Connect and share knowledge within a single location that is structured and easy to search. So if there was an error in the old code this error might still occur and the traceback then points to the line you have just corrected. I was showing a friend something and told him to update his extensions, and he got this error. I was stucked by this problem by few days and I hope someone could help me. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Calling a function of a module by using its name (a string). We tried running your code.The issue seems to be with the quantized.Conv3d, instead you can use normal convolution3d. What platforms do you use to access the UI ? However, the error disappears if not using cuda. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? Is it possible to rotate a window 90 degrees if it has the same length and width? Find centralized, trusted content and collaborate around the technologies you use most. You might want to ask pytorch questions on a pytorch forum. Please click the verification link in your email. Pytorch Simple Linear Sigmoid Network not learning. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Calling a function of a module by using its name (a string). Does your environment recognize torch.cuda? However, the link you referenced for the code contains the following line: PyTorch data types like torch.float came with PyTorch 0.4.0, so when you use something like torch.float in earlier versions like 0.3.1 you will see this error, because torch then actually has no attribute float. Hi, Could you give us an update? CUDA_MODULE_LOADING set to: AC Op-amp integrator with DC Gain Control in LTspice. privacy statement. Press any key to continue . It seems that you need to add --device cpu in the command line to make it work. Find centralized, trusted content and collaborate around the technologies you use most. AttributeError:partially initialized module 'torch' has no attribute 'cuda', How Intuit democratizes AI development across teams through reusability. So for example when changing in the imported code: torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float) to torch.FloatTensor([1,0,0,0,1,0]) it might still complain about torch.float even if the line then doesn't contain a torch.floatanymore (it even shows the new code in the traceback). In following the Pytorch tutorial at https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html. 'numpy.ndarray' object has no attribute 'cuda' - PyTorch Forums In the __init__.py of the module named torch-sparse, it is so bizarre and confusing .And torch.__version__ == 1.8.0 , torch-sparse == 0.6.11. How would "dark matter", subject only to gravity, behave? Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Try to transform the numpy array to a tensor before calling tensor.cuda () Implement Seek on /dev/stdin file descriptor in Rust. GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 or in your case: Why is there a voltage on my HDMI and coaxial cables? AttributeError: module 'torch' has no attribute 'device' By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. https://pytorch.org/. The best approach would be to use the same PyTorch release on both machines. Asking for help, clarification, or responding to other answers. """, def __init__(self, num_classes, pretrained=False): super(C3D, self).__init__() self.conv1 = nn.quantized.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))#..54.14ms self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)), self.conv2 = nn.quantized.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))#**395.749ms** self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), self.conv3a = nn.quantized.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))#..208.237ms self.conv3b = nn.quantized.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))#***..348.491ms*** self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), self.conv4a = nn.quantized.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))#..64.714ms self.conv4b = nn.quantized.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))#..169.855ms self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), self.conv5a = nn.quantized.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))#.27.173ms self.conv5b = nn.quantized.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))#.25.972ms self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1)), self.fc6 = nn.Linear(8192, 4096)#21.852ms self.fc7 = nn.Linear(4096, 4096)#.10.288ms self.fc8 = nn.Linear(4096, num_classes)#0.023ms, self.relu = nn.ReLU() self.softmax = nn.Softmax(dim=1), x = self.relu(self.conv1(x)) x = least_squares(self.pool1(x)), x = self.relu(self.conv2(x)) x = least_squares(self.pool2(x)), x = self.relu(self.conv3a(x)) x = self.relu(self.conv3b(x)) x = least_squares(self.pool3(x)), x = self.relu(self.conv4a(x)) x = self.relu(self.conv4b(x)) x = least_squares(self.pool4(x)), x = self.relu(self.conv5a(x)) x = self.relu(self.conv5b(x)) x = least_squares(self.pool5(x)), x = x.view(-1, 8192) x = self.relu(self.fc6(x)) x = self.dropout(x) x = self.relu(self.fc7(x)) x = self.dropout(x), def __init_weight(self): for m in self.modules(): if isinstance(m, nn.Conv3d): init.xavier_normal_(m.weight.data) init.constant_(m.bias.data, 0.01) elif isinstance(m, nn.Linear): init.xavier_normal_(m.weight.data) init.constant_(m.bias.data, 0.01), import torch.nn.utils.prune as prunedevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")model = C3D(num_classes=2).to(device=device)prune.random_unstructured(module, name="weight", amount=0.3), parameters_to_prune = ( (model.conv2, 'weight'), (model.conv3a, 'weight'), (model.conv3b, 'weight'), (model.conv4a, 'weight'), (model.conv4b, 'weight'), (model.conv5a, 'weight'), (model.conv5b, 'weight'), (model.fc6, 'weight'), (model.fc7, 'weight'), (model.fc8, 'weight'),), prune.global_unstructured( parameters_to_prune, pruning_method=prune.L1Unstructured, amount=0.2), --------------------------------------------------------------------------- AttributeError Traceback (most recent call last)
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