Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 282763,7 "name": "Ajinkya Bankar",8 "username": "Ajinkya.Bankar",9 "avatar_template": "/letter_avatar_proxy/v4/letter/a/bc79bd/{size}.png",10 "created_at": "2021-05-10T14:03:55.286Z",11 "cooked": "<p>Hello,<br>\nI have a PyTorch data_loader with three fields and iterates as follows:<br>\nfor i, (input, target_class, name) in enumerate(data_loader):</p>\n<p>But I want it to enumerate over ‘input’ and ‘target_class’. How can I unpack data_loader in two values given that it has originally three values? Please help. Thanks.</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 3,15 "updated_at": "2021-05-10T14:03:55.286Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 18,20 "reads": 9,21 "readers_count": 8,22 "score": 91.8,23 "yours": false,24 "topic_id": 120838,25 "topic_slug": "how-to-unpack-data-loader-in-small-size",26 "display_username": "Ajinkya Bankar",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": 43539,48 "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/how-to-unpack-data-loader-in-small-size/120838/1",56 "can_accept_answer": false,57 "can_unaccept_answer": false,58 "accepted_answer": false,59 "topic_accepted_answer": null,60 "can_vote": false61 },62 {63 "id": 282767,64 "name": "Aritra Roy Gosthipaty",65 "username": "ariG23498",66 "avatar_template": "/user_avatar/discuss.pytorch.org/arig23498/{size}/69466_2.png",67 "created_at": "2021-05-10T14:14:07.836Z",68 "cooked": "<p>I think either you can create a <code>DataSet</code> that only returns the <code>input</code> and <code>target_class</code>, or you could do something like this:</p>\n<pre><code class=\"lang-python\">for i, element in enumerate(data_loader):\n input, target_class, _ = element\n</code></pre>\n<p>Here you are indeed iterating over the three fields but are not using the name field.</p>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 3,72 "updated_at": "2021-05-10T14:14:07.836Z",73 "reply_count": 0,74 "reply_to_post_number": null,75 "quote_count": 0,76 "incoming_link_count": 0,77 "reads": 7,78 "readers_count": 6,79 "score": 1.4,80 "yours": false,81 "topic_id": 120838,82 "topic_slug": "how-to-unpack-data-loader-in-small-size",83 "display_username": "Aritra Roy Gosthipaty",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": 36965,105 "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/how-to-unpack-data-loader-in-small-size/120838/2",113 "can_accept_answer": false,114 "can_unaccept_answer": false,115 "accepted_answer": false,116 "topic_accepted_answer": null117 },118 {119 "id": 282788,120 "name": "Erjia",121 "username": "ejguan",122 "avatar_template": "/letter_avatar_proxy/v4/letter/e/5f8ce5/{size}.png",123 "created_at": "2021-05-10T16:21:58.294Z",124 "cooked": "<p>Do you want something like this?</p>\n<pre><code class=\"lang-auto\">for i, (input, target, _) in enumerate(dataloader):\n ...\n</code></pre>\n<p>Or, you can change your Dataset to yield (_<em>iter</em>_) or return (_<em>getitem</em>_) only input and target without name.</p>",125 "post_number": 3,126 "post_type": 1,127 "posts_count": 3,128 "updated_at": "2021-05-10T19:36:24.512Z",129 "reply_count": 0,130 "reply_to_post_number": null,131 "quote_count": 0,132 "incoming_link_count": 0,133 "reads": 7,134 "readers_count": 6,135 "score": 1.4,136 "yours": false,137 "topic_id": 120838,138 "topic_slug": "how-to-unpack-data-loader-in-small-size",139 "display_username": "Erjia",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": 4,148 "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": 37796,161 "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/how-to-unpack-data-loader-in-small-size/120838/3",169 "can_accept_answer": false,170 "can_unaccept_answer": false,171 "accepted_answer": false,172 "topic_accepted_answer": null173 }174 ],175 "stream": [176 282763,177 282767,178 282788179 ]180 },181 "timeline_lookup": [182 [183 1,184 1629185 ]186 ],187 "suggested_topics": [188 {189 "fancy_title": "Fold an overlapping 3D tensor?",190 "id": 213879,191 "title": "Fold an overlapping 3D tensor?",192 "slug": "fold-an-overlapping-3d-tensor",193 "posts_count": 2,194 "reply_count": 0,195 "highest_post_number": 2,196 "image_url": null,197 "created_at": "2024-12-05T22:30:45.221Z",198 "last_posted_at": "2024-12-09T20:00:12.156Z",199 "bumped": true,200 "bumped_at": "2024-12-09T20:00:12.156Z",201 "archetype": "regular",202 "unseen": false,203 "pinned": false,204 "unpinned": null,205 "visible": true,206 "closed": false,207 "archived": false,208 "bookmarked": null,209 "liked": null,210 "tags_descriptions": {},211 "like_count": 0,212 "views": 178,213 "category_id": 5,214 "featured_link": null,215 "has_accepted_answer": true,216 "posters": [217 {218 "extras": "latest single",219 "description": "Original Poster, Most Recent Poster, Accepted Answer",220 "user": {221 "id": 66240,222 "username": "bartley",223 "name": "Brendan",224 "avatar_template": "/user_avatar/discuss.pytorch.org/bartley/{size}/60569_2.png",225 "trust_level": 1226 }227 }228 ]229 },230 {231 "fancy_title": "Efficient single object detector",232 "id": 214164,233 "title": "Efficient single object detector",234 "slug": "efficient-single-object-detector",235 "posts_count": 6,236 "reply_count": 3,237 "highest_post_number": 6,238 "image_url": null,239 "created_at": "2024-12-12T18:33:33.575Z",240 "last_posted_at": "2024-12-18T17:00:44.088Z",241 "bumped": true,242 "bumped_at": "2024-12-18T17:00:44.088Z",243 "archetype": "regular",244 "unseen": false,245 "pinned": false,246 "unpinned": null,247 "visible": true,248 "closed": false,249 "archived": false,250 "bookmarked": null,251 "liked": null,252 "tags_descriptions": {},253 "like_count": 2,254 "views": 502,255 "category_id": 5,256 "featured_link": null,257 "has_accepted_answer": false,258 "posters": [259 {260 "extras": null,261 "description": "Original Poster",262 "user": {263 "id": 78029,264 "username": "AviZ",265 "name": "",266 "avatar_template": "/letter_avatar_proxy/v4/letter/a/e0b2c6/{size}.png",267 "trust_level": 1268 }269 },270 {271 "extras": "latest",272 "description": "Most Recent Poster",273 "user": {274 "id": 81089,275 "username": "Aknw_Fen",276 "name": "Aknw Fen",277 "avatar_template": "/user_avatar/discuss.pytorch.org/aknw_fen/{size}/74156_2.png",278 "trust_level": 2279 }280 }281 ]282 },283 {284 "fancy_title": "CrossEntropy Issue",285 "id": 214703,286 "title": "CrossEntropy Issue",287 "slug": "crossentropy-issue",288 "posts_count": 2,289 "reply_count": 0,290 "highest_post_number": 2,291 "image_url": null,292 "created_at": "2024-12-27T14:04:44.755Z",293 "last_posted_at": "2024-12-27T18:27:54.377Z",294 "bumped": true,295 "bumped_at": "2024-12-27T18:27:54.377Z",296 "archetype": "regular",297 "unseen": false,298 "pinned": false,299 "unpinned": null,300 "visible": true,301 "closed": false,302 "archived": false,303 "bookmarked": null,304 "liked": null,305 "tags_descriptions": {},306 "like_count": 0,307 "views": 149,308 "category_id": 5,309 "featured_link": null,310 "has_accepted_answer": false,311 "posters": [312 {313 "extras": null,314 "description": "Original Poster",315 "user": {316 "id": 81646,317 "username": "bruhnugget-nice",318 "name": "bruhnugget",319 "avatar_template": "/user_avatar/discuss.pytorch.org/bruhnugget-nice/{size}/74667_2.png",320 "trust_level": 1321 }322 },323 {324 "extras": "latest",325 "description": "Most Recent Poster",326 "user": {327 "id": 3534,328 "username": "ptrblck",329 "name": "",330 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",331 "admin": true,332 "moderator": true,333 "trust_level": 2334 }335 }336 ]337 },338 {339 "fancy_title": "Training Time is Increasing per epoch, Can somebody help me?",340 "id": 214900,341 "title": "Training Time is Increasing per epoch, Can somebody help me?",342 "slug": "training-time-is-increasing-per-epoch-can-somebody-help-me",343 "posts_count": 6,344 "reply_count": 4,345 "highest_post_number": 6,346 "image_url": null,347 "created_at": "2025-01-02T17:21:10.454Z",348 "last_posted_at": "2025-01-17T18:10:59.673Z",349 "bumped": true,350 "bumped_at": "2025-01-17T18:10:59.673Z",351 "archetype": "regular",352 "unseen": false,353 "pinned": false,354 "unpinned": null,355 "visible": true,356 "closed": false,357 "archived": false,358 "bookmarked": null,359 "liked": null,360 "tags_descriptions": {},361 "like_count": 3,362 "views": 183,363 "category_id": 5,364 "featured_link": null,365 "has_accepted_answer": true,366 "posters": [367 {368 "extras": "latest",369 "description": "Original Poster, Most Recent Poster",370 "user": {371 "id": 81840,372 "username": "iran_boy",373 "name": "iran boy",374 "avatar_template": "/user_avatar/discuss.pytorch.org/iran_boy/{size}/74864_2.png",375 "trust_level": 0376 }377 },378 {379 "extras": null,380 "description": "Frequent Poster, Accepted Answer",381 "user": {382 "id": 3534,383 "username": "ptrblck",384 "name": "",385 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",386 "admin": true,387 "moderator": true,388 "trust_level": 2389 }390 }391 ]392 },393 {394 "fancy_title": "Batch size at inference is influencing accuracy",395 "id": 218096,396 "title": "Batch size at inference is influencing accuracy",397 "slug": "batch-size-at-inference-is-influencing-accuracy",398 "posts_count": 1,399 "reply_count": 0,400 "highest_post_number": 1,401 "image_url": null,402 "created_at": "2025-03-20T21:02:58.000Z",403 "last_posted_at": "2025-03-20T21:02:58.052Z",404 "bumped": true,405 "bumped_at": "2025-03-20T21:02:58.052Z",406 "archetype": "regular",407 "unseen": false,408 "pinned": false,409 "unpinned": null,410 "visible": true,411 "closed": false,412 "archived": false,413 "bookmarked": null,414 "liked": null,415 "tags_descriptions": {},416 "like_count": 0,417 "views": 45,418 "category_id": 5,419 "featured_link": null,420 "has_accepted_answer": false,421 "posters": [422 {423 "extras": "latest single",424 "description": "Original Poster, Most Recent Poster",425 "user": {426 "id": 83393,427 "username": "danbull-scanabull",428 "name": "Danbull Scanabull",429 "avatar_template": "/user_avatar/discuss.pytorch.org/danbull-scanabull/{size}/76268_2.png",430 "trust_level": 1431 }432 }433 ]434 }435 ],436 "tags_descriptions": {},437 "fancy_title": "How to unpack data_loader in small size",438 "id": 120838,439 "title": "How to unpack data_loader in small size",440 "posts_count": 3,441 "created_at": "2021-05-10T14:03:55.220Z",442 "views": 370,443 "reply_count": 0,444 "like_count": 0,445 "last_posted_at": "2021-05-10T16:21:58.294Z",446 "visible": true,447 "closed": false,448 "archived": false,449 "has_summary": false,450 "archetype": "regular",451 "slug": "how-to-unpack-data-loader-in-small-size",452 "category_id": 5,453 "word_count": 130,454 "deleted_at": null,455 "user_id": 43539,456 "featured_link": null,457 "pinned_globally": false,458 "pinned_at": null,459 "pinned_until": null,460 "image_url": null,461 "slow_mode_seconds": 0,462 "draft": null,463 "draft_key": "topic_120838",464 "draft_sequence": null,465 "unpinned": null,466 "pinned": false,467 "current_post_number": 1,468 "highest_post_number": 3,469 "deleted_by": null,470 "actions_summary": [471 {472 "id": 4,473 "count": 0,474 "hidden": false,475 "can_act": false476 },477 {478 "id": 8,479 "count": 0,480 "hidden": false,481 "can_act": false482 },483 {484 "id": 10,485 "count": 0,486 "hidden": false,487 "can_act": false488 },489 {490 "id": 7,491 "count": 0,492 "hidden": false,493 "can_act": false494 }495 ],496 "chunk_size": 20,497 "bookmarked": false,498 "topic_timer": null,499 "message_bus_last_id": 0,500 "participant_count": 3,501 "show_read_indicator": false,502 "thumbnails": null,503 "slow_mode_enabled_until": null,504 "can_vote": false,505 "vote_count": 0,506 "user_voted": false,507 "discourse_zendesk_plugin_zendesk_id": null,508 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",509 "details": {510 "can_edit": false,511 "notification_level": 1,512 "participants": [513 {514 "id": 36965,515 "username": "ariG23498",516 "name": "Aritra Roy Gosthipaty",517 "avatar_template": "/user_avatar/discuss.pytorch.org/arig23498/{size}/69466_2.png",518 "post_count": 1,519 "primary_group_name": null,520 "flair_name": null,521 "flair_url": null,522 "flair_color": null,523 "flair_bg_color": null,524 "flair_group_id": null,525 "trust_level": 2526 },527 {528 "id": 37796,529 "username": "ejguan",530 "name": "Erjia",531 "avatar_template": "/letter_avatar_proxy/v4/letter/e/5f8ce5/{size}.png",532 "post_count": 1,533 "primary_group_name": null,534 "flair_name": null,535 "flair_url": null,536 "flair_color": null,537 "flair_bg_color": null,538 "flair_group_id": null,539 "trust_level": 2540 },541 {542 "id": 43539,543 "username": "Ajinkya.Bankar",544 "name": "Ajinkya Bankar",545 "avatar_template": "/letter_avatar_proxy/v4/letter/a/bc79bd/{size}.png",546 "post_count": 1,547 "primary_group_name": null,548 "flair_name": null,549 "flair_url": null,550 "flair_color": null,551 "flair_bg_color": null,552 "flair_group_id": null,553 "trust_level": 1554 }555 ],556 "created_by": {557 "id": 43539,558 "username": "Ajinkya.Bankar",559 "name": "Ajinkya Bankar",560 "avatar_template": "/letter_avatar_proxy/v4/letter/a/bc79bd/{size}.png"561 },562 "last_poster": {563 "id": 37796,564 "username": "ejguan",565 "name": "Erjia",566 "avatar_template": "/letter_avatar_proxy/v4/letter/e/5f8ce5/{size}.png"567 }568 },569 "bookmarks": []570 },571 {572 "post_stream": {573 "posts": [574 {575 "id": 282812,576 "name": "Omid Erfanmanesh",577 "username": "omiderfanmanesh",578 "avatar_template": "/user_avatar/discuss.pytorch.org/omiderfanmanesh/{size}/37960_2.png",579 "created_at": "2021-05-10T18:44:46.776Z",580 "cooked": "<p>I use my custom dataset class to convert audio files to mel- Spectrogram images. the shape will be padded to (128,1024). I have 10 classes. after a while in the first epoch, my network will be crashed due to this error:</p>\n<pre><code class=\"lang-auto\">Current run is terminating due to exception: Expected hidden size (1, 7, 32), got [1, 16, 32]\nEngine run is terminating due to exception: Expected hidden size (1, 7, 32), got [1, 16, 32]\nTraceback (most recent call last):\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3418, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-2-b8f3a45f8e35>\", line 1, in <module>\n runfile('/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/tools/train_net.py', wdir='/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/tools')\n File \"/home/omid/OMID/program/pycharm-professional-2020.2.4/pycharm-2020.2.4/plugins/python/helpers/pydev/_pydev_bundle/pydev_umd.py\", line 197, in runfile\n pydev_imports.execfile(filename, global_vars, local_vars) # execute the script\n File \"/home/omid/OMID/program/pycharm-professional-2020.2.4/pycharm-2020.2.4/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py\", line 18, in execfile\n exec(compile(contents+\"\\n\", file, 'exec'), glob, loc)\n File \"/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/tools/train_net.py\", line 60, in <module>\n main()\n File \"/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/tools/train_net.py\", line 56, in main\n train(cfg)\n File \"/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/tools/train_net.py\", line 35, in train\n do_train(\n File \"/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/engine/trainer.py\", line 79, in do_train\n trainer.run(train_loader, max_epochs=epochs)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/engine.py\", line 702, in run\n return self._internal_run()\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/engine.py\", line 775, in _internal_run\n self._handle_exception(e)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/engine.py\", line 469, in _handle_exception\n raise e\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/engine.py\", line 745, in _internal_run\n time_taken = self._run_once_on_dataset()\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/engine.py\", line 850, in _run_once_on_dataset\n self._handle_exception(e)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/engine.py\", line 469, in _handle_exception\n raise e\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/engine.py\", line 833, in _run_once_on_dataset\n self.state.output = self._process_function(self, self.state.batch)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/ignite/engine/__init__.py\", line 103, in _update\n y_pred = model(x)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n result = self.forward(*input, **kwargs)\n File \"/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/modeling/model.py\", line 113, in forward\n x, h1 = self.gru1(x, h0)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n result = self.forward(*input, **kwargs)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/rnn.py\", line 819, in forward\n self.check_forward_args(input, hx, batch_sizes)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/rnn.py\", line 229, in check_forward_args\n self.check_hidden_size(hidden, expected_hidden_size)\n File \"/home/omid/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/rnn.py\", line 223, in check_hidden_size\n raise RuntimeError(msg.format(expected_hidden_size, list(hx.size())))\nRuntimeError: Expected hidden size (1, 7, 32), got [1, 16, 32]\n</code></pre>\n<p>my network is :</p>\n<pre><code class=\"lang-auto\">import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nprint('cuda', torch.cuda.is_available())\n\n\nclass MusicClassification(nn.Module):\n def __init__(self, cfg):\n super(MusicClassification, self).__init__()\n device = cfg.MODEL.DEVICE\n num_class = cfg.MODEL.NUM_CLASSES\n\n self.np_layers = 4\n self.np_filters = [64, 128, 128, 128]\n self.kernel_size = (3, 3)\n\n self.pool_size = [(2, 2), (4, 2)]\n\n self.channel_axis = 1\n self.frequency_axis = 2\n self.time_axis = 3\n\n # self.h0 = torch.zeros((1, 16, 32)).to(device)\n\n self.bn0 = nn.BatchNorm2d(num_features=self.channel_axis)\n self.bn1 = nn.BatchNorm2d(num_features=self.np_filters[0])\n self.bn2 = nn.BatchNorm2d(num_features=self.np_filters[1])\n self.bn3 = nn.BatchNorm2d(num_features=self.np_filters[2])\n self.bn4 = nn.BatchNorm2d(num_features=self.np_filters[3])\n\n self.conv1 = nn.Conv2d(1, self.np_filters[0], kernel_size=self.kernel_size)\n self.conv2 = nn.Conv2d(self.np_filters[0], self.np_filters[1], kernel_size=self.kernel_size)\n self.conv3 = nn.Conv2d(self.np_filters[1], self.np_filters[2], kernel_size=self.kernel_size)\n self.conv4 = nn.Conv2d(self.np_filters[2], self.np_filters[3], kernel_size=self.kernel_size)\n\n self.max_pool_2_2 = nn.MaxPool2d(self.pool_size[0])\n self.max_pool_4_2 = nn.MaxPool2d(self.pool_size[1])\n\n self.drop_01 = nn.Dropout(0.1)\n self.drop_03 = nn.Dropout(0.3)\n\n self.gru1 = nn.GRU(input_size=128, hidden_size=32, batch_first=True)\n self.gru2 = nn.GRU(input_size=32, hidden_size=32, batch_first=True)\n\n self.activation = nn.ELU()\n\n self.dense = nn.Linear(32, num_class)\n self.softmax = nn.LogSoftmax(dim=1)\n\n def forward(self, x):\n # x [16, 1, 128,938]\n x = self.bn0(x)\n # x [16, 1, 128,938]\n x = F.pad(x, (0, 0, 2, 1))\n # x [16, 1, 131,938]\n x = self.conv1(x)\n # x [16, 64, 129,936]\n x = self.activation(x)\n # x [16, 64, 129,936]\n x = self.bn1(x)\n # x [16, 64, 129,936]\n x = self.max_pool_2_2(x)\n # x [16, 64, 64,468]\n x = self.drop_01(x)\n # x [16, 64, 64,468]\n x = F.pad(x, (0, 0, 2, 1))\n # x [16, 64, 67,468]\n x = self.conv2(x)\n # x [16, 128, 65,466]\n x = self.activation(x)\n # x [16, 128, 65,466]\n x = self.bn2(x)\n # x [16, 128, 65,455]\n x = self.max_pool_4_2(x)\n # x [16, 128, 16,233]\n x = self.drop_01(x)\n # x [16, 128, 16,233]\n x = F.pad(x, (0, 0, 2, 1))\n # x [16, 128, 19,233]\n x = self.conv3(x)\n # x [16, 128, 17,231]\n x = self.activation(x)\n # x [16, 128, 17,231]\n x = self.bn3(x)\n # x [16, 128, 17,231]\n x = self.max_pool_4_2(x)\n # x [16, 128, 4,115]\n x = self.drop_01(x)\n # x [16, 128, 4,115]\n x = F.pad(x, (0, 0, 2, 1))\n # x [16, 128, 7,115]\n x = self.conv4(x)\n # x [16, 128, 5,113]\n x = self.activation(x)\n # x [16, 128, 5,113]\n x = self.bn4(x)\n # x [16, 128, 5,113]\n x = self.max_pool_4_2(x)\n # x [16, 128, 1,56]\n x = self.drop_01(x)\n # x [16, 128, 1,56]\n\n x = x.permute(0, 3, 1, 2)\n # x [16, 56, 128,1]\n resize_shape = list(x.shape)[2] * list(x.shape)[3]\n # x [16, 128, 56,1], reshape size is 128\n x = torch.reshape(x, (list(x.shape)[0], list(x.shape)[1], resize_shape))\n # x [16, 56, 128]\n device = torch.device(\"cuda\" if torch.cuda.is_available()\n else \"cpu\")\n h0 = torch.zeros((1, 16, 32)).to(device)\n x, h1 = self.gru1(x, h0)\n # x [16, 56, 32]\n x, _ = self.gru2(x, h1)\n # x [16, 56, 32]\n x = x[:, -1, :]\n x = self.dense(x)\n # x [16,10]\n x = self.softmax(x)\n # x [16, 10]\n # x = torch.argmax(x, 1)\n return x\n\n\n\n</code></pre>\n<p>my dataset is :</p>\n<pre><code class=\"lang-auto\">\nfrom __future__ import print_function, division\n\nimport os\n\nimport librosa\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torchaudio\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder\nfrom torch.utils.data import Dataset\nfrom utils.util import pad_along_axis\n\nprint(torch.__version__)\nprint(torchaudio.