Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 407500,7 "name": "Priyanka Perera",8 "username": "Priyanka_Perera",9 "avatar_template": "/user_avatar/discuss.pytorch.org/priyanka_perera/{size}/61648_2.png",10 "created_at": "2023-06-26T18:45:56.613Z",11 "cooked": "<p>Hi all !</p>\n<p>I use below data loaders. How can ensure that workers of the data loaders run on GPU ?</p>\n<p>I’m on below package versions.</p>\n<p>torch 2.0.1<br>\npytorch-forecasting 1.0.0</p>\n<pre><code class=\"lang-auto\"># Training data set timeseries\ntraining_data_timeseries = TimeSeriesDataSet(\n train_data,\n time_idx=\"Timestamp\",\n target=\"Target Variable\",\n group_ids=[\"Timeseries\"],\n min_encoder_length=max_encoder_length // 2,\n max_encoder_length=max_encoder_length,\n min_prediction_length=max_prediction_length // 2,\n max_prediction_length=max_prediction_length,\n time_varying_unknown_reals=['Target Variable'],\n add_relative_time_idx=True,\n add_target_scales=True,\n add_encoder_length=True,\n target_normalizer=None\n)\n\n# Validation data set timeseries\nvalidation_data_timeseries = TimeSeriesDataSet.from_dataset(training_data_timeseries, train_data, predict=True, stop_randomization=True)\n\n\n# Create dataloaders for our model\ntrain_dataloader = training_data_timeseries.to_dataloader(train=True, batch_size=batch_size, num_workers=workers, pin_memory=True)\nval_dataloader = validation_data_timeseries.to_dataloader(train=False, batch_size=batch_size, num_workers=workers, pin_memory=True)\n</code></pre>\n<p>Thanks<br>\nPriyanka</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 1,15 "updated_at": "2023-06-26T18:46:41.466Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 26,20 "reads": 5,21 "readers_count": 4,22 "score": 131.0,23 "yours": false,24 "topic_id": 182923,25 "topic_slug": "running-data-loader-workers-on-gpu",26 "display_username": "Priyanka Perera",27 "primary_group_name": null,28 "flair_name": null,29 "flair_url": null,30 "flair_bg_color": null,31 "flair_color": null,32 "flair_group_id": null,33 "badges_granted": [],34 "version": 1,35 "can_edit": false,36 "can_delete": false,37 "can_recover": false,38 "can_see_hidden_post": false,39 "can_wiki": false,40 "read": true,41 "user_title": null,42 "bookmarked": 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null,422 "slow_mode_enabled_until": null,423 "can_vote": false,424 "vote_count": 0,425 "user_voted": false,426 "discourse_zendesk_plugin_zendesk_id": null,427 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",428 "details": {429 "can_edit": false,430 "notification_level": 1,431 "participants": [432 {433 "id": 67326,434 "username": "Priyanka_Perera",435 "name": "Priyanka Perera",436 "avatar_template": "/user_avatar/discuss.pytorch.org/priyanka_perera/{size}/61648_2.png",437 "post_count": 1,438 "primary_group_name": null,439 "flair_name": null,440 "flair_url": null,441 "flair_color": null,442 "flair_bg_color": null,443 "flair_group_id": null,444 "trust_level": 1445 }446 ],447 "created_by": {448 "id": 67326,449 "username": "Priyanka_Perera",450 "name": "Priyanka Perera",451 "avatar_template": "/user_avatar/discuss.pytorch.org/priyanka_perera/{size}/61648_2.png"452 },453 "last_poster": {454 "id": 67326,455 "username": "Priyanka_Perera",456 "name": "Priyanka Perera",457 "avatar_template": "/user_avatar/discuss.pytorch.org/priyanka_perera/{size}/61648_2.png"458 }459 },460 "bookmarks": []461 },462 {463 "post_stream": {464 "posts": [465 {466 "id": 407344,467 "name": "",468 "username": "jazin",469 "avatar_template": "/letter_avatar_proxy/v4/letter/j/e19b73/{size}.png",470 "created_at": "2023-06-25T08:15:17.778Z",471 "cooked": "<p>Hi<br>\nI’m going to reproduce results using contrastive unpaired translation.