我正在尝试实现一个CNN来识别MNIST数据集中的数字,并且我的代码在数据加载过程中提出了错误。我不明白为什么会发生这种情况。
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5), (0.5))
])
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=20, shuffle=True, num_workers=2)
testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=20, shuffle=False, num_workers=2)
for i, data in enumerate(trainloader, 0):
inputs, labels = data[0], data[1]
错误:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-6-b37c638b6114> in <module>
2
----> 3 for i, data in enumerate(trainloader, 0):
4 inputs, labels = data[0], data[1]
# ...
IndexError: Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 99, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 99, in <listcomp>
samples = collate_fn([dataset[i] for i in batch_indices])
File "/opt/conda/lib/python3.6/site-packages/torchvision/datasets/mnist.py", line 95, in __getitem__
img = self.transform(img)
File "/opt/conda/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 61, in __call__
img = t(img)
File "/opt/conda/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 164, in __call__
return F.normalize(tensor, self.mean, self.std, self.inplace)
File "/opt/conda/lib/python3.6/site-packages/torchvision/transforms/functional.py", line 208, in normalize
tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
IndexError: too many indices for tensor of dimension 0
问题是mean
和std
必须是序列(例如,元组(,因此您应该在值之后添加一个逗号:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
请注意(0.5)
和(0.5,)
之间的差异。您可以在此处检查这些值的使用方式。如果应用相同的过程,您会看到:
import torch
x1 = torch.as_tensor((0.5))
x2 = torch.as_tensor((0.5,))
print(x1.shape, x1.ndim) # output: torch.Size([]) 0
print(x2.shape, x2.ndim) # output: torch.Size([1]) 1
也许您不知道,但是它们在Python中也有所不同:
type((0.5)) # <type 'float'>
type((0.5,)) # <type 'tuple'>
检查火车集是否不是空的,简单的打印输出,对于火车负载器,如果它仍然不起作用,我更喜欢用
手动加载MNISTdef load_mnist_labels(fnlabel):
f = gzip.open(fnlabel, 'rb')
f.read(8)
return np.frombuffer(f.read(), dtype = np.uint8)
def load_mnist_images(fnlabel):
f = gzip.open(fnlabel, 'rb')
f.read(16)
return np.frombuffer(f.read(), dtype = np.uint8)