{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ff9b1a2a", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:29:20.525965Z", "iopub.status.busy": "2022-08-07T14:29:20.525281Z", "iopub.status.idle": "2022-08-07T14:29:23.020847Z", "shell.execute_reply": "2022-08-07T14:29:23.019133Z" }, "papermill": { "duration": 2.505383, "end_time": "2022-08-07T14:29:23.024010", "exception": false, "start_time": "2022-08-07T14:29:20.518627", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'stylegan3'...\r\n", "remote: Enumerating objects: 207, done.\u001B[K\r\n", "remote: Total 207 (delta 0), reused 0 (delta 0), pack-reused 207\u001B[K\r\n", "Receiving objects: 100% (207/207), 4.17 MiB | 9.17 MiB/s, done.\r\n", "Resolving deltas: 100% (98/98), done.\r\n" ] } ], "source": [ "!git clone https://github.com/NVlabs/stylegan3" ] }, { "cell_type": "code", "execution_count": 2, "id": "c15192c3", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:29:23.035022Z", "iopub.status.busy": "2022-08-07T14:29:23.034580Z", "iopub.status.idle": "2022-08-07T14:29:23.043053Z", "shell.execute_reply": "2022-08-07T14:29:23.041771Z" }, "papermill": { "duration": 0.018355, "end_time": "2022-08-07T14:29:23.046808", "exception": false, "start_time": "2022-08-07T14:29:23.028453", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/kaggle/working/stylegan3\n" ] } ], "source": [ "%cd stylegan3" ] }, { "cell_type": "code", "execution_count": 3, "id": "4955b935", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:29:23.057484Z", "iopub.status.busy": "2022-08-07T14:29:23.057042Z", "iopub.status.idle": "2022-08-07T14:30:56.548733Z", "shell.execute_reply": "2022-08-07T14:30:56.546755Z" }, "papermill": { "duration": 93.500964, "end_time": "2022-08-07T14:30:56.552235", "exception": false, "start_time": "2022-08-07T14:29:23.051271", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (8.0.4)\r\n", "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (2.27.1)\r\n", "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (4.64.0)\r\n", "Collecting pyspng\r\n", " Downloading pyspng-0.1.0-cp37-cp37m-manylinux2010_x86_64.whl (195 kB)\r\n", "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m195.2/195.2 kB\u001B[0m \u001B[31m1.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\r\n", "\u001B[?25hCollecting ninja\r\n", " Downloading ninja-1.10.2.3-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)\r\n", "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m 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This behaviour is the source of the following dependency conflicts.\r\n", "torchvision 0.12.0+cpu requires torch==1.11.0, but you have torch 1.7.0 which is incompatible.\r\n", "torchtext 0.12.0 requires torch==1.11.0, but you have torch 1.7.0 which is incompatible.\r\n", "torchaudio 0.11.0+cpu requires torch==1.11.0, but you have torch 1.7.0 which is incompatible.\r\n", "pytorch-lightning 1.6.4 requires torch>=1.8.*, but you have torch 1.7.0 which is incompatible.\r\n", "fairscale 0.4.6 requires torch>=1.8.0, but you have torch 1.7.0 which is incompatible.\u001B[0m\u001B[31m\r\n", "\u001B[0mSuccessfully installed coloredlogs-15.0.1 humanfriendly-10.0 imageio-ffmpeg-0.4.3 ninja-1.10.2.3 onnx-simplifier-0.4.6 onnxruntime-1.12.1 pyspng-0.1.0 torch-1.7.0\r\n", "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\r\n", "\u001B[0m" ] } ], "source": [ "!pip install click requests tqdm pyspng ninja imageio-ffmpeg==0.4.3 psutil onnx==1.11.0 onnx-simplifier torch==1.7.0 onnxruntime\n", "# only works on onnx==1.11.0 torch==1.7.0" ] }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "!wget https://huggingface.co/skytnt/fbanime-gan/resolve/main/fbanime.pkl" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "%cd training\n", "!wget https://huggingface.co/skytnt/fbanime-gan/raw/main/code/networks_stylegan2.py -O networks_stylegan2.py\n", "!wget https://huggingface.co/skytnt/fbanime-gan/raw/main/code/dataset.py -O dataset.py\n", "%cd .." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 6, "id": "2b680036", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:31:04.629796Z", "iopub.status.busy": "2022-08-07T14:31:04.628170Z", "iopub.status.idle": "2022-08-07T14:31:15.044766Z", "shell.execute_reply": "2022-08-07T14:31:15.043398Z" }, "papermill": { "duration": 10.46267, "end_time": "2022-08-07T14:31:15.048871", "exception": false, "start_time": "2022-08-07T14:31:04.586201", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "!sed -i \"s/x.square()/x.mul(x)/g\" training/networks_stylegan2.py\n", "!sed -i \"s/y.square()/y.mul(y)/g\" training/networks_stylegan2.py\n", "!sed -i \"s/w.square()/w.mul(w)/g\" training/networks_stylegan2.py\n", "!sed -i \"s/truncation_psi != 1/truncation_psi != None/g\" training/networks_stylegan2.py\n", "!sed -i \"s/x = self.w_avg.lerp(x, truncation_psi)/x = torch.cat([truncation_psi[0].repeat(5),truncation_psi[1].repeat(self.num_ws-5)]).view(1,self.num_ws,1).repeat(1,1,self.w_dim)*(x - self.w_avg) + self.w_avg/g\" training/networks_stylegan2.py\n", "!sed -i \"s/ noise_mode='random', / noise_mode='const', noise_strength=1, /g\" training/networks_stylegan2.py\n", "!sed -i \"s/noise = self.noise_const \\\\* self.noise_strength/noise = self.noise_const * self.noise_strength * noise_strength/g\" training/networks_stylegan2.py\n", "!sed -i \"s/(self, ws, \\\\*\\\\*block_kwargs)/(self, ws, noise_strength, **block_kwargs)/g\" training/networks_stylegan2.py\n", "!sed -i \"s/block(x, img, cur_ws, \\\\*\\\\*block_kwargs)/block(x, img, cur_ws, noise_strength=noise_strength, **block_kwargs)/g\" training/networks_stylegan2.py" ] }, { "cell_type": "code", "execution_count": 7, "id": "5e334d62", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:31:15.136790Z", "iopub.status.busy": "2022-08-07T14:31:15.135731Z", "iopub.status.idle": "2022-08-07T14:31:17.608742Z", "shell.execute_reply": "2022-08-07T14:31:17.606903Z" }, "papermill": { "duration": 2.517209, "end_time": "2022-08-07T14:31:17.611714", "exception": false, "start_time": "2022-08-07T14:31:15.094505", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import pickle\n", "import onnx\n", "import torch\n", "from onnxsim import simplify\n", "from training import networks_stylegan2\n", "import copy\n", "from torch_utils import misc" ] }, { "cell_type": "code", "execution_count": 8, "id": "5391f358", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:31:17.698077Z", "iopub.status.busy": "2022-08-07T14:31:17.697655Z", "iopub.status.idle": "2022-08-07T14:31:17.705063Z", "shell.execute_reply": "2022-08-07T14:31:17.703936Z" }, "papermill": { "duration": 0.056051, "end_time": "2022-08-07T14:31:17.707361", "exception": false, "start_time": "2022-08-07T14:31:17.651310", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "def convert(model_, x, input_names, output_names, path):\n", " model_ = model_.eval()\n", " torch.onnx.export(model_, # model being run\n", " x, # model input (or a tuple for multiple inputs)\n", " path, # where to save the model (can be a file or file-like object)\n", " export_params=True, # store the trained parameter weights inside the model file\n", " opset_version=11, # the ONNX version to export the model to\n", " do_constant_folding=False, # whether to execute constant folding for optimization\n", " input_names=input_names, # the model's input names\n", " output_names=output_names, # the model's output names\n", " verbose=True\n", " )\n", " onnx_model = onnx.load(path)\n", " model_simp, check = simplify(onnx_model)\n", " assert check, \"Simplified ONNX model could not be validated\"\n", " onnx.save(model_simp, path)\n", " print('finished exporting onnx')" ] }, { "cell_type": "code", "execution_count": 9, "id": "4ea563e4", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:31:17.790816Z", "iopub.status.busy": "2022-08-07T14:31:17.790030Z", "iopub.status.idle": "2022-08-07T14:31:19.011850Z", "shell.execute_reply": "2022-08-07T14:31:19.010773Z" }, "papermill": { "duration": 1.266658, "end_time": "2022-08-07T14:31:19.014918", "exception": false, "start_time": "2022-08-07T14:31:17.748260", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "with open('fbanime.pkl', 'rb') as f:\n", " DG = pickle.load(f)\n", "G = networks_stylegan2.Generator(**copy.deepcopy(DG['G_ema'].init_kwargs)).eval().requires_grad_(False)\n", "misc.copy_params_and_buffers(DG['G_ema'], G, require_all=True)\n", "g_mapping = G.mapping\n", "g_synthesis = G.synthesis" ] }, { "cell_type": "code", "execution_count": 10, "id": "8c774981", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:31:19.106942Z", "iopub.status.busy": "2022-08-07T14:31:19.106472Z", "iopub.status.idle": "2022-08-07T14:31:20.294840Z", "shell.execute_reply": "2022-08-07T14:31:20.293471Z" }, "papermill": { "duration": 1.231414, "end_time": "2022-08-07T14:31:20.297680", "exception": false, "start_time": "2022-08-07T14:31:19.066266", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "! mkdir model" ] }, { "cell_type": "code", "execution_count": 11, "id": "ffa2b206", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:31:20.377431Z", "iopub.status.busy": "2022-08-07T14:31:20.376982Z", "iopub.status.idle": "2022-08-07T14:32:18.322557Z", "shell.execute_reply": "2022-08-07T14:32:18.320985Z" }, "papermill": { "duration": 58.034718, "end_time": "2022-08-07T14:32:18.371238", "exception": false, "start_time": "2022-08-07T14:31:20.336520", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "graph(%z : Float(1:512, 512:1, requires_grad=0, device=cpu),\n", " %psi : Float(2:1, requires_grad=0, device=cpu),\n", " %w_avg : Float(512:1, requires_grad=0, device=cpu),\n", " %fc0.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc0.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %fc1.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc1.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %fc2.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc2.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %fc3.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc3.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %fc4.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc4.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %fc5.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc5.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %fc6.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc6.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %fc7.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %fc7.bias : Float(512:1, requires_grad=0, device=cpu)):\n", " %19 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%z) # /kaggle/working/stylegan3/training/networks_stylegan2.py:247:0\n", " %20 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%19, %19) # /kaggle/working/stylegan3/training/networks_stylegan2.py:28:0\n", " %21 : Float(1:1, 1:1, requires_grad=0, device=cpu) = onnx::ReduceMean[axes=[1], keepdims=1](%20) # /kaggle/working/stylegan3/training/networks_stylegan2.py:28:0\n", " %22 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %23 : Float(1:1, 1:1, requires_grad=0, device=cpu) = onnx::Add(%21, %22)\n", " %24 : Tensor = onnx::Sqrt(%23)\n", " %25 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %26 : Float(1:1, 1:1, requires_grad=0, device=cpu) = onnx::Div(%25, %24) # /kaggle/working/stylegan3/training/networks_stylegan2.py:28:0\n", " %27 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%19, %26) # /kaggle/working/stylegan3/training/networks_stylegan2.py:28:0\n", " %28 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %29 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %30 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%28, %29)\n", " %31 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %32 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %33 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%31, %32)\n", " %34 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%30) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %35 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%27, %34) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %36 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %37 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%33, %36) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %38 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%35, %37) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %39 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%38) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %40 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %41 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%39, %40)\n", " %42 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %43 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %44 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%42, %43)\n", " %45 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %46 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %47 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%45, %46)\n", " %48 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%44) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %49 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%41, %48) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %50 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %51 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%47, %50) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %52 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%49, %51) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %53 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%52) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %54 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %55 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%53, %54)\n", " %56 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc2.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %57 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %58 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%56, %57)\n", " %59 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc2.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %60 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %61 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%59, %60)\n", " %62 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%58) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %63 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%55, %62) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %64 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %65 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%61, %64) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %66 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%63, %65) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %67 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%66) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %68 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %69 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%67, %68)\n", " %70 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc3.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %71 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %72 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%70, %71)\n", " %73 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc3.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %74 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %75 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%73, %74)\n", " %76 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%72) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %77 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%69, %76) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %78 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %79 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%75, %78) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %80 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%77, %79) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %81 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%80) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %82 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %83 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%81, %82)\n", " %84 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc4.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %85 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %86 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%84, %85)\n", " %87 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc4.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %88 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %89 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%87, %88)\n", " %90 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%86) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %91 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%83, %90) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %92 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %93 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%89, %92) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %94 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%91, %93) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %95 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%94) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %96 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %97 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%95, %96)\n", " %98 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc5.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %99 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %100 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%98, %99)\n", " %101 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc5.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %102 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %103 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%101, %102)\n", " %104 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%100) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %105 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%97, %104) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %106 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %107 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%103, %106) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %108 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%105, %107) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %109 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%108) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %110 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %111 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%109, %110)\n", " %112 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc6.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %113 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %114 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%112, %113)\n", " %115 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc6.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %116 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %117 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%115, %116)\n", " %118 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%114) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %119 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%111, %118) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %120 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %121 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%117, %120) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %122 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%119, %121) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %123 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%122) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %124 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %125 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%123, %124)\n", " %126 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc7.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %127 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.000441942}]()\n", " %128 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%126, %127)\n", " %129 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%fc7.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %130 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.01}]()\n", " %131 : Float(512:1, requires_grad=0, device=cpu) = onnx::Mul(%129, %130)\n", " %132 : Float(512:1, 512:512, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0]](%128) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %133 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::MatMul(%125, %132) # /kaggle/working/stylegan3/training/networks_stylegan2.py:130:0\n", " %134 : Tensor = onnx::Constant[value= 1 -1 [ CPULongType{2} ]]()\n", " %135 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%131, %134) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %136 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%133, %135) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %137 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%136) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %138 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %139 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%137, %138)\n", " %140 : Float(1:512, 1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%139) # /kaggle/working/stylegan3/training/networks_stylegan2.py:266:0\n", " %141 : int[] = onnx::Constant[value= 1 16 1 [ CPULongType{3} ]]()\n", " %142 : Tensor = onnx::Shape(%141)\n", " %143 : Tensor = onnx::ConstantOfShape[value={1}](%142)\n", " %144 : Tensor = onnx::Expand(%140, %143)\n", " %145 : Float(1:8192, 16:512, 512:1, requires_grad=0, device=cpu) = onnx::Tile(%144, %141) # /kaggle/working/stylegan3/training/networks_stylegan2.py:266:0\n", " %146 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %147 : Float(requires_grad=0, device=cpu) = onnx::Gather[axis=0](%psi, %146) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %148 : int[] = onnx::Constant[value={5}]()\n", " %149 : Tensor = onnx::Shape(%148)\n", " %150 : Tensor = onnx::ConstantOfShape[value={1}](%149)\n", " %151 : Tensor = onnx::Expand(%147, %150)\n", " %152 : Float(5:1, requires_grad=0, device=cpu) = onnx::Tile(%151, %148) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %153 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %154 : Float(requires_grad=0, device=cpu) = onnx::Gather[axis=0](%psi, %153) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %155 : int[] = onnx::Constant[value={11}]()\n", " %156 : Tensor = onnx::Shape(%155)\n", " %157 : Tensor = onnx::ConstantOfShape[value={1}](%156)\n", " %158 : Tensor = onnx::Expand(%154, %157)\n", " %159 : Float(11:1, requires_grad=0, device=cpu) = onnx::Tile(%158, %155) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %160 : Float(16:1, requires_grad=0, device=cpu) = onnx::Concat[axis=0](%152, %159) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %161 : Tensor = onnx::Constant[value= 1 16 1 [ CPULongType{3} ]]()\n", " %162 : Float(1:16, 16:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%160, %161) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %163 : int[] = onnx::Constant[value= 1 1 512 [ CPULongType{3} ]]()\n", " %164 : Tensor = onnx::Shape(%163)\n", " %165 : Tensor = onnx::ConstantOfShape[value={1}](%164)\n", " %166 : Tensor = onnx::Expand(%162, %165)\n", " %167 : Float(1:8192, 16:512, 512:1, requires_grad=0, device=cpu) = onnx::Tile(%166, %163) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %168 : Float(1:8192, 16:512, 512:1, requires_grad=0, device=cpu) = onnx::Sub(%145, %w_avg) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %169 : Float(1:8192, 16:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%167, %168) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " %w : Float(1:8192, 16:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%169, %w_avg) # /kaggle/working/stylegan3/training/networks_stylegan2.py:273:0\n", " return (%w)\n", "\n", "finished exporting onnx\n", "graph(%w : Float(1:8192, 16:512, 512:1, requires_grad=0, device=cpu),\n", " %noise : Float(1:1, requires_grad=0, device=cpu),\n", " %b4.const : Float(512:32, 8:4, 4:1, requires_grad=0, device=cpu),\n", " %b4.conv1.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b4.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b4.conv1.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b4.conv1.noise_const : Float(8:4, 4:1, requires_grad=0, device=cpu),\n", " %b4.conv1.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b4.conv1.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b4.torgb.weight : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b4.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b4.torgb.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b4.torgb.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b8.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b8.conv0.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b8.conv0.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b8.conv0.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b8.conv0.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b8.conv0.noise_const : Float(16:8, 8:1, requires_grad=0, device=cpu),\n", " %b8.conv0.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b8.conv0.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b8.conv1.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b8.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b8.conv1.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b8.conv1.noise_const : Float(16:8, 8:1, requires_grad=0, device=cpu),\n", " %b8.conv1.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b8.conv1.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b8.torgb.weight : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b8.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b8.torgb.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b8.torgb.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b16.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b16.conv0.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b16.conv0.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b16.conv0.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b16.conv0.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b16.conv0.noise_const : Float(32:16, 16:1, requires_grad=0, device=cpu),\n", " %b16.conv0.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b16.conv0.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b16.conv1.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b16.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b16.conv1.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b16.conv1.noise_const : Float(32:16, 16:1, requires_grad=0, device=cpu),\n", " %b16.conv1.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b16.conv1.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b16.torgb.weight : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b16.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b16.torgb.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b16.torgb.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b32.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b32.conv0.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b32.conv0.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b32.conv0.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b32.conv0.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b32.conv0.noise_const : Float(64:32, 32:1, requires_grad=0, device=cpu),\n", " %b32.conv0.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b32.conv0.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b32.conv1.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b32.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b32.conv1.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b32.conv1.noise_const : Float(64:32, 32:1, requires_grad=0, device=cpu),\n", " %b32.conv1.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b32.conv1.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b32.torgb.weight : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b32.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b32.torgb.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b32.torgb.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b64.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b64.conv0.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b64.conv0.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b64.conv0.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b64.conv0.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b64.conv0.noise_const : Float(128:64, 64:1, requires_grad=0, device=cpu),\n", " %b64.conv0.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b64.conv0.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b64.conv1.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b64.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b64.conv1.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b64.conv1.noise_const : Float(128:64, 64:1, requires_grad=0, device=cpu),\n", " %b64.conv1.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b64.conv1.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b64.torgb.weight : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b64.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b64.torgb.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b64.torgb.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b128.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b128.conv0.weight : Float(256:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b128.conv0.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b128.conv0.bias : Float(256:1, requires_grad=0, device=cpu),\n", " %b128.conv0.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b128.conv0.noise_const : Float(256:128, 128:1, requires_grad=0, device=cpu),\n", " %b128.conv0.affine.weight : Float(512:512, 512:1, requires_grad=0, device=cpu),\n", " %b128.conv0.affine.bias : Float(512:1, requires_grad=0, device=cpu),\n", " %b128.conv1.weight : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b128.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b128.conv1.bias : Float(256:1, requires_grad=0, device=cpu),\n", " %b128.conv1.noise_const : Float(256:128, 128:1, requires_grad=0, device=cpu),\n", " %b128.conv1.affine.weight : Float(256:512, 512:1, requires_grad=0, device=cpu),\n", " %b128.conv1.affine.bias : Float(256:1, requires_grad=0, device=cpu),\n", " %b128.torgb.weight : Float(3:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b128.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b128.torgb.affine.weight : Float(256:512, 512:1, requires_grad=0, device=cpu),\n", " %b128.torgb.affine.bias : Float(256:1, requires_grad=0, device=cpu),\n", " %b256.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b256.conv0.weight : Float(128:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b256.conv0.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b256.conv0.bias : Float(128:1, requires_grad=0, device=cpu),\n", " %b256.conv0.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b256.conv0.noise_const : Float(512:256, 256:1, requires_grad=0, device=cpu),\n", " %b256.conv0.affine.weight : Float(256:512, 512:1, requires_grad=0, device=cpu),\n", " %b256.conv0.affine.bias : Float(256:1, requires_grad=0, device=cpu),\n", " %b256.conv1.weight : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b256.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b256.conv1.bias : Float(128:1, requires_grad=0, device=cpu),\n", " %b256.conv1.noise_const : Float(512:256, 256:1, requires_grad=0, device=cpu),\n", " %b256.conv1.affine.weight : Float(128:512, 512:1, requires_grad=0, device=cpu),\n", " %b256.conv1.affine.bias : Float(128:1, requires_grad=0, device=cpu),\n", " %b256.torgb.weight : Float(3:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b256.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b256.torgb.affine.weight : Float(128:512, 512:1, requires_grad=0, device=cpu),\n", " %b256.torgb.affine.bias : Float(128:1, requires_grad=0, device=cpu),\n", " %b512.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b512.conv0.weight : Float(64:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b512.conv0.