__version__)\n\n# Ignore warnings\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nplt.ion()\n\nimport pathlib\n\nprint(pathlib.Path().absolute())\n\n\nclass GTZANDataset(Dataset):\n def __init__(self,\n genre_folder='/home/omid/OMID/projects/python/mldl/NeuralMusicClassification/data/dataset/genres_original',\n one_hot_encoding=False,\n sr=16000, n_mels=128,\n n_fft=2048, hop_length=512,\n transform=None):\n\n self.genre_folder = genre_folder\n self.one_hot_encoding = one_hot_encoding\n self.audio_address, self.labels = self.extract_address()\n self.sr = sr\n self.n_mels = n_mels\n self.n_fft = n_fft\n self.transform = transform\n self.le = LabelEncoder()\n self.hop_length = hop_length\n\n def __len__(self):\n return len(self.labels)\n\n def __getitem__(self, index):\n address = self.audio_address[index]\n y, sr = librosa.load(address, sr=self.sr)\n S = librosa.feature.melspectrogram(y, sr=sr,\n n_mels=self.n_mels,\n n_fft=self.n_fft,\n hop_length=self.hop_length)\n\n sample = librosa.amplitude_to_db(S, ref=1.0)\n sample = np.expand_dims(sample, axis=0)\n sample = pad_along_axis(sample, 1024, axis=2)\n # print(sample.shape)\n sample = torch.from_numpy(sample)\n\n label = self.labels[index]\n # label = torch.from_numpy(label)\n print(sample.shape,label)\n if self.transform:\n sample = self.transform(sample)\n return sample, label\n\n def extract_address(self):\n label_map = {\n 'blues': 0,\n 'classical': 1,\n 'country': 2,\n 'disco': 3,\n 'hiphop': 4,\n 'jazz': 5,\n 'metal': 6,\n 'pop': 7,\n 'reggae': 8,\n 'rock': 9\n }\n labels = []\n address = []\n # extract all genres' folders\n genres = [path for path in os.listdir(self.genre_folder)]\n for genre in genres:\n # e.g. ./data/generes_original/country\n genre_path = os.path.join(self.genre_folder, genre)\n # extract all sounds from genre_path\n songs = os.listdir(genre_path)\n\n for song in songs:\n song_path = os.path.join(genre_path, song)\n genre_id = label_map[genre]\n # one_hot_targets = torch.eye(10)[genre_id]\n labels.append(genre_id)\n address.append(song_path)\n\n samples = np.array(address)\n labels = np.array(labels)\n # convert labels to one-hot encoding\n # if self.one_hot_encoding:\n # labels = OneHotEncoder(sparse=False).fit_transform(labels)\n # else:\n # labels = LabelEncoder().fit_transform(labels)\n\n return samples, labels\n\n\n</code></pre>\n<p>and trainer :</p>\n<pre><code class=\"lang-auto\">\n# encoding: utf-8\n\n\nimport logging\n\nfrom ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\nfrom ignite.handlers import ModelCheckpoint, Timer\nfrom ignite.metrics import Accuracy, Loss, RunningAverage\n\n\ndef do_train(\n cfg,\n model,\n train_loader,\n val_loader,\n optimizer,\n scheduler,\n loss_fn,\n):\n log_period = cfg.SOLVER.LOG_PERIOD\n checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD\n output_dir = cfg.OUTPUT_DIR\n device = cfg.MODEL.DEVICE\n epochs = cfg.SOLVER.MAX_EPOCHS\n\n model = model.to(device)\n\n logger = logging.getLogger(\"template_model.train\")\n logger.info(\"Start training\")\n trainer = create_supervised_trainer(model, optimizer, loss_fn, device=device)\n evaluator = create_supervised_evaluator(model, metrics={'accuracy': Accuracy(),\n 'ce_loss': Loss(loss_fn)}, device=device)\n checkpointer = ModelCheckpoint(output_dir, 'mnist', None, n_saved=10, require_empty=False)\n timer = Timer(average=True)\n\n trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'model': model.state_dict(),\n 'optimizer': optimizer.state_dict()})\n timer.attach(trainer, start=Events.EPOCH_STARTED, resume=Events.ITERATION_STARTED,\n pause=Events.ITERATION_COMPLETED, step=Events.ITERATION_COMPLETED)\n\n RunningAverage(output_transform=lambda x: x).attach(trainer, 'avg_loss')\n\n @trainer.on(Events.ITERATION_COMPLETED)\n def log_training_loss(engine):\n iter = (engine.state.iteration - 1) % len(train_loader) + 1\n\n if iter % log_period == 0:\n logger.info(\"Epoch[{}] Iteration[{}/{}] Loss: {:.2f}\"\n .format(engine.state.epoch, iter, len(train_loader), engine.state.metrics['avg_loss']))\n\n @trainer.on(Events.EPOCH_COMPLETED)\n def log_training_results(engine):\n evaluator.run(train_loader)\n metrics = evaluator.state.metrics\n avg_accuracy = metrics['accuracy']\n avg_loss = metrics['ce_loss']\n logger.info(\"Training Results - Epoch: {} Avg accuracy: {:.3f} Avg Loss: {:.3f}\"\n .format(engine.state.epoch, avg_accuracy, avg_loss))\n\n if val_loader is not None:\n @trainer.on(Events.EPOCH_COMPLETED)\n def log_validation_results(engine):\n evaluator.run(val_loader)\n metrics = evaluator.state.metrics\n avg_accuracy = metrics['accuracy']\n avg_loss = metrics['ce_loss']\n logger.info(\"Validation Results - Epoch: {} Avg accuracy: {:.3f} Avg Loss: {:.3f}\"\n .format(engine.state.epoch, avg_accuracy, avg_loss)\n )\n\n # adding handlers using `trainer.on` decorator API\n @trainer.on(Events.EPOCH_COMPLETED)\n def print_times(engine):\n logger.info('Epoch {} done. Time per batch: {:.3f}[s] Speed: {:.1f}[samples/s]'\n .format(engine.state.epoch, timer.value() * timer.step_count,\n train_loader.batch_size / timer.value()))\n timer.reset()\n\n trainer.run(train_loader, max_epochs=epochs)\n\n\n</code></pre>",581 "post_number": 1,582 "post_type": 1,583 "posts_count": 1,584 "updated_at": "2021-05-10T18:49:44.587Z",585 "reply_count": 0,586 "reply_to_post_number": null,587 "quote_count": 0,588 "incoming_link_count": 36,589 "reads": 6,590 "readers_count": 