Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 467730,7 "name": "AGH",8 "username": "AGH",9 "avatar_template": "/letter_avatar_proxy/v4/letter/a/90db22/{size}.png",10 "created_at": "2025-03-21T13:03:24.382Z",11 "cooked": "<p>Im using a pretrained model(yolov8 (ultralytics)) which is trained on COCO dataset.(COCO has 80 classes). Now I am validating it on medical-pills dataset which has only 1 class.</p>\n<p>After running this code,</p>\n<p>from ultralytics import YOLO</p>\n<h1><a name=\"p-467730-load-yolov8-pose-model-1\" class=\"anchor\" href=\"#p-467730-load-yolov8-pose-model-1\"></a>Load YOLOv8 pose model</h1>\n<p>model = YOLO(“yolov8n.pt”)</p>\n<h1><a name=\"p-467730-validate-on-the-hand-keypoints-dataset-2\" class=\"anchor\" href=\"#p-467730-validate-on-the-hand-keypoints-dataset-2\"></a>Validate on the hand keypoints dataset</h1>\n<p>metrics = model.val(data=“/content/medical-pills.yaml”, save_json=True)</p>\n<h1><a name=\"p-467730-print-results-3\" class=\"anchor\" href=\"#p-467730-print-results-3\"></a>Print results</h1>\n<p>print(metrics)</p>\n<p>Im getting all evaluation metrics and also val_batch0_labels.jpg and val_batch0_pred.jpg</p>\n<p>In val_batch0_labels.jpg i am getting pills images all with bounding boxes detected as person(which is the first class in COCO)</p>\n<p>and in val_batch0_pred.jpg<br>\ni am getting pills images as cup,cake,dining table,bottle(which all are classes of COCO dataset)</p>\n<p>Why its taking pill as person,instead of taking it as pill.</p>\n<p>Is there any class mismatch? 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{363 "id": 83397,364 "username": "AGH",365 "name": "AGH",366 "avatar_template": "/letter_avatar_proxy/v4/letter/a/90db22/{size}.png",367 "post_count": 1,368 "primary_group_name": null,369 "flair_name": null,370 "flair_url": null,371 "flair_color": null,372 "flair_bg_color": null,373 "flair_group_id": null,374 "trust_level": 0375 }376 ],377 "created_by": {378 "id": 83397,379 "username": "AGH",380 "name": "AGH",381 "avatar_template": "/letter_avatar_proxy/v4/letter/a/90db22/{size}.png"382 },383 "last_poster": {384 "id": 83397,385 "username": "AGH",386 "name": "AGH",387 "avatar_template": "/letter_avatar_proxy/v4/letter/a/90db22/{size}.png"388 }389 },390 "bookmarks": []391 },392 {393 "post_stream": {394 "posts": [395 {396 "id": 422569,397 "name": "Andrew Holmes",398 "username": "Andrew_Holmes",399 "avatar_template": "/user_avatar/discuss.pytorch.org/andrew_holmes/{size}/63127_2.png",400 "created_at": "2023-11-01T20:04:49.731Z",401 "cooked": "<p>I want to understand how autograd works a little bit on the backend, but I have no idea how the <code>ctx</code> variable gets passed for custom <code>Functions</code>. I understand that it’s the backward object that correlates to a Function, but where in the source code are you guys passing the ctx to the <code>forward()</code> and <code>backward()</code> methods so that it’s accessible to users?</p>\n<p><strong>Example:</strong></p>\n<pre><code class=\"lang-python\">import torch\nfrom torch.autograd import Function\n\n\nclass MulConstant(Function):\n @staticmethod\n def forward(tensor, constant):\n return tensor * constant\n\n @staticmethod\n def setup_context(ctx, inputs, output):\n # ctx is a context object that can be used to stash information\n # for backward computation\n tensor, constant = inputs\n print(ctx, type(ctx), ctx.__class__.__name__, sep=\"\\n\")\n ctx.constant = constant\n\n @staticmethod\n def backward(ctx, grad_output):\n # We return as many input gradients as there were arguments.\n # Gradients of non-Tensor arguments to forward must be None.