我想分批放置它，所以我添加了一个进一步的变换CenterCrop（）来调整所有图像的大小相同 . 以下是我的新代码：

    !pip install torch
!pip install torchvision

from __future__ import print_function, division
import os
import torch
import pandas as pd
import numpy as np
# For showing and formatting images
import matplotlib.pyplot as plt
import matplotlib.image as mpimg    
# For importing datasets into pytorch
import torchvision.datasets as dataset    
# Used for dataloaders
from torch.utils.data import DataLoader
# For pretrained resnet34 model
import torchvision.models as models    
# For optimisation function
import torch.nn as nn
import torch.optim as optim    
# For turning data into tensors
import torchvision.transforms as transforms

!wget http://files.fast.ai/data/dogscats.zip
!unzip dogscats.zip

batch_size = 256
sz = 224

train_raw = dataset.ImageFolder(PATH+"train", transform=transforms.Compose([transforms.CenterCrop(sz),transforms.ToTensor()]))
train_loader = DataLoader(train_raw,batch_size=batch_size, shuffle=True)

for batch_idx, (data, target) in enumerate(train_loader):
  print("Data: ", batch_idx)

    谢谢

    回复于

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