Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 189935,7 "name": "",8 "username": "saywwhaaaat",9 "avatar_template": "/letter_avatar_proxy/v4/letter/s/48db29/{size}.png",10 "created_at": "2020-05-06T23:21:57.806Z",11 "cooked": "<p>I am doing semantic segmentation task and I already train a model with a dataset that provides class label.</p>\n<p>Now, I have a new dataset that only provide category label rather than the class labels I used to train. But, to utilize the pretrained model, I have to either adapt the prediction layer channels from number of classes to number of category; or, I map the predicted class labels to category labels, then use this to compute cross entropy loss as before.</p>\n<p>For me, the second option sounds optimal, because all layers in the pretrained model can be applied.</p>\n<p>But, if I modify the predicted label, what would be the consequence to autograd?</p>\n<p>Thanks</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 4,15 "updated_at": "2020-05-06T23:22:55.193Z",16 "reply_count": 1,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 228,20 "reads": 9,21 "readers_count": 8,22 "score": 1146.8,23 "yours": false,24 "topic_id": 80087,25 "topic_slug": "modify-predicted-class-label-to-category-label-before-computing-the-loss",26 "display_username": "",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": 30104,48 "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/modify-predicted-class-label-to-category-label-before-computing-the-loss/80087/1",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": 190031,64 "name": "",65 "username": "ptrblck",66 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",67 "created_at": "2020-05-07T07:09:56.852Z",68 "cooked": "<aside class=\"quote no-group\" data-username=\"saywwhaaaat\" data-post=\"1\" data-topic=\"80087\">\n<div class=\"title\">\n<div class=\"quote-controls\"></div>\n<img loading=\"lazy\" alt=\"\" width=\"24\" height=\"24\" src=\"https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/s/48db29/48.png\" class=\"avatar\"> saywwhaaaat:</div>\n<blockquote>\n<p>But, if I modify the predicted label, what would be the consequence to autograd?</p>\n</blockquote>\n</aside>\n<p>I’m citing <a class=\"mention\" href=\"/u/kfrank\">@KFrank</a> here:</p>\n<blockquote>\n<p>The number-one rule is that your output means whatever you train it to mean…</p>\n</blockquote>\n<p>I’m not sure what kind of classes and categories you are using and how similar they are.<br>\nE.g. if you remap “husky” and “bulldog” to a “dog” class, your model might still work fine.<br>\nDepending how the remapping is done, you might need to retrain the model.</p>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 4,72 "updated_at": "2020-05-07T07:09:56.852Z",73 "reply_count": 1,74 "reply_to_post_number": null,75 "quote_count": 1,76 "incoming_link_count": 0,77 "reads": 7,78 "readers_count": 6,79 "score": 6.4,80 "yours": false,81 "topic_id": 80087,82 "topic_slug": "modify-predicted-class-label-to-category-label-before-computing-the-loss",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 "moderator": true,102 "admin": true,103 "staff": true,104 "user_id": 3534,105 "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/modify-predicted-class-label-to-category-label-before-computing-the-loss/80087/2",113 "can_accept_answer": false,114 "can_unaccept_answer": false,115 "accepted_answer": false,116 "topic_accepted_answer": null117 },118 {119 "id": 190128,120 "name": "",121 "username": "saywwhaaaat",122 "avatar_template": "/letter_avatar_proxy/v4/letter/s/48db29/{size}.png",123 "created_at": "2020-05-07T10:56:03.556Z",124 "cooked": "<p>Basically 1 category is like a collection of class labels, who belongs to this category. For example flat as a category includes road, road line, sidewalk. The pretrained model is trained on class level, but what I want to fine tune is to predict category label.</p>\n<p>I just want to know modify the prediction before computing the loss, whether this is a legal operation, because I feel like manually modification is not differentiable. Maybe I can add a additional mappling layer right after the pretrained model, that learn how to map class label to category?