Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 419396,7 "name": "",8 "username": "TsukeBurrel",9 "avatar_template": "/letter_avatar_proxy/v4/letter/t/977dab/{size}.png",10 "created_at": "2023-10-04T12:54:09.356Z",11 "cooked": "<p>Hello, I am fine-tuning pretrained SSDlite model from torchvision.<br>\nThe training somehow goes well when I feed bounding boxes coordinates in absolute coordinates to the fed image size. However, the bb coordinates “theoretically” should be normalized.<br>\nI’d like to know that In what format does the SSDlite in torchvision expect the annotation?<br>\nIn addition to it, what is the reason that the absolute coordinates make training going well while relative coordinates do not.</p>\n<p>just in case, absolute coordinates look like this (162, 280, 185, 300). relative coordinates look like this (0.52, 0.8, 0.6, 0.9).</p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 3,15 "updated_at": "2023-10-04T12:54:09.356Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 86,20 "reads": 2,21 "readers_count": 1,22 "score": 430.4,23 "yours": false,24 "topic_id": 189376,25 "topic_slug": "ssdlite-annotation-format",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": 69887,48 "hidden": false,49 "trust_level": 0,50 "deleted_at": null,51 "user_deleted": false,52 "edit_reason": null,53 "can_view_edit_history": true,54 "wiki": false,55 "post_url": "/t/ssdlite-annotation-format/189376/1",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": 419420,64 "name": "",65 "username": "ptrblck",66 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",67 "created_at": "2023-10-04T18:02:44.576Z",68 "cooked": "<p>I don’t know how exactly you are feeding the bounding box coordinates to the training, but based on the <a href=\"https://github.com/pytorch/vision/blob/af3077e3d0c3537476ccc2ed3f29a45e56ed30ee/torchvision/models/detection/ssd.py#L138-L139\">model definition</a> the output coordinates don’t seem to be normalized:</p>\n<pre><code class=\"lang-python\"> - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with\n ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.\n</code></pre>",69 "post_number": 2,70 "post_type": 1,71 "posts_count": 3,72 "updated_at": "2023-10-04T18:02:44.576Z",73 "reply_count": 1,74 "reply_to_post_number": null,75 "quote_count": 0,76 "incoming_link_count": 0,77 "reads": 2,78 "readers_count": 1,79 "score": 5.4,80 "yours": false,81 "topic_id": 189376,82 "topic_slug": "ssdlite-annotation-format",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 "link_counts": [98 {99 "url": "https://github.com/pytorch/vision/blob/af3077e3d0c3537476ccc2ed3f29a45e56ed30ee/torchvision/models/detection/ssd.py#L138-L139",100 "internal": false,101 "reflection": false,102 "clicks": 10103 }104 ],105 "read": true,106 "user_title": "",107 "bookmarked": false,108 "actions_summary": [],109 "moderator": true,110 "admin": true,111 "staff": true,112 "user_id": 3534,113 "hidden": false,114 "trust_level": 2,115 "deleted_at": null,116 "user_deleted": false,117 "edit_reason": null,118 "can_view_edit_history": true,119 "wiki": false,120 "post_url": "/t/ssdlite-annotation-format/189376/2",121 "can_accept_answer": false,122 "can_unaccept_answer": false,123 "accepted_answer": true,124 "topic_accepted_answer": true125 },126 {127 "id": 419910,128 "name": "",129 "username": "TsukeBurrel",130 "avatar_template": "/letter_avatar_proxy/v4/letter/t/977dab/{size}.png",131 "created_at": "2023-10-10T09:51:15.396Z",132 "cooked": "<p>thank you for the answer. I could confirm the reason.<br>\nby the way, the classification loss is not decreasing very well after making annotations to absolute coordinates. Is this because of loss functions in SSDlite or something else?</p>",133 "post_number": 3,134 "post_type": 1,135 "posts_count": 3,136 "updated_at": "2023-10-10T09:51:15.396Z",137 "reply_count": 0,138 "reply_to_post_number": 2,139 "quote_count": 0,140 "incoming_link_count": 0,141 "reads": 2,142 "readers_count": 1,143 "score": 0.4,144 "yours": false,145 "topic_id": 189376,146 "topic_slug": "ssdlite-annotation-format",147 "display_username": "",148 "primary_group_name": null,149 "flair_name": null,150 "flair_url": null,151 "flair_bg_color": null,152 "flair_color": null,153 "flair_group_id": null,154 "badges_granted": [],155 "version": 1,156 "can_edit": false,157 "can_delete": false,158 "can_recover": false,159 "can_see_hidden_post": false,160 "can_wiki": false,161 "read": true,162 "user_title": null,163 "reply_to_user": {164 "id": 3534,165 "username": "ptrblck",166 "name": "",167 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png"168 },169 "bookmarked": 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"slow_mode_enabled_until": null,535 "accepted_answer": {536 "post_number": 2,537 "username": "ptrblck",538 "name": "",539 "excerpt": "I don’t know how exactly you are feeding the bounding box coordinates to the training, but based on the <a href=\"https://github.com/pytorch/vision/blob/af3077e3d0c3537476ccc2ed3f29a45e56ed30ee/torchvision/models/detection/ssd.py#L138-L139\">model