<br>\nI used the same seed in the beginning of my train function using:</p>\n<p>def set_seed(seed):<br>\ntorch.manual_seed(seed)<br>\nrandom.seed(seed)<br>\nnp.random.seed(seed)<br>\ntorch.cuda.manual_seed(seed)<br>\ntorch.cuda.manual_seed_all(seed)<br>\ntorch.backends.cudnn.deterministic = True<br>\ntorch.backends.cudnn.benchmark = False<br>\nos.environ[“PYTHONHASHSEED”] = str(seed)</p>\n<p>The batch size of my image is 1 and the patch size is 10, meaning in every epoch I have 10 iterations.<br>\nSince I want to reproduce the same results for two separate runs, the first iteration is consistent, but in the second iteration, the model output after updating the weight is different. All results before the first update are the same, but after updating weight using Adam optimizer, the results are changed.<br>\nit should be noted the data loader is consistent for each iteration and the number of workers is set to zero.<br>\nHere is the optimizer code:</p>\n<pre><code>def optimize_parameters(self):\n \n\n # forward\n self.forward()\n\n \n # update D\n self.set_requires_grad(self.netD, True)\n self.optimizer_D.zero_grad()\n self.loss_D_Y = self.compute_D_loss()\n\n print('self.loss_D_Y :', self.loss_D_Y )\n self.loss_D_Y.backward()\n self.optimizer_D.step()\n\n\n # update G\n self.set_requires_grad(self.netD, False)\n\n self.optimizer_G.zero_grad()\n\n\n self.optimizer_H.zero_grad()\n\n self.loss_G = self.compute_G_loss()\n\n print('self.loss_G :', self.loss_G )\n\n self.loss_G.backward()\n\n self.optimizer_G.step()\n\n self.optimizer_H.step()\n</code></pre>\n<p>And here is the first run:</p>\n<p>-------------------------- itr no.: 0<br>\nself.loss_D_Y : tensor(0.55703777, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.31012464, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 1<br>\nself.loss_D_Y : tensor(1.02021098, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.67312062, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 2<br>\nself.loss_D_Y : tensor(0.81242311, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.35670471, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 3<br>\nself.loss_D_Y : tensor(0.59492981, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.63630438, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 4<br>\nself.loss_D_Y : tensor(0.78508723, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.22501385, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 5<br>\nself.loss_D_Y : tensor(0.37921441, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.00249624, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 6<br>\nself.loss_D_Y : tensor(0.34842652, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.39195514, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 7<br>\nself.loss_D_Y : tensor(0.52215433, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.19204855, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 8<br>\nself.loss_D_Y : tensor(0.39162478, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.22784519, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 9<br>\nself.loss_D_Y : tensor(0.84793001, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.31176651, device=‘cuda:0’, grad_fn=)</p>\n<p>and here in the second run:</p>\n<p>-------------------------- itr no.: 0<br>\nself.loss_D_Y : tensor(0.55703777, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.31012487, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 1<br>\nself.loss_D_Y : tensor(1.02020955, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.67310870, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 2<br>\nself.loss_D_Y : tensor(0.81234312, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.35686684, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 