Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 330474,7 "name": "",8 "username": "vlc",9 "avatar_template": "/letter_avatar_proxy/v4/letter/v/8e7dd6/{size}.png",10 "created_at": "2022-02-09T12:37:44.542Z",11 "cooked": "<p>Hi there,</p>\n<p>I am working to quantizate a semantic segmentation model using the fx api provided by pytorch.</p>\n<p>The model has a swin transformer as a backbone, aspp module and some upconvolutions following a DeepLabv3+ architecture.</p>\n<p>I have followed the steps in the <a href=\"https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_static.html\" rel=\"noopener nofollow ugc\">tutorial</a>l by <a class=\"mention\" href=\"/u/jerryzh168\">@jerryzh168</a></p>\n<p>I am able to run the following lines of code</p>\n<pre><code class=\"lang-auto\">qconfig = get_default_qconfig(\"fbgemm\")\n qconfig_dict = {\"\": qconfig}\n prepare_custom_config_dict = {\n \"non_traceable_module_class\": [PatchEmbed, BasicLayer]\n }\n prepared_model = prepare_fx(model, qconfig_dict, prepare_custom_config_dict)\n print(prepared_model.graph)\n print(prepared_model.code)\n</code></pre>\n<p>I am excluding PatchEmbed and BasicLayer module classes in prepare_fx.<br>\nHere’s their code:</p>\n<pre><code class=\"lang-auto\">class PatchEmbed(nn.Module):\n\n def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n patch_size = to_2tuple(patch_size)\n self.patch_size = patch_size\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n # padding\n _, _, H, W = x.size()\n if W % self.patch_size[1] != 0:\n x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))\n if H % self.patch_size[0] != 0:\n x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n\n x = self.proj(x) # B C Wh Ww\n if self.norm is not None:\n Wh, Ww = x.size(2), x.size(3)\n x = x.flatten(2).transpose(1, 2)\n x = self.norm(x)\n x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)\n\n return x\n\nclass BasicLayer(nn.Module):\n \"\"\" A basic Swin Transformer layer for one stage.\n \"\"\"\n\n def __init__(self,\n dim,\n depth,\n num_heads,\n window_size=7,\n mlp_ratio=4.,\n qkv_bias=True,\n qk_scale=None,\n drop=0.,\n attn_drop=0.,\n drop_path=0.,\n norm_layer=nn.LayerNorm,\n downsample=None,\n use_checkpoint=False):\n super().__init__()\n self.window_size = window_size\n self.shift_size = window_size // 2\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList([\n SwinTransformerBlock(\n dim=dim,\n num_heads=num_heads,\n window_size=window_size,\n shift_size=0 if (i % 2 == 0) else window_size // 2,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop,\n attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer)\n for i in range(depth)])\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(dim=dim, norm_layer=norm_layer)\n else:\n self.downsample = None\n\n def forward(self, x, H, W):\n \"\"\" Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n\n # calculate attention mask for SW-MSA\n Hp = int(np.ceil(H / self.window_size)) * self.window_size\n Wp = int(np.ceil(W / self.window_size)) * self.window_size\n img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1\n h_slices = (slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None))\n w_slices = (slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None))\n cnt = 0\n for h in h_slices:\n for w in w_slices:\n img_mask[:, h, w, :] = cnt\n cnt += 1\n\n mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1\n mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))\n\n for blk in self.blocks:\n blk.H, blk.W = H, W\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x, attn_mask)\n else:\n x = blk(x, attn_mask)\n if self.downsample is not None:\n x_down = self.downsample(x, H, W)\n Wh, Ww = (H + 1) // 2, (W + 1) // 2\n return x, H, W, x_down, Wh, Ww\n else:\n return x, H, W, x, H, W\n</code></pre>\n<p>The PatchEmbed gave me problems due to the presence of if statements.