\n return grad_output * ctx.constant, None\n\n\ndef mul_constant(tensor, c=1):\n return MulConstant.apply(tensor, c)\n\n\ntensor = torch.ones((5, 1), requires_grad=True)\nresult = mul_constant(tensor, c=10)\n</code></pre>\n<p><strong>Output:</strong></p>\n<pre><code class=\"lang-auto\"><torch.autograd.function.MulConstantBackward object at 0x114199300>\n<class 'torch.autograd.function.MulConstantBackward'>\nMulConstantBackward\n</code></pre>",402 "post_number": 1,403 "post_type": 1,404 "posts_count": 5,405 "updated_at": "2023-11-01T20:04:49.731Z",406 "reply_count": 0,407 "reply_to_post_number": null,408 "quote_count": 0,409 "incoming_link_count": 1226,410 "reads": 29,411 "readers_count": 28,412 "score": 6070.6,413 "yours": false,414 "topic_id": 191082,415 "topic_slug": "where-does-the-ctx-variable-come-from",416 "display_username": "Andrew Holmes",417 "primary_group_name": null,418 "flair_name": null,419 "flair_url": null,420 "flair_bg_color": null,421 "flair_color": null,422 "flair_group_id": null,423 "badges_granted": [],424 "version": 1,425 "can_edit": false,426 "can_delete": false,427 "can_recover": false,428 "can_see_hidden_post": false,429 "can_wiki": false,430 "read": true,431 "user_title": null,432 "bookmarked": false,433 "actions_summary": [434 {435 "id": 2,436 "count": 1437 }438 ],439 "moderator": false,440 "admin": false,441 "staff": false,442 "user_id": 68634,443 "hidden": false,444 "trust_level": 1,445 "deleted_at": null,446 "user_deleted": false,447 "edit_reason": null,448 "can_view_edit_history": true,449 "wiki": false,450 "post_url": "/t/where-does-the-ctx-variable-come-from/191082/1",451 "can_accept_answer": false,452 "can_unaccept_answer": false,453 "accepted_answer": false,454 "topic_accepted_answer": null,455 "can_vote": false456 },457 {458 "id": 422613,459 "name": "Thomas V",460 "username": "tom",461 "avatar_template": "/user_avatar/discuss.pytorch.org/tom/{size}/3162_2.png",462 "created_at": "2023-11-02T08:49:57.621Z",463 "cooked": "<p>Hi Andrew,</p>\n<p><code>Function.apply</code> creates the ctx as an instance of the backward node class, this is relatively deep in the C++ guts of the autograd engine, below is the C++ implementation of <code>Function.apply</code>.<br>\nI used to offer an “All about autograd” course, but sadly, I have not updated it to PT2 yet, so it is missing AOTAutograd other things that came after 2021.</p>\n<p>Best regards</p>\n<p>Thomas</p>\n<aside class=\"onebox githubblob\" data-onebox-src=\"https://github.com/pytorch/pytorch/blob/0276d5621ad7cec93a54929ab7221b3bba050dcb/torch/csrc/autograd/python_function.cpp#L1043\">\n <header class=\"source\">\n\n <a href=\"https://github.com/pytorch/pytorch/blob/0276d5621ad7cec93a54929ab7221b3bba050dcb/torch/csrc/autograd/python_function.cpp#L1043\" target=\"_blank\" rel=\"noopener nofollow ugc\">github.com</a>\n </header>\n\n <article class=\"onebox-body\">\n <h4><a href=\"https://github.com/pytorch/pytorch/blob/0276d5621ad7cec93a54929ab7221b3bba050dcb/torch/csrc/autograd/python_function.cpp#L1043\" target=\"_blank\" rel=\"noopener nofollow ugc\">pytorch/pytorch/blob/0276d5621ad7cec93a54929ab7221b3bba050dcb/torch/csrc/autograd/python_function.cpp#L1043</a></h4>\n\n\n\n <pre class=\"onebox\"><code class=\"lang-cpp\">\n <ol class=\"start lines\" start=\"1033\" style=\"counter-reset: li-counter 1032 ;\">\n <li></li>\n <li> // setup_context gets \"leaked\" - we return a new reference and hold onto it</li>\n <li> // forever.</li>\n <li> auto setup_context = PyObject_GetAttrString(function, \"setup_context\");</li>\n <li> if (!setup_context)</li>\n <li> return nullptr;</li>\n <li> THPFunction_setup_context = setup_context;</li>\n <li> return THPFunction_setup_context;</li>\n <li>}</li>\n <li></li>\n <li class=\"selected\">PyObject* THPFunction_apply(PyObject* cls, PyObject* inputs) {</li>\n <li> HANDLE_TH_ERRORS</li>\n <li></li>\n <li> // save a local copy of seq_id before it gets incremented</li>\n <li> auto seq_id = at::sequence_number::peek();</li>\n <li> auto info_pair = unpack_input<false>(inputs);</li>\n <li> UnpackedInput& unpacked_input = info_pair.first;</li>\n <li> InputFlags& input_info = info_pair.second;</li>\n <li></li>\n <li> // Call record function after all the inputs have been decoded, but</li>\n <li> // before context has been allocated.