I tried to fix this problems by refering https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/360 and https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/67 We are closing the case assuming that your issue got resolved.Please raise a new thread in case of any further issues. Connect and share knowledge within a single location that is structured and easy to search. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Follow Up: struct sockaddr storage initialization by network format-string, Full text of the 'Sri Mahalakshmi Dhyanam & Stotram'. On a machine with PyTorch version: 1.12.1+cu116, running the following code gets error message module 'torch.cuda' has no attribute '_UntypedStorage'. AttributeError: 'module' object has no attribute 'urlopen'. I have not tested it on Linux, but I used the command for Windows and it worked great for me on Anaconda. Hi Franck, Thanks for the update. To figure out the exact issue we need your code and steps to test from our end.Could you share the entire code an Thanks for contributing an answer to Stack Overflow! Please click the verification link in your email. WebLKML Archive on lore.kernel.org help / color / mirror / Atom feed * [PATCH v38 00/39] LSM: Module stacking for AppArmor [not found] <20220927195421.14713-1-casey.ref@schaufler-ca.com> @ 2022-09-27 19:53 ` Casey Schaufler 2022-09-27 19:53 ` [PATCH v38 01/39] LSM: Identify modules by more than name Casey Schaufler ` (38 more replies) 0 siblings, Making statements based on opinion; back them up with references or personal experience. If you are wondering whether you have a proper CUDA setup, that question belongs on the CUDA setup forum, and the verification steps are provided in the CUDA linux install guide. Please always post the full error traceback. Thanks a lot! Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Calling a function of a module by using its name (a string). Please see. MIOpen runtime version: N/A We tried running your code.The issue seems to be with the quantized.Conv3d, instead you can use normal convolution3d. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. import torch.nn.utils.prune as prune device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = C3D(num_classes=2).to(device=device) This is the first time for me to run Pytorch with GPU on a linux machine. @emailweixu please reopen if error repros on pytorch 1.13. CUDA_MODULE_LOADING set to: In my code below, I added this statement: But this seems not right or enough. . """, 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)
in 19 parameters_to_prune, 20 pruning_method=prune.L1Unstructured, ---> 21 amount=0.2 22 ) ~/.local/lib/python3.7/site-packages/torch/nn/utils/prune.py in global_unstructured(parameters, pruning_method, **kwargs) 1017 1018 # flatten parameter values to consider them all at once in global pruning -> 1019 t = torch.nn.utils.parameters_to_vector([getattr(*p) for p in parameters]) 1020 # similarly, flatten the masks (if they exist), or use a flattened vector 1021 # of 1s of the same dimensions as t ~/.local/lib/python3.7/site-packages/torch/nn/utils/convert_parameters.py in parameters_to_vector(parameters) 18 for param in parameters: 19 # Ensure the parameters are located in the same device ---> 20 param_device = _check_param_device(param, param_device) 21 22 vec.append(param.view(-1)) ~/.local/lib/python3.7/site-packages/torch/nn/utils/convert_parameters.py in _check_param_device(param, old_param_device) 71 # Meet the first parameter 72 if old_param_device is None: ---> 73 old_param_device = param.get_device() if param.is_cuda else -1 74 else: 75 warn = False AttributeError: 'function' object has no attribute 'is_cuda', prune.global_unstructured when I use prune.global_unstructure I get that error. What is the point of Thrower's Bandolier? You may re-send via your If you have a line like in the example you've linked, it makes perfectly sense to get an error like this. Is there a single-word adjective for "having exceptionally strong moral principles"? raise RuntimeError(f"""{errdesc or 'Error running command'}. """, 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) in 19 parameters_to_prune, 20 pruning_method=prune.L1Unstructured, ---> 21 amount=0.2 22 ) ~/.local/lib/python3.7/site-packages/torch/nn/utils/prune.py in global_unstructured(parameters, pruning_method, **kwargs) 1017 1018 # flatten parameter values to consider them all at once in global pruning -> 1019 t = torch.nn.utils.parameters_to_vector([getattr(*p) for p in parameters]) 1020 # similarly, flatten the masks (if they exist), or use a flattened vector 1021 # of 1s of the same dimensions as t ~/.local/lib/python3.7/site-packages/torch/nn/utils/convert_parameters.py in parameters_to_vector(parameters) 18 for param in parameters: 19 # Ensure the parameters are located in the same device ---> 20 param_device = _check_param_device(param, param_device) 21 22 vec.append(param.view(-1)) ~/.local/lib/python3.7/site-packages/torch/nn/utils/convert_parameters.py in _check_param_device(param, old_param_device) 71 # Meet the first parameter 72 if old_param_device is None: ---> 73 old_param_device = param.get_device() if param.is_cuda else -1 74 else: 75 warn = False AttributeError: 'function' object has no attribute 'is_cuda', prune.global_unstructured when I use prune.global_unstructure I get that error. PyTorch version: 1.12.1+cu116 RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available () is Fal. pytorch1.61.6 Try removing it then reinstalling. Asking for help, clarification, or responding to other answers. stderr: Traceback (most recent call last): "After the incident", I started to be more careful not to trip over things. As you did not include a full error traceback I can only conjecture what the problem is. If thats not possible, and assuming you are using the GPU, use torch.cuda.amp.autocast. Can carbocations exist in a nonpolar solvent? The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cu117 File "C:\ai\stable-diffusion-webui\launch.py", line 272, in prepare_environment Is it suspicious or odd to stand by the gate of a GA airport watching the planes? What else should I do to get right running? Why do I get AttributeError: 'NoneType' object has no attribute 'something'? In such a case restarting the kernel helps. In my case command looks like: But you must obtain package list for yours machine form this site: rev2023.3.3.43278. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Since this issue is not related to Intel Devcloud can we close the case? GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 It seems that you need to add --device cpu in the command line to make it work. Later in the night i did the same and got the same error. Tried doing this and got another error =P Dreambooth can suck it. Thanks! You signed in with another tab or window. Commit hash: 0cc0ee1 Shouldn't this install latest version? What pytorch version are you using? Powered by Discourse, best viewed with JavaScript enabled, AttributeError: module 'torch.cuda' has no attribute 'amp'. prepare_environment() I am actually pruning my model using a particular torch library for pruning, device = torch.device("cuda" if torch.cuda.is_available() else "cpu")class C3D(nn.Module): """ The C3D network. Hi, Could you give us an update? . Libc version: glibc-2.35, Python version: 3.8.15 (default, Oct 12 2022, 19:15:16) [GCC 11.2.0] (64-bit runtime) So I've ditched this extension for now, since I was no longer really using it anyway and updating it regularly breaks my Automatic1111 environment. RuntimeError: Error running command. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Im wondering if my cuda setup is problematic? Have a question about this project? WebThis package adds support for CUDA tensor types, that implement the same function as CPU tensors, but they utilize GPUs for computation. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? You just need to find the line (or lines) where torch.float is used and change it. The text was updated successfully, but these errors were encountered: I don't think the function torch._C._cuda_setDevice or torch.cuda.set_device is available in a cpu-only build. Making statements based on opinion; back them up with references or personal experience. . Difference between "select-editor" and "update-alternatives --config editor". Will Gnome 43 be included in the upgrades of 22.04 Jammy? Is it suspicious or odd to stand by the gate of a GA airport watching the planes? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. You may re-send via your Why does Mister Mxyzptlk need to have a weakness in the comics? Is there a single-word adjective for "having exceptionally strong moral principles"? . with torch.autocast ('cuda'): AttributeError: module 'torch' has no attribute 'autocast' I have this version of PyTorch on Ubuntu 20.04: python Python 3.8.10 (default, To learn more, see our tips on writing great answers. Very strange. Thanks for contributing an answer to Stack Overflow! You have to call the decorator as given in the docs and examples: Powered by Discourse, best viewed with JavaScript enabled, Older version of PyTorch: with torch.autocast('cuda'): AttributeError: module 'torch' has no attribute 'autocast'.
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