问题描述
我正在 CUDA GPU 上训练一个 CNN，它将 3D 医学图像作为输入并输出一个分类器.我怀疑 pytorch 中可能存在错误.我正在运行 pytorch 1.4.0.GPU 是特斯拉 P100-PCIE-16GB".当我在 CUDA 上运行模型时出现错误
I am training a CNN on CUDA GPU which takes 3D medical images as input and outputs a classifier. I suspect there may be a bug in pytorch. I am running pytorch 1.4.0. The GPU is 'Tesla P100-PCIE-16GB'. When I run the model on CUDA I get the error
Traceback (most recent call last):
File "/home/ub/miniconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-55-cc0dd3d9cbb7>", line 1, in <module>
net(cc)
File "/home/ub/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "<ipython-input-2-19e11966d1cd>", line 181, in forward
out = self.layer1(x)
File "/home/ub/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/ub/miniconda3/lib/python3.7/site-packages/torch/nn/modules/container.py", line 100, in forward
input = module(input)
File "/home/ub/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/ub/miniconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 480, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Could not run 'aten::slow_conv3d_forward' with arguments from the 'CUDATensorId' backend. 'aten::slow_conv3d_forward' is only available for these backends: [CPUTensorId, VariableTensorId].
重现问题:
#input is a 64,64,64 3d image batch with 2 channels
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv3d(2, 32, kernel_size=5, stride=1, padding=2),
nn.ReLU(),
nn.MaxPool3d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv3d(32, 64, kernel_size=5, stride=1, padding=2),
nn.ReLU(),
nn.MaxPool3d(kernel_size=2, stride=2))
self.drop_out = nn.Dropout()
self.fc1 = nn.Linear(16 * 16*16 * 64, 1000)
self.fc2 = nn.Linear(1000, 2)
# self.softmax = nn.LogSoftmax(dim=1)
def forward(self, x):
# print(out.shape)
out = self.layer1(x)
# print(out.shape)
out = self.layer2(out)
# print(out.shape)
out = out.reshape(out.size(0), -1)
# print(out.shape)
out = self.drop_out(out)
# print(out.shape)
out = self.fc1(out)
# print(out.shape)
out = self.fc2(out)
# out = self.softmax(out)
# print(out.shape)
return out
net = Convnet()
input = torch.randn(16, 2, 64, 64, 64)
net(input)
推荐答案
最初，我认为错误消息表明 'aten::slow_conv3d_forward' 未通过 GPU (CUDA) 实现.但是看了你的网络后，我觉得没有意义，因为 Conv3D 是一个非常基础的操作，Pytorch 团队应该在 CUDA 中实现它.
Initially, I was thinking the error message indicates that 'aten::slow_conv3d_forward' is not implemented with GPU (CUDA). But after looked at your network, it does not make sense to me, since Conv3D is a very basic op, and Pytorch team should implement this in CUDA.
然后我深入研究了源代码，发现输入不是 CUDA 张量，这导致了问题.
Then I dived a bit about the source code, finding that the input is not a CUDA tensor, which causes the problem.
这是一个工作示例:
import torch
from torch import nn
#input is a 64,64,64 3d image batch with 2 channels
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv3d(2, 32, kernel_size=5, stride=1, padding=2),
nn.ReLU(),
nn.MaxPool3d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv3d(32, 64, kernel_size=5, stride=1, padding=2),
nn.ReLU(),
nn.MaxPool3d(kernel_size=2, stride=2))
self.drop_out = nn.Dropout()
self.fc1 = nn.Linear(16 * 16*16 * 64, 1000)
self.fc2 = nn.Linear(1000, 2)
# self.softmax = nn.LogSoftmax(dim=1)
def forward(self, x):
# print(out.shape)
out = self.layer1(x)
# print(out.shape)
out = self.layer2(out)
# print(out.shape)
out = out.reshape(out.size(0), -1)
# print(out.shape)
out = self.drop_out(out)
# print(out.shape)
out = self.fc1(out)
# print(out.shape)
out = self.fc2(out)
# out = self.softmax(out)
# print(out.shape)
return out
net = ConvNet()
input = torch.randn(16, 2, 64, 64, 64)
net.cuda()
input = input.cuda() # IMPORTANT to reassign your tensor
net(input)
记住当你把一个模型从CPU放到GPU上时，可以直接调用.cuda()，但是如果你把一个张量从CPU放到GPU上，就需要重新赋值，比如tensor = tensor.cuda()，而不是只调用tensor.cuda().希望有所帮助.
Remember when you put a model from CPU to GPU, you can directly call .cuda(), but if you put a tensor from CPU to GPU, you will need to reassign it, such as tensor = tensor.cuda(), instead of only calling tensor.cuda(). Hope that helps.
输出:
tensor([[-0.1588, 0.0680],
[ 0.1514, 0.2078],
[-0.2272, -0.2835],
[-0.1105, 0.0585],
[-0.2300, 0.2517],
[-0.2497, -0.1019],
[ 0.1357, -0.0475],
[-0.0341, -0.3267],
[-0.0207, -0.0451],
[-0.4821, -0.0107],
[-0.1779, 0.1247],
[ 0.1281, 0.1830],
[-0.0595, -0.1259],
[-0.0545, 0.1838],
[-0.0033, -0.1353],
[ 0.0098, -0.0957]], device='cuda:0', grad_fn=<AddmmBackward>)
这篇关于Pytorch 错误:无法使用来自“CUDATensorId"后端的参数运行“aten::slow_conv3d_forward"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！