我正在嘗試自定義加載一些帶有標簽的圖像文件(JPG文件)，并按照示例datasets.html" rel="nofollow noreferrer">here將它們輸入PyTorch中的卷積神經網絡(CNN)。然而，似乎仍然有no decent end-to-end tutorials。我看到的問題如下。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，并被修改為處理NCWH(批處理x通道x寬度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

學習循環(huán)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被拋出。關于我做錯了什么有什么想法嗎？

另外，我知道在不轉置圖像數據的情況下，它的形狀是WHC，但NN模型要求它是CWH。問題是，如果我們從WHC改為CWH，那么如果我們在DataLoader上迭代，就不能再簡單地繪制圖像了。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()

嘗試執(zhí)行此操作將引發(fā)以下錯誤。TypeError: Invalid dimensions for image data

對我來說，那個枕頭給了你WHC，你可以用它來策劃，但是PyTorch CNN想讓CWH來處理，這是個麻煩。你知道如何一致或容易地不做這么多的轉換，但能夠繪圖和輸入數據到CNN嗎？或是WHC與CWH的不匹配只是我們必須面對的問題？

在不轉置圖像的情況下，當將圖像傳送到CNN時，會拋出以下錯誤。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。

總結

以上是生活随笔為你收集整理的pytorch图像和张量的相互转换_如何加载图像并将其转换为PyTorch的适当张量？的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。