我正在尝试自定义加载一些带有标签的图像文件(JPG文件)，并按照示例datasets.html" rel="nofollow noreferrer">here将它们输入PyTorch中的卷积神经网络(CNN)。然而，似乎仍然有no decent end-to-end tutorials。我看到的问题如下。RuntimeError: thnn_conv2d_forward is not implemented for type

torch.ByteTensor

我的Dataset如下所示。class ImageData(Dataset):

def __init__(self, width=256, height=256, transform=None):

self.width = width

self.height = height

self.transform = transform

y, x = get_images() #y is a list of labels, x is a list of file paths

self.y = y

self.x = x

def __getitem__(self, index):

img = Image.open(self.x[index]) # use pillow to open a file

img = img.resize((self.width, self.height)) # resize the file to 256x256

img = img.convert('RGB') #convert image to RGB channel

if self.transform is not None:

img = self.transform(img)

img = np.asarray(img).transpose(-1, 0, 1) # we have to change the dimensions from width x height x channel (WHC) to channel x width x height (CWH)

img = torch.from_numpy(np.asarray(img)) # create the image tensor

label = torch.from_numpy(np.asarray(self.y[index]).reshape([1, 1])) # create the label tensor

return img, label

def __len__(self):

return len(self.x)

CNN取自here，并被修改为处理NCWH(批处理x通道x宽度x高度)，如下所示。class Net(nn.Module):

def __init__(self):

super(Net, self).__init__()

self.conv1 = nn.Conv2d(3, 256, 256)

self.pool = nn.MaxPool2d(2, 2)

self.conv2 = nn.Conv2d(6, 16, 5)

self.fc1 = nn.Linear(16 * 5 * 5, 120)

self.fc2 = nn.Linear(120, 84)

self.fc3 = nn.Linear(84, 10)

def forward(self, x):

x = self.pool(F.relu(self.conv1(x)))

x = self.pool(F.relu(self.conv2(x)))

x = x.view(-1, 16 * 5 * 5)

x = F.relu(self.fc1(x))

x = F.relu(self.fc2(x))

x = self.fc3(x)

return x

学习循环也取自same tutorial，如下所示。for epoch in range(2): # loop over the dataset multiple times

running_loss = 0.0

for i, data in enumerate(dataloader, 0):

# get the inputs

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 % 2000 == 1999: # print every 2000 mini-batches

print('[%d, %5d] loss: %.3f' %

(epoch + 1, i + 1, running_loss / 2000))

running_loss = 0.0

print('Finished Training')

但是，上面提到的RuntimeError被抛出。关于我做错了什么有什么想法吗？

另外，我知道在不转置图像数据的情况下，它的形状是WHC，但NN模型要求它是CWH。问题是，如果我们从WHC改为CWH，那么如果我们在DataLoader上迭代，就不能再简单地绘制图像了。data = ImageData()

dataloader = DataLoader(data, batch_size=10, shuffle=True, num_workers=1)

imgs, labels = next(iter(dataloader))

plt.imshow(imgs.numpy()[0,:,:,:])

plt.show()

尝试执行此操作将引发以下错误。TypeError: Invalid dimensions for image data

对我来说，那个枕头给了你WHC，你可以用它来策划，但是PyTorch CNN想让CWH来处理，这是个麻烦。你知道如何一致或容易地不做这么多的转换，但能够绘图和输入数据到CNN吗？或是WHC与CWH的不匹配只是我们必须面对的问题？

在不转置图像的情况下，当将图像传送到CNN时，会抛出以下错误。RuntimeError: Given groups=1, weight[256, 3, 256, 256], so expected

input[10, 256, 256, 3] to have 3 channels, but got 256 channels

相反。