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b512.conv0.bias : Float(64:1, requires_grad=0, device=cpu),\n", " %b512.conv0.resample_filter : Float(4:4, 4:1, requires_grad=0, device=cpu),\n", " %b512.conv0.noise_const : Float(1024:512, 512:1, requires_grad=0, device=cpu),\n", " %b512.conv0.affine.weight : Float(128:512, 512:1, requires_grad=0, device=cpu),\n", " %b512.conv0.affine.bias : Float(128:1, requires_grad=0, device=cpu),\n", " %b512.conv1.weight : Float(64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu),\n", " %b512.conv1.noise_strength : Float(requires_grad=0, device=cpu),\n", " %b512.conv1.bias : Float(64:1, requires_grad=0, device=cpu),\n", " %b512.conv1.noise_const : Float(1024:512, 512:1, requires_grad=0, device=cpu),\n", " %b512.conv1.affine.weight : Float(64:512, 512:1, requires_grad=0, device=cpu),\n", " %b512.conv1.affine.bias : Float(64:1, requires_grad=0, device=cpu),\n", " %b512.torgb.weight : Float(3:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu),\n", " %b512.torgb.bias : Float(3:1, requires_grad=0, device=cpu),\n", " %b512.torgb.affine.weight : Float(64:512, 512:1, requires_grad=0, device=cpu),\n", " %b512.torgb.affine.bias : Float(64:1, requires_grad=0, device=cpu)):\n", " %148 : Float(1:8192, 16:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%w) # /kaggle/working/stylegan3/training/networks_stylegan2.py:529:0\n", " %149 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %150 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %151 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %152 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %153 : LongTensor = onnx::Add(%151, %152)\n", " %154 : Tensor = onnx::Unsqueeze[axes=[0]](%150)\n", " %155 : Tensor = onnx::Unsqueeze[axes=[0]](%153)\n", " %156 : Tensor = onnx::Unsqueeze[axes=[0]](%149)\n", " %157 : Float(1:8192, 2:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %154, %155, %156) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %158 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %159 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %160 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %161 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %162 : LongTensor = onnx::Add(%160, %161)\n", " %163 : Tensor = onnx::Unsqueeze[axes=[0]](%159)\n", " %164 : Tensor = onnx::Unsqueeze[axes=[0]](%162)\n", " %165 : Tensor = onnx::Unsqueeze[axes=[0]](%158)\n", " %166 : Float(1:8192, 3:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %163, %164, %165) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %167 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %168 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %169 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %170 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %171 : LongTensor = onnx::Add(%169, %170)\n", " %172 : Tensor = onnx::Unsqueeze[axes=[0]](%168)\n", " %173 : Tensor = onnx::Unsqueeze[axes=[0]](%171)\n", " %174 : Tensor = onnx::Unsqueeze[axes=[0]](%167)\n", " %175 : Float(1:8192, 3:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %172, %173, %174) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %176 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %177 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={5}]()\n", " %178 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={5}]()\n", " %179 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %180 : LongTensor = onnx::Add(%178, %179)\n", " %181 : Tensor = onnx::Unsqueeze[axes=[0]](%177)\n", " %182 : Tensor = onnx::Unsqueeze[axes=[0]](%180)\n", " %183 : Tensor = onnx::Unsqueeze[axes=[0]](%176)\n", " %184 : Float(1:8192, 3:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %181, %182, %183) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %185 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %186 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={7}]()\n", " %187 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={7}]()\n", " %188 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %189 : LongTensor = onnx::Add(%187, %188)\n", " %190 : Tensor = onnx::Unsqueeze[axes=[0]](%186)\n", " %191 : Tensor = onnx::Unsqueeze[axes=[0]](%189)\n", " %192 : Tensor = onnx::Unsqueeze[axes=[0]](%185)\n", " %193 : Float(1:8192, 3:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %190, %191, %192) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %194 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %195 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={9}]()\n", " %196 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={9}]()\n", " %197 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %198 : LongTensor = onnx::Add(%196, %197)\n", " %199 : Tensor = onnx::Unsqueeze[axes=[0]](%195)\n", " %200 : Tensor = onnx::Unsqueeze[axes=[0]](%198)\n", " %201 : Tensor = onnx::Unsqueeze[axes=[0]](%194)\n", " %202 : Float(1:8192, 3:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %199, %200, %201) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %203 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %204 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={11}]()\n", " %205 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={11}]()\n", " %206 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %207 : LongTensor = onnx::Add(%205, %206)\n", " %208 : Tensor = onnx::Unsqueeze[axes=[0]](%204)\n", " %209 : Tensor = onnx::Unsqueeze[axes=[0]](%207)\n", " %210 : Tensor = onnx::Unsqueeze[axes=[0]](%203)\n", " %211 : Float(1:8192, 3:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %208, %209, %210) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %212 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %213 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={13}]()\n", " %214 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={13}]()\n", " %215 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %216 : LongTensor = onnx::Add(%214, %215)\n", " %217 : Tensor = onnx::Unsqueeze[axes=[0]](%213)\n", " %218 : Tensor = onnx::Unsqueeze[axes=[0]](%216)\n", " %219 : Tensor = onnx::Unsqueeze[axes=[0]](%212)\n", " %220 : Float(1:8192, 3:512, 512:1, requires_grad=0, device=cpu) = onnx::Slice(%148, %217, %218, %219) # /kaggle/working/stylegan3/training/networks_stylegan2.py:533:0\n", " %221 : Tensor, %222 : Tensor = onnx::Split[axis=1, split=[1, 1]](%157)\n", " %223 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%221) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %224 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%222) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %225 : Float(512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b4.const) # /kaggle/working/stylegan3/training/networks_stylegan2.py:449:0\n", " %226 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%225) # /kaggle/working/stylegan3/training/networks_stylegan2.py:450:0\n", " %227 : Tensor = onnx::Shape(%157)\n", " %228 : Tensor = onnx::Constant[value={0}]()\n", " %229 : Long(device=cpu) = onnx::Gather[axis=0](%227, %228) # /kaggle/working/stylegan3/training/networks_stylegan2.py:450:0\n", " %230 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %231 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %232 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %233 : Tensor = onnx::Unsqueeze[axes=[0]](%229)\n", " %234 : Tensor = onnx::Unsqueeze[axes=[0]](%230)\n", " %235 : Tensor = onnx::Unsqueeze[axes=[0]](%231)\n", " %236 : Tensor = onnx::Unsqueeze[axes=[0]](%232)\n", " %237 : Tensor = onnx::Concat[axis=0](%233, %234, %235, %236)\n", " %238 : Tensor = onnx::Unsqueeze[axes=[0]](%229)\n", " %239 : Tensor = onnx::Unsqueeze[axes=[0]](%230)\n", " %240 : Tensor = onnx::Unsqueeze[axes=[0]](%231)\n", " %241 : Tensor = onnx::Unsqueeze[axes=[0]](%232)\n", " %242 : Tensor = onnx::Concat[axis=0](%238, %239, %240, %241)\n", " %243 : Tensor = onnx::Shape(%237)\n", " %244 : Tensor = onnx::ConstantOfShape[value={1}](%243)\n", " %245 : Tensor = onnx::Expand(%226, %244)\n", " %246 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%245, %242) # /kaggle/working/stylegan3/training/networks_stylegan2.py:450:0\n", " %247 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b4.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %248 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %249 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%247, %248)\n", " %250 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b4.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %251 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%250) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %252 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%223, %249, %251) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %253 : Float(8:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b4.conv1.noise_const, %b4.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %254 : Float(8:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%253, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %255 : Tensor = onnx::Shape(%246)\n", " %256 : Tensor = onnx::Constant[value={0}]()\n", " %257 : Long(device=cpu) = onnx::Gather[axis=0](%255, %256) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %258 : Tensor = onnx::Shape(%b4.conv1.weight)\n", " %259 : Tensor = onnx::Constant[value={1}]()\n", " %260 : Long(device=cpu) = onnx::Gather[axis=0](%258, %259) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %261 : Tensor = onnx::Shape(%b4.conv1.weight)\n", " %262 : Tensor = onnx::Constant[value={2}]()\n", " %263 : Long(device=cpu) = onnx::Gather[axis=0](%261, %262) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %264 : Tensor = onnx::Shape(%b4.conv1.weight)\n", " %265 : Tensor = onnx::Constant[value={3}]()\n", " %266 : Long(device=cpu) = onnx::Gather[axis=0](%264, %265) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %267 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b4.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %268 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %269 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %270 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %271 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %272 : Tensor = onnx::Unsqueeze[axes=[0]](%257)\n", " %273 : Tensor = onnx::Unsqueeze[axes=[0]](%268)\n", " %274 : Tensor = onnx::Unsqueeze[axes=[0]](%269)\n", " %275 : Tensor = onnx::Unsqueeze[axes=[0]](%270)\n", " %276 : Tensor = onnx::Unsqueeze[axes=[0]](%271)\n", " %277 : Tensor = onnx::Concat[axis=0](%272, %273, %274, %275, %276)\n", " %278 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%252, %277) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %279 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%267, %278) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %280 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%279, %279) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %281 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%280) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %282 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %283 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%281, %282)\n", " %284 : Tensor = onnx::Sqrt(%283)\n", " %285 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %286 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%285, %284) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %287 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %288 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %289 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %290 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %291 : Tensor = onnx::Unsqueeze[axes=[0]](%257)\n", " %292 : Tensor = onnx::Unsqueeze[axes=[0]](%287)\n", " %293 : Tensor = onnx::Unsqueeze[axes=[0]](%288)\n", " %294 : Tensor = onnx::Unsqueeze[axes=[0]](%289)\n", " %295 : Tensor = onnx::Unsqueeze[axes=[0]](%290)\n", " %296 : Tensor = onnx::Concat[axis=0](%291, %292, %293, %294, %295)\n", " %297 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%286, %296) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %298 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%279, %297) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %299 : Tensor = onnx::Shape(%246)\n", " %300 : Tensor = onnx::Constant[value={2}]()\n", " %301 : Long(device=cpu) = onnx::Gather[axis=0](%299, %300) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %302 : Tensor = onnx::Shape(%246)\n", " %303 : Tensor = onnx::Constant[value={3}]()\n", " %304 : Long(device=cpu) = onnx::Gather[axis=0](%302, %303) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %305 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %306 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %307 : Tensor = onnx::Unsqueeze[axes=[0]](%305)\n", " %308 : Tensor = onnx::Unsqueeze[axes=[0]](%306)\n", " %309 : Tensor = onnx::Unsqueeze[axes=[0]](%301)\n", " %310 : Tensor = onnx::Unsqueeze[axes=[0]](%304)\n", " %311 : Tensor = onnx::Concat[axis=0](%307, %308, %309, %310)\n", " %312 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Reshape(%246, %311) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %313 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %314 : Tensor = onnx::Unsqueeze[axes=[0]](%313)\n", " %315 : Tensor = onnx::Unsqueeze[axes=[0]](%260)\n", " %316 : Tensor = onnx::Unsqueeze[axes=[0]](%263)\n", " %317 : Tensor = onnx::Unsqueeze[axes=[0]](%266)\n", " %318 : Tensor = onnx::Concat[axis=0](%314, %315, %316, %317)\n", " %319 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%298, %318) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %320 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%319) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %321 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%312, %320) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %322 : Tensor = onnx::Shape(%321)\n", " %323 : Tensor = onnx::Constant[value={2}]()\n", " %324 : Long(device=cpu) = onnx::Gather[axis=0](%322, %323) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %325 : Tensor = onnx::Shape(%321)\n", " %326 : Tensor = onnx::Constant[value={3}]()\n", " %327 : Long(device=cpu) = onnx::Gather[axis=0](%325, %326) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %328 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %329 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %330 : Tensor = onnx::Unsqueeze[axes=[0]](%328)\n", " %331 : Tensor = onnx::Unsqueeze[axes=[0]](%329)\n", " %332 : Tensor = onnx::Unsqueeze[axes=[0]](%324)\n", " %333 : Tensor = onnx::Unsqueeze[axes=[0]](%327)\n", " %334 : Tensor = onnx::Concat[axis=0](%330, %331, %332, %333)\n", " %335 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Reshape(%321, %334) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %336 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Add(%335, %254)\n", " %337 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b4.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %338 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %339 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%337, %338) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %340 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Add(%336, %339) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %341 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%340) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %342 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %343 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%341, %342)\n", " %344 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b4.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %345 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %346 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%344, %345)\n", " %347 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b4.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %348 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%347) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %349 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%224, %346, %348) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %350 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %351 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%349, %350)\n", " %352 : Tensor = onnx::Shape(%343)\n", " %353 : Tensor = onnx::Constant[value={0}]()\n", " %354 : Long(device=cpu) = onnx::Gather[axis=0](%352, %353) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %355 : Tensor = onnx::Shape(%b4.torgb.weight)\n", " %356 : Tensor = onnx::Constant[value={1}]()\n", " %357 : Long(device=cpu) = onnx::Gather[axis=0](%355, %356) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %358 : Tensor = onnx::Shape(%b4.torgb.weight)\n", " %359 : Tensor = onnx::Constant[value={2}]()\n", " %360 : Long(device=cpu) = onnx::Gather[axis=0](%358, %359) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %361 : Tensor = onnx::Shape(%b4.torgb.weight)\n", " %362 : Tensor = onnx::Constant[value={3}]()\n", " %363 : Long(device=cpu) = onnx::Gather[axis=0](%361, %362) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %364 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b4.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %365 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %366 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %367 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %368 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %369 : Tensor = onnx::Unsqueeze[axes=[0]](%354)\n", " %370 : Tensor = onnx::Unsqueeze[axes=[0]](%365)\n", " %371 : Tensor = onnx::Unsqueeze[axes=[0]](%366)\n", " %372 : Tensor = onnx::Unsqueeze[axes=[0]](%367)\n", " %373 : Tensor = onnx::Unsqueeze[axes=[0]](%368)\n", " %374 : Tensor = onnx::Concat[axis=0](%369, %370, %371, %372, %373)\n", " %375 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%351, %374) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %376 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%364, %375) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %377 : Tensor = onnx::Shape(%343)\n", " %378 : Tensor = onnx::Constant[value={2}]()\n", " %379 : Long(device=cpu) = onnx::Gather[axis=0](%377, %378) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %380 : Tensor = onnx::Shape(%343)\n", " %381 : Tensor = onnx::Constant[value={3}]()\n", " %382 : Long(device=cpu) = onnx::Gather[axis=0](%380, %381) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %383 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %384 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %385 : Tensor = onnx::Unsqueeze[axes=[0]](%383)\n", " %386 : Tensor = onnx::Unsqueeze[axes=[0]](%384)\n", " %387 : Tensor = onnx::Unsqueeze[axes=[0]](%379)\n", " %388 : Tensor = onnx::Unsqueeze[axes=[0]](%382)\n", " %389 : Tensor = onnx::Concat[axis=0](%385, %386, %387, %388)\n", " %390 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Reshape(%343, %389) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %391 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %392 : Tensor = onnx::Unsqueeze[axes=[0]](%391)\n", " %393 : Tensor = onnx::Unsqueeze[axes=[0]](%357)\n", " %394 : Tensor = onnx::Unsqueeze[axes=[0]](%360)\n", " %395 : Tensor = onnx::Unsqueeze[axes=[0]](%363)\n", " %396 : Tensor = onnx::Concat[axis=0](%392, %393, %394, %395)\n", " %397 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%376, %396) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %398 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%397) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %399 : Float(1:96, 3:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%390, %398) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %400 : Tensor = onnx::Shape(%399)\n", " %401 : Tensor = onnx::Constant[value={2}]()\n", " %402 : Long(device=cpu) = onnx::Gather[axis=0](%400, %401) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %403 : Tensor = onnx::Shape(%399)\n", " %404 : Tensor = onnx::Constant[value={3}]()\n", " %405 : Long(device=cpu) = onnx::Gather[axis=0](%403, %404) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %406 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %407 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %408 : Tensor = onnx::Unsqueeze[axes=[0]](%406)\n", " %409 : Tensor = onnx::Unsqueeze[axes=[0]](%407)\n", " %410 : Tensor = onnx::Unsqueeze[axes=[0]](%402)\n", " %411 : Tensor = onnx::Unsqueeze[axes=[0]](%405)\n", " %412 : Tensor = onnx::Concat[axis=0](%408, %409, %410, %411)\n", " %413 : Float(1:96, 3:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Reshape(%399, %412) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %414 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b4.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %415 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %416 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%414, %415) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %417 : Float(1:96, 3:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Add(%413, %416) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %418 : Float(1:96, 3:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%417) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %419 : Tensor, %420 : Tensor, %421 : Tensor = onnx::Split[axis=1, split=[1, 1, 1]](%166)\n", " %422 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%419) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %423 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%420) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %424 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%421) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %425 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%343) # /kaggle/working/stylegan3/training/networks_stylegan2.py:453:0\n", " %426 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.conv0.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %427 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %428 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%426, %427)\n", " %429 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.conv0.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %430 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%429) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %431 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%422, %428, %430) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %432 : Float(16:8, 8:1, requires_grad=0, device=cpu) = onnx::Mul(%b8.conv0.noise_const, %b8.conv0.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %433 : Float(16:8, 8:1, requires_grad=0, device=cpu) = onnx::Mul(%432, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %434 : Tensor = onnx::Shape(%425)\n", " %435 : Tensor = onnx::Constant[value={0}]()\n", " %436 : Long(device=cpu) = onnx::Gather[axis=0](%434, %435) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %437 : Tensor = onnx::Shape(%b8.conv0.weight)\n", " %438 : Tensor = onnx::Constant[value={1}]()\n", " %439 : Long(device=cpu) = onnx::Gather[axis=0](%437, %438) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %440 : Tensor = onnx::Shape(%b8.conv0.weight)\n", " %441 : Tensor = onnx::Constant[value={2}]()\n", " %442 : Long(device=cpu) = onnx::Gather[axis=0](%440, %441) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %443 : Tensor = onnx::Shape(%b8.conv0.weight)\n", " %444 : Tensor = onnx::Constant[value={3}]()\n", " %445 : Long(device=cpu) = onnx::Gather[axis=0](%443, %444) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %446 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b8.conv0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %447 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %448 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %449 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %450 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %451 : Tensor = onnx::Unsqueeze[axes=[0]](%436)\n", " %452 : Tensor = onnx::Unsqueeze[axes=[0]](%447)\n", " %453 : Tensor = onnx::Unsqueeze[axes=[0]](%448)\n", " %454 : Tensor = onnx::Unsqueeze[axes=[0]](%449)\n", " %455 : Tensor = onnx::Unsqueeze[axes=[0]](%450)\n", " %456 : Tensor = onnx::Concat[axis=0](%451, %452, %453, %454, %455)\n", " %457 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%431, %456) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %458 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%446, %457) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %459 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%458, %458) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %460 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%459) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %461 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %462 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%460, %461)\n", " %463 : Tensor = onnx::Sqrt(%462)\n", " %464 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %465 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%464, %463) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %466 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %467 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %468 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %469 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %470 : Tensor = onnx::Unsqueeze[axes=[0]](%436)\n", " %471 : Tensor = onnx::Unsqueeze[axes=[0]](%466)\n", " %472 : Tensor = onnx::Unsqueeze[axes=[0]](%467)\n", " %473 : Tensor = onnx::Unsqueeze[axes=[0]](%468)\n", " %474 : Tensor = onnx::Unsqueeze[axes=[0]](%469)\n", " %475 : Tensor = onnx::Concat[axis=0](%470, %471, %472, %473, %474)\n", " %476 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%465, %475) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %477 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%458, %476) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %478 : Tensor = onnx::Shape(%425)\n", " %479 : Tensor = onnx::Constant[value={2}]()\n", " %480 : Long(device=cpu) = onnx::Gather[axis=0](%478, %479) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %481 : Tensor = onnx::Shape(%425)\n", " %482 : Tensor = onnx::Constant[value={3}]()\n", " %483 : Long(device=cpu) = onnx::Gather[axis=0](%481, %482) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %484 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %485 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %486 : Tensor = onnx::Unsqueeze[axes=[0]](%484)\n", " %487 : Tensor = onnx::Unsqueeze[axes=[0]](%485)\n", " %488 : Tensor = onnx::Unsqueeze[axes=[0]](%480)\n", " %489 : Tensor = onnx::Unsqueeze[axes=[0]](%483)\n", " %490 : Tensor = onnx::Concat[axis=0](%486, %487, %488, %489)\n", " %491 : Float(1:16384, 512:32, 8:4, 4:1, requires_grad=0, device=cpu) = onnx::Reshape(%425, %490) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %492 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %493 : Tensor = onnx::Unsqueeze[axes=[0]](%492)\n", " %494 : Tensor = onnx::Unsqueeze[axes=[0]](%439)\n", " %495 : Tensor = onnx::Unsqueeze[axes=[0]](%442)\n", " %496 : Tensor = onnx::Unsqueeze[axes=[0]](%445)\n", " %497 : Tensor = onnx::Concat[axis=0](%493, %494, %495, %496)\n", " %498 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%477, %497) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %499 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%498) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %500 : Float(512:9, 512:4608, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0, 2, 3]](%499) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_resample.py:114:0\n", " %501 : Float(1:78336, 512:153, 17:9, 9:1, requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%491, %500) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:45:0\n", " %502 : Tensor = onnx::Shape(%501)\n", " %503 : Tensor = onnx::Constant[value={0}]()\n", " %504 : Long(device=cpu) = onnx::Gather[axis=0](%502, %503) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %505 : Tensor = onnx::Shape(%501)\n", " %506 : Tensor = onnx::Constant[value={1}]()\n", " %507 : Long(device=cpu) = onnx::Gather[axis=0](%505, %506) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %508 : Tensor = onnx::Shape(%501)\n", " %509 : Tensor = onnx::Constant[value={2}]()\n", " %510 : Long(device=cpu) = onnx::Gather[axis=0](%508, %509) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %511 : Tensor = onnx::Shape(%501)\n", " %512 : Tensor = onnx::Constant[value={3}]()\n", " %513 : Long(device=cpu) = onnx::Gather[axis=0](%511, %512) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %514 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %515 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %516 : Tensor = onnx::Unsqueeze[axes=[0]](%504)\n", " %517 : Tensor = onnx::Unsqueeze[axes=[0]](%507)\n", " %518 : Tensor = onnx::Unsqueeze[axes=[0]](%510)\n", " %519 : Tensor = onnx::Unsqueeze[axes=[0]](%514)\n", " %520 : Tensor = onnx::Unsqueeze[axes=[0]](%513)\n", " %521 : Tensor = onnx::Unsqueeze[axes=[0]](%515)\n", " %522 : Tensor = onnx::Concat[axis=0](%516, %517, %518, %519, %520, %521)\n", " %523 : Float(1:78336, 512:153, 17:9, 1:9, 9:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%501, %522) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %524 : int[] = onnx::Constant[value= 0 0 0 0 0 0 [ CPULongType{6} ]]()\n", " %525 : Tensor = onnx::Constant[value={0}]()\n", " %526 : Tensor = onnx::Shape(%524)\n", " %527 : Tensor = onnx::Gather[axis=0](%526, %525)\n", " %528 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %529 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %530 : LongTensor = onnx::Mul(%528, %529)\n", " %531 : LongTensor = onnx::Sub(%530, %527)\n", " %532 : Tensor = onnx::Cast[to=7](%524)\n", " %533 : Tensor = onnx::ConstantOfShape[value={0}](%531)\n", " %534 : Tensor = onnx::Concat[axis=0](%532, %533)\n", " %535 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %536 : Tensor = onnx::Reshape(%534, %535)\n", " %537 : Tensor = onnx::Constant[value={0}]()\n", " %538 : Tensor = onnx::Constant[value={-1}]()\n", " %539 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %540 : Tensor = onnx::Constant[value={-1}]()\n", " %541 : Tensor = onnx::Slice(%536, %538, %539, %537, %540)\n", " %542 : Tensor = onnx::Transpose[perm=[1, 0]](%541)\n", " %543 : Tensor = onnx::Constant[value={-1}]()\n", " %544 : Tensor = onnx::Reshape(%542, %543)\n", " %545 : Tensor = onnx::Cast[to=7](%544)\n", " %546 : Tensor = onnx::Constant[value={0}]()\n", " %547 : Float(1:78336, 512:153, 17:9, 1:9, 9:1, 1:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%523, %545, %546) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %548 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %549 : Long(requires_grad=0, device=cpu) = onnx::Mul(%510, %548)\n", " %550 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %551 : Long(requires_grad=0, device=cpu) = onnx::Mul(%513, %550)\n", " %552 : Tensor = onnx::Unsqueeze[axes=[0]](%504)\n", " %553 : Tensor = onnx::Unsqueeze[axes=[0]](%507)\n", " %554 : Tensor = onnx::Unsqueeze[axes=[0]](%549)\n", " %555 : Tensor = onnx::Unsqueeze[axes=[0]](%551)\n", " %556 : Tensor = onnx::Concat[axis=0](%552, %553, %554, %555)\n", " %557 : Float(1:78336, 512:153, 17:9, 9:1, requires_grad=0, device=cpu) = onnx::Reshape(%547, %556) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %558 : int[] = onnx::Constant[value= 1 1 1 1 [ CPULongType{4} ]]()\n", " %559 : Tensor = onnx::Constant[value={0}]()\n", " %560 : Tensor = onnx::Shape(%558)\n", " %561 : Tensor = onnx::Gather[axis=0](%560, %559)\n", " %562 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %563 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %564 : LongTensor = onnx::Mul(%562, %563)\n", " %565 : LongTensor = onnx::Sub(%564, %561)\n", " %566 : Tensor = onnx::Cast[to=7](%558)\n", " %567 : Tensor = onnx::ConstantOfShape[value={0}](%565)\n", " %568 : Tensor = onnx::Concat[axis=0](%566, %567)\n", " %569 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %570 : Tensor = onnx::Reshape(%568, %569)\n", " %571 : Tensor = onnx::Constant[value={0}]()\n", " %572 : Tensor = onnx::Constant[value={-1}]()\n", " %573 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %574 : Tensor = onnx::Constant[value={-1}]()\n", " %575 : Tensor = onnx::Slice(%570, %572, %573, %571, %574)\n", " %576 : Tensor = onnx::Transpose[perm=[1, 0]](%575)\n", " %577 : Tensor = onnx::Constant[value={-1}]()\n", " %578 : Tensor = onnx::Reshape(%576, %577)\n", " %579 : Tensor = onnx::Cast[to=7](%578)\n", " %580 : Tensor = onnx::Constant[value={0}]()\n", " %581 : Float(1:107008, 512:209, 19:11, 11:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%557, %579, %580) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %582 : Tensor = onnx::Shape(%581)\n", " %583 : Tensor = onnx::Constant[value={2}]()\n", " %584 : Long(device=cpu) = onnx::Gather[axis=0](%582, %583) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %585 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %586 : Long(requires_grad=0, device=cpu) = onnx::Sub(%584, %585)\n", " %587 : Tensor = onnx::Shape(%581)\n", " %588 : Tensor = onnx::Constant[value={3}]()\n", " %589 : Long(device=cpu) = onnx::Gather[axis=0](%587, %588) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %590 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %591 : Long(requires_grad=0, device=cpu) = onnx::Sub(%589, %590)\n", " %592 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %593 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %594 : Tensor = onnx::Unsqueeze[axes=[0]](%593)\n", " %595 : Tensor = onnx::Unsqueeze[axes=[0]](%586)\n", " %596 : Tensor = onnx::Unsqueeze[axes=[0]](%592)\n", " %597 : Tensor = onnx::Constant[value={1}]()\n", " %598 : Float(1:107008, 512:209, 19:11, 11:1, requires_grad=0, device=cpu) = onnx::Slice(%581, %594, %595, %596, %597) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %599 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %600 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %601 : Tensor = onnx::Unsqueeze[axes=[0]](%600)\n", " %602 : Tensor = onnx::Unsqueeze[axes=[0]](%591)\n", " %603 : Tensor = onnx::Unsqueeze[axes=[0]](%599)\n", " %604 : Tensor = onnx::Constant[value={1}]()\n", " %605 : Float(1:107008, 512:209, 19:11, 11:1, requires_grad=0, device=cpu) = onnx::Slice(%598, %601, %602, %603, %604) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %606 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %607 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b8.conv0.resample_filter, %606)\n", " %608 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%607) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %609 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %610 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %611 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %612 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %613 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%608, %610, %611, %609, %612) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %614 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%613) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %615 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%614) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %616 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %617 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %618 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %619 : Tensor = onnx::Unsqueeze[axes=[0]](%507)\n", " %620 : Tensor = onnx::Unsqueeze[axes=[0]](%616)\n", " %621 : Tensor = onnx::Unsqueeze[axes=[0]](%617)\n", " %622 : Tensor = onnx::Unsqueeze[axes=[0]](%618)\n", " %623 : Tensor = onnx::Concat[axis=0](%619, %620, %621, %622)\n", " %624 : Tensor = onnx::Unsqueeze[axes=[0]](%507)\n", " %625 : Tensor = onnx::Unsqueeze[axes=[0]](%616)\n", " %626 : Tensor = onnx::Unsqueeze[axes=[0]](%617)\n", " %627 : Tensor = onnx::Unsqueeze[axes=[0]](%618)\n", " %628 : Tensor = onnx::Concat[axis=0](%624, %625, %626, %627)\n", " %629 : Tensor = onnx::Shape(%623)\n", " %630 : Tensor = onnx::ConstantOfShape[value={1}](%629)\n", " %631 : Tensor = onnx::Expand(%615, %630)\n", " %632 : Float(512:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%631, %628) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %633 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%605, %632) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %634 : Tensor = onnx::Shape(%633)\n", " %635 : Tensor = onnx::Constant[value={2}]()\n", " %636 : Long(device=cpu) = onnx::Gather[axis=0](%634, %635) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %637 : Tensor = onnx::Shape(%633)\n", " %638 : Tensor = onnx::Constant[value={3}]()\n", " %639 : Long(device=cpu) = onnx::Gather[axis=0](%637, %638) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %640 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %641 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %642 : Tensor = onnx::Unsqueeze[axes=[0]](%640)\n", " %643 : Tensor = onnx::Unsqueeze[axes=[0]](%641)\n", " %644 : Tensor = onnx::Unsqueeze[axes=[0]](%636)\n", " %645 : Tensor = onnx::Unsqueeze[axes=[0]](%639)\n", " %646 : Tensor = onnx::Concat[axis=0](%642, %643, %644, %645)\n", " %647 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Reshape(%633, %646) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %648 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Add(%647, %433)\n", " %649 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.conv0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %650 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %651 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%649, %650) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %652 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Add(%648, %651) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %653 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%652) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %654 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %655 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Mul(%653, %654)\n", " %656 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %657 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %658 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%656, %657)\n", " %659 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %660 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%659) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %661 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%423, %658, %660) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %662 : Float(16:8, 8:1, requires_grad=0, device=cpu) = onnx::Mul(%b8.conv1.noise_const, %b8.