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Anurag1734/cuda-error-resolution-analysis

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1[2  {3    "post_stream": {4      "posts": [5        {6          "id": 353430,7          "name": "Patrick Ferreira",8          "username": "patrickctrf",9          "avatar_template": "/user_avatar/discuss.pytorch.org/patrickctrf/{size}/30332_2.png",10          "created_at": "2022-06-25T22:52:05.016Z",11          "cooked": "<p>I’m debugging my GAN training script because my generator does not learn anything. I’m using a Subset of my dataset with a single sample, so my generator should learn a constant output equal to that sample. But it doesn’t.</p>\n<pre><code class=\"lang-auto\"># Train Data\ntrain_dataset = NsynthDatasetFourier(path=\"nsynth-train/\", noise_length=noise_length)\ntrain_dataset = Subset(train_dataset, [0, ])  # dummy dataset for testing script\ntrain_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1)\n</code></pre>\n<hr>\n<p>Then I decided to create a <code>dummy_generator</code> that only outputs a <strong>Parameter Tensor</strong> saved as instance object. So it definitely should learn that tensor to be equal to the only sample um my dataset, but that tensor never changes.</p>\n<pre><code class=\"lang-auto\">class DummyGenerator(nn.Module):\n    def __init__(self, *args, **kwargs):\n        super().__init__()\n\n        self.dummy_tensor = nn.Parameter(torch.rand((1, 2, 1024, 128), requires_grad=True))\n\n    def forward(self, x):\n        return self.dummy_tensor\n</code></pre>\n<hr>\n<p>Is my approach correct? Neither my original Generator or my DummyGenerator seems to learn, no matter how big is my learning rate. My discriminator seems to learn fine.</p>\n<p><strong>If anybody can help me, I would be very grateful. My complete code is <a href=\"https://github.com/patrickctrf/projeto-ia376/tree/e3ptk/tests\" rel=\"noopener nofollow ugc\">here</a>.</strong></p>\n<hr>\n<p>P.S.: It is necessary to download <a href=\"http://download.magenta.tensorflow.org/datasets/nsynth/nsynth-train.jsonwav.tar.gz\" rel=\"noopener nofollow ugc\">Nsynth dataset</a> to run the complete code.</p>",12          "post_number": 1,13          "post_type": 1,14          "posts_count": 3,15          "updated_at": "2022-06-25T22:58:42.428Z",16          "reply_count": 0,17          "reply_to_post_number": null,18          "quote_count": 0,19          "incoming_link_count": 511,20          "reads": 14,21          "readers_count": 13,22          "score": 2552.8,23          "yours": false,24          "topic_id": 155064,25          "topic_slug": "cant-optimize-single-parameter-tensor",26          "display_username": "Patrick Ferreira",27          "primary_group_name": null,28          "flair_name": null,29          "flair_url": 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false,58          "actions_summary": [],59          "moderator": false,60          "admin": false,61          "staff": false,62          "user_id": 38231,63          "hidden": false,64          "trust_level": 1,65          "deleted_at": null,66          "user_deleted": false,67          "edit_reason": null,68          "can_view_edit_history": true,69          "wiki": false,70          "post_url": "/t/cant-optimize-single-parameter-tensor/155064/1",71          "can_accept_answer": false,72          "can_unaccept_answer": false,73          "accepted_answer": false,74          "topic_accepted_answer": null,75          "can_vote": false76        },77        {78          "id": 353450,79          "name": "",80          "username": "ptrblck",81          "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",82          "created_at": "2022-06-26T00:53:37.096Z",83          "cooked": "<p>The use case is a bit strange, but should generally work as seen in this small example overfitting a static target:</p>\n<pre><code class=\"lang-python\">class DummyGenerator(nn.Module):\n    def __init__(self, *args, **kwargs):\n        super().