You might need to install the nightly binary, since Autocasting wasnt shipped in 1.5. that is, I change the code torch.cuda.set_device(self.opt.gpu_ids[0]) to torch.cuda.set_device(self.opt.gpu_ids[-1]) and torch._C._cuda_setDevice(device) to torch._C._cuda_setDevice(-1)but it still not works. if update to an extension did this, please let us know - in my book, that kind of behavior is borderline hostile as extension should NOT change core libraries, only libraries that are extra for that extension. torch.cuda.amp is available in the nightly binaries, so you would have to update. class GradScaler(torch.cuda.amp.GradScaler): AttributeError: module torch.cuda has no attribute amp Environment: GPU : RTX 8000 CUDA: 10.0 Pytorch By clicking Sign up for GitHub, you agree to our terms of service and update some extensions, and when I restarted stable. So probably you either have somewhere used torch.float in your code or you have imported some code with torch.float. Just renamed it to something else and delete the file named 'torch.py' in the directory CMake version: version 3.22.1 I tried to reinstall the pytorch and update to the newest version (1.4.0), still exists error. Is there a single-word adjective for "having exceptionally strong moral principles"? You may re-send via your. Already on GitHub? File "C:\ai\stable-diffusion-webui\launch.py", line 360, in profile. yes I reported an issue yesterday and met with much the same response. Making statements based on opinion; back them up with references or personal experience. Is there a single-word adjective for "having exceptionally strong moral principles"? Press any key to continue . run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True) Sign up for a free GitHub account to open an issue and contact its maintainers and the community. module 'torch.cuda' has no attribute '_UntypedStorage'. To learn more, see our tips on writing great answers. Thanks! rev2023.3.3.43278. No issues running the same script for a different dataset. Please edit your question with the full stack trace (and remove your comments). Are there tables of wastage rates for different fruit and veg? Asking for help, clarification, or responding to other answers. You signed in with another tab or window. You just need to find the """, 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)
module 'torch' has no attribute 'cuda