Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 295206,7 "name": "seer mer",8 "username": "seer_mer",9 "avatar_template": "/user_avatar/discuss.pytorch.org/seer_mer/{size}/40173_2.png",10 "created_at": "2021-07-11T15:17:57.395Z",11 "cooked": "<p>Can someone point out which <strong>reliable source</strong> does the “mean = [0.485, 0.456, 0.406]” and “std = [0.229, 0.224, 0.225]” normalization for imagenet come from?</p>\n<p>After researching and reading some articles, whenever someone asks this, it seems like all articles I read are just referring to non-academic or unreliable sources like blogs, discussions, or even “by conventions”.</p>\n<p>The reason I have this question is that in TensorFlow/Keras application implementations (for example, EfficientNet), they use [0.229, 0.224, 0.225] as <strong>variance</strong> to standardize the images. However, Pytorch official document uses the exact same value as <strong>standard deviation</strong> to standardize images.</p>\n<p>This causes difference since normalized = (data - mean) / std = (data - mean) / sqrt(variance),<br>\nwhich therefore causes difference in resulting standardized data, the pytorch version results in data ranging in approximately (-2.1, 2.2), while tensorflow version results in data ranging in approximately (-1.0, 1.1).</p>\n<p>From what I learned, standardizing data to approx. the unit length seems to make more sense, but using [0.229, 0.224, 0.225] as std seems to be a more common approach. Can anyone explain which is correct and is there any paper/academic article on it. Thanks</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 2,15 "updated_at": "2021-07-11T15:17:57.395Z",16 "reply_count": 1,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 1463,20 "reads": 21,21 "readers_count": 20,22 "score": 7294.2,23 "yours": false,24 "topic_id": 126430,25 "topic_slug": "imagenet-standard-normalization-standardization",26 "display_username": "seer mer",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": 47099,48 "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/imagenet-standard-normalization-standardization/126430/1",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": 295506,64 "name": "",65 "username": "ptrblck",66 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",67 "created_at": "2021-07-13T08:05:32.535Z",68 "cooked": "<aside class=\"quote no-group\" data-username=\"seer_mer\" data-post=\"1\" data-topic=\"126430\">\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/seer_mer/48/40173_2.png\" class=\"avatar\"> seer_mer:</div>\n<blockquote>\n<p>This causes difference since normalized = (data - mean) / std = (data - mean) / sqrt(variance),<br>\nwhich therefore causes difference in resulting standardized data, the pytorch version results in data ranging in approximately (-2.1, 2.2), while tensorflow version results in data ranging in approximately (-1.0, 1.1).</p>\n</blockquote>\n</aside>\n<p>You could check the stddev and variance of the resulting tensor and make sure it has a unit variance (=1), which is the purpose of this normalization/standardization step.<br>\nI’m not familiar with the TF implementation and don’t know what internally is used.</p>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 2,72 "updated_at": "2021-07-16T12:24:32.206Z",73 "reply_count": 0,74 "reply_to_post_number": null,75 "quote_count": 1,76 "incoming_link_count": 18,77 "reads": 18,78 "readers_count": 17,79 "score": 108.6,80 "yours": false,81 "topic_id": 126430,82 "topic_slug": "imagenet-standard-normalization-standardization",83 "display_username": "",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": "",99 "bookmarked": false,100 "actions_summary": [101 {102 "id": 2,103 "count": 1104 }105 ],106 "moderator": true,107 "admin": true,108 "staff": true,109 "user_id": 3534,110 "hidden": false,111 "trust_level": 2,112 "deleted_at": null,113 "user_deleted": false,114 "edit_reason": null,115 "can_view_edit_history": true,116 "wiki": false,117 "post_url": "/t/imagenet-standard-normalization-standardization/126430/2",118 "can_accept_answer": false,119 "can_unaccept_answer": false,120 "accepted_answer": true,121 "topic_accepted_answer": true122 }123 ],124 "stream": [125 295206,126 295506127 ]128 },129 "timeline_lookup": [130 [131 1,132 1567133 ],134 [135 2,136 1566137 ]138 ],139 "suggested_topics": [140 {141 "fancy_title": "CUDA out of memory. 