__init__()\n\n        self.dummy_tensor = nn.Parameter(torch.rand((1, 2, 1024, 128), requires_grad=True))\n\n    def forward(self):\n        return self.dummy_tensor\n    \nmodel = DummyGenerator()\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\ncriterion = nn.MSELoss()\n\ny = torch.randn(1, 2, 1024, 128)\n\nfor epoch in range(10000):\n    optimizer.zero_grad()\n    out = model()\n    loss = criterion(out, y)\n    loss.backward()\n    optimizer.step()\n    print('epoch {}, loss {}'.format(epoch, loss.item()))\n\n# ...\n# epoch 9998, loss 1.416539715387577e-11\n# epoch 9999, loss 1.4069788736859046e-11\n</code></pre>\n<p>Check if your parameter has a valid <code>.grad</code> attribute after the <code>backward</code> call and is indeed being updated.</p>",84          "post_number": 2,85          "post_type": 1,86          "posts_count": 3,87          "updated_at": "2022-06-26T00:53:37.096Z",88          "reply_count": 1,89          "reply_to_post_number": null,90          "quote_count": 0,91          "incoming_link_count": 23,92          "reads": 12,93          "readers_count": 11,94          "score": 122.4,95          "yours": false,96          "topic_id": 155064,97          "topic_slug": "cant-optimize-single-parameter-tensor",98          "display_username": "",99          "primary_group_name": null,100          "flair_name": null,101          "flair_url": null,102          "flair_bg_color": null,103          "flair_color": null,104          "flair_group_id": null,105          "badges_granted": [],106          "version": 1,107          "can_edit": false,108          "can_delete": false,109          "can_recover": false,110          "can_see_hidden_post": false,111          "can_wiki": false,112          "read": true,113          "user_title": "",114          "bookmarked": false,115        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I tried a case similar to your example and changed my generator <strong>loss</strong> to minimize the difference from the <strong>target</strong>, instead of aiming discriminator’s output:</p>\n<pre><code class=\"lang-auto\">generator_discriminator_out = discriminator(generated_data)\ngenerator_loss = criterion(generated_data, target)\n# generator_loss = criterion(generator_discriminator_out, true_labels)\n</code></pre>\n<p>The generator successfully accomplishes this task and the discriminator’s output becomes 0.5 for every input (this is expected).</p>\n<hr>\n<p>But when I go back to the original loss for GANs (trying to be approved by the discriminator), the output from my generator doesn’t match the sample:</p>\n<pre><code class=\"lang-auto\">generator_discriminator_out = discriminator(generated_data)\n# generator_loss = criterion(generated_data, target)\ngenerator_loss = criterion(generator_discriminator_out, true_labels)\n</code></pre>\n<hr>\n<p>This is an indicator that the discrimination is not learning the correct target, right?</p>\n<p>Extra info: When I train with the <strong>whole dataset</strong> again, generator gets stuck with <strong>constant output</strong> for every input (and MSE loss goes to 1, the maximum value). I don’t know if it’s relevant information, but I don’t understand why my generator would stop in bad local minima like that.</p>",140          "post_number": 3,141          "post_type": 1,142          "posts_count": 3,143          "updated_at": "2022-06-26T21:19:46.921Z",144          "reply_count": 0,145          "reply_to_post_number": 2,146          "quote_count": 0,147          "incoming_link_count": 4,148          "reads": 10,149          "readers_count": 9,150          "score": 22.0,151          "yours": false,152          "topic_id": 155064,153          "topic_slug": "cant-optimize-single-parameter-tensor",154          "display_username": "Patrick Ferreira",155          "primary_group_name": null,156          "flair_name": null,157          "flair_url": null,158          "flair_bg_color": null,159          "flair_color": null,160          "flair_group_id": null,161          "badges_granted": [],162          "version": 1,163          "can_edit": false,164          "can_delete": 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   {610          "id": 353533,611          "name": "iamexperimenting",612          "username": "iamexperimentingnow",613          "avatar_template": "/user_avatar/discuss.pytorch.org/iamexperimentingnow/{size}/43455_2.png",614          "created_at": "2022-06-26T20:29:32.466Z",615          "cooked": "<p>I’m trying to save my model in TorchScript format, but unfortunately getting errors.