</p>",125 "post_number": 3,126 "post_type": 1,127 "posts_count": 4,128 "updated_at": "2020-05-07T11:01:09.148Z",129 "reply_count": 1,130 "reply_to_post_number": 2,131 "quote_count": 0,132 "incoming_link_count": 2,133 "reads": 6,134 "readers_count": 5,135 "score": 16.2,136 "yours": false,137 "topic_id": 80087,138 "topic_slug": "modify-predicted-class-label-to-category-label-before-computing-the-loss",139 "display_username": "",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": 3,148 "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 "reply_to_user": {156 "id": 3534,157 "username": "ptrblck",158 "name": "",159 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png"160 },161 "bookmarked": false,162 "actions_summary": [],163 "moderator": false,164 "admin": false,165 "staff": false,166 "user_id": 30104,167 "hidden": false,168 "trust_level": 1,169 "deleted_at": null,170 "user_deleted": false,171 "edit_reason": null,172 "can_view_edit_history": true,173 "wiki": false,174 "post_url": "/t/modify-predicted-class-label-to-category-label-before-computing-the-loss/80087/3",175 "can_accept_answer": false,176 "can_unaccept_answer": false,177 "accepted_answer": false,178 "topic_accepted_answer": null179 },180 {181 "id": 190307,182 "name": "",183 "username": "ptrblck",184 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",185 "created_at": "2020-05-07T19:44:28.273Z",186 "cooked": "<p>Since the mapping is “hard-coded” you could keep the model with the class outputs and later use the prediction to map it to the corresponding category.</p>\n<p>However, another way would be to let the model learn to combine the classes to categories.<br>\nTo do so you could add another layer, get the class predictions as its input, and output the categories.<br>\nThis might allow the model to learn that e.g. 5 “medium predictions” of flat classes are a stronger signal than a single higher prediction of a non-flat class, assuming that the target is the flat category.</p>",187 "post_number": 4,188 "post_type": 1,189 "posts_count": 4,190 "updated_at": "2020-05-07T19:44:28.273Z",191 "reply_count": 0,192 "reply_to_post_number": 3,193 "quote_count": 0,194 "incoming_link_count": 3,195 "reads": 6,196 "readers_count": 5,197 "score": 16.2,198 "yours": false,199 "topic_id": 80087,200 "topic_slug": "modify-predicted-class-label-to-category-label-before-computing-the-loss",201 "display_username": "",202 "primary_group_name": null,203 "flair_name": null,204 "flair_url": null,205 "flair_bg_color": null,206 "flair_color": null,207 "flair_group_id": null,208 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"created_at": "2020-05-06T16:58:41.165Z",683 "cooked": "<p>I want to consider 2 case:</p>\n<p>First, with reduction = sum</p>\n<p>crit = nn.MSELoss(reduction=‘sum’).to(device)<br>\n…<br>\nfor data, label in batch:<br>\noutput = model(data)<br>\nloss = crit(output, data)<br>\nloss.backward()<br>\nprint(loss.data[0])</p>\n<p>and second, with no reduction, we sum when we want output (print something, write log)</p>\n<p>crit = nn.MSELoss().to(device)</p>\n<p>…<br>\nfor data, label in batch:<br>\noutput = model(data)<br>\nloss = crit(output, data)<br>\nloss.backward()<br>\nprint(loss.data[0].sum)</p>\n<p>The first example is no sum, we sum when we need to print out. The second example is we define reduction=‘sum’. I want to know if it have impact on training process.</p>\n<p>That is for my case. But I don’t think it is importance information here: The code was suppose for computer vision problem. Output is a 2D matrix, with batch also, so output is (batch, a, b), input is (batch, channel, a, b).