</li>\n </ol>\n </code></pre>\n\n\n\n </article>\n\n <div class=\"onebox-metadata\">\n \n \n </div>\n\n <div style=\"clear: both\"></div>\n</aside>\n",464 "post_number": 2,465 "post_type": 1,466 "posts_count": 5,467 "updated_at": "2023-11-02T08:49:57.621Z",468 "reply_count": 0,469 "reply_to_post_number": null,470 "quote_count": 0,471 "incoming_link_count": 13,472 "reads": 26,473 "readers_count": 25,474 "score": 70.0,475 "yours": false,476 "topic_id": 191082,477 "topic_slug": "where-does-the-ctx-variable-come-from",478 "display_username": "Thomas V",479 "primary_group_name": null,480 "flair_name": null,481 "flair_url": null,482 "flair_bg_color": null,483 "flair_color": null,484 "flair_group_id": null,485 "badges_granted": [],486 "version": 1,487 "can_edit": false,488 "can_delete": false,489 "can_recover": false,490 "can_see_hidden_post": false,491 "can_wiki": false,492 "link_counts": [493 {494 "url": "https://github.com/pytorch/pytorch/blob/0276d5621ad7cec93a54929ab7221b3bba050dcb/torch/csrc/autograd/python_function.cpp#L1043",495 "internal": false,496 "reflection": false,497 "clicks": 24498 }499 ],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": 616,508 "hidden": false,509 "trust_level": 2,510 "deleted_at": null,511 "user_deleted": false,512 "edit_reason": null,513 "can_view_edit_history": true,514 "wiki": false,515 "post_url": "/t/where-does-the-ctx-variable-come-from/191082/2",516 "can_accept_answer": false,517 "can_unaccept_answer": false,518 "accepted_answer": false,519 "topic_accepted_answer": null520 },521 {522 "id": 422628,523 "name": "Andrew Holmes",524 "username": "Andrew_Holmes",525 "avatar_template": "/user_avatar/discuss.pytorch.org/andrew_holmes/{size}/63127_2.png",526 "created_at": "2023-11-02T12:20:02.490Z",527 "cooked": "<p>Hey <a class=\"mention\" href=\"/u/tom\">@tom</a>,</p>\n<p>This was pretty helpful. It just peaked my curiosity because I just started learning about autograd and was just really confused on where exactly it originated from. Honestly, I’d still like to check out some parts of your course since I want to learn about eager mode autograd. Do you have a link to it so I can check out some of it?</p>\n<p>Thanks,<br>\nAndrew</p>",528 "post_number": 3,529 "post_type": 1,530 "posts_count": 5,531 "updated_at": "2023-11-02T12:20:02.490Z",532 "reply_count": 1,533 "reply_to_post_number": null,534 "quote_count": 0,535 "incoming_link_count": 1,536 "reads": 20,537 "readers_count": 19,538 "score": 13.8,539 "yours": false,540 "topic_id": 191082,541 "topic_slug": "where-does-the-ctx-variable-come-from",542 "display_username": "Andrew Holmes",543 "primary_group_name": null,544 "flair_name": null,545 "flair_url": null,546 "flair_bg_color": null,547 "flair_color": null,548 "flair_group_id": null,549 "badges_granted": [],550 "version": 1,551 "can_edit": false,552 "can_delete": false,553 "can_recover": false,554 "can_see_hidden_post": false,555 "can_wiki": false,556 "read": true,557 "user_title": null,558 "bookmarked": false,559 "actions_summary": [],560 "moderator": false,561 "admin": false,562 "staff": false,563 "user_id": 68634,564 "hidden": false,565 "trust_level": 1,566 "deleted_at": null,567 "user_deleted": false,568 "edit_reason": null,569 "can_view_edit_history": true,570 "wiki": false,571 "post_url": "/t/where-does-the-ctx-variable-come-from/191082/3",572 "can_accept_answer": false,573 "can_unaccept_answer": false,574 "accepted_answer": false,575 "topic_accepted_answer": null576 },577 {578 "id": 422646,579 "name": "Thomas V",580 "username": "tom",581 "avatar_template": "/user_avatar/discuss.pytorch.org/tom/{size}/3162_2.png",582 "created_at": "2023-11-02T15:06:53.268Z",583 "cooked": "<p>I sent you an invite link via PM.