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %663 : Float(16:8, 8:1, requires_grad=0, device=cpu) = onnx::Mul(%662, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %664 : Tensor = onnx::Shape(%655)\n", " %665 : Tensor = onnx::Constant[value={0}]()\n", " %666 : Long(device=cpu) = onnx::Gather[axis=0](%664, %665) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %667 : Tensor = onnx::Shape(%b8.conv1.weight)\n", " %668 : Tensor = onnx::Constant[value={1}]()\n", " %669 : Long(device=cpu) = onnx::Gather[axis=0](%667, %668) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %670 : Tensor = onnx::Shape(%b8.conv1.weight)\n", " %671 : Tensor = onnx::Constant[value={2}]()\n", " %672 : Long(device=cpu) = onnx::Gather[axis=0](%670, %671) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %673 : Tensor = onnx::Shape(%b8.conv1.weight)\n", " %674 : Tensor = onnx::Constant[value={3}]()\n", " %675 : Long(device=cpu) = onnx::Gather[axis=0](%673, %674) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %676 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b8.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %677 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %678 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %679 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %680 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %681 : Tensor = onnx::Unsqueeze[axes=[0]](%666)\n", " %682 : Tensor = onnx::Unsqueeze[axes=[0]](%677)\n", " %683 : Tensor = onnx::Unsqueeze[axes=[0]](%678)\n", " %684 : Tensor = onnx::Unsqueeze[axes=[0]](%679)\n", " %685 : Tensor = onnx::Unsqueeze[axes=[0]](%680)\n", " %686 : Tensor = onnx::Concat[axis=0](%681, %682, %683, %684, %685)\n", " %687 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%661, %686) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %688 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%676, %687) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %689 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%688, %688) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %690 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%689) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %691 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %692 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%690, %691)\n", " %693 : Tensor = onnx::Sqrt(%692)\n", " %694 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %695 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%694, %693) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %696 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %697 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %698 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %699 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %700 : Tensor = onnx::Unsqueeze[axes=[0]](%666)\n", " %701 : Tensor = onnx::Unsqueeze[axes=[0]](%696)\n", " %702 : Tensor = onnx::Unsqueeze[axes=[0]](%697)\n", " %703 : Tensor = onnx::Unsqueeze[axes=[0]](%698)\n", " %704 : Tensor = onnx::Unsqueeze[axes=[0]](%699)\n", " %705 : Tensor = onnx::Concat[axis=0](%700, %701, %702, %703, %704)\n", " %706 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%695, %705) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %707 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%688, %706) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %708 : Tensor = onnx::Shape(%655)\n", " %709 : Tensor = onnx::Constant[value={2}]()\n", " %710 : Long(device=cpu) = onnx::Gather[axis=0](%708, %709) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %711 : Tensor = onnx::Shape(%655)\n", " %712 : Tensor = onnx::Constant[value={3}]()\n", " %713 : Long(device=cpu) = onnx::Gather[axis=0](%711, %712) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %714 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %715 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %716 : Tensor = onnx::Unsqueeze[axes=[0]](%714)\n", " %717 : Tensor = onnx::Unsqueeze[axes=[0]](%715)\n", " %718 : Tensor = onnx::Unsqueeze[axes=[0]](%710)\n", " %719 : Tensor = onnx::Unsqueeze[axes=[0]](%713)\n", " %720 : Tensor = onnx::Concat[axis=0](%716, %717, %718, %719)\n", " %721 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Reshape(%655, %720) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %722 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %723 : Tensor = onnx::Unsqueeze[axes=[0]](%722)\n", " %724 : Tensor = onnx::Unsqueeze[axes=[0]](%669)\n", " %725 : Tensor = onnx::Unsqueeze[axes=[0]](%672)\n", " %726 : Tensor = onnx::Unsqueeze[axes=[0]](%675)\n", " %727 : Tensor = onnx::Concat[axis=0](%723, %724, %725, %726)\n", " %728 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%707, %727) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %729 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%728) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %730 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%721, %729) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %731 : Tensor = onnx::Shape(%730)\n", " %732 : Tensor = onnx::Constant[value={2}]()\n", " %733 : Long(device=cpu) = onnx::Gather[axis=0](%731, %732) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %734 : Tensor = onnx::Shape(%730)\n", " %735 : Tensor = onnx::Constant[value={3}]()\n", " %736 : Long(device=cpu) = onnx::Gather[axis=0](%734, %735) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %737 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %738 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %739 : Tensor = onnx::Unsqueeze[axes=[0]](%737)\n", " %740 : Tensor = onnx::Unsqueeze[axes=[0]](%738)\n", " %741 : Tensor = onnx::Unsqueeze[axes=[0]](%733)\n", " %742 : Tensor = onnx::Unsqueeze[axes=[0]](%736)\n", " %743 : Tensor = onnx::Concat[axis=0](%739, %740, %741, %742)\n", " %744 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Reshape(%730, %743) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %745 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Add(%744, %663)\n", " %746 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %747 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %748 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%746, %747) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %749 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Add(%745, %748) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %750 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%749) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %751 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %752 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Mul(%750, %751)\n", " %753 : Tensor = onnx::Shape(%418)\n", " %754 : Tensor = onnx::Constant[value={0}]()\n", " %755 : Long(device=cpu) = onnx::Gather[axis=0](%753, %754) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %756 : Tensor = onnx::Shape(%418)\n", " %757 : Tensor = onnx::Constant[value={1}]()\n", " %758 : Long(device=cpu) = onnx::Gather[axis=0](%756, %757) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %759 : Tensor = onnx::Shape(%418)\n", " %760 : Tensor = onnx::Constant[value={2}]()\n", " %761 : Long(device=cpu) = onnx::Gather[axis=0](%759, %760) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %762 : Tensor = onnx::Shape(%418)\n", " %763 : Tensor = onnx::Constant[value={3}]()\n", " %764 : Long(device=cpu) = onnx::Gather[axis=0](%762, %763) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %765 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %766 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %767 : Tensor = onnx::Unsqueeze[axes=[0]](%755)\n", " %768 : Tensor = onnx::Unsqueeze[axes=[0]](%758)\n", " %769 : Tensor = onnx::Unsqueeze[axes=[0]](%761)\n", " %770 : Tensor = onnx::Unsqueeze[axes=[0]](%765)\n", " %771 : Tensor = onnx::Unsqueeze[axes=[0]](%764)\n", " %772 : Tensor = onnx::Unsqueeze[axes=[0]](%766)\n", " %773 : Tensor = onnx::Concat[axis=0](%767, %768, %769, %770, %771, %772)\n", " %774 : Float(1:96, 3:32, 8:4, 1:4, 4:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%418, %773) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %775 : int[] = onnx::Constant[value= 0 1 0 0 0 1 [ CPULongType{6} ]]()\n", " %776 : Tensor = onnx::Constant[value={0}]()\n", " %777 : Tensor = onnx::Shape(%775)\n", " %778 : Tensor = onnx::Gather[axis=0](%777, %776)\n", " %779 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %780 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %781 : LongTensor = onnx::Mul(%779, %780)\n", " %782 : LongTensor = onnx::Sub(%781, %778)\n", " %783 : Tensor = onnx::Cast[to=7](%775)\n", " %784 : Tensor = onnx::ConstantOfShape[value={0}](%782)\n", " %785 : Tensor = onnx::Concat[axis=0](%783, %784)\n", " %786 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %787 : Tensor = onnx::Reshape(%785, %786)\n", " %788 : Tensor = onnx::Constant[value={0}]()\n", " %789 : Tensor = onnx::Constant[value={-1}]()\n", " %790 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %791 : Tensor = onnx::Constant[value={-1}]()\n", " %792 : Tensor = onnx::Slice(%787, %789, %790, %788, %791)\n", " %793 : Tensor = onnx::Transpose[perm=[1, 0]](%792)\n", " %794 : Tensor = onnx::Constant[value={-1}]()\n", " %795 : Tensor = onnx::Reshape(%793, %794)\n", " %796 : Tensor = onnx::Cast[to=7](%795)\n", " %797 : Tensor = onnx::Constant[value={0}]()\n", " %798 : Float(1:384, 3:128, 8:16, 2:8, 4:2, 2:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%774, %796, %797) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %799 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %800 : Long(requires_grad=0, device=cpu) = onnx::Mul(%761, %799)\n", " %801 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %802 : Long(requires_grad=0, device=cpu) = onnx::Mul(%764, %801)\n", " %803 : Tensor = onnx::Unsqueeze[axes=[0]](%755)\n", " %804 : Tensor = onnx::Unsqueeze[axes=[0]](%758)\n", " %805 : Tensor = onnx::Unsqueeze[axes=[0]](%800)\n", " %806 : Tensor = onnx::Unsqueeze[axes=[0]](%802)\n", " %807 : Tensor = onnx::Concat[axis=0](%803, %804, %805, %806)\n", " %808 : Float(1:384, 3:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Reshape(%798, %807) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %809 : int[] = onnx::Constant[value= 2 1 2 1 [ CPULongType{4} ]]()\n", " %810 : Tensor = onnx::Constant[value={0}]()\n", " %811 : Tensor = onnx::Shape(%809)\n", " %812 : Tensor = onnx::Gather[axis=0](%811, %810)\n", " %813 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %814 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %815 : LongTensor = onnx::Mul(%813, %814)\n", " %816 : LongTensor = onnx::Sub(%815, %812)\n", " %817 : Tensor = onnx::Cast[to=7](%809)\n", " %818 : Tensor = onnx::ConstantOfShape[value={0}](%816)\n", " %819 : Tensor = onnx::Concat[axis=0](%817, %818)\n", " %820 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %821 : Tensor = onnx::Reshape(%819, %820)\n", " %822 : Tensor = onnx::Constant[value={0}]()\n", " %823 : Tensor = onnx::Constant[value={-1}]()\n", " %824 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %825 : Tensor = onnx::Constant[value={-1}]()\n", " %826 : Tensor = onnx::Slice(%821, %823, %824, %822, %825)\n", " %827 : Tensor = onnx::Transpose[perm=[1, 0]](%826)\n", " %828 : Tensor = onnx::Constant[value={-1}]()\n", " %829 : Tensor = onnx::Reshape(%827, %828)\n", " %830 : Tensor = onnx::Cast[to=7](%829)\n", " %831 : Tensor = onnx::Constant[value={0}]()\n", " %832 : Float(1:627, 3:209, 19:11, 11:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%808, %830, %831) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %833 : Tensor = onnx::Shape(%832)\n", " %834 : Tensor = onnx::Constant[value={2}]()\n", " %835 : Long(device=cpu) = onnx::Gather[axis=0](%833, %834) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %836 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %837 : Long(requires_grad=0, device=cpu) = onnx::Sub(%835, %836)\n", " %838 : Tensor = onnx::Shape(%832)\n", " %839 : Tensor = onnx::Constant[value={3}]()\n", " %840 : Long(device=cpu) = onnx::Gather[axis=0](%838, %839) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %841 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %842 : Long(requires_grad=0, device=cpu) = onnx::Sub(%840, %841)\n", " %843 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %844 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %845 : Tensor = onnx::Unsqueeze[axes=[0]](%844)\n", " %846 : Tensor = onnx::Unsqueeze[axes=[0]](%837)\n", " %847 : Tensor = onnx::Unsqueeze[axes=[0]](%843)\n", " %848 : Tensor = onnx::Constant[value={1}]()\n", " %849 : Float(1:627, 3:209, 19:11, 11:1, requires_grad=0, device=cpu) = onnx::Slice(%832, %845, %846, %847, %848) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %850 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %851 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %852 : Tensor = onnx::Unsqueeze[axes=[0]](%851)\n", " %853 : Tensor = onnx::Unsqueeze[axes=[0]](%842)\n", " %854 : Tensor = onnx::Unsqueeze[axes=[0]](%850)\n", " %855 : Tensor = onnx::Constant[value={1}]()\n", " %856 : Float(1:627, 3:209, 19:11, 11:1, requires_grad=0, device=cpu) = onnx::Slice(%849, %852, %853, %854, %855) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %857 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %858 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b8.resample_filter, %857)\n", " %859 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%858) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %860 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %861 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %862 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %863 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %864 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%859, %861, %862, %860, %863) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %865 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%864) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %866 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%865) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %867 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %868 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %869 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %870 : Tensor = onnx::Unsqueeze[axes=[0]](%758)\n", " %871 : Tensor = onnx::Unsqueeze[axes=[0]](%867)\n", " %872 : Tensor = onnx::Unsqueeze[axes=[0]](%868)\n", " %873 : Tensor = onnx::Unsqueeze[axes=[0]](%869)\n", " %874 : Tensor = onnx::Concat[axis=0](%870, %871, %872, %873)\n", " %875 : Tensor = onnx::Unsqueeze[axes=[0]](%758)\n", " %876 : Tensor = onnx::Unsqueeze[axes=[0]](%867)\n", " %877 : Tensor = onnx::Unsqueeze[axes=[0]](%868)\n", " %878 : Tensor = onnx::Unsqueeze[axes=[0]](%869)\n", " %879 : Tensor = onnx::Concat[axis=0](%875, %876, %877, %878)\n", " %880 : Tensor = onnx::Shape(%874)\n", " %881 : Tensor = onnx::ConstantOfShape[value={1}](%880)\n", " %882 : Tensor = onnx::Expand(%866, %881)\n", " %883 : Float(3:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%882, %879) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %884 : Float(1:384, 3:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=3, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%856, %883) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %885 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %886 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %887 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%885, %886)\n", " %888 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %889 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%888) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %890 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%424, %887, %889) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %891 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %892 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%890, %891)\n", " %893 : Tensor = onnx::Shape(%752)\n", " %894 : Tensor = onnx::Constant[value={0}]()\n", " %895 : Long(device=cpu) = onnx::Gather[axis=0](%893, %894) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %896 : Tensor = onnx::Shape(%b8.torgb.weight)\n", " %897 : Tensor = onnx::Constant[value={1}]()\n", " %898 : Long(device=cpu) = onnx::Gather[axis=0](%896, %897) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %899 : Tensor = onnx::Shape(%b8.torgb.weight)\n", " %900 : Tensor = onnx::Constant[value={2}]()\n", " %901 : Long(device=cpu) = onnx::Gather[axis=0](%899, %900) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %902 : Tensor = onnx::Shape(%b8.torgb.weight)\n", " %903 : Tensor = onnx::Constant[value={3}]()\n", " %904 : Long(device=cpu) = onnx::Gather[axis=0](%902, %903) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %905 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b8.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %906 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %907 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %908 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %909 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %910 : Tensor = onnx::Unsqueeze[axes=[0]](%895)\n", " %911 : Tensor = onnx::Unsqueeze[axes=[0]](%906)\n", " %912 : Tensor = onnx::Unsqueeze[axes=[0]](%907)\n", " %913 : Tensor = onnx::Unsqueeze[axes=[0]](%908)\n", " %914 : Tensor = onnx::Unsqueeze[axes=[0]](%909)\n", " %915 : Tensor = onnx::Concat[axis=0](%910, %911, %912, %913, %914)\n", " %916 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%892, %915) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %917 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%905, %916) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %918 : Tensor = onnx::Shape(%752)\n", " %919 : Tensor = onnx::Constant[value={2}]()\n", " %920 : Long(device=cpu) = onnx::Gather[axis=0](%918, %919) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %921 : Tensor = onnx::Shape(%752)\n", " %922 : Tensor = onnx::Constant[value={3}]()\n", " %923 : Long(device=cpu) = onnx::Gather[axis=0](%921, %922) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %924 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %925 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %926 : Tensor = onnx::Unsqueeze[axes=[0]](%924)\n", " %927 : Tensor = onnx::Unsqueeze[axes=[0]](%925)\n", " %928 : Tensor = onnx::Unsqueeze[axes=[0]](%920)\n", " %929 : Tensor = onnx::Unsqueeze[axes=[0]](%923)\n", " %930 : Tensor = onnx::Concat[axis=0](%926, %927, %928, %929)\n", " %931 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Reshape(%752, %930) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %932 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %933 : Tensor = onnx::Unsqueeze[axes=[0]](%932)\n", " %934 : Tensor = onnx::Unsqueeze[axes=[0]](%898)\n", " %935 : Tensor = onnx::Unsqueeze[axes=[0]](%901)\n", " %936 : Tensor = onnx::Unsqueeze[axes=[0]](%904)\n", " %937 : Tensor = onnx::Concat[axis=0](%933, %934, %935, %936)\n", " %938 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%917, %937) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %939 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%938) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %940 : Float(1:384, 3:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%931, %939) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %941 : Tensor = onnx::Shape(%940)\n", " %942 : Tensor = onnx::Constant[value={2}]()\n", " %943 : Long(device=cpu) = onnx::Gather[axis=0](%941, %942) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %944 : Tensor = onnx::Shape(%940)\n", " %945 : Tensor = onnx::Constant[value={3}]()\n", " %946 : Long(device=cpu) = onnx::Gather[axis=0](%944, %945) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %947 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %948 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %949 : Tensor = onnx::Unsqueeze[axes=[0]](%947)\n", " %950 : Tensor = onnx::Unsqueeze[axes=[0]](%948)\n", " %951 : Tensor = onnx::Unsqueeze[axes=[0]](%943)\n", " %952 : Tensor = onnx::Unsqueeze[axes=[0]](%946)\n", " %953 : Tensor = onnx::Concat[axis=0](%949, %950, %951, %952)\n", " %954 : Float(1:384, 3:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Reshape(%940, %953) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %955 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b8.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %956 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %957 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%955, %956) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %958 : Float(1:384, 3:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Add(%954, %957) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %959 : Float(1:384, 3:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%958) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %960 : Float(1:384, 3:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Add(%884, %959)\n", " %961 : Tensor, %962 : Tensor, %963 : Tensor = onnx::Split[axis=1, split=[1, 1, 1]](%175)\n", " %964 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%961) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %965 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%962) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %966 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%963) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %967 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%752) # /kaggle/working/stylegan3/training/networks_stylegan2.py:453:0\n", " %968 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.conv0.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %969 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %970 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%968, %969)\n", " %971 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.conv0.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %972 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%971) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %973 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%964, %970, %972) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %974 : Float(32:16, 16:1, requires_grad=0, device=cpu) = onnx::Mul(%b16.conv0.noise_const, %b16.conv0.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %975 : Float(32:16, 16:1, requires_grad=0, device=cpu) = onnx::Mul(%974, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %976 : Tensor = onnx::Shape(%967)\n", " %977 : Tensor = onnx::Constant[value={0}]()\n", " %978 : Long(device=cpu) = onnx::Gather[axis=0](%976, %977) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %979 : Tensor = onnx::Shape(%b16.conv0.weight)\n", " %980 : Tensor = onnx::Constant[value={1}]()\n", " %981 : Long(device=cpu) = onnx::Gather[axis=0](%979, %980) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %982 : Tensor = onnx::Shape(%b16.conv0.weight)\n", " %983 : Tensor = onnx::Constant[value={2}]()\n", " %984 : Long(device=cpu) = onnx::Gather[axis=0](%982, %983) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %985 : Tensor = onnx::Shape(%b16.conv0.weight)\n", " %986 : Tensor = onnx::Constant[value={3}]()\n", " %987 : Long(device=cpu) = onnx::Gather[axis=0](%985, %986) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %988 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b16.conv0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %989 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %990 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %991 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %992 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %993 : Tensor = onnx::Unsqueeze[axes=[0]](%978)\n", " %994 : Tensor = onnx::Unsqueeze[axes=[0]](%989)\n", " %995 : Tensor = onnx::Unsqueeze[axes=[0]](%990)\n", " %996 : Tensor = onnx::Unsqueeze[axes=[0]](%991)\n", " %997 : Tensor = onnx::Unsqueeze[axes=[0]](%992)\n", " %998 : Tensor = onnx::Concat[axis=0](%993, %994, %995, %996, %997)\n", " %999 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%973, %998) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1000 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%988, %999) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1001 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1000, %1000) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1002 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%1001) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1003 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %1004 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%1002, %1003)\n", " %1005 : Tensor = onnx::Sqrt(%1004)\n", " %1006 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1007 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%1006, %1005) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1008 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1009 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1010 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1011 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1012 : Tensor = onnx::Unsqueeze[axes=[0]](%978)\n", " %1013 : Tensor = onnx::Unsqueeze[axes=[0]](%1008)\n", " %1014 : Tensor = onnx::Unsqueeze[axes=[0]](%1009)\n", " %1015 : Tensor = onnx::Unsqueeze[axes=[0]](%1010)\n", " %1016 : Tensor = onnx::Unsqueeze[axes=[0]](%1011)\n", " %1017 : Tensor = onnx::Concat[axis=0](%1012, %1013, %1014, %1015, %1016)\n", " %1018 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1007, %1017) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1019 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1000, %1018) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1020 : Tensor = onnx::Shape(%967)\n", " %1021 : Tensor = onnx::Constant[value={2}]()\n", " %1022 : Long(device=cpu) = onnx::Gather[axis=0](%1020, %1021) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1023 : Tensor = onnx::Shape(%967)\n", " %1024 : Tensor = onnx::Constant[value={3}]()\n", " %1025 : Long(device=cpu) = onnx::Gather[axis=0](%1023, %1024) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1026 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1027 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1028 : Tensor = onnx::Unsqueeze[axes=[0]](%1026)\n", " %1029 : Tensor = onnx::Unsqueeze[axes=[0]](%1027)\n", " %1030 : Tensor = onnx::Unsqueeze[axes=[0]](%1022)\n", " %1031 : Tensor = onnx::Unsqueeze[axes=[0]](%1025)\n", " %1032 : Tensor = onnx::Concat[axis=0](%1028, %1029, %1030, %1031)\n", " %1033 : Float(1:65536, 512:128, 16:8, 8:1, requires_grad=0, device=cpu) = onnx::Reshape(%967, %1032) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1034 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1035 : Tensor = onnx::Unsqueeze[axes=[0]](%1034)\n", " %1036 : Tensor = onnx::Unsqueeze[axes=[0]](%981)\n", " %1037 : Tensor = onnx::Unsqueeze[axes=[0]](%984)\n", " %1038 : Tensor = onnx::Unsqueeze[axes=[0]](%987)\n", " %1039 : Tensor = onnx::Concat[axis=0](%1035, %1036, %1037, %1038)\n", " %1040 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%1019, %1039) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %1041 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1040) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %1042 : Float(512:9, 512:4608, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0, 2, 3]](%1041) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_resample.py:114:0\n", " %1043 : Float(1:287232, 512:561, 33:17, 17:1, requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%1033, %1042) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:45:0\n", " %1044 : Tensor = onnx::Shape(%1043)\n", " %1045 : Tensor = onnx::Constant[value={0}]()\n", " %1046 : Long(device=cpu) = onnx::Gather[axis=0](%1044, %1045) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1047 : Tensor = onnx::Shape(%1043)\n", " %1048 : Tensor = onnx::Constant[value={1}]()\n", " %1049 : Long(device=cpu) = onnx::Gather[axis=0](%1047, %1048) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1050 : Tensor = onnx::Shape(%1043)\n", " %1051 : Tensor = onnx::Constant[value={2}]()\n", " %1052 : Long(device=cpu) = onnx::Gather[axis=0](%1050, %1051) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1053 : Tensor = onnx::Shape(%1043)\n", " %1054 : Tensor = onnx::Constant[value={3}]()\n", " %1055 : Long(device=cpu) = onnx::Gather[axis=0](%1053, %1054) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1056 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1057 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1058 : Tensor = onnx::Unsqueeze[axes=[0]](%1046)\n", " %1059 : Tensor = onnx::Unsqueeze[axes=[0]](%1049)\n", " %1060 : Tensor = onnx::Unsqueeze[axes=[0]](%1052)\n", " %1061 : Tensor = onnx::Unsqueeze[axes=[0]](%1056)\n", " %1062 : Tensor = onnx::Unsqueeze[axes=[0]](%1055)\n", " %1063 : Tensor = onnx::Unsqueeze[axes=[0]](%1057)\n", " %1064 : Tensor = onnx::Concat[axis=0](%1058, %1059, %1060, %1061, %1062, %1063)\n", " %1065 : Float(1:287232, 512:561, 33:17, 1:17, 17:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1043, %1064) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %1066 : int[] = onnx::Constant[value= 0 0 0 0 0 0 [ CPULongType{6} ]]()\n", " %1067 : Tensor = onnx::Constant[value={0}]()\n", " %1068 : Tensor = onnx::Shape(%1066)\n", " %1069 : Tensor = onnx::Gather[axis=0](%1068, %1067)\n", " %1070 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %1071 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1072 : LongTensor = onnx::Mul(%1070, %1071)\n", " %1073 : LongTensor = onnx::Sub(%1072, %1069)\n", " %1074 : Tensor = onnx::Cast[to=7](%1066)\n", " %1075 : Tensor = onnx::ConstantOfShape[value={0}](%1073)\n", " %1076 : Tensor = onnx::Concat[axis=0](%1074, %1075)\n", " %1077 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1078 : Tensor = onnx::Reshape(%1076, %1077)\n", " %1079 : Tensor = onnx::Constant[value={0}]()\n", " %1080 : Tensor = onnx::Constant[value={-1}]()\n", " %1081 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1082 : Tensor = onnx::Constant[value={-1}]()\n", " %1083 : Tensor = onnx::Slice(%1078, %1080, %1081, %1079, %1082)\n", " %1084 : Tensor = onnx::Transpose[perm=[1, 0]](%1083)\n", " %1085 : Tensor = onnx::Constant[value={-1}]()\n", " %1086 : Tensor = onnx::Reshape(%1084, %1085)\n", " %1087 : Tensor = onnx::Cast[to=7](%1086)\n", " %1088 : Tensor = onnx::Constant[value={0}]()\n", " %1089 : Float(1:287232, 512:561, 33:17, 1:17, 17:1, 1:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1065, %1087, %1088) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %1090 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1091 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1052, %1090)\n", " %1092 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1093 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1055, %1092)\n", " %1094 : Tensor = onnx::Unsqueeze[axes=[0]](%1046)\n", " %1095 : Tensor = onnx::Unsqueeze[axes=[0]](%1049)\n", " %1096 : Tensor = onnx::Unsqueeze[axes=[0]](%1091)\n", " %1097 : Tensor = onnx::Unsqueeze[axes=[0]](%1093)\n", " %1098 : Tensor = onnx::Concat[axis=0](%1094, %1095, %1096, %1097)\n", " %1099 : Float(1:287232, 512:561, 33:17, 17:1, requires_grad=0, device=cpu) = onnx::Reshape(%1089, %1098) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %1100 : int[] = onnx::Constant[value= 1 1 1 1 [ CPULongType{4} ]]()\n", " %1101 : Tensor = onnx::Constant[value={0}]()\n", " %1102 : Tensor = onnx::Shape(%1100)\n", " %1103 : Tensor = onnx::Gather[axis=0](%1102, %1101)\n", " %1104 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1105 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1106 : LongTensor = onnx::Mul(%1104, %1105)\n", " %1107 : LongTensor = onnx::Sub(%1106, %1103)\n", " %1108 : Tensor = onnx::Cast[to=7](%1100)\n", " %1109 : Tensor = onnx::ConstantOfShape[value={0}](%1107)\n", " %1110 : Tensor = onnx::Concat[axis=0](%1108, %1109)\n", " %1111 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1112 : Tensor = onnx::Reshape(%1110, %1111)\n", " %1113 : Tensor = onnx::Constant[value={0}]()\n", " %1114 : Tensor = onnx::Constant[value={-1}]()\n", " %1115 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1116 : Tensor = onnx::Constant[value={-1}]()\n", " %1117 : Tensor = onnx::Slice(%1112, %1114, %1115, %1113, %1116)\n", " %1118 : Tensor = onnx::Transpose[perm=[1, 0]](%1117)\n", " %1119 : Tensor = onnx::Constant[value={-1}]()\n", " %1120 : Tensor = onnx::Reshape(%1118, %1119)\n", " %1121 : Tensor = onnx::Cast[to=7](%1120)\n", " %1122 : Tensor = onnx::Constant[value={0}]()\n", " %1123 : Float(1:340480, 512:665, 35:19, 19:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1099, %1121, %1122) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1124 : Tensor = onnx::Shape(%1123)\n", " %1125 : Tensor = onnx::Constant[value={2}]()\n", " %1126 : Long(device=cpu) = onnx::Gather[axis=0](%1124, %1125) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1127 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1128 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1126, %1127)\n", " %1129 : Tensor = onnx::Shape(%1123)\n", " %1130 : Tensor = onnx::Constant[value={3}]()\n", " %1131 : Long(device=cpu) = onnx::Gather[axis=0](%1129, %1130) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1132 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1133 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1131, %1132)\n", " %1134 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1135 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1136 : Tensor = onnx::Unsqueeze[axes=[0]](%1135)\n", " %1137 : Tensor = onnx::Unsqueeze[axes=[0]](%1128)\n", " %1138 : Tensor = onnx::Unsqueeze[axes=[0]](%1134)\n", " %1139 : Tensor = onnx::Constant[value={1}]()\n", " %1140 : Float(1:340480, 512:665, 35:19, 19:1, requires_grad=0, device=cpu) = onnx::Slice(%1123, %1136, %1137, %1138, %1139) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1141 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %1142 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1143 : Tensor = onnx::Unsqueeze[axes=[0]](%1142)\n", " %1144 : Tensor = onnx::Unsqueeze[axes=[0]](%1133)\n", " %1145 : Tensor = onnx::Unsqueeze[axes=[0]](%1141)\n", " %1146 : Tensor = onnx::Constant[value={1}]()\n", " %1147 : Float(1:340480, 512:665, 35:19, 19:1, requires_grad=0, device=cpu) = onnx::Slice(%1140, %1143, %1144, %1145, %1146) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1148 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1149 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b16.conv0.resample_filter, %1148)\n", " %1150 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1149) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %1151 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %1152 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1153 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %1154 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1155 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%1150, %1152, %1153, %1151, %1154) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %1156 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1155) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1157 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%1156) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1158 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1159 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1160 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1161 : Tensor = onnx::Unsqueeze[axes=[0]](%1049)\n", " %1162 : Tensor = onnx::Unsqueeze[axes=[0]](%1158)\n", " %1163 : Tensor = onnx::Unsqueeze[axes=[0]](%1159)\n", " %1164 : Tensor = onnx::Unsqueeze[axes=[0]](%1160)\n", " %1165 : Tensor = onnx::Concat[axis=0](%1161, %1162, %1163, %1164)\n", " %1166 : Tensor = onnx::Unsqueeze[axes=[0]](%1049)\n", " %1167 : Tensor = onnx::Unsqueeze[axes=[0]](%1158)\n", " %1168 : Tensor = onnx::Unsqueeze[axes=[0]](%1159)\n", " %1169 : Tensor = onnx::Unsqueeze[axes=[0]](%1160)\n", " %1170 : Tensor = onnx::Concat[axis=0](%1166, %1167, %1168, %1169)\n", " %1171 : Tensor = onnx::Shape(%1165)\n", " %1172 : Tensor = onnx::ConstantOfShape[value={1}](%1171)\n", " %1173 : Tensor = onnx::Expand(%1157, %1172)\n", " %1174 : Float(512:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%1173, %1170) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1175 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%1147, %1174) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %1176 : Tensor = onnx::Shape(%1175)\n", " %1177 : Tensor = onnx::Constant[value={2}]()\n", " %1178 : Long(device=cpu) = onnx::Gather[axis=0](%1176, %1177) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1179 : Tensor = onnx::Shape(%1175)\n", " %1180 : Tensor = onnx::Constant[value={3}]()\n", " %1181 : Long(device=cpu) = onnx::Gather[axis=0](%1179, %1180) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1182 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1183 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1184 : Tensor = onnx::Unsqueeze[axes=[0]](%1182)\n", " %1185 : Tensor = onnx::Unsqueeze[axes=[0]](%1183)\n", " %1186 : Tensor = onnx::Unsqueeze[axes=[0]](%1178)\n", " %1187 : Tensor = onnx::Unsqueeze[axes=[0]](%1181)\n", " %1188 : Tensor = onnx::Concat[axis=0](%1184, %1185, %1186, %1187)\n", " %1189 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Reshape(%1175, %1188) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1190 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Add(%1189, %975)\n", " %1191 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.conv0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %1192 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %1193 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1191, %1192) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1194 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Add(%1190, %1193) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1195 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%1194) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %1196 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %1197 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Mul(%1195, %1196)\n", " %1198 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %1199 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %1200 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%1198, %1199)\n", " %1201 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %1202 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1201) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1203 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%965, %1200, %1202) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1204 : Float(32:16, 16:1, requires_grad=0, device=cpu) = onnx::Mul(%b16.conv1.noise_const, %b16.