尝试修改pytorch时出现KeyError示例

Question

我试图修改此pytorch示例（https://github.com/pytorch/examples/blob/master/mnist/main.py）以使用我自己的数据集。

我试图将我的数据送入dataloader。我用两种不同的方式封装数据：一次是torch.utils.data.Dataset的扩展，一次是torch.utils.data.TensorDataset。不幸的是，我总是感到我不明白同样的错误：

Traceback (most recent call last): 
    File "main.py", line 142, in <module> 
    train(epoch) 
    File "main.py", line 112, in train 
    output = model(data) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 210, in __call__ 
    result = self.forward(*input, **kwargs) 
    File "main.py", line 90, in forward 
    x = F.relu(F.max_pool2d(self.conv1(x), 2)) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 210, in __call__ 
    result = self.forward(*input, **kwargs) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/conv.py", line 235, in forward 
    self.padding, self.dilation, self.groups) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/functional.py", line 54, in conv2d 
    return f(input, weight, bias) if bias is not None else f(input, weight) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/conv.py", line 33, in forward 
    output = self._update_output(input, weight, bias) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/conv.py", line 88, in _update_output 
    return self._thnn('update_output', input, weight, bias) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/conv.py", line 147, in _thnn 
    return impl[fn_name](self, self._bufs[0], input, weight, *args) 
    File "/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/conv.py", line 213, in call_update_output 
    backend = type2backend[type(input)] 
    File "/usr/local/lib/python2.7/dist-packages/torch/_thnn/__init__.py", line 13, in __getitem__ 
    return self.backends[name].load() 
KeyError: <class 'torch.cuda.ByteTensor'> 

这里是我的main.py，这基本上是这个例子：https://github.com/pytorch/examples/blob/master/mnist/main.py

from __future__ import print_function 
import argparse 
import os 
import glob 
import numpy 
import torch 
import torch.nn as nn 
import torch.nn.functional as F 
import torch.optim as optim 
import torch.utils.data as data_utils 
from PIL import Image 
from torchvision import datasets, transforms 
from torch.autograd import Variable 
from InputData import InputData 

# Training settings 
parser = argparse.ArgumentParser(description='PyTorch MNIST Example') 
parser.add_argument('--batch-size', type=int, default=64, metavar='N', 
        help='input batch size for training (default: 64)') 
parser.add_argument('--test-batch-size', type=int, default=10, metavar='N', 
        help='input batch size for testing (default: 1000)') 
parser.add_argument('--epochs', type=int, default=1, metavar='N', 
        help='number of epochs to train (default: 10)') 
parser.add_argument('--lr', type=float, default=0.01, metavar='LR', 
        help='learning rate (default: 0.01)') 
parser.add_argument('--momentum', type=float, default=0.5, metavar='M', 
        help='SGD momentum (default: 0.5)') 
parser.add_argument('--no-cuda', action='store_true', default=False, 
        help='disables CUDA training') 
parser.add_argument('--seed', type=int, default=1, metavar='S', 
        help='random seed (default: 1)') 
parser.add_argument('--log-interval', type=int, default=10, metavar='N', 
        help='how many batches to wait before logging training status') 
args = parser.parse_args() 
args.cuda = not args.no_cuda and torch.cuda.is_available() 

torch.manual_seed(args.seed) 
if args.cuda: 
    torch.cuda.manual_seed(args.seed) 

kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} 

# Original DataLoader - WORKS: 
''' 
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True, 
        transform=transforms.Compose([ 
         transforms.ToTensor(), 
         transforms.Normalize((0.1307,), (0.3081,)) 
        ])), 
    batch_size=args.batch_size, shuffle=True, **kwargs) 
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([ 
         transforms.ToTensor(), 
         transforms.Normalize((0.1307,), (0.3081,)) 
        ])), 
    batch_size=args.batch_size, shuffle=True, **kwargs) 
''' 

# DataLoader as extension of data.Dataset: 

train_loader = torch.utils.data.DataLoader(InputData('~/bakk-arbeit/data', train=True), 
              batch_size=args.batch_size, shuffle=True, **kwargs) 
test_loader = torch.utils.data.DataLoader(InputData('~/bakk-arbeit/data', train=False), 
              batch_size=args.batch_size, shuffle=True, **kwargs) 



# DataLoader as data.TensorDataset: 
''' 
data_folder = os.path.expanduser('~/bakk-arbeit/data') 
InputData = InputData() 
train = data_utils.TensorDataset(InputData.read_image_files(os.path.join(data_folder, 'training')),InputData.read_label_files(os.path.join(data_folder, 'training'))) 
test = data_utils.TensorDataset(InputData.read_image_files(os.path.join(data_folder, 'test')),InputData.read_label_files(os.path.join(data_folder, 'test'))) 
train_loader = data_utils.DataLoader(train, batch_size=args.batch_size, shuffle=True, **kwargs) 
test_loader = data_utils.DataLoader(test, batch_size=args.batch_size, shuffle=True, **kwargs) 
''' 


