Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 420652,7 "name": "PARUL JOSHI",8 "username": "PARUL_JOSHI",9 "avatar_template": "/user_avatar/discuss.pytorch.org/parul_joshi/{size}/61770_2.png",10 "created_at": "2023-10-17T12:22:35.899Z",11 "cooked": "<p>I have an input <strong>k_norm</strong> of shape(N X D) say 32,250<br>\nI want to design a convolutional kernel name <strong>Wc</strong> in such a way that the output of :<br>\n<strong>Wc * k_norm</strong> would give a result shape of (N X 1)</p>\n<p>So,<br>\nWc = torch.nn.Conv2d(in_channels = ?, out_channels = ?, kernel_size=(?))</p>\n<p>The formula that I’m using is :<br>\nkc_hat = Wc * k_norm</p>\n<p>I want kc_hat shape as N X1</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 3,15 "updated_at": "2023-10-17T12:22:35.899Z",16 "reply_count": 1,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 44,20 "reads": 10,21 "readers_count": 9,22 "score": 227.0,23 "yours": false,24 "topic_id": 190110,25 "topic_slug": "calculation-of-convolutional-kernel-in-pytorch",26 "display_username": "PARUL JOSHI",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": false,43 "actions_summary": [],44 "moderator": false,45 "admin": false,46 "staff": false,47 "user_id": 67427,48 "hidden": false,49 "trust_level": 1,50 "deleted_at": null,51 "user_deleted": false,52 "edit_reason": null,53 "can_view_edit_history": true,54 "wiki": false,55 "post_url": "/t/calculation-of-convolutional-kernel-in-pytorch/190110/1",56 "can_accept_answer": false,57 "can_unaccept_answer": false,58 "accepted_answer": false,59 "topic_accepted_answer": true,60 "can_vote": false61 },62 {63 "id": 420858,64 "name": "K. Frank",65 "username": "KFrank",66 "avatar_template": "/letter_avatar_proxy/v4/letter/k/ecb155/{size}.png",67 "created_at": "2023-10-18T15:25:56.256Z",68 "cooked": "<p>Hi Parul!</p>\n<aside class=\"quote no-group\" data-username=\"PARUL_JOSHI\" data-post=\"1\" data-topic=\"190110\" data-full=\"true\">\n<div class=\"title\">\n<div class=\"quote-controls\"></div>\n<img loading=\"lazy\" alt=\"\" width=\"24\" height=\"24\" src=\"https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/parul_joshi/48/61770_2.png\" class=\"avatar\"> PARUL_JOSHI:</div>\n<blockquote>\n<p>I want to design a convolutional kernel name <strong>Wc</strong> in such a way that the output of :<br>\n<strong>Wc * k_norm</strong> would give a result shape of (N X 1)</p>\n</blockquote>\n</aside>\n<p>Could you explain your use case in a little more detail, maybe illustrating it<br>\nwith some sample code (even if the sample code doesn’t do what you want)?<br>\nWhat goal are you trying to achieve with your specified “result shape?”</p>\n<p>A couple of comments: Pytorch’s <code>Conv2d</code> take a <em>four-dimensional</em> tensor as<br>\ninput, of shape <code>[nBatch, channels, height, width]</code>. (It’s permissible for<br>\nthe <code>nBatch</code> and <code>channels</code> dimensions to have length one, but they still have<br>\nto be there.) Also, you apply such a convolutions, <code>Wc = Conv2d ( stuff )</code><br>\nto its input using function-call notation: <code>kc_hat = Wc (k_norm)</code>.</p>\n<p>Best.</p>\n<p>K. Frank</p>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 3,72 "updated_at": "2023-10-18T15:25:56.256Z",73 "reply_count": 0,74 "reply_to_post_number": null,75 "quote_count": 1,76 "incoming_link_count": 0,77 "reads": 7,78 "readers_count": 6,79 "score": 1.4,80 "yours": false,81 "topic_id": 190110,82 "topic_slug": "calculation-of-convolutional-kernel-in-pytorch",83 "display_username": "K. Frank",84 "primary_group_name": null,85 "flair_name": null,86 "flair_url": null,87 "flair_bg_color": null,88 "flair_color": null,89 "flair_group_id": null,90 "badges_granted": [],91 "version": 1,92 "can_edit": false,93 "can_delete": false,94 "can_recover": false,95 "can_see_hidden_post": false,96 "can_wiki": false,97 "read": true,98 "user_title": null,99 "bookmarked": false,100 "actions_summary": [],101 "moderator": false,102 "admin": false,103 "staff": false,104 "user_id": 18088,105 "hidden": false,106 "trust_level": 2,107 "deleted_at": null,108 "user_deleted": false,109 "edit_reason": null,110 "can_view_edit_history": true,111 "wiki": false,112 "post_url": "/t/calculation-of-convolutional-kernel-in-pytorch/190110/2",113 "can_accept_answer": false,114 "can_unaccept_answer": false,115 "accepted_answer": false,116 "topic_accepted_answer": true117 },118 {119 "id": 421066,120 "name": "J Johnson",121 "username": "J_Johnson",122 "avatar_template": "/user_avatar/discuss.pytorch.org/j_johnson/{size}/55494_2.png",123 "created_at": "2023-10-19T16:01:56.698Z",124 "cooked": "<p>When you take in consideration stride and dilation, there are hundreds of ways you could do this. But, for the sake of simplicity, let’s