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %1205 : Float(32:16, 16:1, requires_grad=0, device=cpu) = onnx::Mul(%1204, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %1206 : Tensor = onnx::Shape(%1197)\n", " %1207 : Tensor = onnx::Constant[value={0}]()\n", " %1208 : Long(device=cpu) = onnx::Gather[axis=0](%1206, %1207) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %1209 : Tensor = onnx::Shape(%b16.conv1.weight)\n", " %1210 : Tensor = onnx::Constant[value={1}]()\n", " %1211 : Long(device=cpu) = onnx::Gather[axis=0](%1209, %1210) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1212 : Tensor = onnx::Shape(%b16.conv1.weight)\n", " %1213 : Tensor = onnx::Constant[value={2}]()\n", " %1214 : Long(device=cpu) = onnx::Gather[axis=0](%1212, %1213) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1215 : Tensor = onnx::Shape(%b16.conv1.weight)\n", " %1216 : Tensor = onnx::Constant[value={3}]()\n", " %1217 : Long(device=cpu) = onnx::Gather[axis=0](%1215, %1216) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1218 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b16.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %1219 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1220 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1221 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1222 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1223 : Tensor = onnx::Unsqueeze[axes=[0]](%1208)\n", " %1224 : Tensor = onnx::Unsqueeze[axes=[0]](%1219)\n", " %1225 : Tensor = onnx::Unsqueeze[axes=[0]](%1220)\n", " %1226 : Tensor = onnx::Unsqueeze[axes=[0]](%1221)\n", " %1227 : Tensor = onnx::Unsqueeze[axes=[0]](%1222)\n", " %1228 : Tensor = onnx::Concat[axis=0](%1223, %1224, %1225, %1226, %1227)\n", " %1229 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1203, %1228) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1230 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1218, %1229) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1231 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1230, %1230) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1232 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%1231) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1233 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %1234 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%1232, %1233)\n", " %1235 : Tensor = onnx::Sqrt(%1234)\n", " %1236 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1237 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%1236, %1235) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1238 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1239 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1240 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1241 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1242 : Tensor = onnx::Unsqueeze[axes=[0]](%1208)\n", " %1243 : Tensor = onnx::Unsqueeze[axes=[0]](%1238)\n", " %1244 : Tensor = onnx::Unsqueeze[axes=[0]](%1239)\n", " %1245 : Tensor = onnx::Unsqueeze[axes=[0]](%1240)\n", " %1246 : Tensor = onnx::Unsqueeze[axes=[0]](%1241)\n", " %1247 : Tensor = onnx::Concat[axis=0](%1242, %1243, %1244, %1245, %1246)\n", " %1248 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1237, %1247) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1249 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1230, %1248) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1250 : Tensor = onnx::Shape(%1197)\n", " %1251 : Tensor = onnx::Constant[value={2}]()\n", " %1252 : Long(device=cpu) = onnx::Gather[axis=0](%1250, %1251) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1253 : Tensor = onnx::Shape(%1197)\n", " %1254 : Tensor = onnx::Constant[value={3}]()\n", " %1255 : Long(device=cpu) = onnx::Gather[axis=0](%1253, %1254) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1256 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1257 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1258 : Tensor = onnx::Unsqueeze[axes=[0]](%1256)\n", " %1259 : Tensor = onnx::Unsqueeze[axes=[0]](%1257)\n", " %1260 : Tensor = onnx::Unsqueeze[axes=[0]](%1252)\n", " %1261 : Tensor = onnx::Unsqueeze[axes=[0]](%1255)\n", " %1262 : Tensor = onnx::Concat[axis=0](%1258, %1259, %1260, %1261)\n", " %1263 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Reshape(%1197, %1262) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1264 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1265 : Tensor = onnx::Unsqueeze[axes=[0]](%1264)\n", " %1266 : Tensor = onnx::Unsqueeze[axes=[0]](%1211)\n", " %1267 : Tensor = onnx::Unsqueeze[axes=[0]](%1214)\n", " %1268 : Tensor = onnx::Unsqueeze[axes=[0]](%1217)\n", " %1269 : Tensor = onnx::Concat[axis=0](%1265, %1266, %1267, %1268)\n", " %1270 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%1249, %1269) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %1271 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1270) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %1272 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%1263, %1271) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %1273 : Tensor = onnx::Shape(%1272)\n", " %1274 : Tensor = onnx::Constant[value={2}]()\n", " %1275 : Long(device=cpu) = onnx::Gather[axis=0](%1273, %1274) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1276 : Tensor = onnx::Shape(%1272)\n", " %1277 : Tensor = onnx::Constant[value={3}]()\n", " %1278 : Long(device=cpu) = onnx::Gather[axis=0](%1276, %1277) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1279 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1280 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1281 : Tensor = onnx::Unsqueeze[axes=[0]](%1279)\n", " %1282 : Tensor = onnx::Unsqueeze[axes=[0]](%1280)\n", " %1283 : Tensor = onnx::Unsqueeze[axes=[0]](%1275)\n", " %1284 : Tensor = onnx::Unsqueeze[axes=[0]](%1278)\n", " %1285 : Tensor = onnx::Concat[axis=0](%1281, %1282, %1283, %1284)\n", " %1286 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Reshape(%1272, %1285) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1287 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Add(%1286, %1205)\n", " %1288 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %1289 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %1290 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1288, %1289) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1291 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Add(%1287, %1290) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1292 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%1291) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %1293 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %1294 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Mul(%1292, %1293)\n", " %1295 : Tensor = onnx::Shape(%960)\n", " %1296 : Tensor = onnx::Constant[value={0}]()\n", " %1297 : Long(device=cpu) = onnx::Gather[axis=0](%1295, %1296) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1298 : Tensor = onnx::Shape(%960)\n", " %1299 : Tensor = onnx::Constant[value={1}]()\n", " %1300 : Long(device=cpu) = onnx::Gather[axis=0](%1298, %1299) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1301 : Tensor = onnx::Shape(%960)\n", " %1302 : Tensor = onnx::Constant[value={2}]()\n", " %1303 : Long(device=cpu) = onnx::Gather[axis=0](%1301, %1302) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1304 : Tensor = onnx::Shape(%960)\n", " %1305 : Tensor = onnx::Constant[value={3}]()\n", " %1306 : Long(device=cpu) = onnx::Gather[axis=0](%1304, %1305) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1307 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1308 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1309 : Tensor = onnx::Unsqueeze[axes=[0]](%1297)\n", " %1310 : Tensor = onnx::Unsqueeze[axes=[0]](%1300)\n", " %1311 : Tensor = onnx::Unsqueeze[axes=[0]](%1303)\n", " %1312 : Tensor = onnx::Unsqueeze[axes=[0]](%1307)\n", " %1313 : Tensor = onnx::Unsqueeze[axes=[0]](%1306)\n", " %1314 : Tensor = onnx::Unsqueeze[axes=[0]](%1308)\n", " %1315 : Tensor = onnx::Concat[axis=0](%1309, %1310, %1311, %1312, %1313, %1314)\n", " %1316 : Float(1:384, 3:128, 16:8, 1:8, 8:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%960, %1315) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %1317 : int[] = onnx::Constant[value= 0 1 0 0 0 1 [ CPULongType{6} ]]()\n", " %1318 : Tensor = onnx::Constant[value={0}]()\n", " %1319 : Tensor = onnx::Shape(%1317)\n", " %1320 : Tensor = onnx::Gather[axis=0](%1319, %1318)\n", " %1321 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %1322 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1323 : LongTensor = onnx::Mul(%1321, %1322)\n", " %1324 : LongTensor = onnx::Sub(%1323, %1320)\n", " %1325 : Tensor = onnx::Cast[to=7](%1317)\n", " %1326 : Tensor = onnx::ConstantOfShape[value={0}](%1324)\n", " %1327 : Tensor = onnx::Concat[axis=0](%1325, %1326)\n", " %1328 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1329 : Tensor = onnx::Reshape(%1327, %1328)\n", " %1330 : Tensor = onnx::Constant[value={0}]()\n", " %1331 : Tensor = onnx::Constant[value={-1}]()\n", " %1332 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1333 : Tensor = onnx::Constant[value={-1}]()\n", " %1334 : Tensor = onnx::Slice(%1329, %1331, %1332, %1330, %1333)\n", " %1335 : Tensor = onnx::Transpose[perm=[1, 0]](%1334)\n", " %1336 : Tensor = onnx::Constant[value={-1}]()\n", " %1337 : Tensor = onnx::Reshape(%1335, %1336)\n", " %1338 : Tensor = onnx::Cast[to=7](%1337)\n", " %1339 : Tensor = onnx::Constant[value={0}]()\n", " %1340 : Float(1:1536, 3:512, 16:32, 2:16, 8:2, 2:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1316, %1338, %1339) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %1341 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1342 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1303, %1341)\n", " %1343 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1344 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1306, %1343)\n", " %1345 : Tensor = onnx::Unsqueeze[axes=[0]](%1297)\n", " %1346 : Tensor = onnx::Unsqueeze[axes=[0]](%1300)\n", " %1347 : Tensor = onnx::Unsqueeze[axes=[0]](%1342)\n", " %1348 : Tensor = onnx::Unsqueeze[axes=[0]](%1344)\n", " %1349 : Tensor = onnx::Concat[axis=0](%1345, %1346, %1347, %1348)\n", " %1350 : Float(1:1536, 3:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Reshape(%1340, %1349) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %1351 : int[] = onnx::Constant[value= 2 1 2 1 [ CPULongType{4} ]]()\n", " %1352 : Tensor = onnx::Constant[value={0}]()\n", " %1353 : Tensor = onnx::Shape(%1351)\n", " %1354 : Tensor = onnx::Gather[axis=0](%1353, %1352)\n", " %1355 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1356 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1357 : LongTensor = onnx::Mul(%1355, %1356)\n", " %1358 : LongTensor = onnx::Sub(%1357, %1354)\n", " %1359 : Tensor = onnx::Cast[to=7](%1351)\n", " %1360 : Tensor = onnx::ConstantOfShape[value={0}](%1358)\n", " %1361 : Tensor = onnx::Concat[axis=0](%1359, %1360)\n", " %1362 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1363 : Tensor = onnx::Reshape(%1361, %1362)\n", " %1364 : Tensor = onnx::Constant[value={0}]()\n", " %1365 : Tensor = onnx::Constant[value={-1}]()\n", " %1366 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1367 : Tensor = onnx::Constant[value={-1}]()\n", " %1368 : Tensor = onnx::Slice(%1363, %1365, %1366, %1364, %1367)\n", " %1369 : Tensor = onnx::Transpose[perm=[1, 0]](%1368)\n", " %1370 : Tensor = onnx::Constant[value={-1}]()\n", " %1371 : Tensor = onnx::Reshape(%1369, %1370)\n", " %1372 : Tensor = onnx::Cast[to=7](%1371)\n", " %1373 : Tensor = onnx::Constant[value={0}]()\n", " %1374 : Float(1:1995, 3:665, 35:19, 19:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1350, %1372, %1373) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1375 : Tensor = onnx::Shape(%1374)\n", " %1376 : Tensor = onnx::Constant[value={2}]()\n", " %1377 : Long(device=cpu) = onnx::Gather[axis=0](%1375, %1376) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1378 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1379 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1377, %1378)\n", " %1380 : Tensor = onnx::Shape(%1374)\n", " %1381 : Tensor = onnx::Constant[value={3}]()\n", " %1382 : Long(device=cpu) = onnx::Gather[axis=0](%1380, %1381) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1383 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1384 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1382, %1383)\n", " %1385 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1386 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1387 : Tensor = onnx::Unsqueeze[axes=[0]](%1386)\n", " %1388 : Tensor = onnx::Unsqueeze[axes=[0]](%1379)\n", " %1389 : Tensor = onnx::Unsqueeze[axes=[0]](%1385)\n", " %1390 : Tensor = onnx::Constant[value={1}]()\n", " %1391 : Float(1:1995, 3:665, 35:19, 19:1, requires_grad=0, device=cpu) = onnx::Slice(%1374, %1387, %1388, %1389, %1390) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1392 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %1393 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1394 : Tensor = onnx::Unsqueeze[axes=[0]](%1393)\n", " %1395 : Tensor = onnx::Unsqueeze[axes=[0]](%1384)\n", " %1396 : Tensor = onnx::Unsqueeze[axes=[0]](%1392)\n", " %1397 : Tensor = onnx::Constant[value={1}]()\n", " %1398 : Float(1:1995, 3:665, 35:19, 19:1, requires_grad=0, device=cpu) = onnx::Slice(%1391, %1394, %1395, %1396, %1397) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1399 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1400 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b16.resample_filter, %1399)\n", " %1401 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1400) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %1402 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %1403 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1404 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %1405 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1406 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%1401, %1403, %1404, %1402, %1405) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %1407 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1406) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1408 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%1407) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1409 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1410 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1411 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1412 : Tensor = onnx::Unsqueeze[axes=[0]](%1300)\n", " %1413 : Tensor = onnx::Unsqueeze[axes=[0]](%1409)\n", " %1414 : Tensor = onnx::Unsqueeze[axes=[0]](%1410)\n", " %1415 : Tensor = onnx::Unsqueeze[axes=[0]](%1411)\n", " %1416 : Tensor = onnx::Concat[axis=0](%1412, %1413, %1414, %1415)\n", " %1417 : Tensor = onnx::Unsqueeze[axes=[0]](%1300)\n", " %1418 : Tensor = onnx::Unsqueeze[axes=[0]](%1409)\n", " %1419 : Tensor = onnx::Unsqueeze[axes=[0]](%1410)\n", " %1420 : Tensor = onnx::Unsqueeze[axes=[0]](%1411)\n", " %1421 : Tensor = onnx::Concat[axis=0](%1417, %1418, %1419, %1420)\n", " %1422 : Tensor = onnx::Shape(%1416)\n", " %1423 : Tensor = onnx::ConstantOfShape[value={1}](%1422)\n", " %1424 : Tensor = onnx::Expand(%1408, %1423)\n", " %1425 : Float(3:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%1424, %1421) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1426 : Float(1:1536, 3:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=3, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%1398, %1425) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %1427 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %1428 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %1429 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%1427, %1428)\n", " %1430 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %1431 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1430) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1432 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%966, %1429, %1431) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1433 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %1434 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%1432, %1433)\n", " %1435 : Tensor = onnx::Shape(%1294)\n", " %1436 : Tensor = onnx::Constant[value={0}]()\n", " %1437 : Long(device=cpu) = onnx::Gather[axis=0](%1435, %1436) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %1438 : Tensor = onnx::Shape(%b16.torgb.weight)\n", " %1439 : Tensor = onnx::Constant[value={1}]()\n", " %1440 : Long(device=cpu) = onnx::Gather[axis=0](%1438, %1439) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1441 : Tensor = onnx::Shape(%b16.torgb.weight)\n", " %1442 : Tensor = onnx::Constant[value={2}]()\n", " %1443 : Long(device=cpu) = onnx::Gather[axis=0](%1441, %1442) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1444 : Tensor = onnx::Shape(%b16.torgb.weight)\n", " %1445 : Tensor = onnx::Constant[value={3}]()\n", " %1446 : Long(device=cpu) = onnx::Gather[axis=0](%1444, %1445) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1447 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b16.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %1448 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1449 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1450 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1451 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1452 : Tensor = onnx::Unsqueeze[axes=[0]](%1437)\n", " %1453 : Tensor = onnx::Unsqueeze[axes=[0]](%1448)\n", " %1454 : Tensor = onnx::Unsqueeze[axes=[0]](%1449)\n", " %1455 : Tensor = onnx::Unsqueeze[axes=[0]](%1450)\n", " %1456 : Tensor = onnx::Unsqueeze[axes=[0]](%1451)\n", " %1457 : Tensor = onnx::Concat[axis=0](%1452, %1453, %1454, %1455, %1456)\n", " %1458 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1434, %1457) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1459 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%1447, %1458) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1460 : Tensor = onnx::Shape(%1294)\n", " %1461 : Tensor = onnx::Constant[value={2}]()\n", " %1462 : Long(device=cpu) = onnx::Gather[axis=0](%1460, %1461) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1463 : Tensor = onnx::Shape(%1294)\n", " %1464 : Tensor = onnx::Constant[value={3}]()\n", " %1465 : Long(device=cpu) = onnx::Gather[axis=0](%1463, %1464) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1466 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1467 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1468 : Tensor = onnx::Unsqueeze[axes=[0]](%1466)\n", " %1469 : Tensor = onnx::Unsqueeze[axes=[0]](%1467)\n", " %1470 : Tensor = onnx::Unsqueeze[axes=[0]](%1462)\n", " %1471 : Tensor = onnx::Unsqueeze[axes=[0]](%1465)\n", " %1472 : Tensor = onnx::Concat[axis=0](%1468, %1469, %1470, %1471)\n", " %1473 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Reshape(%1294, %1472) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1474 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1475 : Tensor = onnx::Unsqueeze[axes=[0]](%1474)\n", " %1476 : Tensor = onnx::Unsqueeze[axes=[0]](%1440)\n", " %1477 : Tensor = onnx::Unsqueeze[axes=[0]](%1443)\n", " %1478 : Tensor = onnx::Unsqueeze[axes=[0]](%1446)\n", " %1479 : Tensor = onnx::Concat[axis=0](%1475, %1476, %1477, %1478)\n", " %1480 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1459, %1479) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %1481 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1480) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %1482 : Float(1:1536, 3:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%1473, %1481) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %1483 : Tensor = onnx::Shape(%1482)\n", " %1484 : Tensor = onnx::Constant[value={2}]()\n", " %1485 : Long(device=cpu) = onnx::Gather[axis=0](%1483, %1484) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1486 : Tensor = onnx::Shape(%1482)\n", " %1487 : Tensor = onnx::Constant[value={3}]()\n", " %1488 : Long(device=cpu) = onnx::Gather[axis=0](%1486, %1487) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1489 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1490 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1491 : Tensor = onnx::Unsqueeze[axes=[0]](%1489)\n", " %1492 : Tensor = onnx::Unsqueeze[axes=[0]](%1490)\n", " %1493 : Tensor = onnx::Unsqueeze[axes=[0]](%1485)\n", " %1494 : Tensor = onnx::Unsqueeze[axes=[0]](%1488)\n", " %1495 : Tensor = onnx::Concat[axis=0](%1491, %1492, %1493, %1494)\n", " %1496 : Float(1:1536, 3:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Reshape(%1482, %1495) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1497 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b16.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %1498 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %1499 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1497, %1498) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1500 : Float(1:1536, 3:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Add(%1496, %1499) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1501 : Float(1:1536, 3:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1500) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %1502 : Float(1:1536, 3:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Add(%1426, %1501)\n", " %1503 : Tensor, %1504 : Tensor, %1505 : Tensor = onnx::Split[axis=1, split=[1, 1, 1]](%184)\n", " %1506 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%1503) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %1507 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%1504) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %1508 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%1505) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %1509 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1294) # /kaggle/working/stylegan3/training/networks_stylegan2.py:453:0\n", " %1510 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.conv0.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %1511 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %1512 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%1510, %1511)\n", " %1513 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.conv0.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %1514 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1513) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1515 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%1506, %1512, %1514) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1516 : Float(64:32, 32:1, requires_grad=0, device=cpu) = onnx::Mul(%b32.conv0.noise_const, %b32.conv0.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %1517 : Float(64:32, 32:1, requires_grad=0, device=cpu) = onnx::Mul(%1516, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %1518 : Tensor = onnx::Shape(%1509)\n", " %1519 : Tensor = onnx::Constant[value={0}]()\n", " %1520 : Long(device=cpu) = onnx::Gather[axis=0](%1518, %1519) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %1521 : Tensor = onnx::Shape(%b32.conv0.weight)\n", " %1522 : Tensor = onnx::Constant[value={1}]()\n", " %1523 : Long(device=cpu) = onnx::Gather[axis=0](%1521, %1522) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1524 : Tensor = onnx::Shape(%b32.conv0.weight)\n", " %1525 : Tensor = onnx::Constant[value={2}]()\n", " %1526 : Long(device=cpu) = onnx::Gather[axis=0](%1524, %1525) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1527 : Tensor = onnx::Shape(%b32.conv0.weight)\n", " %1528 : Tensor = onnx::Constant[value={3}]()\n", " %1529 : Long(device=cpu) = onnx::Gather[axis=0](%1527, %1528) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1530 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b32.conv0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %1531 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1532 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1533 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1534 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1535 : Tensor = onnx::Unsqueeze[axes=[0]](%1520)\n", " %1536 : Tensor = onnx::Unsqueeze[axes=[0]](%1531)\n", " %1537 : Tensor = onnx::Unsqueeze[axes=[0]](%1532)\n", " %1538 : Tensor = onnx::Unsqueeze[axes=[0]](%1533)\n", " %1539 : Tensor = onnx::Unsqueeze[axes=[0]](%1534)\n", " %1540 : Tensor = onnx::Concat[axis=0](%1535, %1536, %1537, %1538, %1539)\n", " %1541 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1515, %1540) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1542 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1530, %1541) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1543 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1542, %1542) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1544 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%1543) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1545 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %1546 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%1544, %1545)\n", " %1547 : Tensor = onnx::Sqrt(%1546)\n", " %1548 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1549 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%1548, %1547) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1550 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1551 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1552 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1553 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1554 : Tensor = onnx::Unsqueeze[axes=[0]](%1520)\n", " %1555 : Tensor = onnx::Unsqueeze[axes=[0]](%1550)\n", " %1556 : Tensor = onnx::Unsqueeze[axes=[0]](%1551)\n", " %1557 : Tensor = onnx::Unsqueeze[axes=[0]](%1552)\n", " %1558 : Tensor = onnx::Unsqueeze[axes=[0]](%1553)\n", " %1559 : Tensor = onnx::Concat[axis=0](%1554, %1555, %1556, %1557, %1558)\n", " %1560 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1549, %1559) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1561 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1542, %1560) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1562 : Tensor = onnx::Shape(%1509)\n", " %1563 : Tensor = onnx::Constant[value={2}]()\n", " %1564 : Long(device=cpu) = onnx::Gather[axis=0](%1562, %1563) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1565 : Tensor = onnx::Shape(%1509)\n", " %1566 : Tensor = onnx::Constant[value={3}]()\n", " %1567 : Long(device=cpu) = onnx::Gather[axis=0](%1565, %1566) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1568 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1569 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1570 : Tensor = onnx::Unsqueeze[axes=[0]](%1568)\n", " %1571 : Tensor = onnx::Unsqueeze[axes=[0]](%1569)\n", " %1572 : Tensor = onnx::Unsqueeze[axes=[0]](%1564)\n", " %1573 : Tensor = onnx::Unsqueeze[axes=[0]](%1567)\n", " %1574 : Tensor = onnx::Concat[axis=0](%1570, %1571, %1572, %1573)\n", " %1575 : Float(1:262144, 512:512, 32:16, 16:1, requires_grad=0, device=cpu) = onnx::Reshape(%1509, %1574) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1576 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1577 : Tensor = onnx::Unsqueeze[axes=[0]](%1576)\n", " %1578 : Tensor = onnx::Unsqueeze[axes=[0]](%1523)\n", " %1579 : Tensor = onnx::Unsqueeze[axes=[0]](%1526)\n", " %1580 : Tensor = onnx::Unsqueeze[axes=[0]](%1529)\n", " %1581 : Tensor = onnx::Concat[axis=0](%1577, %1578, %1579, %1580)\n", " %1582 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%1561, %1581) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %1583 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1582) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %1584 : Float(512:9, 512:4608, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0, 2, 3]](%1583) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_resample.py:114:0\n", " %1585 : Float(1:1098240, 512:2145, 65:33, 33:1, requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%1575, %1584) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:45:0\n", " %1586 : Tensor = onnx::Shape(%1585)\n", " %1587 : Tensor = onnx::Constant[value={0}]()\n", " %1588 : Long(device=cpu) = onnx::Gather[axis=0](%1586, %1587) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1589 : Tensor = onnx::Shape(%1585)\n", " %1590 : Tensor = onnx::Constant[value={1}]()\n", " %1591 : Long(device=cpu) = onnx::Gather[axis=0](%1589, %1590) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1592 : Tensor = onnx::Shape(%1585)\n", " %1593 : Tensor = onnx::Constant[value={2}]()\n", " %1594 : Long(device=cpu) = onnx::Gather[axis=0](%1592, %1593) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1595 : Tensor = onnx::Shape(%1585)\n", " %1596 : Tensor = onnx::Constant[value={3}]()\n", " %1597 : Long(device=cpu) = onnx::Gather[axis=0](%1595, %1596) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1598 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1599 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1600 : Tensor = onnx::Unsqueeze[axes=[0]](%1588)\n", " %1601 : Tensor = onnx::Unsqueeze[axes=[0]](%1591)\n", " %1602 : Tensor = onnx::Unsqueeze[axes=[0]](%1594)\n", " %1603 : Tensor = onnx::Unsqueeze[axes=[0]](%1598)\n", " %1604 : Tensor = onnx::Unsqueeze[axes=[0]](%1597)\n", " %1605 : Tensor = onnx::Unsqueeze[axes=[0]](%1599)\n", " %1606 : Tensor = onnx::Concat[axis=0](%1600, %1601, %1602, %1603, %1604, %1605)\n", " %1607 : Float(1:1098240, 512:2145, 65:33, 1:33, 33:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1585, %1606) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %1608 : int[] = onnx::Constant[value= 0 0 0 0 0 0 [ CPULongType{6} ]]()\n", " %1609 : Tensor = onnx::Constant[value={0}]()\n", " %1610 : Tensor = onnx::Shape(%1608)\n", " %1611 : Tensor = onnx::Gather[axis=0](%1610, %1609)\n", " %1612 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %1613 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1614 : LongTensor = onnx::Mul(%1612, %1613)\n", " %1615 : LongTensor = onnx::Sub(%1614, %1611)\n", " %1616 : Tensor = onnx::Cast[to=7](%1608)\n", " %1617 : Tensor = onnx::ConstantOfShape[value={0}](%1615)\n", " %1618 : Tensor = onnx::Concat[axis=0](%1616, %1617)\n", " %1619 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1620 : Tensor = onnx::Reshape(%1618, %1619)\n", " %1621 : Tensor = onnx::Constant[value={0}]()\n", " %1622 : Tensor = onnx::Constant[value={-1}]()\n", " %1623 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1624 : Tensor = onnx::Constant[value={-1}]()\n", " %1625 : Tensor = onnx::Slice(%1620, %1622, %1623, %1621, %1624)\n", " %1626 : Tensor = onnx::Transpose[perm=[1, 0]](%1625)\n", " %1627 : Tensor = onnx::Constant[value={-1}]()\n", " %1628 : Tensor = onnx::Reshape(%1626, %1627)\n", " %1629 : Tensor = onnx::Cast[to=7](%1628)\n", " %1630 : Tensor = onnx::Constant[value={0}]()\n", " %1631 : Float(1:1098240, 512:2145, 65:33, 1:33, 33:1, 1:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1607, %1629, %1630) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %1632 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1633 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1594, %1632)\n", " %1634 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1635 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1597, %1634)\n", " %1636 : Tensor = onnx::Unsqueeze[axes=[0]](%1588)\n", " %1637 : Tensor = onnx::Unsqueeze[axes=[0]](%1591)\n", " %1638 : Tensor = onnx::Unsqueeze[axes=[0]](%1633)\n", " %1639 : Tensor = onnx::Unsqueeze[axes=[0]](%1635)\n", " %1640 : Tensor = onnx::Concat[axis=0](%1636, %1637, %1638, %1639)\n", " %1641 : Float(1:1098240, 512:2145, 65:33, 33:1, requires_grad=0, device=cpu) = onnx::Reshape(%1631, %1640) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %1642 : int[] = onnx::Constant[value= 1 1 1 1 [ CPULongType{4} ]]()\n", " %1643 : Tensor = onnx::Constant[value={0}]()\n", " %1644 : Tensor = onnx::Shape(%1642)\n", " %1645 : Tensor = onnx::Gather[axis=0](%1644, %1643)\n", " %1646 