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20,449 "bookmarked": false,450 "topic_timer": null,451 "message_bus_last_id": 0,452 "participant_count": 2,453 "show_read_indicator": false,454 "thumbnails": null,455 "slow_mode_enabled_until": null,456 "accepted_answer": {457 "post_number": 2,458 "username": "ptrblck",459 "name": "",460 "excerpt": "You could check the stddev and variance of the resulting tensor and make sure it has a unit variance (=1), which is the purpose of this normalization/standardization step. \nI’m not familiar with the TF implementation and don’t know what internally is used."461 },462 "can_vote": false,463 "vote_count": 0,464 "user_voted": false,465 "discourse_zendesk_plugin_zendesk_id": null,466 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",467 "details": {468 "can_edit": false,469 "notification_level": 1,470 "participants": [471 {472 "id": 3534,473 "username": "ptrblck",474 "name": "",475 "avatar_template": 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"/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png"513 }514 },515 "bookmarks": []516 },517 {518 "post_stream": {519 "posts": [520 {521 "id": 14683,522 "name": "alsharif",523 "username": "mhalsharif",524 "avatar_template": "/letter_avatar_proxy/v4/letter/m/50afbb/{size}.png",525 "created_at": "2017-08-09T23:48:59.626Z",526 "cooked": "<p>When reading through pytorch documentation/codes, one can quickly notice the habit of typing ‘torch’ in full many times instead of using a 2-letter abbreviation such as np for numpy or tf for tensorflow. Of course, you’re free to use whatever abbreviation you want or none at all, but I think it would be better if the pytorch community agreed on a standard convention that is used and seen across all codes/documents. Two possibilities come to mind are:</p>\n<ul>\n<li>import torch as pt # pt for pytorch</li>\n<li>import torch as tc # tc for torch, using the 1st and 4th letters from torch, similar to np for numpy</li>\n</ul>\n<p>Any thoughts?</p>",527 "post_number": 1,528 "post_type": 1,529 "posts_count": 4,530 "updated_at": "2017-08-09T23:48:59.626Z",531 "reply_count": 0,532 "reply_to_post_number": null,533 "quote_count": 0,534 "incoming_link_count": 7274,535 "reads": 76,536 "readers_count": 75,537 "score": 36265.2,538 "yours": false,539 "topic_id": 6007,540 "topic_slug": "convention-for-import-torch-as",541 "display_username": "alsharif",542 "primary_group_name": null,543 "flair_name": null,544 "flair_url": null,545 "flair_bg_color": null,546 "flair_color": null,547 "flair_group_id": null,548 "badges_granted": [],549 "version": 1,550 "can_edit": false,551 "can_delete": false,552 "can_recover": false,553 "can_see_hidden_post": false,554 "can_wiki": false,555 "read": true,556 "user_title": null,557 "bookmarked": false,558 "actions_summary": [559 {560 "id": 2,561 "count": 2562 }563 ],564 "moderator": false,565 "admin": false,566 "staff": false,567 "user_id": 1162,568 "hidden": false,569 "trust_level": 1,570 "deleted_at": null,571 "user_deleted": false,572 "edit_reason": null,573 "can_view_edit_history": true,574 "wiki": false,575 "post_url": "/t/convention-for-import-torch-as/6007/1",576 "can_accept_answer": false,577 "can_unaccept_answer": false,578 "accepted_answer": false,579 "topic_accepted_answer": null,580 "can_vote": false581 },582 {583 "id": 14690,584 "name": "Andy Brock",585 "username": "ajbrock",586 "avatar_template": "/user_avatar/discuss.pytorch.org/ajbrock/{size}/139_2.png",587 "created_at": "2017-08-10T03:25:17.023Z",588 "cooked": "<p>Just “torch” is fine–it’s not like you’re typing “TensorFlow,” all PyTorch code I’ve ever seen uses it (tutorials included), and switching everyone would be an inordinately huge hassle just to save two characters here and there.</p>",589 "post_number": 2,590 "post_type": 1,591 "posts_count": 4,592 "updated_at": "2017-08-10T03:25:17.023Z",593 "reply_count": 0,594 "reply_to_post_number": null,595 "quote_count": 0,596 "incoming_link_count": 35,597 "reads": 70,598 "readers_count": 69,599 "score": 219.0,600 "yours": false,601 "topic_id": 6007,602 "topic_slug": "convention-for-import-torch-as",603 "display_username": "Andy Brock",604 "primary_group_name": null,605 "flair_name": null,606 "flair_url": null,607 "flair_bg_color": null,608 "flair_color": null,609 "flair_group_id": null,610 "badges_granted": [],611 "version": 1,612 "can_edit": false,613 "can_delete": false,614 "can_recover": false,615 "can_see_hidden_post": false,616 "can_wiki": false,617 "read": true,618 "user_title": null,619 "bookmarked": false,620 "actions_summary": [621 {622 "id": 2,623 "count": 2624 }625 ],626 "moderator": false,627 "admin": false,628 "staff": false,629 "user_id": 259,630 "hidden": false,631 "trust_level": 2,632 "deleted_at": null,633 "user_deleted": false,634 "edit_reason": null,635 "can_view_edit_history": true,636 "wiki": false,637 "post_url": "/t/convention-for-import-torch-as/6007/2",638 "can_accept_answer": false,639 "can_unaccept_answer": false,640 "accepted_answer": false,641 "topic_accepted_answer": null642 },643 {644 "id": 187166,645 "name": "Wizard",646 "username": "wizardforcel",647 "avatar_template": "/letter_avatar_proxy/v4/letter/w/4bbf92/{size}.png",648 "created_at": "2020-04-28T15:24:03.802Z",649 "cooked": "<p>Why not be simpler? just</p>\n<pre><code class=\"lang-auto\">import torch as t\n</code></pre>",650 "post_number": 3,651 "post_type": 1,652 "posts_count": 4,653 "updated_at": "2020-04-28T15:24:03.802Z",654 "reply_count": 0,655 "reply_to_post_number": null,656 "quote_count": 0,657 "incoming_link_count": 25,658 "reads": 45,659 "readers_count": 44,660 "score": 134.0,661 "yours": false,662 "topic_id": 6007,663 "topic_slug": "convention-for-import-torch-as",664 "display_username": "Wizard",665 "primary_group_name": null,666 "flair_name": null,667 "flair_url": null,668 "flair_bg_color": null,669 "flair_color": null,670 "flair_group_id": null,671 "badges_granted": [],672 "version": 1,673 "can_edit": false,674 "can_delete": false,675 "can_recover": false,676 "can_see_hidden_post": false,677 "can_wiki": false,678 "read": true,679 "user_title": null,680 "bookmarked": false,681 "actions_summary": [],682 "moderator": false,683 "admin": false,684 "staff": false,685 "user_id": 30778,686 "hidden": false,687 "trust_level": 0,688 "deleted_at": null,689 "user_deleted": false,690 "edit_reason": null,691 "can_view_edit_history": true,692 "wiki": false,693 "post_url": "/t/convention-for-import-torch-as/6007/3",694 "can_accept_answer": false,695 "can_unaccept_answer": false,696 "accepted_answer": false,697 "topic_accepted_answer": null698 },699 {700 "id": 295505,701 "name": "Codingpy",702 "username": "codingpy",703 "avatar_template": "/user_avatar/discuss.pytorch.org/codingpy/{size}/39997_2.png",704 "created_at": "2021-07-13T08:05:08.768Z",705 "cooked": "<p>I like <code>import torch as th</code>, but few people do so.</p>",706 "post_number": 4,707 "post_type": 1,708 "posts_count": 4,709 "updated_at": "2021-07-13T08:05:08.768Z",710 "reply_count": 0,711 "reply_to_post_number": null,712 "quote_count": 0,713 "incoming_link_count": 64,714 "reads": 28,715 "readers_count": 27,716 "score": 355.6,717 "yours": false,718 "topic_id": 6007,719 "topic_slug": "convention-for-import-torch-as",720 "display_username": "Codingpy",721 "primary_group_name": null,722 "flair_name": null,723 "flair_url": null,724 "flair_bg_color": null,725 "flair_color": null,726 "flair_group_id": null,727 "badges_granted": [],728 "version": 1,729 "can_edit": false,730 "can_delete": false,731 "can_recover": false,732 "can_see_hidden_post": false,733 "can_wiki": false,734 "read": true,735 "user_title": null,736 "bookmarked": false,737 "actions_summary": [738 {739 "id": 2,740 "count": 2741 }742 ],743 "moderator": false,744 "admin": false,745 "staff": false,746 "user_id": 46931,747 "hidden": false,748 "trust_level": 1,749 "deleted_at": null,750 "user_deleted": false,751 "edit_reason": null,752 "can_view_edit_history": true,753 "wiki": false,754 "post_url": "/t/convention-for-import-torch-as/6007/4",755 "can_accept_answer": false,756 "can_unaccept_answer": false,757 "accepted_answer": false,758 "topic_accepted_answer": null759 }760 ],761 "stream": [762 14683,763 14690,764 187166,765 295505766 ]767 },768 "timeline_lookup": [769 [770 1,771 2999772 ],773 [774 3,775 2006776 ],777 [778 4,779 1566780 ]781 ],782 "suggested_topics": [783 {784 "fancy_title": "XPU out of memory error with Intel Arc Graphics (Meteor Lake) despite sufficient system memory and reported XPU capacity",785 "id": 219812,786 "title": "XPU out of memory error with Intel Arc Graphics (Meteor Lake) despite sufficient system memory and reported XPU capacity",787 "slug": "xpu-out-of-memory-error-with-intel-arc-graphics-meteor-lake-despite-sufficient-system-memory-and-reported-xpu-capacity",788 "posts_count": 7,789 "reply_count": 6,790 "highest_post_number": 9,791 "image_url": null,792 "created_at": "2025-05-06T14:02:04.746Z",793 "last_posted_at": "2025-08-18T02:55:58.317Z",794 "bumped": true,795 "bumped_at": "2025-08-18T02:55:58.317Z",796 "archetype": "regular",797 "unseen": false,798 "pinned": false,799 "unpinned": null,800 "visible": true,801 "closed": false,802 "archived": false,803 "bookmarked": null,804 "liked": null,805 "tags_descriptions": {},806 "like_count": 1,807 "views": 445,808 "category_id": 1,809 "featured_link": null,810 "has_accepted_answer": true,811 "posters": [812 {813 "extras": null,814 "description": "Original Poster, Accepted Answer",815 "user": {816 "id": 84174,817 "username": "libchara-dev",818 "name": "libchara-dev",819 "avatar_template": "/user_avatar/discuss.pytorch.org/libchara-dev/{size}/76930_2.png",820 "trust_level": 1821 }822 },823 {824 "extras": null,825 "description": "Frequent Poster",826 "user": {827 "id": 18088,828 "username": "KFrank",829 "name": "K. 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