</p>\n<p>Best Regards</p>\n<p>Thomas</p>",584 "post_number": 4,585 "post_type": 1,586 "posts_count": 5,587 "updated_at": "2023-11-02T15:06:53.268Z",588 "reply_count": 0,589 "reply_to_post_number": 3,590 "quote_count": 0,591 "incoming_link_count": 1,592 "reads": 19,593 "readers_count": 18,594 "score": 8.6,595 "yours": false,596 "topic_id": 191082,597 "topic_slug": "where-does-the-ctx-variable-come-from",598 "display_username": "Thomas V",599 "primary_group_name": null,600 "flair_name": null,601 "flair_url": null,602 "flair_bg_color": null,603 "flair_color": null,604 "flair_group_id": null,605 "badges_granted": [],606 "version": 1,607 "can_edit": false,608 "can_delete": false,609 "can_recover": false,610 "can_see_hidden_post": false,611 "can_wiki": false,612 "read": true,613 "user_title": null,614 "reply_to_user": {615 "id": 68634,616 "username": "Andrew_Holmes",617 "name": "Andrew Holmes",618 "avatar_template": "/user_avatar/discuss.pytorch.org/andrew_holmes/{size}/63127_2.png"619 },620 "bookmarked": false,621 "actions_summary": [],622 "moderator": false,623 "admin": false,624 "staff": false,625 "user_id": 616,626 "hidden": false,627 "trust_level": 2,628 "deleted_at": null,629 "user_deleted": false,630 "edit_reason": null,631 "can_view_edit_history": true,632 "wiki": false,633 "post_url": "/t/where-does-the-ctx-variable-come-from/191082/4",634 "can_accept_answer": false,635 "can_unaccept_answer": false,636 "accepted_answer": false,637 "topic_accepted_answer": null638 },639 {640 "id": 467722,641 "name": "Lucia Quirke",642 "username": "luciaquirke",643 "avatar_template": "/user_avatar/discuss.pytorch.org/luciaquirke/{size}/76273_2.png",644 "created_at": "2025-03-21T06:50:56.485Z",645 "cooked": "<p>Hey <a class=\"mention\" href=\"/u/tom\">@tom</a>, could I please get the link too? Thanks <img src=\"https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=12\" title=\":slight_smile:\" class=\"emoji\" alt=\":slight_smile:\" loading=\"lazy\" width=\"20\" height=\"20\"></p>",646 "post_number": 5,647 "post_type": 1,648 "posts_count": 5,649 "updated_at": "2025-03-21T06:50:56.485Z",650 "reply_count": 0,651 "reply_to_post_number": null,652 "quote_count": 0,653 "incoming_link_count": 1,654 "reads": 7,655 "readers_count": 6,656 "score": 6.2,657 "yours": false,658 "topic_id": 191082,659 "topic_slug": "where-does-the-ctx-variable-come-from",660 "display_username": "Lucia Quirke",661 "primary_group_name": null,662 "flair_name": null,663 "flair_url": null,664 "flair_bg_color": null,665 "flair_color": null,666 "flair_group_id": null,667 "badges_granted": [],668 "version": 1,669 "can_edit": false,670 "can_delete": false,671 "can_recover": false,672 "can_see_hidden_post": false,673 "can_wiki": false,674 "read": true,675 "user_title": null,676 "bookmarked": false,677 "actions_summary": [],678 "moderator": false,679 "admin": false,680 "staff": false,681 "user_id": 83399,682 "hidden": false,683 "trust_level": 1,684 "deleted_at": null,685 "user_deleted": false,686 "edit_reason": null,687 "can_view_edit_history": true,688 "wiki": false,689 "post_url": "/t/where-does-the-ctx-variable-come-from/191082/5",690 "can_accept_answer": false,691 "can_unaccept_answer": false,692 "accepted_answer": false,693 "topic_accepted_answer": null694 }695 ],696 "stream": [697 422569,698 422613,699 422628,700 422646,701 467722702 ]703 },704 "timeline_lookup": [705 [706 1,707 724708 ],709 [710 2,711 723712 ],713 [714 5,715 218716 ]717 ],718 "suggested_topics": [719 {720 "fancy_title": "How to preserve computational graph while initializing a network with weights",721 "id": 217388,722 "title": "How to preserve computational graph