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1647 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1648 : LongTensor = onnx::Mul(%1646, %1647)\n", " %1649 : LongTensor = onnx::Sub(%1648, %1645)\n", " %1650 : Tensor = onnx::Cast[to=7](%1642)\n", " %1651 : Tensor = onnx::ConstantOfShape[value={0}](%1649)\n", " %1652 : Tensor = onnx::Concat[axis=0](%1650, %1651)\n", " %1653 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1654 : Tensor = onnx::Reshape(%1652, %1653)\n", " %1655 : Tensor = onnx::Constant[value={0}]()\n", " %1656 : Tensor = onnx::Constant[value={-1}]()\n", " %1657 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1658 : Tensor = onnx::Constant[value={-1}]()\n", " %1659 : Tensor = onnx::Slice(%1654, %1656, %1657, %1655, %1658)\n", " %1660 : Tensor = onnx::Transpose[perm=[1, 0]](%1659)\n", " %1661 : Tensor = onnx::Constant[value={-1}]()\n", " %1662 : Tensor = onnx::Reshape(%1660, %1661)\n", " %1663 : Tensor = onnx::Cast[to=7](%1662)\n", " %1664 : Tensor = onnx::Constant[value={0}]()\n", " %1665 : Float(1:1200640, 512:2345, 67:35, 35:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1641, %1663, %1664) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1666 : Tensor = onnx::Shape(%1665)\n", " %1667 : Tensor = onnx::Constant[value={2}]()\n", " %1668 : Long(device=cpu) = onnx::Gather[axis=0](%1666, %1667) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1669 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1670 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1668, %1669)\n", " %1671 : Tensor = onnx::Shape(%1665)\n", " %1672 : Tensor = onnx::Constant[value={3}]()\n", " %1673 : Long(device=cpu) = onnx::Gather[axis=0](%1671, %1672) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1674 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1675 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1673, %1674)\n", " %1676 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1677 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1678 : Tensor = onnx::Unsqueeze[axes=[0]](%1677)\n", " %1679 : Tensor = onnx::Unsqueeze[axes=[0]](%1670)\n", " %1680 : Tensor = onnx::Unsqueeze[axes=[0]](%1676)\n", " %1681 : Tensor = onnx::Constant[value={1}]()\n", " %1682 : Float(1:1200640, 512:2345, 67:35, 35:1, requires_grad=0, device=cpu) = onnx::Slice(%1665, %1678, %1679, %1680, %1681) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1683 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %1684 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1685 : Tensor = onnx::Unsqueeze[axes=[0]](%1684)\n", " %1686 : Tensor = onnx::Unsqueeze[axes=[0]](%1675)\n", " %1687 : Tensor = onnx::Unsqueeze[axes=[0]](%1683)\n", " %1688 : Tensor = onnx::Constant[value={1}]()\n", " %1689 : Float(1:1200640, 512:2345, 67:35, 35:1, requires_grad=0, device=cpu) = onnx::Slice(%1682, %1685, %1686, %1687, %1688) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1690 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1691 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b32.conv0.resample_filter, %1690)\n", " %1692 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1691) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %1693 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %1694 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1695 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %1696 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1697 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%1692, %1694, %1695, %1693, %1696) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %1698 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1697) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1699 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%1698) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1700 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1701 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1702 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1703 : Tensor = onnx::Unsqueeze[axes=[0]](%1591)\n", " %1704 : Tensor = onnx::Unsqueeze[axes=[0]](%1700)\n", " %1705 : Tensor = onnx::Unsqueeze[axes=[0]](%1701)\n", " %1706 : Tensor = onnx::Unsqueeze[axes=[0]](%1702)\n", " %1707 : Tensor = onnx::Concat[axis=0](%1703, %1704, %1705, %1706)\n", " %1708 : Tensor = onnx::Unsqueeze[axes=[0]](%1591)\n", " %1709 : Tensor = onnx::Unsqueeze[axes=[0]](%1700)\n", " %1710 : Tensor = onnx::Unsqueeze[axes=[0]](%1701)\n", " %1711 : Tensor = onnx::Unsqueeze[axes=[0]](%1702)\n", " %1712 : Tensor = onnx::Concat[axis=0](%1708, %1709, %1710, %1711)\n", " %1713 : Tensor = onnx::Shape(%1707)\n", " %1714 : Tensor = onnx::ConstantOfShape[value={1}](%1713)\n", " %1715 : Tensor = onnx::Expand(%1699, %1714)\n", " %1716 : Float(512:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%1715, %1712) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1717 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%1689, %1716) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %1718 : Tensor = onnx::Shape(%1717)\n", " %1719 : Tensor = onnx::Constant[value={2}]()\n", " %1720 : Long(device=cpu) = onnx::Gather[axis=0](%1718, %1719) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1721 : Tensor = onnx::Shape(%1717)\n", " %1722 : Tensor = onnx::Constant[value={3}]()\n", " %1723 : Long(device=cpu) = onnx::Gather[axis=0](%1721, %1722) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1724 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1725 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1726 : Tensor = onnx::Unsqueeze[axes=[0]](%1724)\n", " %1727 : Tensor = onnx::Unsqueeze[axes=[0]](%1725)\n", " %1728 : Tensor = onnx::Unsqueeze[axes=[0]](%1720)\n", " %1729 : Tensor = onnx::Unsqueeze[axes=[0]](%1723)\n", " %1730 : Tensor = onnx::Concat[axis=0](%1726, %1727, %1728, %1729)\n", " %1731 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Reshape(%1717, %1730) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1732 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Add(%1731, %1517)\n", " %1733 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.conv0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %1734 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %1735 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1733, %1734) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1736 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Add(%1732, %1735) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1737 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%1736) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %1738 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %1739 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Mul(%1737, %1738)\n", " %1740 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %1741 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %1742 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%1740, %1741)\n", " %1743 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %1744 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1743) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1745 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%1507, %1742, %1744) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1746 : Float(64:32, 32:1, requires_grad=0, device=cpu) = onnx::Mul(%b32.conv1.noise_const, %b32.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %1747 : Float(64:32, 32:1, requires_grad=0, device=cpu) = onnx::Mul(%1746, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %1748 : Tensor = onnx::Shape(%1739)\n", " %1749 : Tensor = onnx::Constant[value={0}]()\n", " %1750 : Long(device=cpu) = onnx::Gather[axis=0](%1748, %1749) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %1751 : Tensor = onnx::Shape(%b32.conv1.weight)\n", " %1752 : Tensor = onnx::Constant[value={1}]()\n", " %1753 : Long(device=cpu) = onnx::Gather[axis=0](%1751, %1752) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1754 : Tensor = onnx::Shape(%b32.conv1.weight)\n", " %1755 : Tensor = onnx::Constant[value={2}]()\n", " %1756 : Long(device=cpu) = onnx::Gather[axis=0](%1754, %1755) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1757 : Tensor = onnx::Shape(%b32.conv1.weight)\n", " %1758 : Tensor = onnx::Constant[value={3}]()\n", " %1759 : Long(device=cpu) = onnx::Gather[axis=0](%1757, %1758) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1760 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b32.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %1761 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1762 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1763 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1764 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1765 : Tensor = onnx::Unsqueeze[axes=[0]](%1750)\n", " %1766 : Tensor = onnx::Unsqueeze[axes=[0]](%1761)\n", " %1767 : Tensor = onnx::Unsqueeze[axes=[0]](%1762)\n", " %1768 : Tensor = onnx::Unsqueeze[axes=[0]](%1763)\n", " %1769 : Tensor = onnx::Unsqueeze[axes=[0]](%1764)\n", " %1770 : Tensor = onnx::Concat[axis=0](%1765, %1766, %1767, %1768, %1769)\n", " %1771 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1745, %1770) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1772 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1760, %1771) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %1773 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1772, %1772) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1774 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%1773) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1775 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %1776 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%1774, %1775)\n", " %1777 : Tensor = onnx::Sqrt(%1776)\n", " %1778 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1779 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%1778, %1777) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %1780 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1781 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1782 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1783 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1784 : Tensor = onnx::Unsqueeze[axes=[0]](%1750)\n", " %1785 : Tensor = onnx::Unsqueeze[axes=[0]](%1780)\n", " %1786 : Tensor = onnx::Unsqueeze[axes=[0]](%1781)\n", " %1787 : Tensor = onnx::Unsqueeze[axes=[0]](%1782)\n", " %1788 : Tensor = onnx::Unsqueeze[axes=[0]](%1783)\n", " %1789 : Tensor = onnx::Concat[axis=0](%1784, %1785, %1786, %1787, %1788)\n", " %1790 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1779, %1789) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1791 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%1772, %1790) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %1792 : Tensor = onnx::Shape(%1739)\n", " %1793 : Tensor = onnx::Constant[value={2}]()\n", " %1794 : Long(device=cpu) = onnx::Gather[axis=0](%1792, %1793) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1795 : Tensor = onnx::Shape(%1739)\n", " %1796 : Tensor = onnx::Constant[value={3}]()\n", " %1797 : Long(device=cpu) = onnx::Gather[axis=0](%1795, %1796) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1798 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1799 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1800 : Tensor = onnx::Unsqueeze[axes=[0]](%1798)\n", " %1801 : Tensor = onnx::Unsqueeze[axes=[0]](%1799)\n", " %1802 : Tensor = onnx::Unsqueeze[axes=[0]](%1794)\n", " %1803 : Tensor = onnx::Unsqueeze[axes=[0]](%1797)\n", " %1804 : Tensor = onnx::Concat[axis=0](%1800, %1801, %1802, %1803)\n", " %1805 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Reshape(%1739, %1804) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %1806 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1807 : Tensor = onnx::Unsqueeze[axes=[0]](%1806)\n", " %1808 : Tensor = onnx::Unsqueeze[axes=[0]](%1753)\n", " %1809 : Tensor = onnx::Unsqueeze[axes=[0]](%1756)\n", " %1810 : Tensor = onnx::Unsqueeze[axes=[0]](%1759)\n", " %1811 : Tensor = onnx::Concat[axis=0](%1807, %1808, %1809, %1810)\n", " %1812 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%1791, %1811) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %1813 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1812) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %1814 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%1805, %1813) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %1815 : Tensor = onnx::Shape(%1814)\n", " %1816 : Tensor = onnx::Constant[value={2}]()\n", " %1817 : Long(device=cpu) = onnx::Gather[axis=0](%1815, %1816) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1818 : Tensor = onnx::Shape(%1814)\n", " %1819 : Tensor = onnx::Constant[value={3}]()\n", " %1820 : Long(device=cpu) = onnx::Gather[axis=0](%1818, %1819) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1821 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1822 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1823 : Tensor = onnx::Unsqueeze[axes=[0]](%1821)\n", " %1824 : Tensor = onnx::Unsqueeze[axes=[0]](%1822)\n", " %1825 : Tensor = onnx::Unsqueeze[axes=[0]](%1817)\n", " %1826 : Tensor = onnx::Unsqueeze[axes=[0]](%1820)\n", " %1827 : Tensor = onnx::Concat[axis=0](%1823, %1824, %1825, %1826)\n", " %1828 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Reshape(%1814, %1827) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %1829 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Add(%1828, %1747)\n", " %1830 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %1831 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %1832 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1830, %1831) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1833 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Add(%1829, %1832) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %1834 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%1833) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %1835 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %1836 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Mul(%1834, %1835)\n", " %1837 : Tensor = onnx::Shape(%1502)\n", " %1838 : Tensor = onnx::Constant[value={0}]()\n", " %1839 : Long(device=cpu) = onnx::Gather[axis=0](%1837, %1838) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1840 : Tensor = onnx::Shape(%1502)\n", " %1841 : Tensor = onnx::Constant[value={1}]()\n", " %1842 : Long(device=cpu) = onnx::Gather[axis=0](%1840, %1841) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1843 : Tensor = onnx::Shape(%1502)\n", " %1844 : Tensor = onnx::Constant[value={2}]()\n", " %1845 : Long(device=cpu) = onnx::Gather[axis=0](%1843, %1844) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1846 : Tensor = onnx::Shape(%1502)\n", " %1847 : Tensor = onnx::Constant[value={3}]()\n", " %1848 : Long(device=cpu) = onnx::Gather[axis=0](%1846, %1847) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %1849 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1850 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1851 : Tensor = onnx::Unsqueeze[axes=[0]](%1839)\n", " %1852 : Tensor = onnx::Unsqueeze[axes=[0]](%1842)\n", " %1853 : Tensor = onnx::Unsqueeze[axes=[0]](%1845)\n", " %1854 : Tensor = onnx::Unsqueeze[axes=[0]](%1849)\n", " %1855 : Tensor = onnx::Unsqueeze[axes=[0]](%1848)\n", " %1856 : Tensor = onnx::Unsqueeze[axes=[0]](%1850)\n", " %1857 : Tensor = onnx::Concat[axis=0](%1851, %1852, %1853, %1854, %1855, %1856)\n", " %1858 : Float(1:1536, 3:512, 32:16, 1:16, 16:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1502, %1857) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %1859 : int[] = onnx::Constant[value= 0 1 0 0 0 1 [ CPULongType{6} ]]()\n", " %1860 : Tensor = onnx::Constant[value={0}]()\n", " %1861 : Tensor = onnx::Shape(%1859)\n", " %1862 : Tensor = onnx::Gather[axis=0](%1861, %1860)\n", " %1863 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %1864 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1865 : LongTensor = onnx::Mul(%1863, %1864)\n", " %1866 : LongTensor = onnx::Sub(%1865, %1862)\n", " %1867 : Tensor = onnx::Cast[to=7](%1859)\n", " %1868 : Tensor = onnx::ConstantOfShape[value={0}](%1866)\n", " %1869 : Tensor = onnx::Concat[axis=0](%1867, %1868)\n", " %1870 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1871 : Tensor = onnx::Reshape(%1869, %1870)\n", " %1872 : Tensor = onnx::Constant[value={0}]()\n", " %1873 : Tensor = onnx::Constant[value={-1}]()\n", " %1874 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1875 : Tensor = onnx::Constant[value={-1}]()\n", " %1876 : Tensor = onnx::Slice(%1871, %1873, %1874, %1872, %1875)\n", " %1877 : Tensor = onnx::Transpose[perm=[1, 0]](%1876)\n", " %1878 : Tensor = onnx::Constant[value={-1}]()\n", " %1879 : Tensor = onnx::Reshape(%1877, %1878)\n", " %1880 : Tensor = onnx::Cast[to=7](%1879)\n", " %1881 : Tensor = onnx::Constant[value={0}]()\n", " %1882 : Float(1:6144, 3:2048, 32:64, 2:32, 16:2, 2:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1858, %1880, %1881) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %1883 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1884 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1845, %1883)\n", " %1885 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1886 : Long(requires_grad=0, device=cpu) = onnx::Mul(%1848, %1885)\n", " %1887 : Tensor = onnx::Unsqueeze[axes=[0]](%1839)\n", " %1888 : Tensor = onnx::Unsqueeze[axes=[0]](%1842)\n", " %1889 : Tensor = onnx::Unsqueeze[axes=[0]](%1884)\n", " %1890 : Tensor = onnx::Unsqueeze[axes=[0]](%1886)\n", " %1891 : Tensor = onnx::Concat[axis=0](%1887, %1888, %1889, %1890)\n", " %1892 : Float(1:6144, 3:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Reshape(%1882, %1891) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %1893 : int[] = onnx::Constant[value= 2 1 2 1 [ CPULongType{4} ]]()\n", " %1894 : Tensor = onnx::Constant[value={0}]()\n", " %1895 : Tensor = onnx::Shape(%1893)\n", " %1896 : Tensor = onnx::Gather[axis=0](%1895, %1894)\n", " %1897 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1898 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1899 : LongTensor = onnx::Mul(%1897, %1898)\n", " %1900 : LongTensor = onnx::Sub(%1899, %1896)\n", " %1901 : Tensor = onnx::Cast[to=7](%1893)\n", " %1902 : Tensor = onnx::ConstantOfShape[value={0}](%1900)\n", " %1903 : Tensor = onnx::Concat[axis=0](%1901, %1902)\n", " %1904 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %1905 : Tensor = onnx::Reshape(%1903, %1904)\n", " %1906 : Tensor = onnx::Constant[value={0}]()\n", " %1907 : Tensor = onnx::Constant[value={-1}]()\n", " %1908 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %1909 : Tensor = onnx::Constant[value={-1}]()\n", " %1910 : Tensor = onnx::Slice(%1905, %1907, %1908, %1906, %1909)\n", " %1911 : Tensor = onnx::Transpose[perm=[1, 0]](%1910)\n", " %1912 : Tensor = onnx::Constant[value={-1}]()\n", " %1913 : Tensor = onnx::Reshape(%1911, %1912)\n", " %1914 : Tensor = onnx::Cast[to=7](%1913)\n", " %1915 : Tensor = onnx::Constant[value={0}]()\n", " %1916 : Float(1:7035, 3:2345, 67:35, 35:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%1892, %1914, %1915) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1917 : Tensor = onnx::Shape(%1916)\n", " %1918 : Tensor = onnx::Constant[value={2}]()\n", " %1919 : Long(device=cpu) = onnx::Gather[axis=0](%1917, %1918) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1920 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1921 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1919, %1920)\n", " %1922 : Tensor = onnx::Shape(%1916)\n", " %1923 : Tensor = onnx::Constant[value={3}]()\n", " %1924 : Long(device=cpu) = onnx::Gather[axis=0](%1922, %1923) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1925 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1926 : Long(requires_grad=0, device=cpu) = onnx::Sub(%1924, %1925)\n", " %1927 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %1928 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1929 : Tensor = onnx::Unsqueeze[axes=[0]](%1928)\n", " %1930 : Tensor = onnx::Unsqueeze[axes=[0]](%1921)\n", " %1931 : Tensor = onnx::Unsqueeze[axes=[0]](%1927)\n", " %1932 : Tensor = onnx::Constant[value={1}]()\n", " %1933 : Float(1:7035, 3:2345, 67:35, 35:1, requires_grad=0, device=cpu) = onnx::Slice(%1916, %1929, %1930, %1931, %1932) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1934 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %1935 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %1936 : Tensor = onnx::Unsqueeze[axes=[0]](%1935)\n", " %1937 : Tensor = onnx::Unsqueeze[axes=[0]](%1926)\n", " %1938 : Tensor = onnx::Unsqueeze[axes=[0]](%1934)\n", " %1939 : Tensor = onnx::Constant[value={1}]()\n", " %1940 : Float(1:7035, 3:2345, 67:35, 35:1, requires_grad=0, device=cpu) = onnx::Slice(%1933, %1936, %1937, %1938, %1939) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %1941 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %1942 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b32.resample_filter, %1941)\n", " %1943 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1942) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %1944 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %1945 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1946 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %1947 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %1948 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%1943, %1945, %1946, %1944, %1947) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %1949 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1948) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1950 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%1949) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1951 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1952 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1953 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1954 : Tensor = onnx::Unsqueeze[axes=[0]](%1842)\n", " %1955 : Tensor = onnx::Unsqueeze[axes=[0]](%1951)\n", " %1956 : Tensor = onnx::Unsqueeze[axes=[0]](%1952)\n", " %1957 : Tensor = onnx::Unsqueeze[axes=[0]](%1953)\n", " %1958 : Tensor = onnx::Concat[axis=0](%1954, %1955, %1956, %1957)\n", " %1959 : Tensor = onnx::Unsqueeze[axes=[0]](%1842)\n", " %1960 : Tensor = onnx::Unsqueeze[axes=[0]](%1951)\n", " %1961 : Tensor = onnx::Unsqueeze[axes=[0]](%1952)\n", " %1962 : Tensor = onnx::Unsqueeze[axes=[0]](%1953)\n", " %1963 : Tensor = onnx::Concat[axis=0](%1959, %1960, %1961, %1962)\n", " %1964 : Tensor = onnx::Shape(%1958)\n", " %1965 : Tensor = onnx::ConstantOfShape[value={1}](%1964)\n", " %1966 : Tensor = onnx::Expand(%1950, %1965)\n", " %1967 : Float(3:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%1966, %1963) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %1968 : Float(1:6144, 3:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=3, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%1940, %1967) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %1969 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %1970 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %1971 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%1969, %1970)\n", " %1972 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %1973 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%1972) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1974 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%1508, %1971, %1973) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %1975 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %1976 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%1974, %1975)\n", " %1977 : Tensor = onnx::Shape(%1836)\n", " %1978 : Tensor = onnx::Constant[value={0}]()\n", " %1979 : Long(device=cpu) = onnx::Gather[axis=0](%1977, %1978) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %1980 : Tensor = onnx::Shape(%b32.torgb.weight)\n", " %1981 : Tensor = onnx::Constant[value={1}]()\n", " %1982 : Long(device=cpu) = onnx::Gather[axis=0](%1980, %1981) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1983 : Tensor = onnx::Shape(%b32.torgb.weight)\n", " %1984 : Tensor = onnx::Constant[value={2}]()\n", " %1985 : Long(device=cpu) = onnx::Gather[axis=0](%1983, %1984) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1986 : Tensor = onnx::Shape(%b32.torgb.weight)\n", " %1987 : Tensor = onnx::Constant[value={3}]()\n", " %1988 : Long(device=cpu) = onnx::Gather[axis=0](%1986, %1987) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %1989 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b32.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %1990 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1991 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %1992 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1993 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %1994 : Tensor = onnx::Unsqueeze[axes=[0]](%1979)\n", " %1995 : Tensor = onnx::Unsqueeze[axes=[0]](%1990)\n", " %1996 : Tensor = onnx::Unsqueeze[axes=[0]](%1991)\n", " %1997 : Tensor = onnx::Unsqueeze[axes=[0]](%1992)\n", " %1998 : Tensor = onnx::Unsqueeze[axes=[0]](%1993)\n", " %1999 : Tensor = onnx::Concat[axis=0](%1994, %1995, %1996, %1997, %1998)\n", " %2000 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%1976, %1999) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2001 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%1989, %2000) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2002 : Tensor = onnx::Shape(%1836)\n", " %2003 : Tensor = onnx::Constant[value={2}]()\n", " %2004 : Long(device=cpu) = onnx::Gather[axis=0](%2002, %2003) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2005 : Tensor = onnx::Shape(%1836)\n", " %2006 : Tensor = onnx::Constant[value={3}]()\n", " %2007 : Long(device=cpu) = onnx::Gather[axis=0](%2005, %2006) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2008 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2009 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2010 : Tensor = onnx::Unsqueeze[axes=[0]](%2008)\n", " %2011 : Tensor = onnx::Unsqueeze[axes=[0]](%2009)\n", " %2012 : Tensor = onnx::Unsqueeze[axes=[0]](%2004)\n", " %2013 : Tensor = onnx::Unsqueeze[axes=[0]](%2007)\n", " %2014 : Tensor = onnx::Concat[axis=0](%2010, %2011, %2012, %2013)\n", " %2015 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Reshape(%1836, %2014) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2016 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2017 : Tensor = onnx::Unsqueeze[axes=[0]](%2016)\n", " %2018 : Tensor = onnx::Unsqueeze[axes=[0]](%1982)\n", " %2019 : Tensor = onnx::Unsqueeze[axes=[0]](%1985)\n", " %2020 : Tensor = onnx::Unsqueeze[axes=[0]](%1988)\n", " %2021 : Tensor = onnx::Concat[axis=0](%2017, %2018, %2019, %2020)\n", " %2022 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2001, %2021) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %2023 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2022) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %2024 : Float(1:6144, 3:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%2015, %2023) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %2025 : Tensor = onnx::Shape(%2024)\n", " %2026 : Tensor = onnx::Constant[value={2}]()\n", " %2027 : Long(device=cpu) = onnx::Gather[axis=0](%2025, %2026) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2028 : Tensor = onnx::Shape(%2024)\n", " %2029 : Tensor = onnx::Constant[value={3}]()\n", " %2030 : Long(device=cpu) = onnx::Gather[axis=0](%2028, %2029) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2031 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2032 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2033 : Tensor = onnx::Unsqueeze[axes=[0]](%2031)\n", " %2034 : Tensor = onnx::Unsqueeze[axes=[0]](%2032)\n", " %2035 : Tensor = onnx::Unsqueeze[axes=[0]](%2027)\n", " %2036 : Tensor = onnx::Unsqueeze[axes=[0]](%2030)\n", " %2037 : Tensor = onnx::Concat[axis=0](%2033, %2034, %2035, %2036)\n", " %2038 : Float(1:6144, 3:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Reshape(%2024, %2037) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2039 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b32.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %2040 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %2041 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2039, %2040) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2042 : Float(1:6144, 3:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Add(%2038, %2041) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2043 : Float(1:6144, 3:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2042) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %2044 : Float(1:6144, 3:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Add(%1968, %2043)\n", " %2045 : Tensor, %2046 : Tensor, %2047 : Tensor = onnx::Split[axis=1, split=[1, 1, 1]](%193)\n", " %2048 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%2045) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %2049 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%2046) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %2050 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%2047) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %2051 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%1836) # /kaggle/working/stylegan3/training/networks_stylegan2.py:453:0\n", " %2052 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.conv0.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %2053 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %2054 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%2052, %2053)\n", " %2055 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.conv0.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %2056 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2055) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2057 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%2048, %2054, %2056) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2058 : Float(128:64, 64:1, requires_grad=0, device=cpu) = onnx::Mul(%b64.conv0.noise_const, %b64.conv0.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2059 : Float(128:64, 64:1, requires_grad=0, device=cpu) = onnx::Mul(%2058, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2060 : Tensor = onnx::Shape(%2051)\n", " %2061 : Tensor = onnx::Constant[value={0}]()\n", " %2062 : Long(device=cpu) = onnx::Gather[axis=0](%2060, %2061) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %2063 : Tensor = onnx::Shape(%b64.conv0.weight)\n", " %2064 : Tensor = onnx::Constant[value={1}]()\n", " %2065 : Long(device=cpu) = onnx::Gather[axis=0](%2063, %2064) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2066 : Tensor = onnx::Shape(%b64.conv0.weight)\n", " %2067 : Tensor = onnx::Constant[value={2}]()\n", " %2068 : Long(device=cpu) = onnx::Gather[axis=0](%2066, %2067) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2069 : Tensor = onnx::Shape(%b64.conv0.weight)\n", " %2070 : Tensor = onnx::Constant[value={3}]()\n", " %2071 : Long(device=cpu) = onnx::Gather[axis=0](%2069, %2070) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2072 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b64.conv0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %2073 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2074 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2075 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2076 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2077 : Tensor = onnx::Unsqueeze[axes=[0]](%2062)\n", " %2078 : Tensor = onnx::Unsqueeze[axes=[0]](%2073)\n", " %2079 : Tensor = onnx::Unsqueeze[axes=[0]](%2074)\n", " %2080 : Tensor = onnx::Unsqueeze[axes=[0]](%2075)\n", " %2081 : Tensor = onnx::Unsqueeze[axes=[0]](%2076)\n", " %2082 : Tensor = onnx::Concat[axis=0](%2077, %2078, %2079, %2080, %2081)\n", " %2083 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2057, %2082) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2084 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2072, %2083) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2085 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2084, %2084) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2086 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%2085) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2087 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %2088 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%2086, %2087)\n", " %2089 : Tensor = onnx::Sqrt(%2088)\n", " %2090 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2091 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%2090, %2089) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2092 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2093 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2094 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2095 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2096 : Tensor = onnx::Unsqueeze[axes=[0]](%2062)\n", " %2097 : Tensor = onnx::Unsqueeze[axes=[0]](%2092)\n", " %2098 : Tensor = onnx::Unsqueeze[axes=[0]](%2093)\n", " %2099 : Tensor = onnx::Unsqueeze[axes=[0]](%2094)\n", " %2100 : Tensor = onnx::Unsqueeze[axes=[0]](%2095)\n", " %2101 : Tensor = onnx::Concat[axis=0](%2096, %2097, %2098, %2099, %2100)\n", " %2102 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2091, %2101) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2103 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2084, %2102) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2104 : Tensor = onnx::Shape(%2051)\n", " %2105 : Tensor = onnx::Constant[value={2}]()\n", " %2106 : Long(device=cpu) = onnx::Gather[axis=0](%2104, %2105) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2107 : Tensor = onnx::Shape(%2051)\n", " %2108 : Tensor = onnx::Constant[value={3}]()\n", " %2109 : Long(device=cpu) = onnx::Gather[axis=0](%2107, %2108) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2110 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2111 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2112 : Tensor = onnx::Unsqueeze[axes=[0]](%2110)\n", " %2113 : Tensor = onnx::Unsqueeze[axes=[0]](%2111)\n", " %2114 : Tensor = onnx::Unsqueeze[axes=[0]](%2106)\n", " %2115 : Tensor = onnx::Unsqueeze[axes=[0]](%2109)\n", " %2116 : Tensor = onnx::Concat[axis=0](%2112, %2113, %2114, %2115)\n", " %2117 : Float(1:1048576, 512:2048, 64:32, 32:1, requires_grad=0, device=cpu) = onnx::Reshape(%2051, %2116) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2118 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2119 : Tensor = onnx::Unsqueeze[axes=[0]](%2118)\n", " %2120 : Tensor = onnx::Unsqueeze[axes=[0]](%2065)\n", " %2121 : Tensor = onnx::Unsqueeze[axes=[0]](%2068)\n", " %2122 : Tensor = onnx::Unsqueeze[axes=[0]](%2071)\n", " %2123 : Tensor = onnx::Concat[axis=0](%2119, %2120, %2121, %2122)\n", " %2124 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%2103, %2123) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %2125 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2124) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %2126 : Float(512:9, 512:4608, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0, 2, 3]](%2125) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_resample.py:114:0\n", " %2127 : Float(1:4293120, 512:8385, 129:65, 65:1, requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%2117, %2126) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:45:0\n", " %2128 : Tensor = onnx::Shape(%2127)\n", " %2129 : Tensor = onnx::Constant[value={0}]()\n", " %2130 : Long(device=cpu) = onnx::Gather[axis=0](%2128, %2129) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2131 : Tensor = onnx::Shape(%2127)\n", " %2132 : Tensor = onnx::Constant[value={1}]()\n", " %2133 : Long(device=cpu) = onnx::Gather[axis=0](%2131, %2132) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2134 : Tensor = onnx::Shape(%2127)\n", " %2135 : Tensor = onnx::Constant[value={2}]()\n", " %2136 : Long(device=cpu) = onnx::Gather[axis=0](%2134, %2135) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2137 : Tensor = onnx::Shape(%2127)\n", " %2138 : Tensor = onnx::Constant[value={3}]()\n", " %2139 : Long(device=cpu) = onnx::Gather[axis=0](%2137, %2138) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2140 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2141 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2142 : Tensor = onnx::Unsqueeze[axes=[0]](%2130)\n", " %2143 : Tensor = onnx::Unsqueeze[axes=[0]](%2133)\n", " %2144 : Tensor = onnx::Unsqueeze[axes=[0]](%2136)\n", " %2145 : Tensor = onnx::Unsqueeze[axes=[0]](%2140)\n", " %2146 : Tensor = onnx::Unsqueeze[axes=[0]](%2139)\n", " %2147 : Tensor = onnx::Unsqueeze[axes=[0]](%2141)\n", " %2148 : Tensor = onnx::Concat[axis=0](%2142, %2143, %2144, %2145, %2146, %2147)\n", " %2149 : Float(1:4293120, 512:8385, 129:65, 1:65, 65:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2127, %2148) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %2150 : int[] = onnx::Constant[value= 0 0 0 0 0 0 [ CPULongType{6} ]]()\n", " %2151 : Tensor = onnx::Constant[value={0}]()\n", " %2152 : Tensor = onnx::Shape(%2150)\n", " %2153 : Tensor = onnx::Gather[axis=0](%2152, %2151)\n", " %2154 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %2155 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2156 : LongTensor = onnx::Mul(%2154, %2155)\n", " %2157 : LongTensor = onnx::Sub(%2156, %2153)\n", " %2158 : Tensor = onnx::Cast[to=7](%2150)\n", " %2159 : Tensor = onnx::ConstantOfShape[value={0}](%2157)\n", " %2160 : Tensor = onnx::Concat[axis=0](%2158, %2159)\n", " %2161 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2162 : Tensor = onnx::Reshape(%2160, %2161)\n", " %2163 : Tensor = onnx::Constant[value={0}]()\n", " %2164 : Tensor = onnx::Constant[value={-1}]()\n", " %2165 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2166 : Tensor = onnx::Constant[value={-1}]()\n", " %2167 : Tensor = onnx::Slice(%2162, %2164, %2165, %2163, %2166)\n", " %2168 : Tensor = onnx::Transpose[perm=[1, 0]](%2167)\n", " %2169 : Tensor = onnx::Constant[value={-1}]()\n", " %2170 : Tensor = onnx::Reshape(%2168, %2169)\n", " %2171 : Tensor = onnx::Cast[to=7](%2170)\n", " %2172 : Tensor = onnx::Constant[value={0}]()\n", " %2173 : Float(1:4293120, 512:8385, 129:65, 1:65, 65:1, 1:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2149, %2171, %2172) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %2174 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2175 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2136, %2174)\n", " %2176 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2177 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2139, %2176)\n", " %2178 : Tensor = onnx::Unsqueeze[axes=[0]](%2130)\n", " %2179 : Tensor = onnx::Unsqueeze[axes=[0]](%2133)\n", " %2180 : Tensor = onnx::Unsqueeze[axes=[0]](%2175)\n", " %2181 : Tensor = onnx::Unsqueeze[axes=[0]](%2177)\n", " %2182 : Tensor = onnx::Concat[axis=0](%2178, %2179, %2180, %2181)\n", " %2183 : Float(1:4293120, 512:8385, 129:65, 65:1, requires_grad=0, device=cpu) = onnx::Reshape(%2173, %2182) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %2184 : int[] = onnx::Constant[value= 1 1 1 1 [ CPULongType{4} ]]()\n", " %2185 : Tensor = onnx::Constant[value={0}]()\n", " %2186 : Tensor = onnx::Shape(%2184)\n", " %2187 : Tensor = onnx::Gather[axis=0](%2186, %2185)\n", " %2188 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %2189 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2190 : LongTensor = onnx::Mul(%2188, %2189)\n", " %2191 : LongTensor = onnx::Sub(%2190, %2187)\n", " %2192 : Tensor = onnx::Cast[to=7](%2184)\n", " %2193 : Tensor = onnx::ConstantOfShape[value={0}](%2191)\n", " %2194 : Tensor = onnx::Concat[axis=0](%2192, %2193)\n", " %2195 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2196 : Tensor = onnx::Reshape(%2194, %2195)\n", " %2197 : Tensor = onnx::Constant[value={0}]()\n", " %2198 : Tensor = onnx::Constant[value={-1}]()\n", " %2199 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2200 : Tensor = onnx::Constant[value={-1}]()\n", " %2201 : Tensor = onnx::Slice(%2196, %2198, %2199, %2197, %2200)\n", " %2202 : Tensor = onnx::Transpose[perm=[1, 0]](%2201)\n", " %2203 : Tensor = onnx::Constant[value={-1}]()\n", " %2204 : Tensor = onnx::Reshape(%2202, %2203)\n", " %2205 : Tensor = onnx::Cast[to=7](%2204)\n", " %2206 : Tensor = onnx::Constant[value={0}]()\n", " %2207 : Float(1:4493824, 512:8777, 131:67, 67:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2183, %2205, %2206) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2208 : Tensor = onnx::Shape(%2207)\n", " %2209 : Tensor = onnx::Constant[value={2}]()\n", " %2210 : Long(device=cpu) = onnx::Gather[axis=0](%2208, %2209) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2211 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2212 : Long(requires_grad=0, device=cpu) = onnx::Sub(%2210, %2211)\n", " %2213 : Tensor = onnx::Shape(%2207)\n", " %2214 : Tensor = onnx::Constant[value={3}]()\n", " %2215 : Long(device=cpu) = onnx::Gather[axis=0](%2213, %2214) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2216 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2217 : Long(requires_grad=0, device=cpu) = onnx::Sub(%2215, %2216)\n", " %2218 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2219 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2220 : Tensor = onnx::Unsqueeze[axes=[0]](%2219)\n", " %2221 : Tensor = onnx::Unsqueeze[axes=[0]](%2212)\n", " %2222 : Tensor = onnx::Unsqueeze[axes=[0]](%2218)\n", " %2223 : Tensor = onnx::Constant[value={1}]()\n", " %2224 : Float(1:4493824, 512:8777, 131:67, 67:1, requires_grad=0, device=cpu) = onnx::Slice(%2207, %2220, %2221, %2222, %2223) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2225 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %2226 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2227 : Tensor = onnx::Unsqueeze[axes=[0]](%2226)\n", " %2228 : Tensor = onnx::Unsqueeze[axes=[0]](%2217)\n", " %2229 : Tensor = onnx::Unsqueeze[axes=[0]](%2225)\n", " %2230 : Tensor = onnx::Constant[value={1}]()\n", " %2231 : Float(1:4493824, 512:8777, 131:67, 67:1, requires_grad=0, device=cpu) = onnx::Slice(%2224, %2227, %2228, %2229, %2230) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2232 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %2233 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b64.conv0.resample_filter, %2232)\n", " %2234 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2233) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %2235 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %2236 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %2237 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %2238 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %2239 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%2234, %2236, %2237, %2235, %2238) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %2240 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2239) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2241 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%2240) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2242 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2243 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2244 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2245 : Tensor = onnx::Unsqueeze[axes=[0]](%2133)\n", " %2246 : Tensor = onnx::Unsqueeze[axes=[0]](%2242)\n", " %2247 : Tensor = onnx::Unsqueeze[axes=[0]](%2243)\n", " %2248 : Tensor = onnx::Unsqueeze[axes=[0]](%2244)\n", " %2249 : Tensor = onnx::Concat[axis=0](%2245, %2246, %2247, %2248)\n", " %2250 : Tensor = onnx::Unsqueeze[axes=[0]](%2133)\n", " %2251 : Tensor = onnx::Unsqueeze[axes=[0]](%2242)\n", " %2252 : Tensor = onnx::Unsqueeze[axes=[0]](%2243)\n", " %2253 : Tensor = onnx::Unsqueeze[axes=[0]](%2244)\n", " %2254 : Tensor = onnx::Concat[axis=0](%2250, %2251, %2252, %2253)\n", " %2255 : Tensor = onnx::Shape(%2249)\n", " %2256 : Tensor = onnx::ConstantOfShape[value={1}](%2255)\n", " %2257 : Tensor = onnx::Expand(%2241, %2256)\n", " %2258 : Float(512:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%2257, %2254) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2259 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%2231, %2258) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %2260 : Tensor = onnx::Shape(%2259)\n", " %2261 : Tensor = onnx::Constant[value={2}]()\n", " %2262 : Long(device=cpu) = onnx::Gather[axis=0](%2260, %2261) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2263 : Tensor = onnx::Shape(%2259)\n", " %2264 : Tensor = onnx::Constant[value={3}]()\n", " %2265 : Long(device=cpu) = onnx::Gather[axis=0](%2263, %2264) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2266 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2267 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2268 : Tensor = onnx::Unsqueeze[axes=[0]](%2266)\n", " %2269 : Tensor = onnx::Unsqueeze[axes=[0]](%2267)\n", " %2270 : Tensor = onnx::Unsqueeze[axes=[0]](%2262)\n", " %2271 : Tensor = onnx::Unsqueeze[axes=[0]](%2265)\n", " %2272 : Tensor = onnx::Concat[axis=0](%2268, %2269, %2270, %2271)\n", " %2273 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Reshape(%2259, %2272) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2274 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%2273, %2059)\n", " %2275 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.conv0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %2276 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %2277 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2275, %2276) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2278 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%2274, %2277) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2279 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%2278) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %2280 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %2281 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Mul(%2279, %2280)\n", " %2282 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %2283 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %2284 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%2282, %2283)\n", " %2285 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %2286 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2285) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2287 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%2049, %2284, %2286) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2288 : Float(128:64, 64:1, requires_grad=0, device=cpu) = onnx::Mul(%b64.conv1.noise_const, %b64.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2289 : Float(128:64, 64:1, requires_grad=0, device=cpu) = onnx::Mul(%2288, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2290 : Tensor = onnx::Shape(%2281)\n", " %2291 : Tensor = onnx::Constant[value={0}]()\n", " %2292 : Long(device=cpu) = onnx::Gather[axis=0](%2290, %2291) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %2293 : Tensor = onnx::Shape(%b64.conv1.weight)\n", " %2294 : Tensor = onnx::Constant[value={1}]()\n", " %2295 : Long(device=cpu) = onnx::Gather[axis=0](%2293, %2294) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2296 : Tensor = onnx::Shape(%b64.conv1.weight)\n", " %2297 : Tensor = onnx::Constant[value={2}]()\n", " %2298 : Long(device=cpu) = onnx::Gather[axis=0](%2296, %2297) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2299 : Tensor = onnx::Shape(%b64.conv1.weight)\n", " %2300 : Tensor = onnx::Constant[value={3}]()\n", " %2301 : Long(device=cpu) = onnx::Gather[axis=0](%2299, %2300) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2302 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b64.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %2303 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2304 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2305 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2306 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2307 : Tensor = onnx::Unsqueeze[axes=[0]](%2292)\n", " %2308 : Tensor = onnx::Unsqueeze[axes=[0]](%2303)\n", " %2309 : Tensor = onnx::Unsqueeze[axes=[0]](%2304)\n", " %2310 : Tensor = onnx::Unsqueeze[axes=[0]](%2305)\n", " %2311 : Tensor = onnx::Unsqueeze[axes=[0]](%2306)\n", " %2312 : Tensor = onnx::Concat[axis=0](%2307, %2308, %2309, %2310, %2311)\n", " %2313 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2287, %2312) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2314 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2302, %2313) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2315 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2314, %2314) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2316 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%2315) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2317 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %2318 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%2316, %2317)\n", " %2319 : Tensor = onnx::Sqrt(%2318)\n", " %2320 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2321 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Div(%2320, %2319) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2322 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2323 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2324 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2325 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2326 : Tensor = onnx::Unsqueeze[axes=[0]](%2292)\n", " %2327 : Tensor = onnx::Unsqueeze[axes=[0]](%2322)\n", " %2328 : Tensor = onnx::Unsqueeze[axes=[0]](%2323)\n", " %2329 : Tensor = onnx::Unsqueeze[axes=[0]](%2324)\n", " %2330 : Tensor = onnx::Unsqueeze[axes=[0]](%2325)\n", " %2331 : Tensor = onnx::Concat[axis=0](%2326, %2327, %2328, %2329, %2330)\n", " %2332 : Float(1:512, 512:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2321, %2331) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2333 : Float(1:2359296, 512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2314, %2332) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2334 : Tensor = onnx::Shape(%2281)\n", " %2335 : Tensor = onnx::Constant[value={2}]()\n", " %2336 : Long(device=cpu) = onnx::Gather[axis=0](%2334, %2335) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2337 : Tensor = onnx::Shape(%2281)\n", " %2338 : Tensor = onnx::Constant[value={3}]()\n", " %2339 : Long(device=cpu) = onnx::Gather[axis=0](%2337, %2338) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2340 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2341 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2342 : Tensor = onnx::Unsqueeze[axes=[0]](%2340)\n", " %2343 : Tensor = onnx::Unsqueeze[axes=[0]](%2341)\n", " %2344 : Tensor = onnx::Unsqueeze[axes=[0]](%2336)\n", " %2345 : Tensor = onnx::Unsqueeze[axes=[0]](%2339)\n", " %2346 : Tensor = onnx::Concat[axis=0](%2342, %2343, %2344, %2345)\n", " %2347 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Reshape(%2281, %2346) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2348 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2349 : Tensor = onnx::Unsqueeze[axes=[0]](%2348)\n", " %2350 : Tensor = onnx::Unsqueeze[axes=[0]](%2295)\n", " %2351 : Tensor = onnx::Unsqueeze[axes=[0]](%2298)\n", " %2352 : Tensor = onnx::Unsqueeze[axes=[0]](%2301)\n", " %2353 : Tensor = onnx::Concat[axis=0](%2349, %2350, %2351, %2352)\n", " %2354 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%2333, %2353) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %2355 : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2354) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %2356 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%2347, %2355) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %2357 : Tensor = onnx::Shape(%2356)\n", " %2358 : Tensor = onnx::Constant[value={2}]()\n", " %2359 : Long(device=cpu) = onnx::Gather[axis=0](%2357, %2358) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2360 : Tensor = onnx::Shape(%2356)\n", " %2361 : Tensor = onnx::Constant[value={3}]()\n", " %2362 : Long(device=cpu) = onnx::Gather[axis=0](%2360, %2361) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2363 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2364 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2365 : Tensor = onnx::Unsqueeze[axes=[0]](%2363)\n", " %2366 : Tensor = onnx::Unsqueeze[axes=[0]](%2364)\n", " %2367 : Tensor = onnx::Unsqueeze[axes=[0]](%2359)\n", " %2368 : Tensor = onnx::Unsqueeze[axes=[0]](%2362)\n", " %2369 : Tensor = onnx::Concat[axis=0](%2365, %2366, %2367, %2368)\n", " %2370 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Reshape(%2356, %2369) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2371 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%2370, %2289)\n", " %2372 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %2373 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %2374 : Float(1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2372, %2373) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2375 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%2371, %2374) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2376 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%2375) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %2377 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %2378 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Mul(%2376, %2377)\n", " %2379 : Tensor = onnx::Shape(%2044)\n", " %2380 : Tensor = onnx::Constant[value={0}]()\n", " %2381 : Long(device=cpu) = onnx::Gather[axis=0](%2379, %2380) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2382 : Tensor = onnx::Shape(%2044)\n", " %2383 : Tensor = onnx::Constant[value={1}]()\n", " %2384 : Long(device=cpu) = onnx::Gather[axis=0](%2382, %2383) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2385 : Tensor = onnx::Shape(%2044)\n", " %2386 : Tensor = onnx::Constant[value={2}]()\n", " %2387 : Long(device=cpu) = onnx::Gather[axis=0](%2385, %2386) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2388 : Tensor = onnx::Shape(%2044)\n", " %2389 : Tensor = onnx::Constant[value={3}]()\n", " %2390 : Long(device=cpu) = onnx::Gather[axis=0](%2388, %2389) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2391 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2392 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2393 : Tensor = onnx::Unsqueeze[axes=[0]](%2381)\n", " %2394 : Tensor = onnx::Unsqueeze[axes=[0]](%2384)\n", " %2395 : Tensor = onnx::Unsqueeze[axes=[0]](%2387)\n", " %2396 : Tensor = onnx::Unsqueeze[axes=[0]](%2391)\n", " %2397 : Tensor = onnx::Unsqueeze[axes=[0]](%2390)\n", " %2398 : Tensor = onnx::Unsqueeze[axes=[0]](%2392)\n", " %2399 : Tensor = onnx::Concat[axis=0](%2393, %2394, %2395, %2396, %2397, %2398)\n", " %2400 : Float(1:6144, 3:2048, 64:32, 1:32, 32:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2044, %2399) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %2401 : int[] = onnx::Constant[value= 0 1 0 0 0 1 [ CPULongType{6} ]]()\n", " %2402 : Tensor = onnx::Constant[value={0}]()\n", " %2403 : Tensor = onnx::Shape(%2401)\n", " %2404 : Tensor = onnx::Gather[axis=0](%2403, %2402)\n", " %2405 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %2406 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2407 : LongTensor = onnx::Mul(%2405, %2406)\n", " %2408 : LongTensor = onnx::Sub(%2407, %2404)\n", " %2409 : Tensor = onnx::Cast[to=7](%2401)\n", " %2410 : Tensor = onnx::ConstantOfShape[value={0}](%2408)\n", " %2411 : Tensor = onnx::Concat[axis=0](%2409, %2410)\n", " %2412 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2413 : Tensor = onnx::Reshape(%2411, %2412)\n", " %2414 : Tensor = onnx::Constant[value={0}]()\n", " %2415 : Tensor = onnx::Constant[value={-1}]()\n", " %2416 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2417 : Tensor = onnx::Constant[value={-1}]()\n", " %2418 : Tensor = onnx::Slice(%2413, %2415, %2416, %2414, %2417)\n", " %2419 : Tensor = onnx::Transpose[perm=[1, 0]](%2418)\n", " %2420 : Tensor = onnx::Constant[value={-1}]()\n", " %2421 : Tensor = onnx::Reshape(%2419, %2420)\n", " %2422 : Tensor = onnx::Cast[to=7](%2421)\n", " %2423 : Tensor = onnx::Constant[value={0}]()\n", " %2424 : Float(1:24576, 3:8192, 64:128, 2:64, 32:2, 2:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2400, %2422, %2423) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %2425 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2426 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2387, %2425)\n", " %2427 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2428 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2390, %2427)\n", " %2429 : Tensor = onnx::Unsqueeze[axes=[0]](%2381)\n", " %2430 : Tensor = onnx::Unsqueeze[axes=[0]](%2384)\n", " %2431 : Tensor = onnx::Unsqueeze[axes=[0]](%2426)\n", " %2432 : Tensor = onnx::Unsqueeze[axes=[0]](%2428)\n", " %2433 : Tensor = onnx::Concat[axis=0](%2429, %2430, %2431, %2432)\n", " %2434 : Float(1:24576, 3:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Reshape(%2424, %2433) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %2435 : int[] = onnx::Constant[value= 2 1 2 1 [ CPULongType{4} ]]()\n", " %2436 : Tensor = onnx::Constant[value={0}]()\n", " %2437 : Tensor = onnx::Shape(%2435)\n", " %2438 : Tensor = onnx::Gather[axis=0](%2437, %2436)\n", " %2439 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %2440 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2441 : LongTensor = onnx::Mul(%2439, %2440)\n", " %2442 : LongTensor = onnx::Sub(%2441, %2438)\n", " %2443 : Tensor = onnx::Cast[to=7](%2435)\n", " %2444 : Tensor = onnx::ConstantOfShape[value={0}](%2442)\n", " %2445 : Tensor = onnx::Concat[axis=0](%2443, %2444)\n", " %2446 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2447 : Tensor = onnx::Reshape(%2445, %2446)\n", " %2448 : Tensor = onnx::Constant[value={0}]()\n", " %2449 : Tensor = onnx::Constant[value={-1}]()\n", " %2450 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2451 : Tensor = onnx::Constant[value={-1}]()\n", " %2452 : Tensor = onnx::Slice(%2447, %2449, %2450, %2448, %2451)\n", " %2453 : Tensor = onnx::Transpose[perm=[1, 0]](%2452)\n", " %2454 : Tensor = onnx::Constant[value={-1}]()\n", " %2455 : Tensor = onnx::Reshape(%2453, %2454)\n", " %2456 : Tensor = onnx::Cast[to=7](%2455)\n", " %2457 : Tensor = onnx::Constant[value={0}]()\n", " %2458 : Float(1:26331, 3:8777, 131:67, 67:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2434, %2456, %2457) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2459 : Tensor = onnx::Shape(%2458)\n", " %2460 : Tensor = onnx::Constant[value={2}]()\n", " %2461 : Long(device=cpu) = onnx::Gather[axis=0](%2459, %2460) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2462 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2463 : Long(requires_grad=0, device=cpu) = onnx::Sub(%2461, %2462)\n", " %2464 : Tensor = onnx::Shape(%2458)\n", " %2465 : Tensor = onnx::Constant[value={3}]()\n", " %2466 : Long(device=cpu) = onnx::Gather[axis=0](%2464, %2465) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2467 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2468 : Long(requires_grad=0, device=cpu) = onnx::Sub(%2466, %2467)\n", " %2469 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2470 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2471 : Tensor = onnx::Unsqueeze[axes=[0]](%2470)\n", " %2472 : Tensor = onnx::Unsqueeze[axes=[0]](%2463)\n", " %2473 : Tensor = onnx::Unsqueeze[axes=[0]](%2469)\n", " %2474 : Tensor = onnx::Constant[value={1}]()\n", " %2475 : Float(1:26331, 3:8777, 131:67, 67:1, requires_grad=0, device=cpu) = onnx::Slice(%2458, %2471, %2472, %2473, %2474) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2476 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %2477 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2478 : Tensor = onnx::Unsqueeze[axes=[0]](%2477)\n", " %2479 : Tensor = onnx::Unsqueeze[axes=[0]](%2468)\n", " %2480 : Tensor = onnx::Unsqueeze[axes=[0]](%2476)\n", " %2481 : Tensor = onnx::Constant[value={1}]()\n", " %2482 : Float(1:26331, 3:8777, 131:67, 67:1, requires_grad=0, device=cpu) = onnx::Slice(%2475, %2478, %2479, %2480, %2481) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2483 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %2484 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b64.resample_filter, %2483)\n", " %2485 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2484) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %2486 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %2487 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %2488 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %2489 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %2490 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%2485, %2487, %2488, %2486, %2489) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %2491 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2490) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2492 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%2491) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2493 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2494 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2495 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2496 : Tensor = onnx::Unsqueeze[axes=[0]](%2384)\n", " %2497 : Tensor = onnx::Unsqueeze[axes=[0]](%2493)\n", " %2498 : Tensor = onnx::Unsqueeze[axes=[0]](%2494)\n", " %2499 : Tensor = onnx::Unsqueeze[axes=[0]](%2495)\n", " %2500 : Tensor = onnx::Concat[axis=0](%2496, %2497, %2498, %2499)\n", " %2501 : Tensor = onnx::Unsqueeze[axes=[0]](%2384)\n", " %2502 : Tensor = onnx::Unsqueeze[axes=[0]](%2493)\n", " %2503 : Tensor = onnx::Unsqueeze[axes=[0]](%2494)\n", " %2504 : Tensor = onnx::Unsqueeze[axes=[0]](%2495)\n", " %2505 : Tensor = onnx::Concat[axis=0](%2501, %2502, %2503, %2504)\n", " %2506 : Tensor = onnx::Shape(%2500)\n", " %2507 : Tensor = onnx::ConstantOfShape[value={1}](%2506)\n", " %2508 : Tensor = onnx::Expand(%2492, %2507)\n", " %2509 : Float(3:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%2508, %2505) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2510 : Float(1:24576, 3:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=3, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%2482, %2509) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %2511 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %2512 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %2513 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%2511, %2512)\n", " %2514 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %2515 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2514) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2516 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%2050, %2513, %2515) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2517 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %2518 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%2516, %2517)\n", " %2519 : Tensor = onnx::Shape(%2378)\n", " %2520 : Tensor = onnx::Constant[value={0}]()\n", " %2521 : Long(device=cpu) = onnx::Gather[axis=0](%2519, %2520) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %2522 : Tensor = onnx::Shape(%b64.torgb.weight)\n", " %2523 : Tensor = onnx::Constant[value={1}]()\n", " %2524 : Long(device=cpu) = onnx::Gather[axis=0](%2522, %2523) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2525 : Tensor = onnx::Shape(%b64.torgb.weight)\n", " %2526 : Tensor = onnx::Constant[value={2}]()\n", " %2527 : Long(device=cpu) = onnx::Gather[axis=0](%2525, %2526) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2528 : Tensor = onnx::Shape(%b64.torgb.weight)\n", " %2529 : Tensor = onnx::Constant[value={3}]()\n", " %2530 : Long(device=cpu) = onnx::Gather[axis=0](%2528, %2529) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2531 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b64.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %2532 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2533 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2534 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2535 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2536 : Tensor = onnx::Unsqueeze[axes=[0]](%2521)\n", " %2537 : Tensor = onnx::Unsqueeze[axes=[0]](%2532)\n", " %2538 : Tensor = onnx::Unsqueeze[axes=[0]](%2533)\n", " %2539 : Tensor = onnx::Unsqueeze[axes=[0]](%2534)\n", " %2540 : Tensor = onnx::Unsqueeze[axes=[0]](%2535)\n", " %2541 : Tensor = onnx::Concat[axis=0](%2536, %2537, %2538, %2539, %2540)\n", " %2542 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2518, %2541) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2543 : Float(1:1536, 3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%2531, %2542) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2544 : Tensor = onnx::Shape(%2378)\n", " %2545 : Tensor = onnx::Constant[value={2}]()\n", " %2546 : Long(device=cpu) = onnx::Gather[axis=0](%2544, %2545) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2547 : Tensor = onnx::Shape(%2378)\n", " %2548 : Tensor = onnx::Constant[value={3}]()\n", " %2549 : Long(device=cpu) = onnx::Gather[axis=0](%2547, %2548) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2550 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2551 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2552 : Tensor = onnx::Unsqueeze[axes=[0]](%2550)\n", " %2553 : Tensor = onnx::Unsqueeze[axes=[0]](%2551)\n", " %2554 : Tensor = onnx::Unsqueeze[axes=[0]](%2546)\n", " %2555 : Tensor = onnx::Unsqueeze[axes=[0]](%2549)\n", " %2556 : Tensor = onnx::Concat[axis=0](%2552, %2553, %2554, %2555)\n", " %2557 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Reshape(%2378, %2556) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2558 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2559 : Tensor = onnx::Unsqueeze[axes=[0]](%2558)\n", " %2560 : Tensor = onnx::Unsqueeze[axes=[0]](%2524)\n", " %2561 : Tensor = onnx::Unsqueeze[axes=[0]](%2527)\n", " %2562 : Tensor = onnx::Unsqueeze[axes=[0]](%2530)\n", " %2563 : Tensor = onnx::Concat[axis=0](%2559, %2560, %2561, %2562)\n", " %2564 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2543, %2563) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %2565 : Float(3:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2564) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %2566 : Float(1:24576, 3:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%2557, %2565) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %2567 : Tensor = onnx::Shape(%2566)\n", " %2568 : Tensor = onnx::Constant[value={2}]()\n", " %2569 : Long(device=cpu) = onnx::Gather[axis=0](%2567, %2568) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2570 : Tensor = onnx::Shape(%2566)\n", " %2571 : Tensor = onnx::Constant[value={3}]()\n", " %2572 : Long(device=cpu) = onnx::Gather[axis=0](%2570, %2571) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2573 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2574 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2575 : Tensor = onnx::Unsqueeze[axes=[0]](%2573)\n", " %2576 : Tensor = onnx::Unsqueeze[axes=[0]](%2574)\n", " %2577 : Tensor = onnx::Unsqueeze[axes=[0]](%2569)\n", " %2578 : Tensor = onnx::Unsqueeze[axes=[0]](%2572)\n", " %2579 : Tensor = onnx::Concat[axis=0](%2575, %2576, %2577, %2578)\n", " %2580 : Float(1:24576, 3:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Reshape(%2566, %2579) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2581 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b64.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %2582 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %2583 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2581, %2582) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2584 : Float(1:24576, 3:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%2580, %2583) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2585 : Float(1:24576, 3:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2584) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %2586 : Float(1:24576, 3:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%2510, %2585)\n", " %2587 : Tensor, %2588 : Tensor, %2589 : Tensor = onnx::Split[axis=1, split=[1, 1, 1]](%202)\n", " %2590 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%2587) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %2591 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%2588) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %2592 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%2589) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %2593 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2378) # /kaggle/working/stylegan3/training/networks_stylegan2.py:453:0\n", " %2594 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.conv0.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %2595 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %2596 : Float(512:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%2594, %2595)\n", " %2597 : Float(512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.conv0.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %2598 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2597) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2599 : Float(1:512, 512:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%2590, %2596, %2598) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2600 : Float(256:128, 128:1, requires_grad=0, device=cpu) = onnx::Mul(%b128.conv0.noise_const, %b128.conv0.