</p>\n<h2><a name=\"what-you-have-already-tried-1\" class=\"anchor\" href=\"#what-you-have-already-tried-1\"></a>What you have already tried</h2>\n<p><code>torch.jit.script(model)</code></p>\n<h2><a name=\"environment-2\" class=\"anchor\" href=\"#environment-2\"></a>Environment</h2>\n<p>python</p>\n<blockquote>\n<p>Build information about Torch-TensorRT can be found by turning on debug messages</p>\n</blockquote>\n<ul>\n<li>PyTorch Version (e.g., 1.0):1.11.0+cu113</li>\n<li>CPU Architecture:</li>\n<li>OS (e.g., Linux): ubuntu 20.04</li>\n<li>How you installed PyTorch (<code>conda</code>, <code>pip</code>, <code>libtorch</code>, source): pip</li>\n<li>Build command you used (if compiling from source):</li>\n<li>Are you using local sources or building from archives:</li>\n<li>Python version:3.9.7</li>\n<li>CUDA version:11.7</li>\n<li>GPU models and configuration: RTX GEFORCE 2060</li>\n<li>Any other relevant information:</li>\n</ul>\n<p><div class=\"lightbox-wrapper\"><a class=\"lightbox\" href=\"https://discuss.pytorch.org/uploads/default/original/3X/7/e/7e264f0357986feccf6427a8d2a8b6f06959129e.png\" data-download-href=\"https://discuss.pytorch.org/uploads/default/7e264f0357986feccf6427a8d2a8b6f06959129e\" title=\"image\"><img src=\"https://discuss.pytorch.org/uploads/default/optimized/3X/7/e/7e264f0357986feccf6427a8d2a8b6f06959129e_2_690x388.png\" alt=\"image\" data-base62-sha1=\"hZYeHG74na1togHinZ3EswbNCEK\" width=\"690\" height=\"388\" srcset=\"https://discuss.pytorch.org/uploads/default/optimized/3X/7/e/7e264f0357986feccf6427a8d2a8b6f06959129e_2_690x388.png, https://discuss.pytorch.org/uploads/default/optimized/3X/7/e/7e264f0357986feccf6427a8d2a8b6f06959129e_2_1035x582.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/7/e/7e264f0357986feccf6427a8d2a8b6f06959129e_2_1380x776.png 2x\" data-dominant-color=\"3C1B32\"><div class=\"meta\"><svg class=\"fa d-icon d-icon-far-image svg-icon\" aria-hidden=\"true\"><use href=\"#far-image\"></use></svg><span class=\"filename\">image</span><span class=\"informations\">1920×1080 394 KB</span><svg class=\"fa d-icon d-icon-discourse-expand svg-icon\" aria-hidden=\"true\"><use href=\"#discourse-expand\"></use></svg></div></a></div></p>\n<h2><a name=\"to-reproduce-3\" class=\"anchor\" href=\"#to-reproduce-3\"></a>To Reproduce</h2>\n<p>please find the original git repo. Here, I’m using <code>ade20k-hrnetv2.yaml</code> config file.</p><aside class=\"onebox allowlistedgeneric\" data-onebox-src=\"https://github.com/CSAILVision/semantic-segmentation-pytorch\">\n  <header class=\"source\">\n      <img src=\"https://github.githubassets.com/favicons/favicon.svg\" class=\"site-icon\" width=\"32\" height=\"32\">\n\n      <a href=\"https://github.com/CSAILVision/semantic-segmentation-pytorch\" target=\"_blank\" rel=\"noopener nofollow ugc\">GitHub</a>\n  </header>\n\n  <article class=\"onebox-body\">\n    <div class=\"aspect-image\" style=\"--aspect-ratio:690/344;\"><img src=\"https://opengraph.githubassets.com/a9cdfa5321964e5c753904082779fcc13a4897f613b47a0e61ed34d6bbaf53ac/CSAILVision/semantic-segmentation-pytorch\" class=\"thumbnail\" width=\"690\" height=\"345\"></div>\n\n<h3><a href=\"https://github.com/CSAILVision/semantic-segmentation-pytorch\" target=\"_blank\" rel=\"noopener nofollow ugc\">GitHub - CSAILVision/semantic-segmentation-pytorch: Pytorch implementation...</a></h3>\n\n  <p>Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset - GitHub - CSAILVision/semantic-segmentation-pytorch: Pytorch implementation for Semantic Segmentation/Scene Par...