5,591 "score": 181.2,592 "yours": false,593 "topic_id": 120862,594 "topic_slug": "training-will-be-stop-after-a-while-in-gru-layer",595 "display_username": "Omid Erfanmanesh",596 "primary_group_name": null,597 "flair_name": null,598 "flair_url": null,599 "flair_bg_color": null,600 "flair_color": null,601 "flair_group_id": null,602 "badges_granted": [],603 "version": 1,604 "can_edit": false,605 "can_delete": false,606 "can_recover": false,607 "can_see_hidden_post": false,608 "can_wiki": false,609 "read": true,610 "user_title": null,611 "bookmarked": false,612 "actions_summary": [],613 "moderator": false,614 "admin": false,615 "staff": false,616 "user_id": 45082,617 "hidden": false,618 "trust_level": 1,619 "deleted_at": null,620 "user_deleted": false,621 "edit_reason": null,622 "can_view_edit_history": true,623 "wiki": false,624 "post_url": "/t/training-will-be-stop-after-a-while-in-gru-layer/120862/1",625 "can_accept_answer": false,626 "can_unaccept_answer": false,627 "accepted_answer": false,628 "topic_accepted_answer": null,629 "can_vote": false630 }631 ],632 "stream": [633 282812634 ]635 },636 "timeline_lookup": [637 [638 1,639 1629640 ]641 ],642 "suggested_topics": [643 {644 "fancy_title": "[PR] Torchaudio incompatible with python flag -OO due to __doc__ being None",645 "id": 216897,646 "title": "[PR] Torchaudio incompatible with python flag -OO due to __doc__ being None",647 "slug": "pr-torchaudio-incompatible-with-python-flag-oo-due-to-doc-being-none",648 "posts_count": 3,649 "reply_count": 1,650 "highest_post_number": 3,651 "image_url": null,652 "created_at": "2025-02-19T14:32:46.063Z",653 "last_posted_at": "2025-02-19T15:16:06.680Z",654 "bumped": true,655 "bumped_at": "2025-02-19T15:16:06.680Z",656 "archetype": "regular",657 "unseen": false,658 "pinned": false,659 "unpinned": null,660 "visible": true,661 "closed": false,662 "archived": false,663 "bookmarked": null,664 "liked": null,665 "tags_descriptions": {},666 "like_count": 0,667 "views": 100,668 "category_id": 9,669 "featured_link": null,670 "has_accepted_answer": false,671 "posters": [672 {673 "extras": "latest",674 "description": "Original Poster, Most Recent Poster",675 "user": {676 "id": 82796,677 "username": "FremyCompany",678 "name": "François REMY",679 "avatar_template": "/user_avatar/discuss.pytorch.org/fremycompany/{size}/75760_2.png",680 "trust_level": 0681 }682 },683 {684 "extras": null,685 "description": "Frequent Poster",686 "user": {687 "id": 3534,688 "username": "ptrblck",689 "name": "",690 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",691 "admin": true,692 "moderator": true,693 "trust_level": 2694 }695 }696 ]697 },698 {699 "fancy_title": "Can’t run forward pass of WaveRNN model due to unsuccessful GPU RAM allocation",700 "id": 219358,701 "title": "Can't run forward pass of WaveRNN model due to unsuccessful GPU RAM allocation",702 "slug": "cant-run-forward-pass-of-wavernn-model-due-to-unsuccessful-gpu-ram-allocation",703 "posts_count": 4,704 "reply_count": 2,705 "highest_post_number": 4,706 "image_url": null,707 "created_at": "2025-04-23T03:28:01.943Z",708 "last_posted_at": "2025-05-24T22:16:30.465Z",709 "bumped": true,710 "bumped_at": "2025-05-24T22:16:30.465Z",711 "archetype": "regular",712 "unseen": false,713 "pinned": false,714 "unpinned": null,715 "visible": true,716 "closed": false,717 "archived": false,718 "bookmarked": null,719 "liked": null,720 "tags_descriptions": {},721 "like_count": 0,722 "views": 112,723 "category_id": 9,724 "featured_link": null,725 "has_accepted_answer": false,726 "posters": [727 {728 "extras": "latest",729 "description": "Original Poster, Most Recent Poster",730 "user": {731 "id": 83956,732 "username": "j-silv",733 "name": "Justin",734 "avatar_template": "/letter_avatar_proxy/v4/letter/j/e36b37/{size}.png",735 "trust_level": 1736 }737 },738 {739 "extras": null,740 "description": "Frequent Poster",741 "user": {742 "id": 9081,743 "username": "JuanFMontesinos",744 "name": "Juan Montesinos",745 "avatar_template": "/user_avatar/discuss.pytorch.org/juanfmontesinos/{size}/76115_2.png",746 "trust_level": 2747 }748 }749 ]750 },751 {752 "fancy_title": "Torchaudio.functional.speed very slow when called repeatedly using randomly generated factors",753 "id": 212781,754 "title": "Torchaudio.functional.speed very slow when called repeatedly using randomly generated factors",755 "slug": "torchaudio-functional-speed-very-slow-when-called-repeatedly-using-randomly-generated-factors",756 "posts_count": 1,757 "reply_count": 0,758 "highest_post_number": 1,759 "image_url": null,760 "created_at": "2024-11-11T01:35:41.065Z",761 "last_posted_at": "2024-11-11T01:35:41.142Z",762 "bumped": true,763 "bumped_at": "2024-11-11T01:35:41.142Z",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": 62,776 "category_id": 9,777 "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": 77838,785 "username": "MikeK",786 "name": "MikeK",787 "avatar_template": "/letter_avatar_proxy/v4/letter/m/258eb7/{size}.png",788 "trust_level": 1789 }790 }791 ]792 },793 {794 "fancy_title": "How to use filtfilt() function?",795 "id": 216443,796 "title": "How to use filtfilt() function?",797 "slug": "how-to-use-filtfilt-function",798 "posts_count": 2,799 "reply_count": 0,800 "highest_post_number": 2,801 "image_url": null,802 "created_at": "2025-02-10T03:01:31.509Z",803 "last_posted_at": "2025-02-10T08:55:11.729Z",804 "bumped": true,805 "bumped_at": "2025-02-10T08:55:23.471Z",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": 115,818 "category_id": 9,819 "featured_link": null,820 "has_accepted_answer": false,821 "posters": [822 {823 "extras": "latest single",824 "description": "Original Poster, Most Recent Poster",825 "user": {826 "id": 73868,827 "username": "elinliu0823",828 "name": "轶霖 柳",829 "avatar_template": "/user_avatar/discuss.pytorch.org/elinliu0823/{size}/68215_2.png",830 "trust_level": 1831 }832 }833 ]834 },835 {836 "fancy_title": "CTC loss inputs and input lengths",837 "id": 219837,838 "title": "CTC loss inputs and input lengths",839 "slug": "ctc-loss-inputs-and-input-lengths",840 "posts_count": 1,841 "reply_count": 0,842 "highest_post_number": 1,843 "image_url": null,844 "created_at": "2025-05-07T13:49:43.750Z",845 "last_posted_at": "2025-05-07T13:49:43.799Z",846 "bumped": true,847 "bumped_at": "2025-05-07T13:49:43.799Z",848 "archetype": "regular",849 "unseen": false,850 "pinned": false,851 "unpinned": null,852 "visible": true,853 "closed": false,854 "archived": false,855 "bookmarked": null,856 "liked": null,857 "tags_descriptions": {},858 "like_count": 0,859 "views": 67,860 "category_id": 9,861 "featured_link": null,862 "has_accepted_answer": false,863 "posters": [864 {865 "extras": "latest single",866 "description": "Original Poster, Most Recent Poster",867 "user": {868 "id": 83384,869 "username": "alicemabille",870 "name": "Alice Mabille",871 "avatar_template": "/user_avatar/discuss.pytorch.org/alicemabille/{size}/76264_2.png",872 "trust_level": 1873 }874 }875 ]876 }877 ],878 "tags_descriptions": {},879 "fancy_title": "Training will be stop after a while in GRU layer",880 "id": 120862,881 "title": "Training will be stop after a while in GRU layer",882 "posts_count": 1,883 "created_at": "2021-05-10T18:44:46.612Z",884 "views": 472,885 "reply_count": 0,886 "like_count": 0,887 "last_posted_at": "2021-05-10T18:44:46.776Z",888 "visible": true,889 "closed": false,890 "archived": false,891 "has_summary": false,892 "archetype": "regular",893 "slug": "training-will-be-stop-after-a-while-in-gru-layer",894 "category_id": 9,895 "word_count": 1819,896 "deleted_at": null,897 "user_id": 45082,898 "featured_link": null,899 "pinned_globally": false,900 "pinned_at": null,901 "pinned_until": null,902 "image_url": null,903 "slow_mode_seconds": 0,904 "draft": null,905 "draft_key": "topic_120862",906 "draft_sequence": null,907 "unpinned": null,908 "pinned": false,909 "current_post_number": 1,910 "highest_post_number": 1,911 "deleted_by": null,912 "actions_summary": [913 {914 "id": 4,915 "count": 0,916 "hidden": false,917 "can_act": false918 },919 {920 "id": 8,921 "count": 0,922 "hidden": false,923 "can_act": false924 },925 {926 "id": 10,927 "count": 0,928 "hidden": false,929 "can_act": false930 },931 {932 "id": 7,933 "count": 0,934 "hidden": false,935 "can_act": false936 }937 ],938 "chunk_size": 20,939 "bookmarked": false,940 "topic_timer": null,941 "message_bus_last_id": 0,942 "participant_count": 1,943 "show_read_indicator": false,944 "thumbnails": null,945 "slow_mode_enabled_until": null,946 "can_vote": false,947 "vote_count": 0,948 "user_voted": false,949 "discourse_zendesk_plugin_zendesk_id": null,950 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",951 "details": {952 "can_edit": false,953 "notification_level": 1,954 "participants": [955 {956 "id": 45082,957 "username": "omiderfanmanesh",958 "name": "Omid Erfanmanesh",959 "avatar_template": "/user_avatar/discuss.pytorch.org/omiderfanmanesh/{size}/37960_2.png",960 "post_count": 1,961 "primary_group_name": null,962 "flair_name": null,963 "flair_url": null,964 "flair_color": null,965 "flair_bg_color": null,966 "flair_group_id": null,967 "trust_level": 1968 }969 ],970 "created_by": {971 "id": 45082,972 "username": "omiderfanmanesh",973 "name": "Omid Erfanmanesh",974 "avatar_template": "/user_avatar/discuss.pytorch.org/omiderfanmanesh/{size}/37960_2.png"975 },976 "last_poster": {977 "id": 45082,978 "username": "omiderfanmanesh",979 "name": "Omid Erfanmanesh",980 "avatar_template": "/user_avatar/discuss.pytorch.org/omiderfanmanesh/{size}/37960_2.png"981 }982 },983 "bookmarks": []984 },985 {986 "post_stream": {987 "posts": [988 {989 "id": 282800,990 "name": "Tornike",991 "username": "Tornike",992 "avatar_template": "/user_avatar/discuss.pytorch.org/tornike/{size}/36047_2.png",993 "created_at": "2021-05-10T17:44:27.556Z",994 "cooked": "<p>Hello, i want to try some medical problems with PyTorch. Would be very helpful if you shared your experience with me. How should i start? What articles or video tutorials would you recommend? What are the steps you go through while working on it ?