definition</a> the output coordinates don’t seem to be normalized: \n - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with\n ``0 <= x1 < x2 <…"540 },541 "can_vote": false,542 "vote_count": 0,543 "user_voted": false,544 "discourse_zendesk_plugin_zendesk_id": null,545 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",546 "details": {547 "can_edit": false,548 "notification_level": 1,549 "participants": [550 {551 "id": 69887,552 "username": "TsukeBurrel",553 "name": "",554 "avatar_template": 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"https://github.com/pytorch/vision/blob/af3077e3d0c3537476ccc2ed3f29a45e56ed30ee/torchvision/models/detection/ssd.py#L138-L139",596 "title": null,597 "internal": false,598 "attachment": false,599 "reflection": false,600 "clicks": 10,601 "user_id": 3534,602 "domain": "github.com",603 "root_domain": "github.com"604 }605 ]606 },607 "bookmarks": []608 },609 {610 "post_stream": {611 "posts": [612 {613 "id": 419903,614 "name": "",615 "username": "sdg91",616 "avatar_template": "/letter_avatar_proxy/v4/letter/s/ee7513/{size}.png",617 "created_at": "2023-10-10T08:33:04.991Z",618 "cooked": "<p>PyTorch TimeSeries Dataset compaints about NaN; when there is no NaN in the data.</p>\n<p>To confirm, created a sample dataset with no NaN values in the data of 1000 rows.</p>\n<pre data-code-wrap=\"training\"><code class=\"lang-plaintext\"> df[lambda x: x.index <= training_cutoff],\n time_idx='index',\n target='Target',\n group_ids=['Region'],\n min_encoder_length=1,\n max_encoder_length=max_encoder_length,\n min_prediction_length=1,\n max_prediction_length=max_prediction_length,\n static_categoricals=['categorical_feature'],\n time_varying_unknown_reals=['numerical_feature1'],\n target_normalizer=GroupNormalizer(groups=['Target']),\n add_relative_time_idx=True,\n add_target_scales=True,\n add_encoder_length=True)\n</code></pre>\n<p>It raises following error:</p>\n<pre><code class=\"lang-auto\">ValueError: 995 (100.00%) of Target_scale values were found to be NA or infinite (even after encoding). NA values are not allowed `allow_missing_timesteps` refers to missing rows, not to missing values. Possible strategies to fix the issue are (a) dropping the variable Target_scale, (b) using `NaNLabelEncoder(add_nan=True)` for categorical variables, (c) filling missing values and/or (d) optionally adding a variable indicating filled values\n</code></pre>\n<p>What is causing this issue and what is the possible fix?</p>",619 "post_number": 1,620 "post_type": 1,621 "posts_count": 1,622 "updated_at": "2023-10-10T08:35:52.038Z",623 "reply_count": 0,624 "reply_to_post_number": null,625 "quote_count": 0,626 "incoming_link_count": 269,627 "reads": 7,628 "readers_count": 6,629 "score": 1331.4,630 "yours": false,631 "topic_id": 189667,632 "topic_slug": "error-in-timeseies-dataset-pytorch-forecasting",633 "display_username": "",634 "primary_group_name": null,635 "flair_name": null,636 "flair_url": null,637 "flair_bg_color": null,638 "flair_color": null,639 "flair_group_id": null,640 "badges_granted": [],641 "version": 1,642 "can_edit": false,643 "can_delete": false,644 "can_recover": false,645 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"posts": [1002 {1003 "id": 419902,1004 "name": "Thibaud LE GALL",1005 "username": "Thibaud_LE_GALL",1006 "avatar_template": "/user_avatar/discuss.pytorch.org/thibaud_le_gall/{size}/64506_2.png",1007 "created_at": "2023-10-10T08:32:45.815Z",1008 "cooked": "<p>Hi here,</p>\n<p>We are using Pytorch 1.10 and Cuda 11.x</p>\n<p>my question would be related to the topic <a href=\"https://discuss.pytorch.org/t/disk-ssd-or-hdd-space-increase-decrease-ing-during-training/137107\" class=\"inline-onebox\">Disk(SSD or HDD) space increase(decrease)ing during training</a></p>\n<p>We are training a model on-prem and on GCP. We are observing that the space available on disk is constantly diminishing during training. We have observed this behavior with our own algorithm and with a random basic algorithm found on the net (that i can provide if required).</p>\n<p>Given the previously quoted article, we understand that it could be because we run out RAM, which would be understandable with our algorithm but not with the one pick-up from internet.</p>\n<p>The writing on disk get to a problematic level with our in-house algorithm getting to 4Gb per epoch, which is something we will sort out, but the behavior of a constant increase in use of disk space is something we can’t find any explanation about.</p>\n<p>We are posting here to check if this behavior is consistent with Pytorch expected behavior, and if anyone else has had any trouble with it before ?</p>\n<p>Regards</p>\n<p>PS: We do have all sorts of logs from shutils or du commands in different environments (mostly on GCP), We’ll provide the relevant files/lines of logs in line with the discussion</p>",1009 "post_number": 1,1010 "post_type": 1,1011 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