</p>\n\n\n  </article>\n\n  <div class=\"onebox-metadata\">\n    \n    \n  </div>\n\n  <div style=\"clear: both\"></div>\n</aside>\n\n<p><a class=\"mention\" href=\"/u/ptrblck\">@ptrblck</a></p>",616          "post_number": 1,617          "post_type": 1,618          "posts_count": 1,619          "updated_at": "2022-06-26T20:29:32.466Z",620          "reply_count": 0,621          "reply_to_post_number": null,622          "quote_count": 0,623          "incoming_link_count": 87,624          "reads": 7,625          "readers_count": 6,626          "score": 436.4,627          "yours": false,628          "topic_id": 155114,629          "topic_slug": "unable-to-save-the-model-in-torchscript-format",630          "display_username": "iamexperimenting",631          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"domain": "github.com",1044          "root_domain": "github.com"1045        }1046      ]1047    },1048    "bookmarks": []1049  },1050  {1051    "post_stream": {1052      "posts": [1053        {1054          "id": 353467,1055          "name": "amit",1056          "username": "laro",1057          "avatar_template": "/user_avatar/discuss.pytorch.org/laro/{size}/47125_2.png",1058          "created_at": "2022-06-26T07:11:51.567Z",1059          "cooked": "<p>I Have the following model:</p>\n<p>import torch<br>\nimport torch.nn as nn<br>\nimport torch.nn.functional as F</p>\n<p>class MyModel(nn.Module):</p>\n<pre><code>def __init__(self, input_size, num_classes):\n    super(MyModel, self).__init__()\n    \n    \n    \n    self.layer_1      = nn.Conv1d(1,   16,  3, bias=False, stride=2)\n    self.activation_1 = F.relu       \n    self.adap         = nn.AdaptiveAvgPool1d(1)\n    self.flatten      = nn.Flatten          (),  \n    \n    self.layer_2      = torch.nn.Linear(2249, 500)\n    self.activation_2 = F.relu\n    self.layer_3      = torch.nn.Linear(500, 2)\n   \n    \n    pass\n\ndef forward(self, x, labels=None):\n    \n    \n    x = x.reshape(256, 1, -1)                        \n    x = self.layer_1(x)     \n    x = self.activation_1(x)            \n    x = self.flatten(x)\n    return x\n</code></pre>\n<p>When running torchinfo<br>\nmodel = MyModel(input_size=4500, num_classes=2)<br>\ntorchinfo.summary(model, (256, 4500))</p>\n<p>I’m Getting error:</p>\n<p>Input In [101], in MyModel.forward(self, x, labels)<br>\n30 x = self.activation_1(x)<br>\n—&gt; 31 x = self.flatten(x)<br>\n32 return x</p>\n<p>TypeError: ‘tuple’ object is not callable</p>\n<ol>\n<li>What is wrong ?</li>\n<li>What do I need to change ?</li>\n</ol>",1060          "post_number": 1,1061          "post_type": 1,1062          "posts_count": 2,1063          "updated_at": "2022-06-26T07:11:51.567Z",1064          "reply_count": 0,1065          "reply_to_post_number": null,1066          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"staff": false,1095          "user_id": 50872,1096          "hidden": false,1097          "trust_level": 1,1098          "deleted_at": null,1099          "user_deleted": false,1100          "edit_reason": null,1101          "can_view_edit_history": true,1102          "wiki": false,1103          "post_url": "/t/typeerror-tuple-object-is-not-callable-when-using-flatten-layer/155084/1",1104          "can_accept_answer": false,1105          "can_unaccept_answer": false,1106          "accepted_answer": false,1107          "topic_accepted_answer": true,1108          "can_vote": false1109        },1110        {1111          "id": 353526,1112          "name": "",1113          "username": "ptrblck",1114          "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",1115          "created_at": "2022-06-26T19:22:29.243Z",1116          "cooked": "<p>Remove the comma after <code>nn.Flatten()</code> and the error should be gone:</p>\n<pre><code class=\"lang-python\">self.flatten      = nn.Flatten          (),  # &lt;- note the comma\n</code></pre>",1117          "post_number": 2,1118          "post_type": 1,1119          "posts_count": 2,1120          "updated_at": "2022-06-26T19:22:29.243Z",1121          "reply_count": 0,1122          "reply_to_post_number": null,1123          "quote_count": 0,1124          "incoming_link_count": 0,1125          "reads": 2,1126          "readers_count": 1,1127          "score": 15.4,1128          "yours": false,1129          "topic_id": 155084,1130          "topic_slug": "typeerror-tuple-object-is-not-callable-when-using-flatten-layer",1131          "display_username": "",1132          "primary_group_name": null,1133          "flair_name": null,1134          "flair_url": null,1135          "flair_bg_color": null,1136          "flair_color": null,1137          "flair_group_id": null,1138          "badges_granted": [],1139          "version": 1,1140          "can_edit": false,1141          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