</p>\n<p>Thanks in Advance</p>",995 "post_number": 1,996 "post_type": 1,997 "posts_count": 2,998 "updated_at": "2021-05-10T17:44:27.556Z",999 "reply_count": 0,1000 "reply_to_post_number": null,1001 "quote_count": 0,1002 "incoming_link_count": 9,1003 "reads": 7,1004 "readers_count": 6,1005 "score": 46.4,1006 "yours": false,1007 "topic_id": 120852,1008 "topic_slug": "ai-for-medical-diagnoses",1009 "display_username": "Tornike",1010 "primary_group_name": null,1011 "flair_name": null,1012 "flair_url": null,1013 "flair_bg_color": null,1014 "flair_color": null,1015 "flair_group_id": null,1016 "badges_granted": [],1017 "version": 1,1018 "can_edit": false,1019 "can_delete": false,1020 "can_recover": false,1021 "can_see_hidden_post": false,1022 "can_wiki": false,1023 "read": true,1024 "user_title": null,1025 "bookmarked": false,1026 "actions_summary": [],1027 "moderator": false,1028 "admin": false,1029 "staff": false,1030 "user_id": 43317,1031 "hidden": false,1032 "trust_level": 1,1033 "deleted_at": null,1034 "user_deleted": false,1035 "edit_reason": null,1036 "can_view_edit_history": true,1037 "wiki": false,1038 "post_url": "/t/ai-for-medical-diagnoses/120852/1",1039 "can_accept_answer": false,1040 "can_unaccept_answer": false,1041 "accepted_answer": false,1042 "topic_accepted_answer": null,1043 "can_vote": false1044 },1045 {1046 "id": 282805,1047 "name": "Akshay Goel",1048 "username": "aksg87",1049 "avatar_template": "/user_avatar/discuss.pytorch.org/aksg87/{size}/15602_2.png",1050 "created_at": "2021-05-10T17:55:36.831Z",1051 "cooked": "<p>There are lots of places to start <a class=\"mention\" href=\"/u/tornike\">@Tornike</a></p>\n<p>If you are interested in radiology-focused problems.</p>\n<p>You could start by looking at some of the Kaggle competitions which have been on Pneumonia, Pneumothorax, Intracranial hemorrhage. Those discussion forums are full of more resources that you will be linked to!</p>",1052 "post_number": 2,1053 "post_type": 1,1054 "posts_count": 2,1055 "updated_at": "2021-05-10T17:55:36.831Z",1056 "reply_count": 0,1057 "reply_to_post_number": null,1058 "quote_count": 0,1059 "incoming_link_count": 0,1060 "reads": 7,1061 "readers_count": 6,1062 "score": 16.4,1063 "yours": false,1064 "topic_id": 120852,1065 "topic_slug": "ai-for-medical-diagnoses",1066 "display_username": "Akshay Goel",1067 "primary_group_name": null,1068 "flair_name": null,1069 "flair_url": null,1070 "flair_bg_color": null,1071 "flair_color": null,1072 "flair_group_id": null,1073 "badges_granted": [],1074 "version": 1,1075 "can_edit": false,1076 "can_delete": false,1077 "can_recover": false,1078 "can_see_hidden_post": false,1079 "can_wiki": false,1080 "read": true,1081 "user_title": null,1082 "bookmarked": false,1083 "actions_summary": [1084 {1085 "id": 2,1086 "count": 11087 }1088 ],1089 "moderator": false,1090 "admin": false,1091 "staff": false,1092 "user_id": 22194,1093 "hidden": false,1094 "trust_level": 2,1095 "deleted_at": null,1096 "user_deleted": false,1097 "edit_reason": null,1098 "can_view_edit_history": true,1099 "wiki": false,1100 "post_url": "/t/ai-for-medical-diagnoses/120852/2",1101 "can_accept_answer": false,1102 "can_unaccept_answer": false,1103 "accepted_answer": false,1104 "topic_accepted_answer": null1105 }1106 ],1107 "stream": [1108 282800,1109 2828051110 ]1111 },1112 "timeline_lookup": [1113 [1114 1,1115 16291116 ]1117 ],1118 "suggested_topics": [1119 {1120 "fancy_title": "Compile the PyTorch from source code by using Dockerfile, I got a fishy error. It seems to be related to the ld linker",1121 "id": 212866,1122 "title": "Compile the PyTorch from source code by using Dockerfile, I got a fishy error. It seems to be related to the ld linker",1123 "slug": "compile-the-pytorch-from-source-code-by-using-dockerfile-i-got-a-fishy-error-it-seems-to-be-related-to-the-ld-linker",1124 "posts_count": 5,1125 "reply_count": 3,1126 "highest_post_number": 6,1127 "image_url": null,1128 "created_at": "2024-11-12T13:21:13.716Z",1129 "last_posted_at": "2024-11-25T07:09:59.728Z",1130 "bumped": true,1131 "bumped_at": "2024-11-25T07:09:59.728Z",1132 "archetype": "regular",1133 "unseen": false,1134 "pinned": false,1135 "unpinned": null,1136 "visible": true,1137 "closed": false,1138 "archived": false,1139 "bookmarked": null,1140 "liked": null,1141 "tags_descriptions": {},1142 "like_count": 1,1143 "views": 90,1144 "category_id": 1,1145 "featured_link": null,1146 "has_accepted_answer": true,1147 "posters": [1148 {1149 "extras": "latest",1150 "description": "Original Poster, Most Recent Poster, Accepted Answer",1151 "user": {1152 "id": 80843,1153 "username": "shysuen001",1154 "name": "",1155 "avatar_template": "/user_avatar/discuss.pytorch.org/shysuen001/{size}/73944_2.png",1156 "trust_level": 11157 }1158 },1159 {1160 "extras": null,1161 "description": "Frequent Poster",1162 "user": {1163 "id": 3534,1164 "username": "ptrblck",1165 "name": "",1166 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",1167 "admin": true,1168 "moderator": true,1169 "trust_level": 21170 }1171 },1172 {1173 "extras": null,1174 "description": "Frequent Poster",1175 "user": {1176 "id": 81111,1177 "username": "sgomber",1178 "name": "Shaurya Gomber",1179 "avatar_template": "/user_avatar/discuss.pytorch.org/sgomber/{size}/74173_2.png",1180 "trust_level": 11181 }1182 }1183 ]1184 },1185 {1186 "fancy_title": "Cant pip install torch on MacOS Sonoma 14.6.1",1187 "id": 214968,1188 "title": "Cant pip install torch on MacOS Sonoma 14.6.1",1189 "slug": "cant-pip-install-torch-on-macos-sonoma-14-6-1",1190 "posts_count": 2,1191 "reply_count": 0,1192 "highest_post_number": 2,1193 "image_url": null,1194 "created_at": "2025-01-04T15:16:37.304Z",1195 "last_posted_at": "2025-01-05T21:31:30.954Z",1196 "bumped": true,1197 "bumped_at": "2025-01-05T21:31:30.954Z",1198 "archetype": "regular",1199 "unseen": false,1200 "pinned": false,