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我正在尝试在 Pytorch 中运行以下代码：

import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data


class H5Dataset(data.Dataset):

    def __init__(self, trainx_path, trainy_path):

        super(H5Dataset, self).__init__()
        x_file = h5py.File(trainx_path)
        y_file = h5py.File(trainy_path)

        self.data = x_file.get('X')        
        self.target = y_file.get('y')

    def __getitem__(self, size):

      permutation1 = list(np.random.permutation(249000))  
      permutation2 = list(np.random.permutation(np.arange(249000,498000)))

      size1 = int(size/2)
      index1=list(permutation1[0:size1])
      index2=list(permutation2[0:size1])
      index = index1+index2
      labels=np.array(self.target).reshape(498000,-1)
      train_labels=labels[index]
      train_batch=[]

      for i in range(size):
          img=(self.data)[index[i]]
          train_batch.append(img)    
      train_batch=np.array(train_batch)

      return (torch.from_numpy(train_batch).float(), torch.from_numpy(train_labels).float())


    def __len__(self):
        return len(self.data)


dataset = H5Dataset('//content//drive//My Drive//E2E_1//train_x.hdf5','//content//drive//My Drive//E2E_1//train_y.hdf5')
train_loader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True, num_workers=2, pin_memory=False) 


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(2, 64, kernel_size=(2,2), padding=(8,8), stride=(2,2),padding_mode='zeros')
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=(2,2), padding=(8,8), stride=(2,2),padding_mode='zeros')
        self.fc1 = nn.Linear(32768, 500) 
        self.drop = nn.Dropout(p=0.5) 
        self.fc2 = nn.Linear(500, 1)
        self.fc3 = nn.Linear(1,1)  

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = F.relu(self.conv2(x))
        x = x.view(-1, 16 * 16 * 128)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()
print(net)

import torch.optim as optim

criterion = nn.BCELoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 32 == 31:    # print every 32 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 32))
            running_loss = 0.0


print('Finished Training')

但我收到以下错误。我的 train_x 和 train_y 数据存储在 2 个单独的 .hdf5 文件中，当我在训练时尝试读取它们时，会弹出此错误。请任何人都可以告诉必须进行哪些更改。

OSError                                   Traceback (most recent call last)
<ipython-input-8-a8f66fac8b5c> in <module>()
      2 
      3     running_loss = 0.0
----> 4     for i, data in enumerate(train_loader, 0):
      5         # get the inputs; data is a list of [inputs, labels]
      6         inputs, labels = data

3 frames
/usr/local/lib/python3.6/dist-packages/torch/_utils.py in reraise(self)
    392             # (https://bugs.python.org/issue2651), so we work around it.
    393             msg = KeyErrorMessage(msg)
--> 394         raise self.exc_type(msg)

OSError: Caught OSError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "<ipython-input-5-0df8cfc88081>", line 37, in __getitem__
    img=(self.data)[index[i]]
  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "/usr/local/lib/python3.6/dist-packages/h5py/_hl/dataset.py", line 573, in __getitem__
    self.id.read(mspace, fspace, arr, mtype, dxpl=self._dxpl)
  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "h5py/h5d.pyx", line 182, in h5py.h5d.DatasetID.read
  File "h5py/_proxy.pyx", line 130, in h5py._proxy.dset_rw
  File "h5py/_proxy.pyx", line 84, in h5py._proxy.H5PY_H5Dread
OSError: Can't read data (wrong B-tree signature)

我收到上面显示的错误。我是 PyTorch 的新手，所以请你建议可以做什么？

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0 回答 0