class Net(nn.Module): 
    def __init__(self): 
     super(Net, self).__init__() 
     self.conv1 = nn.Conv2d(1, 10, kernel_size=5) # change to 3 input channels for InputData! 
     self.conv2 = nn.Conv2d(10, 20, kernel_size=5) 
     self.conv2_drop = nn.Dropout2d() 
     self.fc1 = nn.Linear(320, 50) # change 320 to 500 for InputData to match 32x32 
     self.fc2 = nn.Linear(50, 10) 

    def forward(self, x): 
     x = F.relu(F.max_pool2d(self.conv1(x), 2)) 
     x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) 
     x = x.view(-1, 320) # # change 320 to 500 for InputData to match 32x32 
     x = F.relu(self.fc1(x)) 
     x = F.dropout(x, training=self.training) 
     x = self.fc2(x) 
     return F.log_softmax(x) 

model = Net() 
if args.cuda: 
    model.cuda() 

optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) 

def train(epoch): 
    model.train() 
    for batch_idx, (data, target) in enumerate(train_loader): 
     # data = data.numpy() 
     if args.cuda: 
      data, target = data.cuda(), target.cuda() 
     data, target = Variable(data), Variable(target) 
     optimizer.zero_grad() 
     output = model(data) 
     loss = F.nll_loss(output, target) 
     loss.backward() 
     optimizer.step() 
     if batch_idx % args.log_interval == 0: 
      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
       epoch, batch_idx * len(data), len(train_loader.dataset), 
       100. * batch_idx/len(train_loader), loss.data[0])) 

def test(): 
    model.eval() 
    test_loss = 0 
    correct = 0 
    for data, target in test_loader: 
     if args.cuda: 
      data, target = data.cuda(), target.cuda() 
     data, target = Variable(data, volatile=True), Variable(target) 
     output = model(data) 
     test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss 
     pred = output.data.max(1)[1] # get the index of the max log-probability 
     correct += pred.eq(target.data.view_as(pred)).cpu().sum() 

    test_loss /= len(test_loader.dataset) 
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
     test_loss, correct, len(test_loader.dataset), 
     100. * correct/len(test_loader.dataset))) 



for epoch in range(1, args.epochs + 1): 
    train(epoch) 
    test() 

...这是我的InputData.py这扩大了数据。数据集：

import torch 
import numpy 
import torch.utils.data as data 
import glob 
import os 
from PIL import Image 


class InputData(data.Dataset): 
    train_folder = 'training' 
    test_folder = 'test' 

    def __init__(self, root='', train=True): 
     self.root = os.path.expanduser(root) 
     self.train = train # training set or test set 
     if root: 
      if self.train: 
       self.training_labels = self.read_label_files(os.path.join(self.root, self.train_folder)) 
       #with open(os.path.join(self.root, 'training_labels.pt'), 'wb') as f: 
        # torch.save(self.read_label_files(os.path.join(self.root, self.train_folder)), f) 
       # with open(os.path.join(self.root, 'training_images.pt'), 'wb') as f: 
        #torch.save(self.read_image_files(os.path.join(self.root, self.train_folder)), f) 
       self.training_images = self.read_image_files(os.path.join(self.root, self.train_folder)) 
      else: 
       self.test_images = self.read_image_files(os.path.join(self.root, self.test_folder)) 
       self.test_labels = self.read_label_files(os.path.join(self.root, self.test_folder)) 
     print('initialized') 

    def read_image_files(self, path): 
     print('reading image files...') 
     image_list = [] 
     # ten = torch.ByteTensor(3,32,32) 
     for filename in glob.glob(path + '/*.png'): 
      im = Image.open(filename) 
      data = numpy.asarray(im) 
      data = numpy.swapaxes(data,0,2) 
      image_list.append(data) 
     image_list = numpy.asarray(image_list) 
     t = torch.from_numpy(image_list) 
     # ten = torch.stack([ten, t]) 
     print('done!') 
     return t 

    def read_label_files(self, path): 
     print('reading labels...') 
     labels = [] 
     for filename in glob.glob(path + '/*.png'): 
      base = os.path.basename(filename) 
      im_class = int(base[:1]) 
      labels.append(im_class) 
     print('done!') 
     return torch.LongTensor(labels) 

    def __getitem__(self, index): 
     """ 
     Args: 
      index (int): Index 

     Returns: 
      tuple: (image, target) where target is index of the target class. 
     """ 
     if self.train: 
      img, target = self.training_images[Index], self.training_labels[Index] 
     else: 
      img, target = self.test_images[Index], self.test_labels[Index] 

     # img = Image.fromarray(img.numpy(), mode='RGB') 
     # -> won't work for me??? returns TypeError: batch must contain tensors, numbers, or lists; found <class 'PIL.Image.Image'> 

     return img, target 

    def __len__(self): 
     if self.train: 
      return len(self.training_images) 
     else: 
      return len(self.test_images) 

我在做什么错？

Answer 1

似乎大多数操作是在FloatTensor和DoubleTensor（source）定义，你的模型获取model(data)一个ByteTensor。

我会继续确保我的dataset对象输出FloatTensor s。调试model(data)之前的行并查看张量类型data。我想这是一个ByteTensor，这将是一个很好的开始。