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2601 : Float(256:128, 128:1, requires_grad=0, device=cpu) = onnx::Mul(%2600, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2602 : Tensor = onnx::Shape(%2593)\n", " %2603 : Tensor = onnx::Constant[value={0}]()\n", " %2604 : Long(device=cpu) = onnx::Gather[axis=0](%2602, %2603) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %2605 : Tensor = onnx::Shape(%b128.conv0.weight)\n", " %2606 : Tensor = onnx::Constant[value={1}]()\n", " %2607 : Long(device=cpu) = onnx::Gather[axis=0](%2605, %2606) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2608 : Tensor = onnx::Shape(%b128.conv0.weight)\n", " %2609 : Tensor = onnx::Constant[value={2}]()\n", " %2610 : Long(device=cpu) = onnx::Gather[axis=0](%2608, %2609) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2611 : Tensor = onnx::Shape(%b128.conv0.weight)\n", " %2612 : Tensor = onnx::Constant[value={3}]()\n", " %2613 : Long(device=cpu) = onnx::Gather[axis=0](%2611, %2612) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2614 : Float(1:1179648, 256:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b128.conv0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %2615 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2616 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2617 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2618 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2619 : Tensor = onnx::Unsqueeze[axes=[0]](%2604)\n", " %2620 : Tensor = onnx::Unsqueeze[axes=[0]](%2615)\n", " %2621 : Tensor = onnx::Unsqueeze[axes=[0]](%2616)\n", " %2622 : Tensor = onnx::Unsqueeze[axes=[0]](%2617)\n", " %2623 : Tensor = onnx::Unsqueeze[axes=[0]](%2618)\n", " %2624 : Tensor = onnx::Concat[axis=0](%2619, %2620, %2621, %2622, %2623)\n", " %2625 : Float(1:512, 1:512, 512:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2599, %2624) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2626 : Float(1:1179648, 256:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2614, %2625) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2627 : Float(1:1179648, 256:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2626, %2626) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2628 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%2627) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2629 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %2630 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%2628, %2629)\n", " %2631 : Tensor = onnx::Sqrt(%2630)\n", " %2632 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2633 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Div(%2632, %2631) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2634 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2635 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2636 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2637 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2638 : Tensor = onnx::Unsqueeze[axes=[0]](%2604)\n", " %2639 : Tensor = onnx::Unsqueeze[axes=[0]](%2634)\n", " %2640 : Tensor = onnx::Unsqueeze[axes=[0]](%2635)\n", " %2641 : Tensor = onnx::Unsqueeze[axes=[0]](%2636)\n", " %2642 : Tensor = onnx::Unsqueeze[axes=[0]](%2637)\n", " %2643 : Tensor = onnx::Concat[axis=0](%2638, %2639, %2640, %2641, %2642)\n", " %2644 : Float(1:256, 256:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2633, %2643) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2645 : Float(1:1179648, 256:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2626, %2644) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2646 : Tensor = onnx::Shape(%2593)\n", " %2647 : Tensor = onnx::Constant[value={2}]()\n", " %2648 : Long(device=cpu) = onnx::Gather[axis=0](%2646, %2647) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2649 : Tensor = onnx::Shape(%2593)\n", " %2650 : Tensor = onnx::Constant[value={3}]()\n", " %2651 : Long(device=cpu) = onnx::Gather[axis=0](%2649, %2650) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2652 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2653 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2654 : Tensor = onnx::Unsqueeze[axes=[0]](%2652)\n", " %2655 : Tensor = onnx::Unsqueeze[axes=[0]](%2653)\n", " %2656 : Tensor = onnx::Unsqueeze[axes=[0]](%2648)\n", " %2657 : Tensor = onnx::Unsqueeze[axes=[0]](%2651)\n", " %2658 : Tensor = onnx::Concat[axis=0](%2654, %2655, %2656, %2657)\n", " %2659 : Float(1:4194304, 512:8192, 128:64, 64:1, requires_grad=0, device=cpu) = onnx::Reshape(%2593, %2658) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2660 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2661 : Tensor = onnx::Unsqueeze[axes=[0]](%2660)\n", " %2662 : Tensor = onnx::Unsqueeze[axes=[0]](%2607)\n", " %2663 : Tensor = onnx::Unsqueeze[axes=[0]](%2610)\n", " %2664 : Tensor = onnx::Unsqueeze[axes=[0]](%2613)\n", " %2665 : Tensor = onnx::Concat[axis=0](%2661, %2662, %2663, %2664)\n", " %2666 : Float(256:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%2645, %2665) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %2667 : Float(256:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2666) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %2668 : Float(512:9, 256:4608, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0, 2, 3]](%2667) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_resample.py:114:0\n", " %2669 : Float(1:8487168, 256:33153, 257:129, 129:1, requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%2659, %2668) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:45:0\n", " %2670 : Tensor = onnx::Shape(%2669)\n", " %2671 : Tensor = onnx::Constant[value={0}]()\n", " %2672 : Long(device=cpu) = onnx::Gather[axis=0](%2670, %2671) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2673 : Tensor = onnx::Shape(%2669)\n", " %2674 : Tensor = onnx::Constant[value={1}]()\n", " %2675 : Long(device=cpu) = onnx::Gather[axis=0](%2673, %2674) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2676 : Tensor = onnx::Shape(%2669)\n", " %2677 : Tensor = onnx::Constant[value={2}]()\n", " %2678 : Long(device=cpu) = onnx::Gather[axis=0](%2676, %2677) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2679 : Tensor = onnx::Shape(%2669)\n", " %2680 : Tensor = onnx::Constant[value={3}]()\n", " %2681 : Long(device=cpu) = onnx::Gather[axis=0](%2679, %2680) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2682 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2683 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2684 : Tensor = onnx::Unsqueeze[axes=[0]](%2672)\n", " %2685 : Tensor = onnx::Unsqueeze[axes=[0]](%2675)\n", " %2686 : Tensor = onnx::Unsqueeze[axes=[0]](%2678)\n", " %2687 : Tensor = onnx::Unsqueeze[axes=[0]](%2682)\n", " %2688 : Tensor = onnx::Unsqueeze[axes=[0]](%2681)\n", " %2689 : Tensor = onnx::Unsqueeze[axes=[0]](%2683)\n", " %2690 : Tensor = onnx::Concat[axis=0](%2684, %2685, %2686, %2687, %2688, %2689)\n", " %2691 : Float(1:8487168, 256:33153, 257:129, 1:129, 129:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2669, %2690) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %2692 : int[] = onnx::Constant[value= 0 0 0 0 0 0 [ CPULongType{6} ]]()\n", " %2693 : Tensor = onnx::Constant[value={0}]()\n", " %2694 : Tensor = onnx::Shape(%2692)\n", " %2695 : Tensor = onnx::Gather[axis=0](%2694, %2693)\n", " %2696 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %2697 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2698 : LongTensor = onnx::Mul(%2696, %2697)\n", " %2699 : LongTensor = onnx::Sub(%2698, %2695)\n", " %2700 : Tensor = onnx::Cast[to=7](%2692)\n", " %2701 : Tensor = onnx::ConstantOfShape[value={0}](%2699)\n", " %2702 : Tensor = onnx::Concat[axis=0](%2700, %2701)\n", " %2703 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2704 : Tensor = onnx::Reshape(%2702, %2703)\n", " %2705 : Tensor = onnx::Constant[value={0}]()\n", " %2706 : Tensor = onnx::Constant[value={-1}]()\n", " %2707 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2708 : Tensor = onnx::Constant[value={-1}]()\n", " %2709 : Tensor = onnx::Slice(%2704, %2706, %2707, %2705, %2708)\n", " %2710 : Tensor = onnx::Transpose[perm=[1, 0]](%2709)\n", " %2711 : Tensor = onnx::Constant[value={-1}]()\n", " %2712 : Tensor = onnx::Reshape(%2710, %2711)\n", " %2713 : Tensor = onnx::Cast[to=7](%2712)\n", " %2714 : Tensor = onnx::Constant[value={0}]()\n", " %2715 : Float(1:8487168, 256:33153, 257:129, 1:129, 129:1, 1:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2691, %2713, %2714) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %2716 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2717 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2678, %2716)\n", " %2718 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2719 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2681, %2718)\n", " %2720 : Tensor = onnx::Unsqueeze[axes=[0]](%2672)\n", " %2721 : Tensor = onnx::Unsqueeze[axes=[0]](%2675)\n", " %2722 : Tensor = onnx::Unsqueeze[axes=[0]](%2717)\n", " %2723 : Tensor = onnx::Unsqueeze[axes=[0]](%2719)\n", " %2724 : Tensor = onnx::Concat[axis=0](%2720, %2721, %2722, %2723)\n", " %2725 : Float(1:8487168, 256:33153, 257:129, 129:1, requires_grad=0, device=cpu) = onnx::Reshape(%2715, %2724) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %2726 : int[] = onnx::Constant[value= 1 1 1 1 [ CPULongType{4} ]]()\n", " %2727 : Tensor = onnx::Constant[value={0}]()\n", " %2728 : Tensor = onnx::Shape(%2726)\n", " %2729 : Tensor = onnx::Gather[axis=0](%2728, %2727)\n", " %2730 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %2731 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2732 : LongTensor = onnx::Mul(%2730, %2731)\n", " %2733 : LongTensor = onnx::Sub(%2732, %2729)\n", " %2734 : Tensor = onnx::Cast[to=7](%2726)\n", " %2735 : Tensor = onnx::ConstantOfShape[value={0}](%2733)\n", " %2736 : Tensor = onnx::Concat[axis=0](%2734, %2735)\n", " %2737 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2738 : Tensor = onnx::Reshape(%2736, %2737)\n", " %2739 : Tensor = onnx::Constant[value={0}]()\n", " %2740 : Tensor = onnx::Constant[value={-1}]()\n", " %2741 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2742 : Tensor = onnx::Constant[value={-1}]()\n", " %2743 : Tensor = onnx::Slice(%2738, %2740, %2741, %2739, %2742)\n", " %2744 : Tensor = onnx::Transpose[perm=[1, 0]](%2743)\n", " %2745 : Tensor = onnx::Constant[value={-1}]()\n", " %2746 : Tensor = onnx::Reshape(%2744, %2745)\n", " %2747 : Tensor = onnx::Cast[to=7](%2746)\n", " %2748 : Tensor = onnx::Constant[value={0}]()\n", " %2749 : Float(1:8685824, 256:33929, 259:131, 131:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2725, %2747, %2748) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2750 : Tensor = onnx::Shape(%2749)\n", " %2751 : Tensor = onnx::Constant[value={2}]()\n", " %2752 : Long(device=cpu) = onnx::Gather[axis=0](%2750, %2751) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2753 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2754 : Long(requires_grad=0, device=cpu) = onnx::Sub(%2752, %2753)\n", " %2755 : Tensor = onnx::Shape(%2749)\n", " %2756 : Tensor = onnx::Constant[value={3}]()\n", " %2757 : Long(device=cpu) = onnx::Gather[axis=0](%2755, %2756) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2758 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2759 : Long(requires_grad=0, device=cpu) = onnx::Sub(%2757, %2758)\n", " %2760 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2761 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2762 : Tensor = onnx::Unsqueeze[axes=[0]](%2761)\n", " %2763 : Tensor = onnx::Unsqueeze[axes=[0]](%2754)\n", " %2764 : Tensor = onnx::Unsqueeze[axes=[0]](%2760)\n", " %2765 : Tensor = onnx::Constant[value={1}]()\n", " %2766 : Float(1:8685824, 256:33929, 259:131, 131:1, requires_grad=0, device=cpu) = onnx::Slice(%2749, %2762, %2763, %2764, %2765) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2767 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %2768 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %2769 : Tensor = onnx::Unsqueeze[axes=[0]](%2768)\n", " %2770 : Tensor = onnx::Unsqueeze[axes=[0]](%2759)\n", " %2771 : Tensor = onnx::Unsqueeze[axes=[0]](%2767)\n", " %2772 : Tensor = onnx::Constant[value={1}]()\n", " %2773 : Float(1:8685824, 256:33929, 259:131, 131:1, requires_grad=0, device=cpu) = onnx::Slice(%2766, %2769, %2770, %2771, %2772) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %2774 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %2775 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b128.conv0.resample_filter, %2774)\n", " %2776 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2775) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %2777 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %2778 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %2779 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %2780 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %2781 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%2776, %2778, %2779, %2777, %2780) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %2782 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2781) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2783 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%2782) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2784 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2785 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2786 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2787 : Tensor = onnx::Unsqueeze[axes=[0]](%2675)\n", " %2788 : Tensor = onnx::Unsqueeze[axes=[0]](%2784)\n", " %2789 : Tensor = onnx::Unsqueeze[axes=[0]](%2785)\n", " %2790 : Tensor = onnx::Unsqueeze[axes=[0]](%2786)\n", " %2791 : Tensor = onnx::Concat[axis=0](%2787, %2788, %2789, %2790)\n", " %2792 : Tensor = onnx::Unsqueeze[axes=[0]](%2675)\n", " %2793 : Tensor = onnx::Unsqueeze[axes=[0]](%2784)\n", " %2794 : Tensor = onnx::Unsqueeze[axes=[0]](%2785)\n", " %2795 : Tensor = onnx::Unsqueeze[axes=[0]](%2786)\n", " %2796 : Tensor = onnx::Concat[axis=0](%2792, %2793, %2794, %2795)\n", " %2797 : Tensor = onnx::Shape(%2791)\n", " %2798 : Tensor = onnx::ConstantOfShape[value={1}](%2797)\n", " %2799 : Tensor = onnx::Expand(%2783, %2798)\n", " %2800 : Float(256:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%2799, %2796) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %2801 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=256, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%2773, %2800) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %2802 : Tensor = onnx::Shape(%2801)\n", " %2803 : Tensor = onnx::Constant[value={2}]()\n", " %2804 : Long(device=cpu) = onnx::Gather[axis=0](%2802, %2803) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2805 : Tensor = onnx::Shape(%2801)\n", " %2806 : Tensor = onnx::Constant[value={3}]()\n", " %2807 : Long(device=cpu) = onnx::Gather[axis=0](%2805, %2806) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2808 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2809 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2810 : Tensor = onnx::Unsqueeze[axes=[0]](%2808)\n", " %2811 : Tensor = onnx::Unsqueeze[axes=[0]](%2809)\n", " %2812 : Tensor = onnx::Unsqueeze[axes=[0]](%2804)\n", " %2813 : Tensor = onnx::Unsqueeze[axes=[0]](%2807)\n", " %2814 : Tensor = onnx::Concat[axis=0](%2810, %2811, %2812, %2813)\n", " %2815 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Reshape(%2801, %2814) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2816 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%2815, %2601)\n", " %2817 : Float(256:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.conv0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %2818 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %2819 : Float(1:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2817, %2818) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2820 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%2816, %2819) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2821 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%2820) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %2822 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %2823 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Mul(%2821, %2822)\n", " %2824 : Float(256:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %2825 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %2826 : Float(256:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%2824, %2825)\n", " %2827 : Float(256:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %2828 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%2827) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2829 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%2591, %2826, %2828) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %2830 : Float(256:128, 128:1, requires_grad=0, device=cpu) = onnx::Mul(%b128.conv1.noise_const, %b128.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2831 : Float(256:128, 128:1, requires_grad=0, device=cpu) = onnx::Mul(%2830, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %2832 : Tensor = onnx::Shape(%2823)\n", " %2833 : Tensor = onnx::Constant[value={0}]()\n", " %2834 : Long(device=cpu) = onnx::Gather[axis=0](%2832, %2833) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %2835 : Tensor = onnx::Shape(%b128.conv1.weight)\n", " %2836 : Tensor = onnx::Constant[value={1}]()\n", " %2837 : Long(device=cpu) = onnx::Gather[axis=0](%2835, %2836) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2838 : Tensor = onnx::Shape(%b128.conv1.weight)\n", " %2839 : Tensor = onnx::Constant[value={2}]()\n", " %2840 : Long(device=cpu) = onnx::Gather[axis=0](%2838, %2839) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2841 : Tensor = onnx::Shape(%b128.conv1.weight)\n", " %2842 : Tensor = onnx::Constant[value={3}]()\n", " %2843 : Long(device=cpu) = onnx::Gather[axis=0](%2841, %2842) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %2844 : Float(1:589824, 256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b128.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %2845 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2846 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2847 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2848 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2849 : Tensor = onnx::Unsqueeze[axes=[0]](%2834)\n", " %2850 : Tensor = onnx::Unsqueeze[axes=[0]](%2845)\n", " %2851 : Tensor = onnx::Unsqueeze[axes=[0]](%2846)\n", " %2852 : Tensor = onnx::Unsqueeze[axes=[0]](%2847)\n", " %2853 : Tensor = onnx::Unsqueeze[axes=[0]](%2848)\n", " %2854 : Tensor = onnx::Concat[axis=0](%2849, %2850, %2851, %2852, %2853)\n", " %2855 : Float(1:256, 1:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2829, %2854) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2856 : Float(1:589824, 256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2844, %2855) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %2857 : Float(1:589824, 256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2856, %2856) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2858 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%2857) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2859 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %2860 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%2858, %2859)\n", " %2861 : Tensor = onnx::Sqrt(%2860)\n", " %2862 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2863 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Div(%2862, %2861) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %2864 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2865 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2866 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2867 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2868 : Tensor = onnx::Unsqueeze[axes=[0]](%2834)\n", " %2869 : Tensor = onnx::Unsqueeze[axes=[0]](%2864)\n", " %2870 : Tensor = onnx::Unsqueeze[axes=[0]](%2865)\n", " %2871 : Tensor = onnx::Unsqueeze[axes=[0]](%2866)\n", " %2872 : Tensor = onnx::Unsqueeze[axes=[0]](%2867)\n", " %2873 : Tensor = onnx::Concat[axis=0](%2868, %2869, %2870, %2871, %2872)\n", " %2874 : Float(1:256, 256:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2863, %2873) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2875 : Float(1:589824, 256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%2856, %2874) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %2876 : Tensor = onnx::Shape(%2823)\n", " %2877 : Tensor = onnx::Constant[value={2}]()\n", " %2878 : Long(device=cpu) = onnx::Gather[axis=0](%2876, %2877) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2879 : Tensor = onnx::Shape(%2823)\n", " %2880 : Tensor = onnx::Constant[value={3}]()\n", " %2881 : Long(device=cpu) = onnx::Gather[axis=0](%2879, %2880) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2882 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2883 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2884 : Tensor = onnx::Unsqueeze[axes=[0]](%2882)\n", " %2885 : Tensor = onnx::Unsqueeze[axes=[0]](%2883)\n", " %2886 : Tensor = onnx::Unsqueeze[axes=[0]](%2878)\n", " %2887 : Tensor = onnx::Unsqueeze[axes=[0]](%2881)\n", " %2888 : Tensor = onnx::Concat[axis=0](%2884, %2885, %2886, %2887)\n", " %2889 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Reshape(%2823, %2888) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %2890 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2891 : Tensor = onnx::Unsqueeze[axes=[0]](%2890)\n", " %2892 : Tensor = onnx::Unsqueeze[axes=[0]](%2837)\n", " %2893 : Tensor = onnx::Unsqueeze[axes=[0]](%2840)\n", " %2894 : Tensor = onnx::Unsqueeze[axes=[0]](%2843)\n", " %2895 : Tensor = onnx::Concat[axis=0](%2891, %2892, %2893, %2894)\n", " %2896 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%2875, %2895) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %2897 : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2896) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %2898 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%2889, %2897) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %2899 : Tensor = onnx::Shape(%2898)\n", " %2900 : Tensor = onnx::Constant[value={2}]()\n", " %2901 : Long(device=cpu) = onnx::Gather[axis=0](%2899, %2900) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2902 : Tensor = onnx::Shape(%2898)\n", " %2903 : Tensor = onnx::Constant[value={3}]()\n", " %2904 : Long(device=cpu) = onnx::Gather[axis=0](%2902, %2903) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2905 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2906 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %2907 : Tensor = onnx::Unsqueeze[axes=[0]](%2905)\n", " %2908 : Tensor = onnx::Unsqueeze[axes=[0]](%2906)\n", " %2909 : Tensor = onnx::Unsqueeze[axes=[0]](%2901)\n", " %2910 : Tensor = onnx::Unsqueeze[axes=[0]](%2904)\n", " %2911 : Tensor = onnx::Concat[axis=0](%2907, %2908, %2909, %2910)\n", " %2912 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Reshape(%2898, %2911) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %2913 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%2912, %2831)\n", " %2914 : Float(256:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %2915 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %2916 : Float(1:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2914, %2915) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2917 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%2913, %2916) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %2918 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%2917) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %2919 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %2920 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Mul(%2918, %2919)\n", " %2921 : Tensor = onnx::Shape(%2586)\n", " %2922 : Tensor = onnx::Constant[value={0}]()\n", " %2923 : Long(device=cpu) = onnx::Gather[axis=0](%2921, %2922) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2924 : Tensor = onnx::Shape(%2586)\n", " %2925 : Tensor = onnx::Constant[value={1}]()\n", " %2926 : Long(device=cpu) = onnx::Gather[axis=0](%2924, %2925) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2927 : Tensor = onnx::Shape(%2586)\n", " %2928 : Tensor = onnx::Constant[value={2}]()\n", " %2929 : Long(device=cpu) = onnx::Gather[axis=0](%2927, %2928) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2930 : Tensor = onnx::Shape(%2586)\n", " %2931 : Tensor = onnx::Constant[value={3}]()\n", " %2932 : Long(device=cpu) = onnx::Gather[axis=0](%2930, %2931) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %2933 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2934 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %2935 : Tensor = onnx::Unsqueeze[axes=[0]](%2923)\n", " %2936 : Tensor = onnx::Unsqueeze[axes=[0]](%2926)\n", " %2937 : Tensor = onnx::Unsqueeze[axes=[0]](%2929)\n", " %2938 : Tensor = onnx::Unsqueeze[axes=[0]](%2933)\n", " %2939 : Tensor = onnx::Unsqueeze[axes=[0]](%2932)\n", " %2940 : Tensor = onnx::Unsqueeze[axes=[0]](%2934)\n", " %2941 : Tensor = onnx::Concat[axis=0](%2935, %2936, %2937, %2938, %2939, %2940)\n", " %2942 : Float(1:24576, 3:8192, 128:64, 1:64, 64:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%2586, %2941) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %2943 : int[] = onnx::Constant[value= 0 1 0 0 0 1 [ CPULongType{6} ]]()\n", " %2944 : Tensor = onnx::Constant[value={0}]()\n", " %2945 : Tensor = onnx::Shape(%2943)\n", " %2946 : Tensor = onnx::Gather[axis=0](%2945, %2944)\n", " %2947 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %2948 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2949 : LongTensor = onnx::Mul(%2947, %2948)\n", " %2950 : LongTensor = onnx::Sub(%2949, %2946)\n", " %2951 : Tensor = onnx::Cast[to=7](%2943)\n", " %2952 : Tensor = onnx::ConstantOfShape[value={0}](%2950)\n", " %2953 : Tensor = onnx::Concat[axis=0](%2951, %2952)\n", " %2954 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2955 : Tensor = onnx::Reshape(%2953, %2954)\n", " %2956 : Tensor = onnx::Constant[value={0}]()\n", " %2957 : Tensor = onnx::Constant[value={-1}]()\n", " %2958 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2959 : Tensor = onnx::Constant[value={-1}]()\n", " %2960 : Tensor = onnx::Slice(%2955, %2957, %2958, %2956, %2959)\n", " %2961 : Tensor = onnx::Transpose[perm=[1, 0]](%2960)\n", " %2962 : Tensor = onnx::Constant[value={-1}]()\n", " %2963 : Tensor = onnx::Reshape(%2961, %2962)\n", " %2964 : Tensor = onnx::Cast[to=7](%2963)\n", " %2965 : Tensor = onnx::Constant[value={0}]()\n", " %2966 : Float(1:98304, 3:32768, 128:256, 2:128, 64:2, 2:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2942, %2964, %2965) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %2967 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2968 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2929, %2967)\n", " %2969 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2970 : Long(requires_grad=0, device=cpu) = onnx::Mul(%2932, %2969)\n", " %2971 : Tensor = onnx::Unsqueeze[axes=[0]](%2923)\n", " %2972 : Tensor = onnx::Unsqueeze[axes=[0]](%2926)\n", " %2973 : Tensor = onnx::Unsqueeze[axes=[0]](%2968)\n", " %2974 : Tensor = onnx::Unsqueeze[axes=[0]](%2970)\n", " %2975 : Tensor = onnx::Concat[axis=0](%2971, %2972, %2973, %2974)\n", " %2976 : Float(1:98304, 3:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Reshape(%2966, %2975) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %2977 : int[] = onnx::Constant[value= 2 1 2 1 [ CPULongType{4} ]]()\n", " %2978 : Tensor = onnx::Constant[value={0}]()\n", " %2979 : Tensor = onnx::Shape(%2977)\n", " %2980 : Tensor = onnx::Gather[axis=0](%2979, %2978)\n", " %2981 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %2982 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %2983 : LongTensor = onnx::Mul(%2981, %2982)\n", " %2984 : LongTensor = onnx::Sub(%2983, %2980)\n", " %2985 : Tensor = onnx::Cast[to=7](%2977)\n", " %2986 : Tensor = onnx::ConstantOfShape[value={0}](%2984)\n", " %2987 : Tensor = onnx::Concat[axis=0](%2985, %2986)\n", " %2988 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %2989 : Tensor = onnx::Reshape(%2987, %2988)\n", " %2990 : Tensor = onnx::Constant[value={0}]()\n", " %2991 : Tensor = onnx::Constant[value={-1}]()\n", " %2992 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %2993 : Tensor = onnx::Constant[value={-1}]()\n", " %2994 : Tensor = onnx::Slice(%2989, %2991, %2992, %2990, %2993)\n", " %2995 : Tensor = onnx::Transpose[perm=[1, 0]](%2994)\n", " %2996 : Tensor = onnx::Constant[value={-1}]()\n", " %2997 : Tensor = onnx::Reshape(%2995, %2996)\n", " %2998 : Tensor = onnx::Cast[to=7](%2997)\n", " %2999 : Tensor = onnx::Constant[value={0}]()\n", " %3000 : Float(1:101787, 3:33929, 259:131, 131:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%2976, %2998, %2999) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3001 : Tensor = onnx::Shape(%3000)\n", " %3002 : Tensor = onnx::Constant[value={2}]()\n", " %3003 : Long(device=cpu) = onnx::Gather[axis=0](%3001, %3002) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3004 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3005 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3003, %3004)\n", " %3006 : Tensor = onnx::Shape(%3000)\n", " %3007 : Tensor = onnx::Constant[value={3}]()\n", " %3008 : Long(device=cpu) = onnx::Gather[axis=0](%3006, %3007) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3009 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3010 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3008, %3009)\n", " %3011 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3012 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3013 : Tensor = onnx::Unsqueeze[axes=[0]](%3012)\n", " %3014 : Tensor = onnx::Unsqueeze[axes=[0]](%3005)\n", " %3015 : Tensor = onnx::Unsqueeze[axes=[0]](%3011)\n", " %3016 : Tensor = onnx::Constant[value={1}]()\n", " %3017 : Float(1:101787, 3:33929, 259:131, 131:1, requires_grad=0, device=cpu) = onnx::Slice(%3000, %3013, %3014, %3015, %3016) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3018 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %3019 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3020 : Tensor = onnx::Unsqueeze[axes=[0]](%3019)\n", " %3021 : Tensor = onnx::Unsqueeze[axes=[0]](%3010)\n", " %3022 : Tensor = onnx::Unsqueeze[axes=[0]](%3018)\n", " %3023 : Tensor = onnx::Constant[value={1}]()\n", " %3024 : Float(1:101787, 3:33929, 259:131, 131:1, requires_grad=0, device=cpu) = onnx::Slice(%3017, %3020, %3021, %3022, %3023) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3025 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %3026 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b128.resample_filter, %3025)\n", " %3027 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3026) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %3028 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %3029 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3030 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %3031 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3032 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%3027, %3029, %3030, %3028, %3031) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %3033 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3032) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3034 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%3033) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3035 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3036 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3037 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3038 : Tensor = onnx::Unsqueeze[axes=[0]](%2926)\n", " %3039 : Tensor = onnx::Unsqueeze[axes=[0]](%3035)\n", " %3040 : Tensor = onnx::Unsqueeze[axes=[0]](%3036)\n", " %3041 : Tensor = onnx::Unsqueeze[axes=[0]](%3037)\n", " %3042 : Tensor = onnx::Concat[axis=0](%3038, %3039, %3040, %3041)\n", " %3043 : Tensor = onnx::Unsqueeze[axes=[0]](%2926)\n", " %3044 : Tensor = onnx::Unsqueeze[axes=[0]](%3035)\n", " %3045 : Tensor = onnx::Unsqueeze[axes=[0]](%3036)\n", " %3046 : Tensor = onnx::Unsqueeze[axes=[0]](%3037)\n", " %3047 : Tensor = onnx::Concat[axis=0](%3043, %3044, %3045, %3046)\n", " %3048 : Tensor = onnx::Shape(%3042)\n", " %3049 : Tensor = onnx::ConstantOfShape[value={1}](%3048)\n", " %3050 : Tensor = onnx::Expand(%3034, %3049)\n", " %3051 : Float(3:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%3050, %3047) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3052 : Float(1:98304, 3:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=3, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%3024, %3051) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %3053 : Float(256:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %3054 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %3055 : Float(256:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3053, %3054)\n", " %3056 : Float(256:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %3057 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3056) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3058 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%2592, %3055, %3057) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3059 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0625}]()\n", " %3060 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Mul(%3058, %3059)\n", " %3061 : Tensor = onnx::Shape(%2920)\n", " %3062 : Tensor = onnx::Constant[value={0}]()\n", " %3063 : Long(device=cpu) = onnx::Gather[axis=0](%3061, %3062) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %3064 : Tensor = onnx::Shape(%b128.torgb.weight)\n", " %3065 : Tensor = onnx::Constant[value={1}]()\n", " %3066 : Long(device=cpu) = onnx::Gather[axis=0](%3064, %3065) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3067 : Tensor = onnx::Shape(%b128.torgb.weight)\n", " %3068 : Tensor = onnx::Constant[value={2}]()\n", " %3069 : Long(device=cpu) = onnx::Gather[axis=0](%3067, %3068) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3070 : Tensor = onnx::Shape(%b128.torgb.weight)\n", " %3071 : Tensor = onnx::Constant[value={3}]()\n", " %3072 : Long(device=cpu) = onnx::Gather[axis=0](%3070, %3071) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3073 : Float(1:768, 3:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b128.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %3074 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3075 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3076 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3077 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3078 : Tensor = onnx::Unsqueeze[axes=[0]](%3063)\n", " %3079 : Tensor = onnx::Unsqueeze[axes=[0]](%3074)\n", " %3080 : Tensor = onnx::Unsqueeze[axes=[0]](%3075)\n", " %3081 : Tensor = onnx::Unsqueeze[axes=[0]](%3076)\n", " %3082 : Tensor = onnx::Unsqueeze[axes=[0]](%3077)\n", " %3083 : Tensor = onnx::Concat[axis=0](%3078, %3079, %3080, %3081, %3082)\n", " %3084 : Float(1:256, 1:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3060, %3083) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3085 : Float(1:768, 3:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%3073, %3084) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3086 : Tensor = onnx::Shape(%2920)\n", " %3087 : Tensor = onnx::Constant[value={2}]()\n", " %3088 : Long(device=cpu) = onnx::Gather[axis=0](%3086, %3087) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3089 : Tensor = onnx::Shape(%2920)\n", " %3090 : Tensor = onnx::Constant[value={3}]()\n", " %3091 : Long(device=cpu) = onnx::Gather[axis=0](%3089, %3090) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3092 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3093 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3094 : Tensor = onnx::Unsqueeze[axes=[0]](%3092)\n", " %3095 : Tensor = onnx::Unsqueeze[axes=[0]](%3093)\n", " %3096 : Tensor = onnx::Unsqueeze[axes=[0]](%3088)\n", " %3097 : Tensor = onnx::Unsqueeze[axes=[0]](%3091)\n", " %3098 : Tensor = onnx::Concat[axis=0](%3094, %3095, %3096, %3097)\n", " %3099 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Reshape(%2920, %3098) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3100 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3101 : Tensor = onnx::Unsqueeze[axes=[0]](%3100)\n", " %3102 : Tensor = onnx::Unsqueeze[axes=[0]](%3066)\n", " %3103 : Tensor = onnx::Unsqueeze[axes=[0]](%3069)\n", " %3104 : Tensor = onnx::Unsqueeze[axes=[0]](%3072)\n", " %3105 : Tensor = onnx::Concat[axis=0](%3101, %3102, %3103, %3104)\n", " %3106 : Float(3:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3085, %3105) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %3107 : Float(3:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3106) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %3108 : Float(1:98304, 3:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%3099, %3107) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %3109 : Tensor = onnx::Shape(%3108)\n", " %3110 : Tensor = onnx::Constant[value={2}]()\n", " %3111 : Long(device=cpu) = onnx::Gather[axis=0](%3109, %3110) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3112 : Tensor = onnx::Shape(%3108)\n", " %3113 : Tensor = onnx::Constant[value={3}]()\n", " %3114 : Long(device=cpu) = onnx::Gather[axis=0](%3112, %3113) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3115 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3116 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3117 : Tensor = onnx::Unsqueeze[axes=[0]](%3115)\n", " %3118 : Tensor = onnx::Unsqueeze[axes=[0]](%3116)\n", " %3119 : Tensor = onnx::Unsqueeze[axes=[0]](%3111)\n", " %3120 : Tensor = onnx::Unsqueeze[axes=[0]](%3114)\n", " %3121 : Tensor = onnx::Concat[axis=0](%3117, %3118, %3119, %3120)\n", " %3122 : Float(1:98304, 3:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Reshape(%3108, %3121) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3123 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b128.