just assume those are 1 and 1 respectively. In that case, you’d need a kernel size of (1, D):</p>\n<pre><code class=\"lang-auto\">import torch\nimport torch.nn as nn\n\nN = 32\nD = 250\nbatch_size = 1\nchannels = 1\n\nkernel = nn.Conv2d(channels, channels, (1, D), bias = False)\n\nx = torch.rand((batch_size, channels, N, D))\n\nprint(kernel(x).size())\n</code></pre>",125 "post_number": 3,126 "post_type": 1,127 "posts_count": 3,128 "updated_at": "2023-10-19T16:01:56.698Z",129 "reply_count": 0,130 "reply_to_post_number": null,131 "quote_count": 0,132 "incoming_link_count": 2,133 "reads": 5,134 "readers_count": 4,135 "score": 11.0,136 "yours": false,137 "topic_id": 190110,138 "topic_slug": "calculation-of-convolutional-kernel-in-pytorch",139 "display_username": "J Johnson",140 "primary_group_name": null,141 "flair_name": null,142 "flair_url": null,143 "flair_bg_color": null,144 "flair_color": null,145 "flair_group_id": null,146 "badges_granted": [],147 "version": 1,148 "can_edit": false,149 "can_delete": false,150 "can_recover": false,151 "can_see_hidden_post": false,152 "can_wiki": false,153 "read": true,154 "user_title": null,155 "bookmarked": false,156 "actions_summary": [],157 "moderator": false,158 "admin": false,159 "staff": false,160 "user_id": 41458,161 "hidden": false,162 "trust_level": 2,163 "deleted_at": null,164 "user_deleted": false,165 "edit_reason": null,166 "can_view_edit_history": true,167 "wiki": false,168 "post_url": "/t/calculation-of-convolutional-kernel-in-pytorch/190110/3",169 "can_accept_answer": false,170 "can_unaccept_answer": false,171 "accepted_answer": true,172 "topic_accepted_answer": true173 }174 ],175 "stream": [176 420652,177 420858,178 421066179 ]180 },181 "timeline_lookup": [182 [183 1,184 739185 ],186 [187 2,188 738189 ],190 [191 3,192 737193 ]194 ],195 "suggested_topics": [196 {197 "fancy_title": "Temporal Fusion Transformer Error",198 "id": 213925,199 "title": "Temporal Fusion Transformer Error",200 "slug": "temporal-fusion-transformer-error",201 "posts_count": 3,202 "reply_count": 1,203 "highest_post_number": 3,204 "image_url": null,205 "created_at": "2024-12-06T20:29:08.583Z",206 "last_posted_at": "2024-12-17T19:48:46.480Z",207 "bumped": true,208 "bumped_at": "2024-12-17T19:48:46.480Z",209 "archetype": "regular",210 "unseen": false,211 "pinned": false,212 "unpinned": null,213 "visible": true,214 "closed": false,215 "archived": false,216 "bookmarked": null,217 "liked": null,218 "tags_descriptions": {},219 "like_count": 0,220 "views": 290,221 "category_id": 1,222 "featured_link": null,223 "has_accepted_answer": false,224 "posters": [225 {226 "extras": null,227 "description": "Original Poster",228 "user": {229 "id": 81366,230 "username": "himanshu_birla",231 "name": "himanshu birla",232 "avatar_template": "/user_avatar/discuss.pytorch.org/himanshu_birla/{size}/74403_2.png",233 "trust_level": 0234 }235 },236 {237 "extras": null,238 "description": "Frequent Poster",239 "user": {240 "id": 3534,241 "username": "ptrblck",242 "name": "",243 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",244 "admin": true,245 "moderator": true,246 "trust_level": 2247 }248 },249 {250 "extras": "latest",251 "description": "Most Recent Poster",252 "user": {253 "id": 81553,254 "username": "Juan_pablo_Mazo_quin",255 "name": "Juan pablo Mazo quintero",256 "avatar_template": "/user_avatar/discuss.pytorch.org/juan_pablo_mazo_quin/{size}/74572_2.png",257 "trust_level": 1258 }259 }260 ]261 },262 {263 "fancy_title": "How to add value to tensor multiple times by index",264 "id": 212382,265 "title": "How to add value to tensor multiple times by index",266 "slug": "how-to-add-value-to-tensor-multiple-times-by-index",267 "posts_count": 3,268 "reply_count": 0,269 "highest_post_number": 3,270 "image_url": null,271 "created_at": "2024-10-31T20:08:52.361Z",272 "last_posted_at": "2024-11-06T07:14:05.689Z",273 "bumped": true,274 "bumped_at": "2024-11-06T07:14:05.689Z",275 "archetype": "regular",276 "unseen": false,277 "pinned": false,278 "unpinned": null,279 "visible": true,280 "closed": false,281 "archived": false,282 "bookmarked": null,283 "liked": null,284 "tags_descriptions": {},285 "like_count": 1,286 "views": 181,287 "category_id": 1,288 "featured_link": null,289 "has_accepted_answer": false,290 "posters": [291 {292 "extras": "latest",293 "description": "Original Poster, Most Recent Poster",294 "user": {295 "id": 80623,296 "username": "dronnet1",297 "name": "Андрей Медведев",298 "avatar_template": "/user_avatar/discuss.pytorch.org/dronnet1/{size}/73708_2.png",299 "trust_level": 1300 }301 },302 {303 "extras": null,304 "description": "Frequent Poster",305 "user": {306 "id": 72430,307 "username": "Eduardo_Lawson",308 "name": "Eduardo Lawson da Silva",309 "avatar_template": "/user_avatar/discuss.pytorch.org/eduardo_lawson/{size}/66899_2.png",310 "trust_level": 2311 }312 }313 ]314 },315 {316 "fancy_title": "Permute inside nn.Sequential",317 "id": 214877,318 "title": "Permute inside nn.Sequential",319 "slug": "permute-inside-nn-sequential",320 "posts_count": 3,321 "reply_count": 0,322 "highest_post_number": 3,323 "image_url": null,324 "created_at": "2025-01-02T08:19:47.254Z",325 "last_posted_at": "2025-09-15T19:19:55.247Z",326 "bumped": true,327 "bumped_at": "2025-09-15T19:19:55.247Z",328 "archetype": "regular",329 "unseen": false,330 "pinned": false,331 "unpinned": null,332 "visible": true,333 "closed": false,334 "archived": false,335 "bookmarked": null,336 "liked": null,337 "tags_descriptions": {},338 "like_count": 1,339 "views": 476,340 "category_id": 1,341 "featured_link": null,342 "has_accepted_answer": false,343 "posters": [344 {345 "extras": null,346 "description": "Original Poster",347 "user": {348 "id": 81597,349 "username": "nada",350 "name": null,351 "avatar_template": "/letter_avatar_proxy/v4/letter/n/a88e4f/{size}.png",352 "trust_level": 1353 }354 },355 {356 "extras": null,357 "description": "Frequent Poster",358 "user": {359 "id": 3534,360 "username": "ptrblck",361 "name": "",362 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",363 "admin": true,364 "moderator": true,365 "trust_level": 2366 }367 },368 {369 "extras": "latest",370 "description": "Most Recent Poster",371 "user": {372 "id": 32812,373 "username": "Bjorn_Lindqvist",374 "name": "Björn Lindqvist",375 "avatar_template": "/user_avatar/discuss.pytorch.org/bjorn_lindqvist/{size}/25326_2.png",376 "trust_level": 2377 }378 }379 ]380 },381 {382 "fancy_title": "ImportError : cannot import name ‘DiagnosticOptions’ from ‘torch.onnx._internal.exporter’",383 "id": 215989,384 "title": "ImportError : cannot import name ‘DiagnosticOptions’ from ‘torch.onnx._internal.exporter’",385 "slug": "importerror-cannot-import-name-diagnosticoptions-from-torch-onnx-internal-exporter",386 "posts_count": 2,387 "reply_count": 0,388 "highest_post_number": 2,389 "image_url": "https://discuss.pytorch.org/uploads/default/optimized/3X/a/3/a3a542821235a5d5307f1577a9ede9d33da98334_2_1024x282.png",390 "created_at": "2025-01-28T13:17:41.487Z",391 "last_posted_at": "2025-02-10T06:40:11.050Z",392 "bumped": true,393 "bumped_at": "2025-02-10T06:40:11.050Z",394 "archetype": "regular",395 "unseen": false,396 "pinned": false,397 "unpinned": null,398 "visible": true,399 "closed": false,400 "archived": false,401 "bookmarked": null,402 "liked": null,403 "tags_descriptions": {},404 "like_count": 0,405 "views": 1679,406 "category_id": 1,407 "featured_link": null,408 "has_accepted_answer": false,409 "posters": [410 {411 "extras": null,412 "description": "Original Poster",413 "user": {414 "id": 82357,415 "username": "cyy",416 "name": "adqdqefwe",417 "avatar_template": "/letter_avatar_proxy/v4/letter/c/e480ec/{size}.png",418 "trust_level": 0419 }420 },421 {422 "extras": "latest",423 "description": "Most Recent Poster",424 "user": {425 "id": 71955,426 "username": "ashok_arora",427 "name": "Ashok Arora",428 "avatar_template": "/user_avatar/discuss.pytorch.org/ashok_arora/{size}/66772_2.png",429 "trust_level": 1430 }431 }432 ]433 },434 {435 "fancy_title": "Precision-ill is undefined while training Pytorch Models",436 "id": 218235,437 "title": "Precision-ill is undefined while training Pytorch Models",438 "slug": "precision-ill-is-undefined-while-training-pytorch-models",439 "posts_count": 1,440 "reply_count": 0,441 "highest_post_number": 1,442 "image_url": null,443 "created_at": "2025-03-25T10:39:35.467Z",444 "last_posted_at": "2025-03-25T10:39:35.509Z",445 "bumped": true,446 "bumped_at": "2025-03-25T15:22:02.738Z",447 "archetype": "regular",448 "unseen": false,449 "pinned": false,450 "unpinned": null,451 "visible": true,452 "closed": false,453 "archived": false,454 "bookmarked": null,455 "liked": null,456 "tags_descriptions": {},457 "like_count": 0,458 "views": 37,459 "category_id": 1,460 "featured_link": null,461 "has_accepted_answer": false,462 "posters": [463 {464 "extras": "latest single",465 "description": "Original Poster, Most Recent Poster",466 "user": {467 "id": 83401,468 "username": "koussayinsat12",469 "name": "GHAOUARI KOUSAI",470 "avatar_template": "/user_avatar/discuss.pytorch.org/koussayinsat12/{size}/76275_2.png",471 "trust_level": 1472 }473 }474 ]475 }476 ],477 "tags_descriptions": {},478 "fancy_title": "Calculation of Convolutional Kernel in Pytorch",479 "id": 190110,480 "title": "Calculation of Convolutional Kernel in Pytorch",481 "posts_count": 3,482 "created_at": "2023-10-17T12:22:35.837Z",483 "views": 380,484 "reply_count": 0,485 "like_count": 0,486 "last_posted_at": "2023-10-19T16:01:56.698Z",487 "visible": true,488 "closed": false,489 "archived": false,490 "has_summary": false,491 "archetype": "regular",492 "slug": "calculation-of-convolutional-kernel-in-pytorch",493 "category_id": 1,494 "word_count": 288,495 "deleted_at": null,496 "user_id": 67427,497 "featured_link": null,498 "pinned_globally": false,499 "pinned_at": null,500 "pinned_until": null,501 "image_url": null,502 "slow_mode_seconds": 0,503 "draft": null,504 "draft_key": "topic_190110",505 "draft_sequence": null,506 "unpinned": null,507 "pinned": false,508 "current_post_number": 1,509 "highest_post_number": 3,510 "deleted_by": null,511 "actions_summary": [512 {513 "id": 4,514 "count": 0,515 "hidden": false,516 "can_act": false517 },518 {519 "id": 8,520 "count": 0,521 "hidden": false,522 "can_act": false523 },524 {525 "id": 10,526 "count": 0,527 "hidden": false,528 "can_act": false529 },530 {531 "id": 7,532 "count": 0,533 "hidden": false,534 "can_act": false535 }536 ],537 "chunk_size": 20,538 "bookmarked": false,539 "topic_timer": null,540 "message_bus_last_id": 0,541 "participant_count": 3,542 "show_read_indicator": false,543 "thumbnails": null,544 "slow_mode_enabled_until": null,545 "accepted_answer": {546 "post_number": 3,547 "username": "J_Johnson",548 "name": "J Johnson",549 "excerpt": "When you take in consideration stride and dilation, there are hundreds of ways you could do this. But, for the sake of simplicity, let’s just assume those are 1 and 1 respectively. In that case, you’d need a kernel size of (1, D): \nimport torch\nimport torch.nn as nn\n\nN = 32\nD = 250\nbatch_size = 1\nch…"550 },551 "can_vote": false,552 "vote_count": 0,553 "user_voted": false,554 "discourse_zendesk_plugin_zendesk_id": null,555 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",556 "details": {557 "can_edit": false,558 "notification_level": 1,559 "participants": [560 {561 "id": 18088,562 "username": "KFrank",563 "name": "K. Frank",564 "avatar_template": "/letter_avatar_proxy/v4/letter/k/ecb155/{size}.png",565 "post_count": 1,566 "primary_group_name": null,567 "flair_name": null,568 "flair_url": null,569 "flair_color": null,570 "flair_bg_color": null,571 "flair_group_id": null,572 "trust_level": 2573 },574 {575 "id": 41458,576 "username": "J_Johnson",577 "name": "J Johnson",578 "avatar_template": "/user_avatar/discuss.pytorch.org/j_johnson/{size}/55494_2.png",579 "post_count": 1,580 "primary_group_name": null,581 "flair_name": null,582 "flair_url": null,583 "flair_color": null,584 "flair_bg_color": null,585 "flair_group_id": null,586 "trust_level": 2587 },588 {589 "id": 67427,590 "username": "PARUL_JOSHI",591 "name": "PARUL JOSHI",592 "avatar_template": "/user_avatar/discuss.pytorch.org/parul_joshi/{size}/61770_2.png",593 "post_count": 1,594 "primary_group_name": null,595 "flair_name": null,596 "flair_url": null,597 "flair_color": null,598 "flair_bg_color": null,599 "flair_group_id": null,600 "trust_level": 1601 }602 ],603 "created_by": {604 "id": 67427,605 "username": "PARUL_JOSHI",606 "name": "PARUL JOSHI",607 "avatar_template": "/user_avatar/discuss.pytorch.org/parul_joshi/{size}/61770_2.png"608 },609 "last_poster": {610 "id": 41458,611 "username": "J_Johnson",612 "name": "J Johnson",613 "avatar_template": "/user_avatar/discuss.pytorch.org/j_johnson/{size}/55494_2.png"614 }615 },616 "bookmarks": []617 },618 {619 "post_stream": {620 "posts": [621 {622 "id": 420969,623 "name": "Aniruth Sundararajan",624 "username": "Aniruth_Sundararajan",625 "avatar_template": "/user_avatar/discuss.pytorch.org/aniruth_sundararajan/{size}/63308_2.png",626 "created_at": "2023-10-19T03:23:13.759Z",627 "cooked": "<p>I am trying to load my shanghai dataset for training . When I used the normal default Data Loader , i kept getting an empty dataset , even if my path was correct .<br>\nSo , i circumvented it by using torch.utils.data.subset , and the same dataset was read .</p>\n<p>My Problem is , when I check the length of the subset , it is giving me correctly , the length of the dataset I have, but when I run my code for training , I keep getting a None Type Error .</p>\n<p>Another issue is , if I give subset size as just 2 , then my training occurs , only for those 2 samples .