Pytorch，INPUT(正常张量)和WEIGHT(CUDA张量)不匹配 [英] Pytorch, INPUT (normal tensor) and WEIGHT (cuda tensor) mismatch
问题描述
免责声明，我知道，这个问题已经问了很多遍了，但是我尝试了他们的解决方案，但没有一个对我有用，因此，经过所有这些努力，我什么也找不到了，最终我不得不再问一次.
DISCLAIMER I know, this question has already asked multiple times, but i tried their solutions, none of them worked for me, so after all those effort, i can't find anything else and eventually i have to ask again.
我正在使用cnns(PYTORCH)进行图像分类，我不想在GPU(nvidia gpu，与已安装的cuda/cuda兼容)上进行训练，我成功地在其上安装了网络，但问题出在数据.
I'm doing image classification with cnns (PYTORCH), i wan't to train it on GPU (nvidia gpu, compatible with cuda/cuda installed), i successfully managed to put net on it, but the problem is with data.
if torch.cuda.is_available():
device = torch.device("cuda:0")
print("Running on the GPU")
print("Available GPU", torch.cuda.device_count())
Running on the GPU
Available GPU 1
net = Net()
net.to(device)
for epoch in range(2):
running_loss=0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data[0].to(device), data[1].to(device) # putting data on gpu
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
dataiter = iter(testloader)
images, labels = dataiter.next()
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
out = net(images)
ERROR
----------------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-81-76c52eabb174> in <module>
----> 1 out = net(images)
~/anaconda3/envs/home/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548 result = self._slow_forward(*input, **kwargs)
549 else:
--> 550 result = self.forward(*input, **kwargs)
551 for hook in self._forward_hooks.values():
552 hook_result = hook(self, input, result)
<ipython-input-57-c74f8361a10b> in forward(self, x)
11
12 def forward(self, x):
---> 13 x = self.pool(F.relu(self.conv1(x)))
14 x = self.pool(F.relu(self.conv2(x)))
15 x = x.view(-1, 16*5*5)
~/anaconda3/envs/home/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
548 result = self._slow_forward(*input, **kwargs)
549 else:
--> 550 result = self.forward(*input, **kwargs)
551 for hook in self._forward_hooks.values():
552 hook_result = hook(self, input, result)
~/anaconda3/envs/home/lib/python3.8/site-packages/torch/nn/modules/conv.py in forward(self, input)
351
352 def forward(self, input):
--> 353 return self._conv_forward(input, self.weight)
354
355 class Conv3d(_ConvNd):
~/anaconda3/envs/home/lib/python3.8/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
347 weight, self.bias, self.stride,
348 _pair(0), self.dilation, self.groups)
--> 349 return F.conv2d(input, weight, self.bias, self.stride,
350 self.padding, self.dilation, self.groups)
351
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same
inputs.is_cuda
True
与标签相同.
我尝试过的事情:
https://stackoverflow.com/q/59013109/13287412
https://github.com/sksq96/pytorch-summary/issues/57
https://blog.csdn.net/qq_27261889/article/details/86575033
https://blog.csdn.net/public669/article/details/96510293
但到目前为止没有任何效果...
but nothing worked so far...
推荐答案
您的图像张量位于CPU上，而 net 则位于GPU上.即使在评估时，您也要确保输入张量和模型位于同一设备上，否则会出现张量数据类型错误.
Your images tensor is located on the CPU while your net is located on the GPU. Even when evaluating you want to make sure that your input tensors and model are located on the same device otherwise you will get tensor data type errors.
out = net(images.to(device))
这篇关于Pytorch，INPUT(正常张量)和WEIGHT(CUDA张量)不匹配的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