</p>",684 "post_number": 1,685 "post_type": 1,686 "posts_count": 3,687 "updated_at": "2020-05-06T17:22:41.212Z",688 "reply_count": 0,689 "reply_to_post_number": null,690 "quote_count": 0,691 "incoming_link_count": 1274,692 "reads": 52,693 "readers_count": 51,694 "score": 6395.4,695 "yours": false,696 "topic_id": 80038,697 "topic_slug": "is-loss-function-reduction-something-have-significant-effect-on-training-performance",698 "display_username": "Thai Thien",699 "primary_group_name": null,700 "flair_name": null,701 "flair_url": null,702 "flair_bg_color": null,703 "flair_color": null,704 "flair_group_id": null,705 "badges_granted": [],706 "version": 2,707 "can_edit": false,708 "can_delete": false,709 "can_recover": false,710 "can_see_hidden_post": false,711 "can_wiki": false,712 "read": true,713 "user_title": null,714 "bookmarked": false,715 "actions_summary": [716 {717 "id": 2,718 "count": 1719 }720 ],721 "moderator": false,722 "admin": false,723 "staff": false,724 "user_id": 1381,725 "hidden": false,726 "trust_level": 2,727 "deleted_at": null,728 "user_deleted": false,729 "edit_reason": null,730 "can_view_edit_history": true,731 "wiki": false,732 "post_url": "/t/is-loss-function-reduction-something-have-significant-effect-on-training-performance/80038/1",733 "can_accept_answer": false,734 "can_unaccept_answer": false,735 "accepted_answer": false,736 "topic_accepted_answer": null,737 "can_vote": false738 },739 {740 "id": 189860,741 "name": "Russell Izadi",742 "username": "russellizadi",743 "avatar_template": "/user_avatar/discuss.pytorch.org/russellizadi/{size}/23162_2.png",744 "created_at": "2020-05-06T18:00:53.149Z",745 "cooked": "<p>Depending on the definition of your specific loss function, the reduction may affect the training performance. One of the advantages of <code>reduction=mean</code> is that it makes the update term independent of the batch size. So if you want to use a constant learning rate, you better take the average of loss values. Again, it totally depends on the task and the derivation of the loss function.</p>",746 "post_number": 2,747 "post_type": 1,748 "posts_count": 3,749 "updated_at": "2020-05-06T18:00:53.149Z",750 "reply_count": 1,751 "reply_to_post_number": null,752 "quote_count": 0,753 "incoming_link_count": 10,754 "reads": 47,755 "readers_count": 46,756 "score": 64.4,757 "yours": false,758 "topic_id": 80038,759 "topic_slug": "is-loss-function-reduction-something-have-significant-effect-on-training-performance",760 "display_username": "Russell Izadi",761 "primary_group_name": null,762 "flair_name": null,763 "flair_url": null,764 "flair_bg_color": null,765 "flair_color": null,766 "flair_group_id": null,767 "badges_granted": [],768 "version": 1,769 "can_edit": false,770 "can_delete": false,771 "can_recover": false,772 "can_see_hidden_post": false,773 "can_wiki": false,774 "read": true,775 "user_title": null,776 "bookmarked": false,777 "actions_summary": [],778 "moderator": false,779 "admin": false,780 "staff": false,781 "user_id": 30442,782 "hidden": false,783 "trust_level": 2,784 "deleted_at": null,785 "user_deleted": false,786 "edit_reason": null,787 "can_view_edit_history": true,788 "wiki": false,789 "post_url": "/t/is-loss-function-reduction-something-have-significant-effect-on-training-performance/80038/2",790 "can_accept_answer": false,791 "can_unaccept_answer": false,792 "accepted_answer": false,793 "topic_accepted_answer": null794 },795 {796 "id": 190298,797 "name": "N P ",798 "username": "nmtp",799 "avatar_template": "/letter_avatar_proxy/v4/letter/n/71e660/{size}.png",800 "created_at": "2020-05-07T19:24:19.659Z",801 "cooked": "<p>Just curious, is there ever a time when the reduction=“sum” would be preferable? It seems like a lot more effort to keep track of batch size but I’ve seen “sum” used as well</p>",802 "post_number": 3,803 "post_type": 1,804 "posts_count": 3,805 "updated_at": "2020-05-07T19:24:19.659Z",806 "reply_count": 0,807 "reply_to_post_number": 2,808 "quote_count": 0,809 "incoming_link_count": 7,810 "reads": 45,811 "readers_count": 44,812 "score": 74.0,813 "yours": false,814 "topic_id": 80038,815 "topic_slug": "is-loss-function-reduction-something-have-significant-effect-on-training-performance",816 "display_username": "N P ",817 "primary_group_name": null,818 "flair_name": null,819 "flair_url": null,820 "flair_bg_color": null,821 "flair_color": null,822 "flair_group_id": null,823 "badges_granted": [],824 "version": 1,825 "can_edit": false,826 "can_delete": false,827 "can_recover": false,828 "can_see_hidden_post": false,829 "can_wiki": false,830 "read": true,831 "user_title": null,832 "reply_to_user": {833 "id": 30442,834 "username": "russellizadi",835 "name": "Russell 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