while initializing a network with weights",723 "slug": "how-to-preserve-computational-graph-while-initializing-a-network-with-weights",724 "posts_count": 3,725 "reply_count": 1,726 "highest_post_number": 3,727 "image_url": null,728 "created_at": "2025-03-03T16:08:29.829Z",729 "last_posted_at": "2025-03-04T05:38:35.265Z",730 "bumped": true,731 "bumped_at": "2025-03-04T05:38:35.265Z",732 "archetype": "regular",733 "unseen": false,734 "pinned": false,735 "unpinned": null,736 "visible": true,737 "closed": false,738 "archived": false,739 "bookmarked": null,740 "liked": null,741 "tags_descriptions": {},742 "like_count": 0,743 "views": 50,744 "category_id": 7,745 "featured_link": null,746 "has_accepted_answer": true,747 "posters": [748 {749 "extras": "latest",750 "description": "Original Poster, Most Recent Poster",751 "user": {752 "id": 83043,753 "username": "Charley_Xiao",754 "name": "Charley Xiao",755 "avatar_template": "/user_avatar/discuss.pytorch.org/charley_xiao/{size}/75963_2.png",756 "trust_level": 1757 }758 },759 {760 "extras": null,761 "description": "Frequent Poster, Accepted Answer",762 "user": {763 "id": 18088,764 "username": "KFrank",765 "name": "K. 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"https://github.com/pytorch/pytorch/blob/0276d5621ad7cec93a54929ab7221b3bba050dcb/torch/csrc/autograd/python_function.cpp#L1043",1122 "title": null,1123 "internal": false,1124 "attachment": false,1125 "reflection": false,1126 "clicks": 24,1127 "user_id": 616,1128 "domain": "github.com",1129 "root_domain": "github.com"1130 }1131 ]1132 },1133 "bookmarks": []1134 },1135 {1136 "post_stream": {1137 "posts": [1138 {1139 "id": 467718,1140 "name": "Muhammad Irfan",1141 "username": "Muhammad_Irfan",1142 "avatar_template": "/user_avatar/discuss.pytorch.org/muhammad_irfan/{size}/76272_2.png",1143 "created_at": "2025-03-21T06:12:00.808Z",1144 "cooked": "<p>I am facing a problem, i have a point error dataset of 74 rows in which 3 are my inputs and 16 are output features. Well when I trained my model using polynomial regression, if I increased the degree I got good results in train r2 and test r2 and this is overfitting. when I used DNN model with complex hidden layer I got good train r2 but test r2 fails so what you suggest to get good train and test r2, either I need to change my model as when I tuned my polynomial model the results not improved or we have to collect more data ?</p>",1145 "post_number": 1,1146 "post_type": 1,1147 "posts_count": 1,1148 "updated_at": "2025-03-21T06:12:00.808Z",1149 "reply_count": 0,1150 "reply_to_post_number": null,1151 "quote_count": 0,1152 "incoming_link_count": 3,1153 "reads": 5,1154 "readers_count": 4,1155 "score": 16.0,1156 "yours": false,1157 "topic_id": 218106,1158 "topic_slug": "dataset-handling-and-model-selection",1159 "display_username": "Muhammad Irfan",1160 "primary_group_name": null,1161 "flair_name": null,1162 "flair_url": null,1163 "flair_bg_color": null,1164 "flair_color": null,1165 "flair_group_id": null,1166 "badges_granted": [],1167 "version": 1,1168 "can_edit": false,1169 "can_delete": false,1170 "can_recover": false,1171 "can_see_hidden_post": false,1172 "can_wiki": false,1173 "read": true,1174 "user_title": null,1175 "bookmarked": false,1176 "actions_summary": [],1177 "moderator": false,1178 "admin": false,1179 "staff": false,1180 "user_id": 83398,1181 "hidden": false,1182 "trust_level": 0,1183 "deleted_at": null,1184 "user_deleted": false,1185 "edit_reason": null,1186 "can_view_edit_history": true,1187 "wiki": false,1188 "post_url": "/t/dataset-handling-and-model-selection/218106/1",1189 "can_accept_answer": false,1190 "can_unaccept_answer": false,1191 "accepted_answer": false,1192 "topic_accepted_answer": null,1193 "can_vote": false1194 }1195 ],1196 "stream": [1197 4677181198 ]1199 },1200 "timeline_lookup": [