</p>\n<p>BasicLayer was failing when executing numpy operations with Proxys in these lines:</p>\n<pre><code class=\"lang-auto\">Hp = int(np.ceil(H / self.window_size)) * self.window_size\nWp = int(np.ceil(W / self.window_size)) * self.window_size\n</code></pre>\n<p>My problem comes, when I try to calibrate the model.</p>\n<pre><code class=\"lang-auto\">def calibrate(model, data_loader):\n with torch.no_grad():\n for inp, target, _, _ in tqdm(data_loader, total=len(data_loader.dataset),\n desc='Calibrating model for post training static quantization...'):\n model(inp)\n\n\ncalibrate(prepared_model, data_loader)\n</code></pre>\n<p>That’s when I get the following error:</p>\n<pre><code class=\"lang-auto\">Traceback (most recent call last):\n File \"/home/victor/proyectos/roof_segmentation/increase_speed_model_production/roof_condition_semseg/utils/quantization.py\", line 37, in calibrate\n model(inp)\n File \"/home/victor/proyectos/roof_segmentation/increase_speed_model_production/roof_condition_semseg/venv/lib/python3.8/site-packages/torch/fx/graph_module.py\", line 513, in wrapped_call\n raise e.with_traceback(None)\nAttributeError: 'int' object has no attribute 'numel'\n\n</code></pre>\n<p>The weird part is that my input is torch.tensor not an int. So I guess the generated graph may have a problem somewhere, after executing prepare_fx.</p>\n<p>I haven’t found any information regarding problems when calibrating the model.</p>\n<p>Any ideas on how to solve the problem?</p>\n<p>Thanks.</p>\n<p><em>Note: I have already tried post training dynamic quantization using eager mode and it works fine. However, it only allows me to quantizate nn.Linear and activation functions of my model.</em></p>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 6,15 "updated_at": "2022-02-09T12:37:44.542Z",16 "reply_count": 0,17 "reply_to_post_number": null,18 "quote_count": 0,19 "incoming_link_count": 361,20 "reads": 21,21 "readers_count": 20,22 "score": 1804.2,23 "yours": false,24 "topic_id": 143661,25 "topic_slug": "calibration-of-model-in-post-training-static-quantization-using-fx-api",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 "link_counts": [41 {42 "url": "https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_static.html",43 "internal": false,44 "reflection": false,45 "title": "(prototype) FX Graph Mode Post Training Static Quantization — PyTorch Tutorials 1.10.1+cu102 documentation",46 "clicks": 947 }48 ],49 "read": true,50 "user_title": null,51 "bookmarked": false,52 "actions_summary": [],53 "moderator": false,54 "admin": false,55 "staff": false,56 "user_id": 53071,57 "hidden": false,58 "trust_level": 1,59 "deleted_at": null,60 "user_deleted": false,61 "edit_reason": null,62 "can_view_edit_history": true,63 "wiki": false,64 "post_url": "/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/1",65 "can_accept_answer": false,66 "can_unaccept_answer": false,67 "accepted_answer": false,68 "topic_accepted_answer": null,69 "can_vote": false70 },71 {72 "id": 330546,73 "name": "Hd Charles",74 "username": "HDCharles",75 "avatar_template": "/user_avatar/discuss.pytorch.org/hdcharles/{size}/38408_2.png",76 "created_at": "2022-02-09T19:29:52.190Z",77 "cooked": "<p>Hey,</p>\n<p>If you could give us a minimal reproducible example, that would be helpful.</p>\n<p>my guess is that an observer is getting attached to one of the modules with an int input and, assuming its a tensor, calls numel() on it.</p>",78 "post_number": 2,79 "post_type": 1,80 "posts_count": 6,81 "updated_at": "2022-02-09T19:29:52.190Z",82 "reply_count": 0,83 "reply_to_post_number": null,84 "quote_count": 0,85 "incoming_link_count": 1,86 "reads": 19,87 "readers_count": 18,88 "score": 8.8,89 "yours": false,90 "topic_id": 143661,91 "topic_slug": "calibration-of-model-in-post-training-static-quantization-using-fx-api",92 "display_username": "Hd Charles",93 "primary_group_name": null,94 "flair_name": null,95 "flair_url": null,96 "flair_bg_color": null,97 "flair_color": null,98 "flair_group_id": null,99 "badges_granted": [],100 "version": 1,101 "can_edit": false,102 "can_delete": false,103 "can_recover": false,104 "can_see_hidden_post": false,105 "can_wiki": false,106 "read": true,107 "user_title": null,108 "bookmarked": false,109 "actions_summary": [],110 "moderator": false,111 "admin": false,112 "staff": false,113 "user_id": 45475,114 "hidden": false,115 "trust_level": 2,116 "deleted_at": null,117 "user_deleted": false,118 "edit_reason": null,119 "can_view_edit_history": true,120 "wiki": false,121 "post_url": "/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/2",122 "can_accept_answer": false,123 "can_unaccept_answer": false,124 "accepted_answer": false,125 "topic_accepted_answer": null126 },127 {128 "id": 330708,129 "name": "",130 "username": "vlc",131 "avatar_template": "/letter_avatar_proxy/v4/letter/v/8e7dd6/{size}.png",132 "created_at": "2022-02-10T15:59:31.152Z",133 "cooked": "<p>Hey,</p>\n<p>Thanks for the answer.</p>\n<p>I can’t share all the details on the model’s architecture due to my company policy but I can share the workflow I use:</p>\n<pre><code class=\"lang-auto\">from models import DeeplabV3X\nfrom models.backbone import PatchEmbed, BasicLayer\nfrom datasets import Dataset, build_dataloader\n\nmodel = DeeplabV3X()\n\n# Use a dataset to compare inference times and evaluation metrics\ntest_dataset = Dataset()\n\n# Dataloader getitem generates input (n, 3, 512, 512) tensor image, target (n, 1, 512, 512) tensor with masks\ntest_dataloader = build_dataloader(test_dataset)\n\n# Deepcopying the original model because quantization api changes the model inplace and we want\n# to keep the original model for future comparison\n q_model = copy.deepcopy(model)\n\n# Function to calibrate graph module\ndef calibrate(model, data_loader):\n with torch.no_grad():\n for inp, target, _, _ in tqdm(data_loader, total=len(data_loader.dataset),\n desc='Calibrating model for post training static quantization...'):\n model(inp)\n\n# Function to convert model\ndef quantizate_ptq_static_fx(model, data_loader):\n qconfig = get_default_qconfig(\"fbgemm\")\n qconfig_dict = {\"\": qconfig}\n prepare_custom_config_dict = {\n \"non_traceable_module_class\": [PatchEmbed, BasicLayer]\n }\n prepared_model = prepare_fx(model, qconfig_dict, prepare_custom_config_dict) # Generate graph\n print(prepared_model.graph)\n print(prepared_model.code)\n calibrate(prepared_model, data_loader) # Code breaks here\n q_model = convert_fx(prepared_model)\n return q_model\n\nq_model = quantizate_ptq_static_fx(q_model, test_dataloader)\n\nfor inp, target in test_loader:\n \n logits = model(inp)\n q_logits = model(inp)\n\n</code></pre>\n<p>I have generated the logs of <a href=\"https://justpaste.it/7ugzk\" rel=\"noopener nofollow ugc\">graph</a> and <a href=\"https://justpaste.it/6vc6g\" rel=\"noopener nofollow ugc\">code</a> for prepared_model.</p>\n<p>Let me know if this is helpful.</p>",134 "post_number": 3,135 "post_type": 1,136 "posts_count": 6,137 "updated_at": "2022-02-10T15:59:31.152Z",138 "reply_count": 1,139 "reply_to_post_number": null,140 "quote_count": 0,141 "incoming_link_count": 6,142 "reads": 20,143 "readers_count": 19,144 "score": 39.0,145 "yours": false,146 "topic_id": 143661,147 "topic_slug": "calibration-of-model-in-post-training-static-quantization-using-fx-api",148 "display_username": "",149 "primary_group_name": null,150 "flair_name": null,151 "flair_url": null,152 "flair_bg_color": null,153 "flair_color": null,154 "flair_group_id": null,155 "badges_granted": [],156 "version": 1,157 "can_edit": false,158 "can_delete": false,159 "can_recover": false,160 "can_see_hidden_post": false,161 "can_wiki": false,162 "link_counts": [163 {164 "url": "https://justpaste.it/6vc6g",165 "internal": false,166 "reflection": false,167 "title": "JustPaste.it - Share Text & Images the Easy Way",168 "clicks": 4169 },170 {171 "url": "https://justpaste.it/7ugzk",172 "internal": false,173 "reflection": false,174 "title": "JustPaste.it - Share Text & Images the Easy Way",175 "clicks": 3176 }177 ],178 "read": true,179 "user_title": null,180 "bookmarked": false,181 "actions_summary": [],182 "moderator": false,183 "admin": false,184 "staff": false,185 "user_id": 53071,186 "hidden": false,187 "trust_level": 1,188 "deleted_at": null,189 "user_deleted": false,190 "edit_reason": null,191 "can_view_edit_history": true,192 "wiki": false,193 "post_url": "/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/3",194 "can_accept_answer": false,195 "can_unaccept_answer": false,196 "accepted_answer": false,197 "topic_accepted_answer": null198 },199 {200 "id": 330736,201 "name": "Hd Charles",202 "username": "HDCharles",203 "avatar_template": "/user_avatar/discuss.pytorch.org/hdcharles/{size}/38408_2.png",204 "created_at": "2022-02-10T18:08:24.448Z",205 "cooked": "<p>A minimal reproducible example doesn’t generally involve details about model architecture, ideally it’d be a toy model with only the problematic piece.</p>\n<p>The issue is in one of the modules and without code I can’t determine more than that. You could probably just use print statements to figure out which one is the issue and go from there.</p>\n<p>Again, if i were to guess, its probably because you’re passing in/returning a mixture of different dtypes (i know you said your input is not an int but the input to BasicLayer does contain an int) in some of these modules when fx is probably assuming its all tensors. You could fix this by passing/returning everything as tensors (if thats actually the issue).</p>",206 "post_number": 4,207 "post_type": 1,208 "posts_count": 6,209 "updated_at": "2022-02-10T18:08:24.448Z",210 "reply_count": 0,211 "reply_to_post_number": 3,212 "quote_count": 0,213 "incoming_link_count": 0,214 "reads": 20,215 "readers_count": 19,216 "score": 4.0,217 "yours": false,218 "topic_id": 143661,219 "topic_slug": "calibration-of-model-in-post-training-static-quantization-using-fx-api",220 "display_username": "Hd Charles",221 "primary_group_name": null,222 "flair_name": null,223 "flair_url": null,224 "flair_bg_color": null,225 "flair_color": null,226 "flair_group_id": null,227 "badges_granted": [],228 "version": 1,229 "can_edit": false,230 "can_delete": false,231 "can_recover": false,232 "can_see_hidden_post": false,233 "can_wiki": false,234 "read": true,235 "user_title": null,236 "reply_to_user": {237 "id": 53071,238 "username": "vlc",239 "name": "",240 "avatar_template": "/letter_avatar_proxy/v4/letter/v/8e7dd6/{size}.png"241 },242 "bookmarked": false,243 "actions_summary": [],244 "moderator": false,245 "admin": false,246 "staff": false,247 "user_id": 45475,248 "hidden": false,249 "trust_level": 2,250 "deleted_at": null,251 "user_deleted": false,252 "edit_reason": null,253 "can_view_edit_history": true,254 "wiki": false,255 "post_url": "/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/4",256 "can_accept_answer": false,257 "can_unaccept_answer": false,258 "accepted_answer": false,259 "topic_accepted_answer": null260 },261 {262 "id": 330822,263 "name": "Jerry Zhang",264 "username": "jerryzh168",265 "avatar_template": "/user_avatar/discuss.pytorch.org/jerryzh168/{size}/15217_2.png",266 "created_at": "2022-02-11T01:06:39.637Z",267 "cooked": "<p><a class=\"mention\" href=\"/u/hdcharles\">@HDCharles</a> we can probably get some hint from the graph and code that is attached.</p>\n<p>here:</p>\n<pre><code class=\"lang-auto\">view = model_model_backbone_norm0_activation_post_process_0.view(-1, getitem_2_activation_post_process_0, getitem_3_activation_post_process_0, 96); \n</code></pre>\n<p>looks like <code>getitem_2_activation_post_process_0</code> and <code>getitem_3_activation_post_process_0</code> are expected to be <code>int</code>? yet it is observed?