3<br>\nself.loss_D_Y : tensor(0.59236562, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.62531424, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 4<br>\nself.loss_D_Y : tensor(0.78288686, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.22760558, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 5<br>\nself.loss_D_Y : tensor(0.36207247, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.04722524, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 6<br>\nself.loss_D_Y : tensor(0.39092541, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.51113224, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 7<br>\nself.loss_D_Y : tensor(0.57800019, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.30624080, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 8<br>\nself.loss_D_Y : tensor(0.33473897, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.16657138, device=‘cuda:0’, grad_fn=)<br>\n-------------------------- itr no.: 9<br>\nself.loss_D_Y : tensor(0.82777905, device=‘cuda:0’, grad_fn=)<br>\nself.loss_G : tensor(1.19245756, device=‘cuda:0’, grad_fn=)</p>\n<p>Could you please help me understand to the source of this problem?</p>",472 "post_number": 1,473 "post_type": 1,474 "posts_count": 3,475 "updated_at": "2023-06-25T08:15:17.778Z",476 "reply_count": 0,477 "reply_to_post_number": null,478 "quote_count": 0,479 "incoming_link_count": 155,480 "reads": 8,481 "readers_count": 7,482 "score": 746.6,483 "yours": false,484 "topic_id": 182833,485 "topic_slug": "problem-with-reproduce-result-even-with-same-seed",486 "display_username": "",487 "primary_group_name": null,488 "flair_name": null,489 "flair_url": null,490 "flair_bg_color": null,491 "flair_color": null,492 "flair_group_id": null,493 "badges_granted": [],494 "version": 1,495 "can_edit": false,496 "can_delete": false,497 "can_recover": false,498 "can_see_hidden_post": false,499 "can_wiki": false,500 "read": true,501 "user_title": null,502 "bookmarked": false,503 "actions_summary": [],504 "moderator": false,505 "admin": false,506 "staff": false,507 "user_id": 60088,508 "hidden": false,509 "trust_level": 1,510 "deleted_at": null,511 "user_deleted": false,512 "edit_reason": null,513 "can_view_edit_history": true,514 "wiki": false,515 "post_url": "/t/problem-with-reproduce-result-even-with-same-seed/182833/1",516 "can_accept_answer": false,517 "can_unaccept_answer": false,518 "accepted_answer": false,519 "topic_accepted_answer": null,520 "can_vote": false521 },522 {523 "id": 407386,524 "name": "",525 "username": "ptrblck",526 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",527 "created_at": "2023-06-25T20:54:22.223Z",528 "cooked": "<p>Check the <a href=\"https://pytorch.org/docs/stable/notes/randomness.html\">Reproducibility docs</a> which explain how to use deterministic algorithms via <code>torch.use_deterministic_algorithms(True)</code>.</p>",529 "post_number": 2,530 "post_type": 1,531 "posts_count": 3,532 "updated_at": "2023-06-25T20:54:22.223Z",533 "reply_count": 1,534 "reply_to_post_number": null,535 "quote_count": 0,536 "incoming_link_count": 2,537 "reads": 6,538 "readers_count": 5,539 "score": 16.2,540 "yours": false,541 "topic_id": 182833,542 "topic_slug": "problem-with-reproduce-result-even-with-same-seed",543 "display_username": "",544 "primary_group_name": null,545 "flair_name": null,546 "flair_url": null,547 "flair_bg_color": null,548 "flair_color": null,549 "flair_group_id": null,550 "badges_granted": [],551 "version": 1,552 "can_edit": false,553 "can_delete": false,554 "can_recover": false,555 "can_see_hidden_post": false,556 "can_wiki": false,557 "link_counts": [558 {559 "url": "https://pytorch.org/docs/stable/notes/randomness.html",560 "internal": false,561 "reflection": false,562 "title": "Reproducibility — PyTorch 2.0 documentation",563 "clicks": 24564 }565 ],566 "read": true,567 "user_title": "",568 "bookmarked": false,569 "actions_summary": [],570 "moderator": true,571 "admin": true,572 "staff": true,573 "user_id": 3534,574 "hidden": false,575 "trust_level": 2,576 "deleted_at": null,577 "user_deleted": false,578 "edit_reason": null,579 "can_view_edit_history": true,580 "wiki": false,581 "post_url": "/t/problem-with-reproduce-result-even-with-same-seed/182833/2",582 "can_accept_answer": false,583 "can_unaccept_answer": false,584 "accepted_answer": false,585 "topic_accepted_answer": null586 },587 {588 "id": 407497,589 "name": "",590 "username": "jazin",591 "avatar_template": "/letter_avatar_proxy/v4/letter/j/e19b73/{size}.png",592 "created_at": "2023-06-26T18:13:41.915Z",593 "cooked": "<p>Thank you so much.<br>\nIt works for me. I activate torch.use_deterministic_algorithms(True).<br>\nI used nn.ReflectionPad3d(1), which is not deterministic. I changed it into zero padding and it works know.</p>",594 "post_number": 3,595 "post_type": 1,596 "posts_count": 3,597 "updated_at": "2023-06-26T18:13:41.915Z",598 "reply_count": 0,599 "reply_to_post_number": 2,600 "quote_count": 0,601 "incoming_link_count": 1,602 "reads": 5,603 "readers_count": 4,604 "score": 6.0,605 "yours": false,606 "topic_id": 182833,607 "topic_slug": "problem-with-reproduce-result-even-with-same-seed",608 "display_username": "",609 "primary_group_name": null,610 "flair_name": null,611 "flair_url": null,612 "flair_bg_color": null,613 "flair_color": null,614 "flair_group_id": null,615 "badges_granted": [],616 "version": 1,617 "can_edit": false,618 "can_delete": false,619 "can_recover": false,620 "can_see_hidden_post": false,621 "can_wiki": false,622 "read": true,623 "user_title": null,624 "reply_to_user": {625 "id": 3534,626 "username": "ptrblck",627 "name": "",628 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png"629 },630 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Filter out rows with missing values\n self.data = self.data.dropna(subset=['Chip_Image_Name', 'Present'])\n \ndef __len__(self):\n return len(self.data)\n\ndef __getitem__(self, idx):\n row = self.data.iloc[idx]\n image_name = row['Chip_Image_Name']\n \n image_path = os.path.join(self.image_folder, f'{image_name}.bmp')\n \n image = Image.open(image_path[:-4]).convert('RGB')\n \n label = row['Present']\n \n if self.transform is not None:\n image = self.transform(image)\n \n return image, label\n</code></pre>\n<h1>\n<a name=\"set-the-path-to-your-dataset-csv-file-and-image-folder-2\" class=\"anchor\" href=\"#set-the-path-to-your-dataset-csv-file-and-image-folder-2\"></a>Set the path to your dataset CSV file and image folder</h1>\n<p>csv_file = ‘data/train.csv’<br>\nimage_folder = ‘data/images’</p>\n<h1>\n<a name=\"set-the-transformation-for-your-images-3\" class=\"anchor\" href=\"#set-the-transformation-for-your-images-3\"></a>Set the transformation for your images</h1>\n<p>transform = transforms.Compose([<br>\ntransforms.Resize((224, 224)),<br>\ntransforms.ToTensor(),<br>\ntransforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])<br>\n])</p>\n<h1>\n<a name=\"create-the-dataset-4\" class=\"anchor\" href=\"#create-the-dataset-4\"></a>Create the dataset</h1>\n<p>dataset = AllergyDataset(csv_file, image_folder, transform=transform)</p>\n<h1>\n<a name=\"create-data-loaders-5\" class=\"anchor\" href=\"#create-data-loaders-5\"></a>Create data loaders</h1>\n<p>batch_size = 32<br>\ntrain_ratio = 0.8<br>\ntrain_size = int(train_ratio * len(dataset))<br>\nval_size = len(dataset) - train_size</p>\n<p>train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])</p>\n<p>train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)<br>\nval_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)</p>\n<h1>\n<a name=\"define-your-neural-network-model-6\" class=\"anchor\" href=\"#define-your-neural-network-model-6\"></a>Define your neural network model</h1>\n<p>class AllergyClassifier(nn.Module):<br>\ndef <strong>init</strong>(self):<br>\nsuper(AllergyClassifier, self).