试图将 png 图像导入到 torchvision

Question

我正在尝试导入用于 torch 和 torchvision 的图像。但我收到此错误：

TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "c:\python38\lib\site-packages\torch\utils\data\_utils\worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "c:\python38\lib\site-packages\torch\utils\data\_utils\fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "c:\python38\lib\site-packages\torch\utils\data\_utils\collate.py", line 79, in default_collate
    return [default_collate(samples) for samples in transposed]
  File "c:\python38\lib\site-packages\torch\utils\data\_utils\collate.py", line 79, in <listcomp>
    return [default_collate(samples) for samples in transposed]
  File "c:\python38\lib\site-packages\torch\utils\data\_utils\collate.py", line 81, in default_collate
    raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'PIL.Image.Image'>

根据这篇文章，我将它们转换为张量：

https://discuss.pytorch.org/t/typeerror-default-collate-batch-must-contain-tensors-numpy-arrays-numbers-dicts-or-lists-found-class-imageio-core-util-array/62667

这是我的代码：

import torch
import torchvision
import torchvision.transforms
from torchvision import datasets, transforms

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.ToTensor()
])

dataset = torchvision.datasets.ImageFolder('datasets')

dataloader = torch.utils.data.DataLoader(dataset,
                                          batch_size=16,
                                          shuffle=True,
                                          num_workers=12)

tensor_dataset = []

for i, data in enumerate(dataloader, 0):
    Tensor = torch.tensor(data)
    tensor_dataset.append(Tensor.flatten)

最后一部分来自https://github.com/TerragonDE/PyTorch，但我没有成功。我要加载的数据来自这里：

http://www.cvlibs.net/datasets/kitti/

我该如何解决这个问题？

更新：

感谢@trialNerror，但现在我收到此错误：

ValueError                                Traceback (most recent call last)
<ipython-input-6-aa72392b67e8> in <module>
      1 for i, data in enumerate(dataloader, 0):
----> 2     Tensor = torch.tensor(data)
      3     tensor_dataset.append(Tensor.flatten)

ValueError: only one element tensors can be converted to Python scalars

这是我目前发现的，但不知道如何应用它：

https://discuss.pytorch.org/t/pytorch-autograd-grad-only-one-element-tensors-can-be-converted-to-python-scalars/56681

更新 2：

我最终没有使用数据加载器的原因是因为我最终得到了这个错误：

num_epochs = 10
loss_values = list()

for epoch in range(1, num_epochs):
    for i, data in enumerate(train_array, 0):     
        outputs = model(data.unsqueeze(0))
        loss = criterion(outputs,data.unsqueeze(0))
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
        print('Epoch - %d, loss - %0.5f '%(epoch, loss.item()))
        loss_values.append(loss.item())

torch.Size([1, 16, 198, 660]) 火炬尺寸（[1, 32, 97, 328]） torch.Size([1, 1018112])

RuntimeError                              Traceback (most recent call last)
<ipython-input-106-5e6fa86df079> in <module>
      4 for epoch in range(1, num_epochs):
      5     for i, data in enumerate(train_array, 0):
----> 6         outputs = model(data.unsqueeze(0))
      7         loss = criterion(outputs,data.unsqueeze(0))
      8 

c:\python38\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

<ipython-input-90-467a3f84a03f> in forward(self, x)
     29         print(out.shape)
     30 
---> 31         out = self.fc(out)
     32         print(out.shape)
     33 

c:\python38\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

c:\python38\lib\site-packages\torch\nn\modules\linear.py in forward(self, input)
     85 
     86     def forward(self, input):
---> 87         return F.linear(input, self.weight, self.bias)
     88 
     89     def extra_repr(self):

c:\python38\lib\site-packages\torch\nn\functional.py in linear(input, weight, bias)
   1608     if input.dim() == 2 and bias is not None:
   1609         # fused op is marginally faster
-> 1610         ret = torch.addmm(bias, input, weight.t())
   1611     else:
   1612         output = input.matmul(weight.t())

RuntimeError: size mismatch, m1: [1 x 1018112], m2: [512 x 10] at C:\w\b\windows\pytorch\aten\src\TH/generic/THTensorMath.cpp:41

我意识到如果你有 m1: [a * b] 和 m2: [c * d] 那么 b 和 c 必须是相同的值，但我不确定，调整图像大小的最佳方法是什么?

Answer 1

您的 transform 变量未使用，应将其传递给 Dataset 构造函数：

`dataset = torchvision.datasets.ImageFolder('datasets', transform=transform)`

因此，ToTensor 永远不会应用于您的数据，因此它们仍然是 PIL 图像，而不是张量。

Answer 2

请注意，我想将目录中的所有 PNG 图像自动加载为 pytorch 张量。我以前看过这样的帖子（以及许多其他网页）：

Loading a huge dataset batch-wise to train pytorch

但我最终在目录中的所有图像上使用Image.open，而不是torch DataLoader：

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

image = Image.open("datasets/image_02/data/my_image.png").convert('RGB')

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms as tf

transforms = tf.Compose([tf.Resize(400), 
                        tf.ToTensor()])

img_tensor = transforms(image)