</p>\n<p>Is this intended? we could potentially remove the quantization support for view, it’s probably not needed</p>",268 "post_number": 5,269 "post_type": 1,270 "posts_count": 6,271 "updated_at": "2022-02-11T01:06:39.637Z",272 "reply_count": 1,273 "reply_to_post_number": null,274 "quote_count": 0,275 "incoming_link_count": 13,276 "reads": 19,277 "readers_count": 18,278 "score": 73.8,279 "yours": false,280 "topic_id": 143661,281 "topic_slug": "calibration-of-model-in-post-training-static-quantization-using-fx-api",282 "display_username": "Jerry Zhang",283 "primary_group_name": null,284 "flair_name": null,285 "flair_url": null,286 "flair_bg_color": null,287 "flair_color": null,288 "flair_group_id": null,289 "badges_granted": [],290 "version": 1,291 "can_edit": false,292 "can_delete": false,293 "can_recover": false,294 "can_see_hidden_post": false,295 "can_wiki": false,296 "read": true,297 "user_title": null,298 "bookmarked": false,299 "actions_summary": [],300 "moderator": false,301 "admin": false,302 "staff": false,303 "user_id": 21770,304 "hidden": false,305 "trust_level": 2,306 "deleted_at": null,307 "user_deleted": false,308 "edit_reason": null,309 "can_view_edit_history": true,310 "wiki": false,311 "post_url": "/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/5",312 "can_accept_answer": false,313 "can_unaccept_answer": false,314 "accepted_answer": false,315 "topic_accepted_answer": null316 },317 {318 "id": 330850,319 "name": "Hd Charles",320 "username": "HDCharles",321 "avatar_template": "/user_avatar/discuss.pytorch.org/hdcharles/{size}/38408_2.png",322 "created_at": "2022-02-11T05:16:30.969Z",323 "cooked": "<p>Good catch.</p>\n<p>Its bigger than view though, pretty much every quant pattern in GeneralTensorShapeOpQuantizeHandler has the same issue if you do anything but hard code the non tensor arguments. To be honest I’m not sure why these need a quant handler since they can handle both normal and qtensors, they don’t break anything if they are excluded from the flow.</p>\n<p>e.g.</p>\n<pre><code class=\"lang-auto\">import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.quantization.quantize_fx import prepare_fx, convert_fx\n\nclass Net(nn.Module):\n def __init__(self):\n super().__init__()\n self.conv1 = nn.Conv2d(3, 6, 5)\n self.pool = nn.MaxPool2d(2, 2)\n self.lin = nn.Linear(5,1)\n\n def forward(self, x, y):\n x = self.pool(F.relu(self.conv1(x)))\n x = torch.flatten(x, 1) # flatten all dimensions except batch\n x = x.view(-1, y)\n x = self.lin(x)\n return x\n\nmodel=Net().eval()\nmodel(torch.randn(5,3,32,32), 5)\nqconfig = torch.ao.quantization.get_default_qconfig(\"fbgemm\")\nqconfig_dict = {\"\": qconfig}\n# qconfig_dict = {\"\": qconfig, \"object_type\": [('view', None)]}\nprepared_model = prepare_fx(model, qconfig_dict)\nprint(prepared_model.code)\nprepared_model(torch.randn(5,3,32,32), 5)\nfinal_model = convert_fx(prepared_model)\nprint(final_model.code)\nfinal_model(torch.randn(5,3,32,32), 5)\n</code></pre>\n<p><a class=\"mention\" href=\"/u/vlc\">@vlc</a><br>\nyou can solve the issue by specifying None as the qconfig for view (see the commented out qconfig dict in the repro) to exclude it.</p>",324 "post_number": 6,325 "post_type": 1,326 "posts_count": 6,327 "updated_at": "2022-02-11T05:16:30.969Z",328 "reply_count": 0,329 "reply_to_post_number": 5,330 "quote_count": 0,331 "incoming_link_count": 21,332 "reads": 18,333 "readers_count": 17,334 "score": 108.6,335 "yours": false,336 "topic_id": 143661,337 "topic_slug": "calibration-of-model-in-post-training-static-quantization-using-fx-api",338 "display_username": "Hd Charles",339 "primary_group_name": null,340 "flair_name": null,341 "flair_url": null,342 "flair_bg_color": null,343 "flair_color": null,344 "flair_group_id": null,345 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"pytorch.org"811 },812 {813 "url": "https://justpaste.it/6vc6g",814 "title": "JustPaste.it - Share Text & Images the Easy Way",815 "internal": false,816 "attachment": false,817 "reflection": false,818 "clicks": 4,819 "user_id": 53071,820 "domain": "justpaste.it",821 "root_domain": "justpaste.it"822 },823 {824 "url": "https://justpaste.it/7ugzk",825 "title": "JustPaste.it - Share Text & Images the Easy Way",826 "internal": false,827 "attachment": false,828 "reflection": false,829 "clicks": 3,830 "user_id": 53071,831 "domain": "justpaste.it",832 "root_domain": "justpaste.it"833 }834 ]835 },836 "bookmarks": []837 },838 {839 "post_stream": {840 "posts": [841 {842 "id": 330841,843 "name": "Matheus Silva de Paula",844 "username": "Matheus_Silva_de_Pau",845 "avatar_template": "/user_avatar/discuss.pytorch.org/matheus_silva_de_pau/{size}/46521_2.png",846 "created_at": "2022-02-11T03:53:35.083Z",847 "cooked": "<p>I’m trying to remove layers from this model and at the same time keep the “TorchModel”, how can I do that? I tried using .children, but a problem occurs in another line of code.