</p>\n<p>My Code :<br>\nshanghai_dataset = SHANGHAITECH(path_ )</p>\n<p><span class=\"hashtag-raw\">#data_loader2</span> = torch.utils.data.DataLoader(shanghai_dataset) <span class=\"hashtag-raw\">#This</span> is loading an empty dataset</p>\n<p>data_loader2 = torch.utils.data.Subset(shanghai_dataset , indices = [i for i in range(0 , 300)] )</p>\n<p>num_epochs = 800<br>\nfor epoch in range(num_epochs):<br>\nfor batch_idx, batch in enumerate(data_loader2):<br>\nvideo_frames = batch[batch_idx]<br>\nprint(batch_idx)<br>\nprint(video_frames)<br>\nvisual_features = combined_model(video_frames)<br>\nvisual_features = visual_features.view(-1)</p>\n<pre><code>\t#anomaly_scores = anomaly_detection_head(video_frames)\n labels=torch.tensor([0.0 , 0.0 , 0.0 , 0.0])\n loss = criterion(visual_features, labels )\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \nprint(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item()}')\n</code></pre>\n<p>Output , if subset length is 300<br>\n<First 2 video array values are printed><br>\n2<br>\nNone<br>\nTraceback (most recent call last):<br>\nFile “/content/gdrive/MyDrive/Final/video_swin_transformer/train.py”, line 289, in <br>\nvisual_features = combined_model(video_frames)<br>\nFile “/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py”, line 1518, in _wrapped_call_impl<br>\nreturn self._call_impl(*args, **kwargs)<br>\nFile “/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py”, line 1527, in _call_impl<br>\nreturn forward_call(*args, **kwargs)<br>\nFile “/usr/local/lib/python3.10/site-packages/torch/nn/modules/container.py”, line 215, in forward<br>\ninput = module(input)<br>\nFile “/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py”, line 1518, in _wrapped_call_impl<br>\nreturn self._call_impl(*args, **kwargs)<br>\nFile “/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py”, line 1527, in _call_impl<br>\nreturn forward_call(*args, **kwargs)<br>\nFile “/content/gdrive/MyDrive/Final/video_swin_transformer/mmaction/models/backbones/swin_transformer.py”, line 652, in forward<br>\nx = self.patch_embed(x)<br>\nFile “/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py”, line 1518, in _wrapped_call_impl<br>\nreturn self._call_impl(*args, **kwargs)<br>\nFile “/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py”, line 1527, in _call_impl<br>\nreturn forward_call(*args, **kwargs)<br>\nFile “/content/gdrive/MyDrive/Final/video_swin_transformer/mmaction/models/backbones/swin_transformer.py”, line 441, in forward<br>\n_, _, D, H, W = x.size()<br>\nAttributeError: ‘NoneType’ object has no attribute ‘size’</p>\n<p>Output if length of the subset is 2</p>\n<p>Normal epoch vs loss gets printed without any issues</p>",628 "post_number": 1,629 "post_type": 1,630 "posts_count": 2,631 "updated_at": "2023-10-19T03:23:13.759Z",632 "reply_count": 0,633 "reply_to_post_number": null,634 "quote_count": 0,635 "incoming_link_count": 56,636 "reads": 10,637 "readers_count": 9,638 "score": 277.0,639 "yours": false,640 "topic_id": 190254,641 "topic_slug": "data-loading-using-pytorch",642 "display_username": "Aniruth Sundararajan",643 "primary_group_name": null,644 "flair_name": null,645 "flair_url": null,646 "flair_bg_color": null,647 "flair_color": null,648 "flair_group_id": null,649 "badges_granted": [],650 "version": 1,651 "can_edit": false,652 "can_delete": false,653 "can_recover": false,654 "can_see_hidden_post": false,655 "can_wiki": false,656 "read": true,657 "user_title": null,658 "bookmarked": false,659 "actions_summary": [],660 "moderator": false,661 "admin": false,662 "staff": false,663 "user_id": 70254,664 "hidden": false,665 "trust_level": 0,666 "deleted_at": null,667 "user_deleted": false,668 "edit_reason": null,669 "can_view_edit_history": true,670 "wiki": false,671 "post_url": "/t/data-loading-using-pytorch/190254/1",672 "can_accept_answer": false,673 "can_unaccept_answer": false,674 "accepted_answer": false,675 "topic_accepted_answer": null,676 "can_vote": false677 },678 {679 "id": 421062,680 "name": "Roopali Bhavanasi",681 "username": "Roopali_Bhavanasi",682 "avatar_template": "/user_avatar/discuss.pytorch.org/roopali_bhavanasi/{size}/61570_2.png",683 "created_at": "2023-10-19T15:43:01.497Z",684 "cooked": "<p>I am facing a similar issue.Even I used torch.utils. DataLoader() but then I was not able read any of the data files even though my path was correct .I tried the same using subset but then when I give index length more than two.It is giving me null.</p>",685 "post_number": 2,686 "post_type": 1,687 "posts_count": 2,688 "updated_at": "2023-10-19T15:43:01.497Z",689 "reply_count": 0,690 "reply_to_post_number": null,691 "quote_count": 0,692 "incoming_link_count": 1,693 "reads": 8,694 "readers_count": 7,695 "score": 6.6,696 "yours": false,697 "topic_id": 190254,698 "topic_slug": "data-loading-using-pytorch",699 "display_username": "Roopali Bhavanasi",700 "primary_group_name": null,701 "flair_name": null,702 "flair_url": null,703 "flair_bg_color": null,704 "flair_color": null,705 "flair_group_id": null,706 "badges_granted": [],707 "version": 1,708 "can_edit": false,709 "can_delete": false,710 "can_recover": false,711 "can_see_hidden_post": false,712 "can_wiki": false,713 "read": true,714 "user_title": null,715 "bookmarked": false,716 "actions_summary": [],717 "moderator": false,718 "admin": false,719 "staff": false,720 "user_id": 70274,721 "hidden": false,722 "trust_level": 0,723 "deleted_at": null,724 "user_deleted": false,725 "edit_reason": null,726 "can_view_edit_history": true,727 "wiki": false,728 "post_url": "/t/data-loading-using-pytorch/190254/2",729 "can_accept_answer": false,730 "can_unaccept_answer": false,731 "accepted_answer": false,732 "topic_accepted_answer": null733 }734 ],735 "stream": [736 420969,737 421062738 ]739 },740 "timeline_lookup": [741 [742 1,743 738744 ],745 [746 2,747 737748 ]749 ],750 "suggested_topics": [751 {752 "fancy_title": "Reusing worker processes across different DataLoader instances",753 "id": 216387,754 "title": "Reusing worker processes across different DataLoader instances",755 "slug": "reusing-worker-processes-across-different-dataloader-instances",756 "posts_count": 1,757 "reply_count": 0,758 "highest_post_number": 1,759 "image_url": null,760 "created_at": "2025-02-07T23:36:36.020Z",761 "last_posted_at": "2025-02-07T23:36:36.064Z",762 "bumped": true,763 "bumped_at": "2025-02-07T23:36:36.064Z",764 "archetype": "regular",765 "unseen": false,766 "pinned": false,767 "unpinned": null,768 "visible": true,769 "closed": false,770 "archived": false,771 "bookmarked": null,772 "liked": null,773 "tags_descriptions": {},774 "like_count": 0,775 "views": 52,776 "category_id": 37,777 "featured_link": null,778 "has_accepted_answer": false,779 "posters": [780 {781 "extras": "latest single",782 "description": "Original Poster, Most Recent Poster",783 "user": {784 "id": 82554,785 "username": "smolboii",786 "name": "smolboii",787 "avatar_template": "/user_avatar/discuss.pytorch.org/smolboii/{size}/75537_2.png",788 "trust_level": 1789 }790 }791 ]792 },793 {794 "fancy_title": "Encountered strange overfitting during training, with `num_worker > 0`, when shuffling is off",795 "id": 213263,796 "title": "Encountered strange overfitting during training, with `num_worker > 0`, when shuffling is off",797 "slug": "encountered-strange-overfitting-during-training-with-num-worker-0-when-shuffling-is-off",798 "posts_count": 5,799 "reply_count": 2,800 "highest_post_number": 5,801 "image_url": "https://discuss.pytorch.org/uploads/default/original/3X/a/9/a92b412f4e5991ec0528b44bea73d1764d1f8838.png",802 "created_at": "2024-11-21T10:55:19.879Z",803 "last_posted_at": "2025-02-24T04:10:33.257Z",804 "bumped": true,805 "bumped_at": "2025-02-24T04:10:33.257Z",806 "archetype": "regular",807 "unseen": false,808 "pinned": false,809 "unpinned": null,810 "visible": true,811 "closed": false,812 "archived": false,813 "bookmarked": null,814 "liked": null,815 "tags_descriptions": {},816 "like_count": 0,817 "views": 411,818 "category_id": 37,819 "featured_link": null,820 "has_accepted_answer": false,821 "posters": [822 {823 "extras": null,824 "description": "Original Poster",825 "user": {826 "id": 80706,827 "username": "melisande-c",828 "name": "Melisande Croft",829 "avatar_template": "/user_avatar/discuss.pytorch.org/melisande-c/{size}/73788_2.png",830 "trust_level": 1831 }832 },833 {834 "extras": "latest",835 "description": "Most Recent Poster",836 "user": {837 "id": 81103,838 "username": "traincheck-team",839 "name": "",840 "avatar_template": "/user_avatar/discuss.pytorch.org/traincheck-team/{size}/73668_2.png",841 "trust_level": 1842 }843 }844 ]845 },846 {847 "fancy_title": "Low GPU Utilization (~10%) with HF Datasets Streaming on Vertex with PyTorch Trainer",848 "id": 213157,849 "title": "Low GPU Utilization (~10%) with HF Datasets Streaming on Vertex with PyTorch Trainer",850 "slug": "low-gpu-utilization-10-with-hf-datasets-streaming-on-vertex-with-pytorch-trainer",851 "posts_count": 1,852 "reply_count": 0,853 "highest_post_number": 1,854 "image_url": null,855 "created_at": "2024-11-19T12:51:35.715Z",856 "last_posted_at": "2024-11-19T12:51:35.774Z",857 "bumped": true,858 "bumped_at": "2024-11-19T13:55:15.111Z",859 "archetype": "regular",860 "unseen": false,861 "pinned": false,862 "unpinned": null,863 "visible": true,864 "closed": false,865 "archived": false,866 "bookmarked": null,867 "liked": null,868 "tags_descriptions": {},869 "like_count": 0,870 "views": 66,871 "category_id": 37,872 "featured_link": null,873 "has_accepted_answer": false,874 "posters": [875 {876 "extras": "latest single",877 "description": "Original Poster, Most Recent Poster",878 "user": {879 "id": 68538,880 "username": "lpolisi",881 "name": "",882 "avatar_template": "/user_avatar/discuss.pytorch.org/lpolisi/{size}/63030_2.png",883 "trust_level": 1884 }885 }886 ]887 },888 {889 "fancy_title": "Losing Mask Data",890 "id": 213711,891 "title": "Losing Mask Data",892 "slug": "losing-mask-data",893 "posts_count": 5,894 "reply_count": 1,895 "highest_post_number": 5,896 "image_url": null,897 "created_at": "2024-12-02T15:28:21.758Z",898 "last_posted_at": "2024-12-04T13:32:52.827Z",899 "bumped": true,900 "bumped_at": "2024-12-04T13:32:52.827Z",901 "archetype": "regular",902 "unseen": false,903 "pinned": false,904 "unpinned": null,905 "visible": true,906 "closed": false,907 "archived": false,908 "bookmarked": null,909 "liked": null,910 "tags_descriptions": {},911 "like_count": 0,912 "views": 268,913 "category_id": 37,914 "featured_link": null,915 "has_accepted_answer": false,916 "posters": [917 {918 "extras": null,919 "description": "Original Poster",920 "user": {921 "id": 81251,922 "username": "NoahKling",923 "name": "Noah Kling",924 "avatar_template": "/user_avatar/discuss.pytorch.org/noahkling/{size}/74303_2.png",925 "trust_level": 0926 }927 },928 {929 "extras": "latest",930 "description": "Most Recent Poster",931 "user": {932 "id": 3534,933 "username": "ptrblck",934 "name": "",935 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",936 "admin": true,937 "moderator": true,938 "trust_level": 2939 }940 }941 ]942 },943 {944 "fancy_title": "Sizes of tensors must match except in dimension 0. Expected size 914 but got size 531 for tensor number 1 in the list",945 "id": 216813,946 "title": "Sizes of tensors must match except in dimension 0. Expected size 914 but got size 531 for tensor number 1 in the list",947 "slug": "sizes-of-tensors-must-match-except-in-dimension-0-expected-size-914-but-got-size-531-for-tensor-number-1-in-the-list",948 "posts_count": 3,949 "reply_count": 1,950 "highest_post_number": 3,951 "image_url": null,952 "created_at": "2025-02-18T08:08:49.761Z",953 "last_posted_at": "2025-02-20T15:43:24.065Z",954 "bumped": true,955 "bumped_at": "2025-02-20T15:43:24.065Z",956 "archetype": "regular",957 "unseen": false,958 "pinned": false,959 "unpinned": null,960 "visible": true,961 "closed": false,962 "archived": false,963 "bookmarked": null,964 "liked": null,965 "tags_descriptions": {},966 "like_count": 0,967 "views": 129,968 "category_id": 37,969 "featured_link": null,970 "has_accepted_answer": false,971 "posters": [972 {973 "extras": "latest",974 "description": "Original Poster, Most Recent Poster",975 "user": {976 "id": 82744,977 "username": "Maria_Djeblahi",978 "name": "Maria Djeblahi",979 "avatar_template": "/user_avatar/discuss.pytorch.org/maria_djeblahi/{size}/75704_2.png",980 "trust_level": 1981 }982 },983 {984 "extras": null,985 "description": "Frequent Poster",986 "user": {987 "id": 3534,988 "username": "ptrblck",989 "name": "",990 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",991 "admin": true,992 "moderator": true,993 "trust_level": 2994 }995 }996 ]997 }998 ],999 "tags_descriptions": {},1000 "fancy_title": "Data Loading using Pytorch",1001 "id": 190254,1002 "title": "Data Loading using Pytorch",1003 "posts_count": 2,1004 "created_at": "2023-10-19T03:23:13.699Z",1005 "views": 377,1006 "reply_count": 0,1007 "like_count": 0,1008 "last_posted_at": "2023-10-19T15:43:01.497Z",1009 "visible": true,1010 "closed": false,1011 "archived": false,1012 "has_summary": false,1013 "archetype": "regular",1014 "slug": "data-loading-using-pytorch",1015 "category_id": 37,1016 "word_count": 495,1017 "deleted_at": null,1018 "user_id": 70254,1019 "featured_link": null,1020 "pinned_globally": false,1021 "pinned_at": null,1022 "pinned_until": null,1023 "image_url": null,1024 "slow_mode_seconds": 0,1025 "draft": null,1026 "draft_key": "topic_190254",1027 "draft_sequence": null,1028 "unpinned": null,1029 "pinned": false,1030 "current_post_number": 1,1031 "highest_post_number": 2,1032 "deleted_by": null,1033 "actions_summary": [1034 {1035 "id": 4,1036 "count": 0,1037 "hidden": false,1038 "can_act": false1039 },1040 {1041 "id": 8,1042 "count": 0,1043 "hidden": false,1044 "can_act": false1045 },1046 {1047 "id": 10,1048 "count": 0,1049 "hidden": false,1050 "can_act": false1051 },1052 {1053 "id": 7,1054 "count": 0,1055 "hidden": false,1056 "can_act": false1057 }1058 ],1059 "chunk_size": 20,1060 "bookmarked": false,1061 "topic_timer": null,1062 "message_bus_last_id": 0,1063 "participant_count": 2,1064 "show_read_indicator": false,1065 "thumbnails": null,1066 "slow_mode_enabled_until": null,1067 "can_vote": false,1068 "vote_count": 0,1069 "user_voted": false,1070 "discourse_zendesk_plugin_zendesk_id": null,1071 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",1072 "details": {1073 "can_edit": false,1074 "notification_level": 1,1075 "participants": [1076 {1077 "id": 70254,1078 "username": "Aniruth_Sundararajan",1079 "name": "Aniruth Sundararajan",1080 "avatar_template": "/user_avatar/discuss.pytorch.org/aniruth_sundararajan/{size}/63308_2.png",1081 "post_count": 1,1082 "primary_group_name": null,1083 "flair_name": null,1084 "flair_url": null,1085 "flair_color": null,1086 "flair_bg_color": null,1087 "flair_group_id": null,1088 "trust_level": 01089 },1090 {1091 "id": 70274,1092 "username": "Roopali_Bhavanasi",1093 "name": "Roopali Bhavanasi",1094 "avatar_template": "/user_avatar/discuss.pytorch.org/roopali_bhavanasi/{size}/61570_2.png",1095 "post_count": 1,1096 "primary_group_name": null,1097 "flair_name": null,1098 "flair_url": null,1099 "flair_color": null,1100 "flair_bg_color": null,1101 "flair_group_id": null,1102 "trust_level": 01103 }1104 ],1105 "created_by": {1106 "id": 70254,1107 "username": "Aniruth_Sundararajan",1108 "name": "Aniruth Sundararajan",1109 "avatar_template": "/user_avatar/discuss.pytorch.org/aniruth_sundararajan/{size}/63308_2.png"1110 },1111 "last_poster": {1112 "id": 70274,1113 "username": "Roopali_Bhavanasi",1114 "name": "Roopali Bhavanasi",1115 "avatar_template": "/user_avatar/discuss.pytorch.org/roopali_bhavanasi/{size}/61570_2.png"1116 }1117 },1118 "bookmarks": []1119 },1120 {1121 "post_stream": {1122 "posts": [1123 {1124 "id": 420825,1125 "name": "",1126 "username": "yxz77777",1127 "avatar_template": "/letter_avatar_proxy/v4/letter/y/6bbea6/{size}.png",1128 "created_at": "2023-10-18T13:26:57.317Z",1129 "cooked": "<p><strong>My algorithm is DDPG, and my actor network is unable to backpropagate. How should I handle this issue?</strong><br>\nclass ActorNet(nn.Module):<br>\ndef <strong>init</strong>(self):<br>\nsuper(ActorNet, self).<strong>init</strong>()<br>\ninit_w = 1e-3<br>\nself.input_size = 3<br>\nself.output_size = 1 + 1<br>\nself.fc1 = nn.Linear(self.input_size, HIDDEN_SIZE_1)<br>\nself.fc2 = nn.Linear(HIDDEN_SIZE_1, HIDDEN_SIZE_2)<br>\nself.fc3 = nn.Linear(HIDDEN_SIZE_2, self.output_size)</p>\n<pre><code> init.kaiming_uniform_(self.fc1.weight)\n init.kaiming_uniform_(self.fc2.weight)\n init.kaiming_uniform_(self.fc3.weight)\n\n\ndef forward(self, x):\n x = self.fc1(x)\n x = torch.relu(x)\n x = self.fc2(x)\n x = torch.relu(x)\n x = self.fc3(x)\n x = torch.relu(x)\n return x\n</code></pre>\n<p><strong>this is my actor-net frame,and the update is below</strong><br>\nstate, action, reward, next_state = self.memory.sample(self.batch_size)<br>\nstate_batch = torch.FloatTensor(np.array(state))<br>\naction_batch = torch.FloatTensor(np.array(action))<br>\nreward_batch = torch.FloatTensor(reward).unsqueeze(1)<br>\nnext_state_batch = torch.FloatTensor(np.array(next_state))<br>\nstate_actor_batch = torch.cat((state_batch, action_batch), 1)<br>\npolicy_Q = torch.mean(self.critic(state_actor_batch))<br>\nactor_loss = -policy_Q<br>\nself.actor_optimizer.zero_grad()<br>\ntorch.nn.utils.clip_grad_norm_(self.actor.parameters(), 1)<br>\nactor_loss.backward(retain_graph=True)<br>\nself.actor_optimizer.step()<br>\nand my optim is<br>\nself.actor_optimizer = optim.Adam(self.actor.parameters(), lr=1e-3, weight_decay=1e-5)</p>",1130 "post_number": 1,1131 "post_type": 1,1132 "posts_count": 8,1133 "updated_at": "2023-10-18T13:50:48.420Z",1134 "reply_count": 0,1135 "reply_to_post_number": null,1136 "quote_count": 0,1137 "incoming_link_count": 52,1138 "reads": 11,1139 "readers_count": 10,1140 "score": 262.2,1141 "yours": false,1142 "topic_id": 190201,1143 "topic_slug": "backward-error-in-ddpg",1144 "display_username": "",1145 "primary_group_name": null,1146 "flair_name": null,1147 "flair_url": null,1148 "flair_bg_color": null,1149 "flair_color": null,1150 "flair_group_id": null,1151 "badges_granted": [],1152 "version": 2,1153 "can_edit": false,1154 "can_delete": false,1155 "can_recover": false,1156 "can_see_hidden_post": false,1157 "can_wiki": false,1158 "read": true,1159 "user_title": null,1160 "bookmarked": false,1161 "actions_summary": [],1162 "moderator": false,1163 "admin": false,1164 "staff": false,1165 "user_id": 70238,1166 "hidden": false,1167 "trust_level": 1,1168 "deleted_at": null,1169 "user_deleted": false,1170 "edit_reason": null,1171 "can_view_edit_history": true,1172 "wiki": false,1173 "post_url": "/t/backward-error-in-ddpg/190201/1",1174 "can_accept_answer": false,1175 "can_unaccept_answer": false,1176 "accepted_answer": false,1177 "topic_accepted_answer": true,1178 "can_vote": false1179 },1180 {1181 "id": 420880,1182 "name": "",1183 "username": "ptrblck",1184 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",1185 "created_at": "2023-10-18T17:43:52.573Z",1186 "cooked": "<p>I don’t know what the error is but often using <code>retain_graph=True</code> is wrong and causes issues trying to calculate gradients from stale forward activations, so could you explain why this argument is used?</p>",1187 "post_number": 2,1188 "post_type": 1,1189 "posts_count": 8,1190 "updated_at": "2023-10-18T17:43:52.573Z",1191 "reply_count": 1,1192 "reply_to_post_number": null,1193 "quote_count": 0,1194 "incoming_link_count": 2,1195 "reads": 7,1196 "readers_count": 6,1197 "score": 31.4,1198 "yours": false,1199 "topic_id": 190201,1200 "topic_slug": "backward-error-in-ddpg",