<strong>init</strong>()<br>\nself.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)<br>\nself.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)<br>\nself.fc1 = nn.Linear(128 * 28 * 28, 256) # Update the input size here<br>\nself.fc2 = nn.Linear(256, 1)<br>\nself.sigmoid = nn.Sigmoid()</p>\n<pre><code>def forward(self, x):\n x = nn.functional.relu(self.conv1(x))\n x = nn.functional.relu(self.conv2(x))\n x = x.view(x.size(0), -1)\n x = nn.functional.relu(self.fc1(x))\n x = self.fc2(x)\n x = self.sigmoid(x)\n return x\n</code></pre>\n<h1>\n<a name=\"create-an-instance-of-your-model-7\" class=\"anchor\" href=\"#create-an-instance-of-your-model-7\"></a>Create an instance of your model</h1>\n<p>model = AllergyClassifier()</p>\n<h1>\n<a name=\"define-the-loss-function-and-optimizer-8\" class=\"anchor\" href=\"#define-the-loss-function-and-optimizer-8\"></a>Define the loss function and optimizer</h1>\n<p>criterion = nn.BCELoss()<br>\noptimizer = optim.Adam(model.parameters(), lr=0.001)</p>\n<h1>\n<a name=\"set-the-device-9\" class=\"anchor\" href=\"#set-the-device-9\"></a>Set the device</h1>\n<p>device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)<br>\nmodel.to(device)</p>\n<h1>\n<a name=\"training-loop-10\" class=\"anchor\" href=\"#training-loop-10\"></a>Training loop</h1>\n<p>num_epochs = 10<br>\nfor epoch in range(num_epochs):<br>\nmodel.train()<br>\nrunning_loss = 0.0</p>\n<pre><code>for images, labels in train_loader:\n images = images.to(device)\n labels = labels.to(device)\n \n optimizer.zero_grad()\n \n outputs = model(images)\n loss = criterion(outputs.squeeze(), labels.float())\n \n loss.backward()\n optimizer.step()\n \n running_loss += loss.item() * images.size(0)\n\nepoch_loss = running_loss / len(train_dataset)\nprint(f'Training Loss - Epoch {epoch+1}: {epoch_loss:.4f}')\n</code></pre>\n<h1>\n<a name=\"validation-loop-11\" class=\"anchor\" href=\"#validation-loop-11\"></a>Validation loop</h1>\n<p>model.eval()<br>\nnum_correct = 0<br>\nnum_samples = 0</p>\n<p>with torch.no_grad():<br>\nfor images, labels in val_loader:<br>\nimages = images.to(device)<br>\nlabels = labels.to(device)</p>\n<pre><code> outputs = model(images)\n predictions = (outputs >= 0.5).squeeze().long()\n \n num_correct += (predictions == labels).sum().item()\n num_samples += labels.size(0)\n\naccuracy = num_correct / num_samples\nprint(f'Validation Accuracy: {accuracy:.4f}')\n</code></pre>\n<p>facing some issue</p>",1037 "post_number": 1,1038 "post_type": 1,1039 "posts_count": 2,1040 "updated_at": "2023-06-26T16:01:06.988Z",1041 "reply_count": 0,1042 "reply_to_post_number": null,1043 "quote_count": 0,1044 "incoming_link_count": 13,1045 "reads": 3,1046 "readers_count": 2,1047 "score": 60.6,1048 "yours": false,1049 "topic_id": 182916,1050 "topic_slug": "runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x6422528-and-100352x256",1051 "display_username": "vervit khandelwal",1052 "primary_group_name": null,1053 "flair_name": null,1054 "flair_url": null,1055 "flair_bg_color": null,1056 "flair_color": null,1057 "flair_group_id": null,1058 "badges_granted": [],1059 "version": 1,1060 "can_edit": false,1061 "can_delete": false,1062 "can_recover": false,1063 "can_see_hidden_post": false,1064 "can_wiki": false,1065 "read": true,1066 "user_title": null,1067 "bookmarked": false,1068 "actions_summary": [],1069 "moderator": false,1070 "admin": false,1071 "staff": false,1072 "user_id": 67317,1073 "hidden": false,1074 "trust_level": 0,1075 "deleted_at": null,1076 "user_deleted": false,1077 "edit_reason": null,1078 "can_view_edit_history": true,1079 "wiki": false,1080 "post_url": "/t/runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x6422528-and-100352x256/182916/1",1081 "can_accept_answer": false,1082 "can_unaccept_answer": false,1083 "accepted_answer": false,1084 "topic_accepted_answer": null,1085 "can_vote": false1086 },1087 {1088 "id": 407492,1089 "name": "",1090 "username": "ptrblck",1091 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",1092 "created_at": "2023-06-26T16:49:28.822Z",1093 "cooked": "<p>The error is raised in <code>self.fc1</code> as its <code>in_features</code> do not match the number of features of the input activation. The error message shows the feature size of the activation, so change the <code>in_features</code> to this value and it should work.</p>",1094 "post_number": 2,1095 "post_type": 1,1096 "posts_count": 2,1097 "updated_at": "2023-06-26T16:49:28.822Z",1098 "reply_count": 0,1099 "reply_to_post_number": null,1100 "quote_count": 0,1101 "incoming_link_count": 0,1102 "reads": 3,1103 "readers_count": 2,1104 "score": 0.6,1105 "yours": false,1106 "topic_id": 182916,1107 "topic_slug": "runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x6422528-and-100352x256",1108 "display_username": "",1109 "primary_group_name": null,1110 "flair_name": null,1111 "flair_url": null,1112 "flair_bg_color": null,1113 "flair_color": null,1114 "flair_group_id": null,1115 "badges_granted": [],1116 "version": 1,1117 "can_edit": false,1118 "can_delete": false,1119 "can_recover": false,1120 "can_see_hidden_post": false,1121 "can_wiki": false,1122 "read": true,1123 "user_title": "",1124 "bookmarked": false,1125 "actions_summary": [],1126 "moderator": true,1127 "admin": true,1128 "staff": true,1129 "user_id": 3534,1130 "hidden": false,1131 "trust_level": 2,1132 "deleted_at": null,1133 "user_deleted": false,1134 "edit_reason": null,1135 "can_view_edit_history": true,1136 "wiki": false,1137 "post_url": "/t/runtimeerror-mat1-and-mat2-shapes-cannot-be-multiplied-32x6422528-and-100352x256/182916/2",1138 "can_accept_answer": false,1139 "can_unaccept_answer": false,1140 "accepted_answer": false,1141 "topic_accepted_answer": null1142 }1143 ],1144 "stream": [1145 407479,1146 4074921147 ]1148 },1149 "timeline_lookup": [1150 [1151 1,1152 8521153 ]1154 ],1155 "suggested_topics": [1156 {1157 "fancy_title": "Torchvision sm_120 failed installed",1158 "id": 217725,1159 "title": "Torchvision sm_120 failed installed",1160 "slug": "torchvision-sm-120-failed-installed",1161 "posts_count": 2,1162 "reply_count": 0,1163 "highest_post_number": 2,1164 "image_url": null,1165 "created_at": "2025-03-12T04:29:40.562Z",1166 "last_posted_at": "2025-03-12T13:14:27.243Z",1167 "bumped": true,1168 "bumped_at": "2025-03-12T13:14:27.243Z",1169 "archetype": "regular",1170 "unseen": false,1171 "pinned": false,1172 "unpinned": null,1173 "visible": true,1174 "closed": false,1175 "archived": false,1176 "bookmarked": null,1177 "liked": null,1178 "tags_descriptions": {},1179 "like_count": 0,1180 "views": 305,1181 "category_id": 5,1182 "featured_link": null,1183 "has_accepted_answer": false,1184 "posters": [1185 {1186 "extras": null,1187 "description": "Original Poster",1188 "user": {1189 "id": 83206,1190 "username": "Desire6666",1191 "name": null,1192 "avatar_template": "/letter_avatar_proxy/v4/letter/d/e36b37/{size}.png",1193 "trust_level": 01194 }1195 },1196 {1197 "extras": "latest",1198 "description": "Most Recent Poster",1199 "user": {1200 "id": 3534,