</p>\n<p><div class=\"lightbox-wrapper\"><a class=\"lightbox\" href=\"https://discuss.pytorch.org/uploads/default/original/3X/9/7/97494d7fea78b7aedb5ee066ed5345e4c210417d.png\" data-download-href=\"https://discuss.pytorch.org/uploads/default/97494d7fea78b7aedb5ee066ed5345e4c210417d\" title=\"image\"><img src=\"https://discuss.pytorch.org/uploads/default/original/3X/9/7/97494d7fea78b7aedb5ee066ed5345e4c210417d.png\" alt=\"image\" data-base62-sha1=\"lAl9cwrc093T55U0Od2D5eey5Q1\" width=\"690\" height=\"430\" data-dominant-color=\"F3F3F3\"><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\">783×489 19.2 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>",848 "post_number": 1,849 "post_type": 1,850 "posts_count": 2,851 "updated_at": "2022-02-11T03:53:35.083Z",852 "reply_count": 0,853 "reply_to_post_number": null,854 "quote_count": 0,855 "incoming_link_count": 54,856 "reads": 3,857 "readers_count": 2,858 "score": 270.6,859 "yours": false,860 "topic_id": 143862,861 "topic_slug": "how-can-i-remove-layers-from-torch-model",862 "display_username": "Matheus Silva de Paula",863 "primary_group_name": null,864 "flair_name": null,865 "flair_url": null,866 "flair_bg_color": null,867 "flair_color": null,868 "flair_group_id": null,869 "badges_granted": [],870 "version": 1,871 "can_edit": false,872 "can_delete": false,873 "can_recover": false,874 "can_see_hidden_post": false,875 "can_wiki": false,876 "link_counts": [877 {878 "url": "https://discuss.pytorch.org/uploads/default/original/3X/9/7/97494d7fea78b7aedb5ee066ed5345e4c210417d.png",879 "internal": true,880 "reflection": false,881 "clicks": 0882 }883 ],884 "read": true,885 "user_title": null,886 "bookmarked": false,887 "actions_summary": [],888 "moderator": false,889 "admin": false,890 "staff": false,891 "user_id": 53118,892 "hidden": false,893 "trust_level": 1,894 "deleted_at": null,895 "user_deleted": false,896 "edit_reason": null,897 "can_view_edit_history": true,898 "wiki": false,899 "post_url": "/t/how-can-i-remove-layers-from-torch-model/143862/1",900 "can_accept_answer": false,901 "can_unaccept_answer": false,902 "accepted_answer": false,903 "topic_accepted_answer": null,904 "can_vote": false905 },906 {907 "id": 330848,908 "name": "Suho Cho",909 "username": "thecho7",910 "avatar_template": "/letter_avatar_proxy/v4/letter/t/eada6e/{size}.png",911 "created_at": "2022-02-11T04:46:25.990Z",912 "cooked": "<p>I don’t know the direct method to modify the name of class. Instead, use <code>.named_children()</code></p>\n<pre><code class=\"lang-auto\">class TorchModel(nn.Module):\n def __init__(self, torch_model):\n super(TorchModel, self).__init__()\n for n, m in torch_model.named_children():\n self.__setattr__(n, m)\n\n def forward(self, x):\n # whatever you want\n\nmy_model = TorchModel(torch_model)\n</code></pre>",913 "post_number": 2,914 "post_type": 1,915 "posts_count": 2,916 "updated_at": "2022-02-11T04:47:11.237Z",917 "reply_count": 0,918 "reply_to_post_number": null,919 "quote_count": 0,920 "incoming_link_count": 0,921 "reads": 3,922 "readers_count": 2,923 "score": 0.6,924 "yours": false,925 "topic_id": 143862,926 "topic_slug": "how-can-i-remove-layers-from-torch-model",927 "display_username": "Suho Cho",928 "primary_group_name": null,929 "flair_name": null,930 "flair_url": null,931 "flair_bg_color": null,932 "flair_color": null,933 "flair_group_id": null,934 "badges_granted": [],935 "version": 1,936 "can_edit": false,937 "can_delete": false,938 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