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %3124 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %3125 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3123, %3124) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3126 : Float(1:98304, 3:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%3122, %3125) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3127 : Float(1:98304, 3:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3126) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %3128 : Float(1:98304, 3:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%3052, %3127)\n", " %3129 : Tensor, %3130 : Tensor, %3131 : Tensor = onnx::Split[axis=1, split=[1, 1, 1]](%211)\n", " %3132 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%3129) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %3133 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%3130) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %3134 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%3131) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %3135 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%2920) # /kaggle/working/stylegan3/training/networks_stylegan2.py:453:0\n", " %3136 : Float(256:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.conv0.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %3137 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %3138 : Float(256:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3136, %3137)\n", " %3139 : Float(256:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.conv0.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %3140 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3139) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3141 : Float(1:256, 256:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%3132, %3138, %3140) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3142 : Float(512:256, 256:1, requires_grad=0, device=cpu) = onnx::Mul(%b256.conv0.noise_const, %b256.conv0.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3143 : Float(512:256, 256:1, requires_grad=0, device=cpu) = onnx::Mul(%3142, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3144 : Tensor = onnx::Shape(%3135)\n", " %3145 : Tensor = onnx::Constant[value={0}]()\n", " %3146 : Long(device=cpu) = onnx::Gather[axis=0](%3144, %3145) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %3147 : Tensor = onnx::Shape(%b256.conv0.weight)\n", " %3148 : Tensor = onnx::Constant[value={1}]()\n", " %3149 : Long(device=cpu) = onnx::Gather[axis=0](%3147, %3148) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3150 : Tensor = onnx::Shape(%b256.conv0.weight)\n", " %3151 : Tensor = onnx::Constant[value={2}]()\n", " %3152 : Long(device=cpu) = onnx::Gather[axis=0](%3150, %3151) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3153 : Tensor = onnx::Shape(%b256.conv0.weight)\n", " %3154 : Tensor = onnx::Constant[value={3}]()\n", " %3155 : Long(device=cpu) = onnx::Gather[axis=0](%3153, %3154) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3156 : Float(1:294912, 128:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b256.conv0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %3157 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3158 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3159 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3160 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3161 : Tensor = onnx::Unsqueeze[axes=[0]](%3146)\n", " %3162 : Tensor = onnx::Unsqueeze[axes=[0]](%3157)\n", " %3163 : Tensor = onnx::Unsqueeze[axes=[0]](%3158)\n", " %3164 : Tensor = onnx::Unsqueeze[axes=[0]](%3159)\n", " %3165 : Tensor = onnx::Unsqueeze[axes=[0]](%3160)\n", " %3166 : Tensor = onnx::Concat[axis=0](%3161, %3162, %3163, %3164, %3165)\n", " %3167 : Float(1:256, 1:256, 256:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3141, %3166) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3168 : Float(1:294912, 128:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3156, %3167) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3169 : Float(1:294912, 128:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3168, %3168) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3170 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%3169) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3171 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %3172 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%3170, %3171)\n", " %3173 : Tensor = onnx::Sqrt(%3172)\n", " %3174 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3175 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Div(%3174, %3173) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3176 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3177 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3178 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3179 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3180 : Tensor = onnx::Unsqueeze[axes=[0]](%3146)\n", " %3181 : Tensor = onnx::Unsqueeze[axes=[0]](%3176)\n", " %3182 : Tensor = onnx::Unsqueeze[axes=[0]](%3177)\n", " %3183 : Tensor = onnx::Unsqueeze[axes=[0]](%3178)\n", " %3184 : Tensor = onnx::Unsqueeze[axes=[0]](%3179)\n", " %3185 : Tensor = onnx::Concat[axis=0](%3180, %3181, %3182, %3183, %3184)\n", " %3186 : Float(1:128, 128:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3175, %3185) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3187 : Float(1:294912, 128:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3168, %3186) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3188 : Tensor = onnx::Shape(%3135)\n", " %3189 : Tensor = onnx::Constant[value={2}]()\n", " %3190 : Long(device=cpu) = onnx::Gather[axis=0](%3188, %3189) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3191 : Tensor = onnx::Shape(%3135)\n", " %3192 : Tensor = onnx::Constant[value={3}]()\n", " %3193 : Long(device=cpu) = onnx::Gather[axis=0](%3191, %3192) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3194 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3195 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3196 : Tensor = onnx::Unsqueeze[axes=[0]](%3194)\n", " %3197 : Tensor = onnx::Unsqueeze[axes=[0]](%3195)\n", " %3198 : Tensor = onnx::Unsqueeze[axes=[0]](%3190)\n", " %3199 : Tensor = onnx::Unsqueeze[axes=[0]](%3193)\n", " %3200 : Tensor = onnx::Concat[axis=0](%3196, %3197, %3198, %3199)\n", " %3201 : Float(1:8388608, 256:32768, 256:128, 128:1, requires_grad=0, device=cpu) = onnx::Reshape(%3135, %3200) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3202 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3203 : Tensor = onnx::Unsqueeze[axes=[0]](%3202)\n", " %3204 : Tensor = onnx::Unsqueeze[axes=[0]](%3149)\n", " %3205 : Tensor = onnx::Unsqueeze[axes=[0]](%3152)\n", " %3206 : Tensor = onnx::Unsqueeze[axes=[0]](%3155)\n", " %3207 : Tensor = onnx::Concat[axis=0](%3203, %3204, %3205, %3206)\n", " %3208 : Float(128:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%3187, %3207) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %3209 : Float(128:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3208) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %3210 : Float(256:9, 128:2304, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0, 2, 3]](%3209) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_resample.py:114:0\n", " %3211 : Float(1:16875648, 128:131841, 513:257, 257:1, requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%3201, %3210) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:45:0\n", " %3212 : Tensor = onnx::Shape(%3211)\n", " %3213 : Tensor = onnx::Constant[value={0}]()\n", " %3214 : Long(device=cpu) = onnx::Gather[axis=0](%3212, %3213) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3215 : Tensor = onnx::Shape(%3211)\n", " %3216 : Tensor = onnx::Constant[value={1}]()\n", " %3217 : Long(device=cpu) = onnx::Gather[axis=0](%3215, %3216) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3218 : Tensor = onnx::Shape(%3211)\n", " %3219 : Tensor = onnx::Constant[value={2}]()\n", " %3220 : Long(device=cpu) = onnx::Gather[axis=0](%3218, %3219) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3221 : Tensor = onnx::Shape(%3211)\n", " %3222 : Tensor = onnx::Constant[value={3}]()\n", " %3223 : Long(device=cpu) = onnx::Gather[axis=0](%3221, %3222) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3224 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3225 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3226 : Tensor = onnx::Unsqueeze[axes=[0]](%3214)\n", " %3227 : Tensor = onnx::Unsqueeze[axes=[0]](%3217)\n", " %3228 : Tensor = onnx::Unsqueeze[axes=[0]](%3220)\n", " %3229 : Tensor = onnx::Unsqueeze[axes=[0]](%3224)\n", " %3230 : Tensor = onnx::Unsqueeze[axes=[0]](%3223)\n", " %3231 : Tensor = onnx::Unsqueeze[axes=[0]](%3225)\n", " %3232 : Tensor = onnx::Concat[axis=0](%3226, %3227, %3228, %3229, %3230, %3231)\n", " %3233 : Float(1:16875648, 128:131841, 513:257, 1:257, 257:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3211, %3232) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %3234 : int[] = onnx::Constant[value= 0 0 0 0 0 0 [ CPULongType{6} ]]()\n", " %3235 : Tensor = onnx::Constant[value={0}]()\n", " %3236 : Tensor = onnx::Shape(%3234)\n", " %3237 : Tensor = onnx::Gather[axis=0](%3236, %3235)\n", " %3238 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %3239 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3240 : LongTensor = onnx::Mul(%3238, %3239)\n", " %3241 : LongTensor = onnx::Sub(%3240, %3237)\n", " %3242 : Tensor = onnx::Cast[to=7](%3234)\n", " %3243 : Tensor = onnx::ConstantOfShape[value={0}](%3241)\n", " %3244 : Tensor = onnx::Concat[axis=0](%3242, %3243)\n", " %3245 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %3246 : Tensor = onnx::Reshape(%3244, %3245)\n", " %3247 : Tensor = onnx::Constant[value={0}]()\n", " %3248 : Tensor = onnx::Constant[value={-1}]()\n", " %3249 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %3250 : Tensor = onnx::Constant[value={-1}]()\n", " %3251 : Tensor = onnx::Slice(%3246, %3248, %3249, %3247, %3250)\n", " %3252 : Tensor = onnx::Transpose[perm=[1, 0]](%3251)\n", " %3253 : Tensor = onnx::Constant[value={-1}]()\n", " %3254 : Tensor = onnx::Reshape(%3252, %3253)\n", " %3255 : Tensor = onnx::Cast[to=7](%3254)\n", " %3256 : Tensor = onnx::Constant[value={0}]()\n", " %3257 : Float(1:16875648, 128:131841, 513:257, 1:257, 257:1, 1:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%3233, %3255, %3256) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %3258 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3259 : Long(requires_grad=0, device=cpu) = onnx::Mul(%3220, %3258)\n", " %3260 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3261 : Long(requires_grad=0, device=cpu) = onnx::Mul(%3223, %3260)\n", " %3262 : Tensor = onnx::Unsqueeze[axes=[0]](%3214)\n", " %3263 : Tensor = onnx::Unsqueeze[axes=[0]](%3217)\n", " %3264 : Tensor = onnx::Unsqueeze[axes=[0]](%3259)\n", " %3265 : Tensor = onnx::Unsqueeze[axes=[0]](%3261)\n", " %3266 : Tensor = onnx::Concat[axis=0](%3262, %3263, %3264, %3265)\n", " %3267 : Float(1:16875648, 128:131841, 513:257, 257:1, requires_grad=0, device=cpu) = onnx::Reshape(%3257, %3266) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %3268 : int[] = onnx::Constant[value= 1 1 1 1 [ CPULongType{4} ]]()\n", " %3269 : Tensor = onnx::Constant[value={0}]()\n", " %3270 : Tensor = onnx::Shape(%3268)\n", " %3271 : Tensor = onnx::Gather[axis=0](%3270, %3269)\n", " %3272 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %3273 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3274 : LongTensor = onnx::Mul(%3272, %3273)\n", " %3275 : LongTensor = onnx::Sub(%3274, %3271)\n", " %3276 : Tensor = onnx::Cast[to=7](%3268)\n", " %3277 : Tensor = onnx::ConstantOfShape[value={0}](%3275)\n", " %3278 : Tensor = onnx::Concat[axis=0](%3276, %3277)\n", " %3279 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %3280 : Tensor = onnx::Reshape(%3278, %3279)\n", " %3281 : Tensor = onnx::Constant[value={0}]()\n", " %3282 : Tensor = onnx::Constant[value={-1}]()\n", " %3283 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %3284 : Tensor = onnx::Constant[value={-1}]()\n", " %3285 : Tensor = onnx::Slice(%3280, %3282, %3283, %3281, %3284)\n", " %3286 : Tensor = onnx::Transpose[perm=[1, 0]](%3285)\n", " %3287 : Tensor = onnx::Constant[value={-1}]()\n", " %3288 : Tensor = onnx::Reshape(%3286, %3287)\n", " %3289 : Tensor = onnx::Cast[to=7](%3288)\n", " %3290 : Tensor = onnx::Constant[value={0}]()\n", " %3291 : Float(1:17073280, 128:133385, 515:259, 259:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%3267, %3289, %3290) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3292 : Tensor = onnx::Shape(%3291)\n", " %3293 : Tensor = onnx::Constant[value={2}]()\n", " %3294 : Long(device=cpu) = onnx::Gather[axis=0](%3292, %3293) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3295 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3296 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3294, %3295)\n", " %3297 : Tensor = onnx::Shape(%3291)\n", " %3298 : Tensor = onnx::Constant[value={3}]()\n", " %3299 : Long(device=cpu) = onnx::Gather[axis=0](%3297, %3298) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3300 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3301 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3299, %3300)\n", " %3302 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3303 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3304 : Tensor = onnx::Unsqueeze[axes=[0]](%3303)\n", " %3305 : Tensor = onnx::Unsqueeze[axes=[0]](%3296)\n", " %3306 : Tensor = onnx::Unsqueeze[axes=[0]](%3302)\n", " %3307 : Tensor = onnx::Constant[value={1}]()\n", " %3308 : Float(1:17073280, 128:133385, 515:259, 259:1, requires_grad=0, device=cpu) = onnx::Slice(%3291, %3304, %3305, %3306, %3307) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3309 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %3310 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3311 : Tensor = onnx::Unsqueeze[axes=[0]](%3310)\n", " %3312 : Tensor = onnx::Unsqueeze[axes=[0]](%3301)\n", " %3313 : Tensor = onnx::Unsqueeze[axes=[0]](%3309)\n", " %3314 : Tensor = onnx::Constant[value={1}]()\n", " %3315 : Float(1:17073280, 128:133385, 515:259, 259:1, requires_grad=0, device=cpu) = onnx::Slice(%3308, %3311, %3312, %3313, %3314) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3316 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %3317 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b256.conv0.resample_filter, %3316)\n", " %3318 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3317) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %3319 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %3320 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3321 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %3322 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3323 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%3318, %3320, %3321, %3319, %3322) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %3324 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3323) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3325 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%3324) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3326 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3327 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3328 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3329 : Tensor = onnx::Unsqueeze[axes=[0]](%3217)\n", " %3330 : Tensor = onnx::Unsqueeze[axes=[0]](%3326)\n", " %3331 : Tensor = onnx::Unsqueeze[axes=[0]](%3327)\n", " %3332 : Tensor = onnx::Unsqueeze[axes=[0]](%3328)\n", " %3333 : Tensor = onnx::Concat[axis=0](%3329, %3330, %3331, %3332)\n", " %3334 : Tensor = onnx::Unsqueeze[axes=[0]](%3217)\n", " %3335 : Tensor = onnx::Unsqueeze[axes=[0]](%3326)\n", " %3336 : Tensor = onnx::Unsqueeze[axes=[0]](%3327)\n", " %3337 : Tensor = onnx::Unsqueeze[axes=[0]](%3328)\n", " %3338 : Tensor = onnx::Concat[axis=0](%3334, %3335, %3336, %3337)\n", " %3339 : Tensor = onnx::Shape(%3333)\n", " %3340 : Tensor = onnx::ConstantOfShape[value={1}](%3339)\n", " %3341 : Tensor = onnx::Expand(%3325, %3340)\n", " %3342 : Float(128:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%3341, %3338) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3343 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=128, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%3315, %3342) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %3344 : Tensor = onnx::Shape(%3343)\n", " %3345 : Tensor = onnx::Constant[value={2}]()\n", " %3346 : Long(device=cpu) = onnx::Gather[axis=0](%3344, %3345) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3347 : Tensor = onnx::Shape(%3343)\n", " %3348 : Tensor = onnx::Constant[value={3}]()\n", " %3349 : Long(device=cpu) = onnx::Gather[axis=0](%3347, %3348) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3350 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3351 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3352 : Tensor = onnx::Unsqueeze[axes=[0]](%3350)\n", " %3353 : Tensor = onnx::Unsqueeze[axes=[0]](%3351)\n", " %3354 : Tensor = onnx::Unsqueeze[axes=[0]](%3346)\n", " %3355 : Tensor = onnx::Unsqueeze[axes=[0]](%3349)\n", " %3356 : Tensor = onnx::Concat[axis=0](%3352, %3353, %3354, %3355)\n", " %3357 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Reshape(%3343, %3356) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3358 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%3357, %3143)\n", " %3359 : Float(128:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.conv0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %3360 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %3361 : Float(1:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3359, %3360) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3362 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%3358, %3361) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3363 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%3362) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %3364 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %3365 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Mul(%3363, %3364)\n", " %3366 : Float(128:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %3367 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %3368 : Float(128:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3366, %3367)\n", " %3369 : Float(128:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %3370 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3369) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3371 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%3133, %3368, %3370) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3372 : Float(512:256, 256:1, requires_grad=0, device=cpu) = onnx::Mul(%b256.conv1.noise_const, %b256.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3373 : Float(512:256, 256:1, requires_grad=0, device=cpu) = onnx::Mul(%3372, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3374 : Tensor = onnx::Shape(%3365)\n", " %3375 : Tensor = onnx::Constant[value={0}]()\n", " %3376 : Long(device=cpu) = onnx::Gather[axis=0](%3374, %3375) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %3377 : Tensor = onnx::Shape(%b256.conv1.weight)\n", " %3378 : Tensor = onnx::Constant[value={1}]()\n", " %3379 : Long(device=cpu) = onnx::Gather[axis=0](%3377, %3378) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3380 : Tensor = onnx::Shape(%b256.conv1.weight)\n", " %3381 : Tensor = onnx::Constant[value={2}]()\n", " %3382 : Long(device=cpu) = onnx::Gather[axis=0](%3380, %3381) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3383 : Tensor = onnx::Shape(%b256.conv1.weight)\n", " %3384 : Tensor = onnx::Constant[value={3}]()\n", " %3385 : Long(device=cpu) = onnx::Gather[axis=0](%3383, %3384) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3386 : Float(1:147456, 128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b256.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %3387 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3388 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3389 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3390 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3391 : Tensor = onnx::Unsqueeze[axes=[0]](%3376)\n", " %3392 : Tensor = onnx::Unsqueeze[axes=[0]](%3387)\n", " %3393 : Tensor = onnx::Unsqueeze[axes=[0]](%3388)\n", " %3394 : Tensor = onnx::Unsqueeze[axes=[0]](%3389)\n", " %3395 : Tensor = onnx::Unsqueeze[axes=[0]](%3390)\n", " %3396 : Tensor = onnx::Concat[axis=0](%3391, %3392, %3393, %3394, %3395)\n", " %3397 : Float(1:128, 1:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3371, %3396) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3398 : Float(1:147456, 128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3386, %3397) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3399 : Float(1:147456, 128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3398, %3398) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3400 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%3399) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3401 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %3402 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Add(%3400, %3401)\n", " %3403 : Tensor = onnx::Sqrt(%3402)\n", " %3404 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3405 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Div(%3404, %3403) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3406 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3407 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3408 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3409 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3410 : Tensor = onnx::Unsqueeze[axes=[0]](%3376)\n", " %3411 : Tensor = onnx::Unsqueeze[axes=[0]](%3406)\n", " %3412 : Tensor = onnx::Unsqueeze[axes=[0]](%3407)\n", " %3413 : Tensor = onnx::Unsqueeze[axes=[0]](%3408)\n", " %3414 : Tensor = onnx::Unsqueeze[axes=[0]](%3409)\n", " %3415 : Tensor = onnx::Concat[axis=0](%3410, %3411, %3412, %3413, %3414)\n", " %3416 : Float(1:128, 128:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3405, %3415) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3417 : Float(1:147456, 128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3398, %3416) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3418 : Tensor = onnx::Shape(%3365)\n", " %3419 : Tensor = onnx::Constant[value={2}]()\n", " %3420 : Long(device=cpu) = onnx::Gather[axis=0](%3418, %3419) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3421 : Tensor = onnx::Shape(%3365)\n", " %3422 : Tensor = onnx::Constant[value={3}]()\n", " %3423 : Long(device=cpu) = onnx::Gather[axis=0](%3421, %3422) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3424 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3425 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3426 : Tensor = onnx::Unsqueeze[axes=[0]](%3424)\n", " %3427 : Tensor = onnx::Unsqueeze[axes=[0]](%3425)\n", " %3428 : Tensor = onnx::Unsqueeze[axes=[0]](%3420)\n", " %3429 : Tensor = onnx::Unsqueeze[axes=[0]](%3423)\n", " %3430 : Tensor = onnx::Concat[axis=0](%3426, %3427, %3428, %3429)\n", " %3431 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Reshape(%3365, %3430) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3432 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3433 : Tensor = onnx::Unsqueeze[axes=[0]](%3432)\n", " %3434 : Tensor = onnx::Unsqueeze[axes=[0]](%3379)\n", " %3435 : Tensor = onnx::Unsqueeze[axes=[0]](%3382)\n", " %3436 : Tensor = onnx::Unsqueeze[axes=[0]](%3385)\n", " %3437 : Tensor = onnx::Concat[axis=0](%3433, %3434, %3435, %3436)\n", " %3438 : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%3417, %3437) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %3439 : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3438) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %3440 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%3431, %3439) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %3441 : Tensor = onnx::Shape(%3440)\n", " %3442 : Tensor = onnx::Constant[value={2}]()\n", " %3443 : Long(device=cpu) = onnx::Gather[axis=0](%3441, %3442) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3444 : Tensor = onnx::Shape(%3440)\n", " %3445 : Tensor = onnx::Constant[value={3}]()\n", " %3446 : Long(device=cpu) = onnx::Gather[axis=0](%3444, %3445) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3447 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3448 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3449 : Tensor = onnx::Unsqueeze[axes=[0]](%3447)\n", " %3450 : Tensor = onnx::Unsqueeze[axes=[0]](%3448)\n", " %3451 : Tensor = onnx::Unsqueeze[axes=[0]](%3443)\n", " %3452 : Tensor = onnx::Unsqueeze[axes=[0]](%3446)\n", " %3453 : Tensor = onnx::Concat[axis=0](%3449, %3450, %3451, %3452)\n", " %3454 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Reshape(%3440, %3453) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3455 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%3454, %3373)\n", " %3456 : Float(128:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %3457 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %3458 : Float(1:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3456, %3457) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3459 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%3455, %3458) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3460 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%3459) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %3461 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %3462 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Mul(%3460, %3461)\n", " %3463 : Tensor = onnx::Shape(%3128)\n", " %3464 : Tensor = onnx::Constant[value={0}]()\n", " %3465 : Long(device=cpu) = onnx::Gather[axis=0](%3463, %3464) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3466 : Tensor = onnx::Shape(%3128)\n", " %3467 : Tensor = onnx::Constant[value={1}]()\n", " %3468 : Long(device=cpu) = onnx::Gather[axis=0](%3466, %3467) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3469 : Tensor = onnx::Shape(%3128)\n", " %3470 : Tensor = onnx::Constant[value={2}]()\n", " %3471 : Long(device=cpu) = onnx::Gather[axis=0](%3469, %3470) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3472 : Tensor = onnx::Shape(%3128)\n", " %3473 : Tensor = onnx::Constant[value={3}]()\n", " %3474 : Long(device=cpu) = onnx::Gather[axis=0](%3472, %3473) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3475 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3476 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3477 : Tensor = onnx::Unsqueeze[axes=[0]](%3465)\n", " %3478 : Tensor = onnx::Unsqueeze[axes=[0]](%3468)\n", " %3479 : Tensor = onnx::Unsqueeze[axes=[0]](%3471)\n", " %3480 : Tensor = onnx::Unsqueeze[axes=[0]](%3475)\n", " %3481 : Tensor = onnx::Unsqueeze[axes=[0]](%3474)\n", " %3482 : Tensor = onnx::Unsqueeze[axes=[0]](%3476)\n", " %3483 : Tensor = onnx::Concat[axis=0](%3477, %3478, %3479, %3480, %3481, %3482)\n", " %3484 : Float(1:98304, 3:32768, 256:128, 1:128, 128:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3128, %3483) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %3485 : int[] = onnx::Constant[value= 0 1 0 0 0 1 [ CPULongType{6} ]]()\n", " %3486 : Tensor = onnx::Constant[value={0}]()\n", " %3487 : Tensor = onnx::Shape(%3485)\n", " %3488 : Tensor = onnx::Gather[axis=0](%3487, %3486)\n", " %3489 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %3490 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3491 : LongTensor = onnx::Mul(%3489, %3490)\n", " %3492 : LongTensor = onnx::Sub(%3491, %3488)\n", " %3493 : Tensor = onnx::Cast[to=7](%3485)\n", " %3494 : Tensor = onnx::ConstantOfShape[value={0}](%3492)\n", " %3495 : Tensor = onnx::Concat[axis=0](%3493, %3494)\n", " %3496 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %3497 : Tensor = onnx::Reshape(%3495, %3496)\n", " %3498 : Tensor = onnx::Constant[value={0}]()\n", " %3499 : Tensor = onnx::Constant[value={-1}]()\n", " %3500 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %3501 : Tensor = onnx::Constant[value={-1}]()\n", " %3502 : Tensor = onnx::Slice(%3497, %3499, %3500, %3498, %3501)\n", " %3503 : Tensor = onnx::Transpose[perm=[1, 0]](%3502)\n", " %3504 : Tensor = onnx::Constant[value={-1}]()\n", " %3505 : Tensor = onnx::Reshape(%3503, %3504)\n", " %3506 : Tensor = onnx::Cast[to=7](%3505)\n", " %3507 : Tensor = onnx::Constant[value={0}]()\n", " %3508 : Float(1:393216, 3:131072, 256:512, 2:256, 128:2, 2:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%3484, %3506, %3507) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %3509 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3510 : Long(requires_grad=0, device=cpu) = onnx::Mul(%3471, %3509)\n", " %3511 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3512 : Long(requires_grad=0, device=cpu) = onnx::Mul(%3474, %3511)\n", " %3513 : Tensor = onnx::Unsqueeze[axes=[0]](%3465)\n", " %3514 : Tensor = onnx::Unsqueeze[axes=[0]](%3468)\n", " %3515 : Tensor = onnx::Unsqueeze[axes=[0]](%3510)\n", " %3516 : Tensor = onnx::Unsqueeze[axes=[0]](%3512)\n", " %3517 : Tensor = onnx::Concat[axis=0](%3513, %3514, %3515, %3516)\n", " %3518 : Float(1:393216, 3:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Reshape(%3508, %3517) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %3519 : int[] = onnx::Constant[value= 2 1 2 1 [ CPULongType{4} ]]()\n", " %3520 : Tensor = onnx::Constant[value={0}]()\n", " %3521 : Tensor = onnx::Shape(%3519)\n", " %3522 : Tensor = onnx::Gather[axis=0](%3521, %3520)\n", " %3523 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %3524 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3525 : LongTensor = onnx::Mul(%3523, %3524)\n", " %3526 : LongTensor = onnx::Sub(%3525, %3522)\n", " %3527 : Tensor = onnx::Cast[to=7](%3519)\n", " %3528 : Tensor = onnx::ConstantOfShape[value={0}](%3526)\n", " %3529 : Tensor = onnx::Concat[axis=0](%3527, %3528)\n", " %3530 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %3531 : Tensor = onnx::Reshape(%3529, %3530)\n", " %3532 : Tensor = onnx::Constant[value={0}]()\n", " %3533 : Tensor = onnx::Constant[value={-1}]()\n", " %3534 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %3535 : Tensor = onnx::Constant[value={-1}]()\n", " %3536 : Tensor = onnx::Slice(%3531, %3533, %3534, %3532, %3535)\n", " %3537 : Tensor = onnx::Transpose[perm=[1, 0]](%3536)\n", " %3538 : Tensor = onnx::Constant[value={-1}]()\n", " %3539 : Tensor = onnx::Reshape(%3537, %3538)\n", " %3540 : Tensor = onnx::Cast[to=7](%3539)\n", " %3541 : Tensor = onnx::Constant[value={0}]()\n", " %3542 : Float(1:400155, 3:133385, 515:259, 259:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%3518, %3540, %3541) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3543 : Tensor = onnx::Shape(%3542)\n", " %3544 : Tensor = onnx::Constant[value={2}]()\n", " %3545 : Long(device=cpu) = onnx::Gather[axis=0](%3543, %3544) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3546 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3547 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3545, %3546)\n", " %3548 : Tensor = onnx::Shape(%3542)\n", " %3549 : Tensor = onnx::Constant[value={3}]()\n", " %3550 : Long(device=cpu) = onnx::Gather[axis=0](%3548, %3549) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3551 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3552 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3550, %3551)\n", " %3553 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3554 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3555 : Tensor = onnx::Unsqueeze[axes=[0]](%3554)\n", " %3556 : Tensor = onnx::Unsqueeze[axes=[0]](%3547)\n", " %3557 : Tensor = onnx::Unsqueeze[axes=[0]](%3553)\n", " %3558 : Tensor = onnx::Constant[value={1}]()\n", " %3559 : Float(1:400155, 3:133385, 515:259, 259:1, requires_grad=0, device=cpu) = onnx::Slice(%3542, %3555, %3556, %3557, %3558) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3560 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %3561 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3562 : Tensor = onnx::Unsqueeze[axes=[0]](%3561)\n", " %3563 : Tensor = onnx::Unsqueeze[axes=[0]](%3552)\n", " %3564 : Tensor = onnx::Unsqueeze[axes=[0]](%3560)\n", " %3565 : Tensor = onnx::Constant[value={1}]()\n", " %3566 : Float(1:400155, 3:133385, 515:259, 259:1, requires_grad=0, device=cpu) = onnx::Slice(%3559, %3562, %3563, %3564, %3565) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3567 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %3568 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b256.resample_filter, %3567)\n", " %3569 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3568) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %3570 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %3571 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3572 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %3573 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3574 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%3569, %3571, %3572, %3570, %3573) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %3575 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3574) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3576 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%3575) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3577 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3578 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3579 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3580 : Tensor = onnx::Unsqueeze[axes=[0]](%3468)\n", " %3581 : Tensor = onnx::Unsqueeze[axes=[0]](%3577)\n", " %3582 : Tensor = onnx::Unsqueeze[axes=[0]](%3578)\n", " %3583 : Tensor = onnx::Unsqueeze[axes=[0]](%3579)\n", " %3584 : Tensor = onnx::Concat[axis=0](%3580, %3581, %3582, %3583)\n", " %3585 : Tensor = onnx::Unsqueeze[axes=[0]](%3468)\n", " %3586 : Tensor = onnx::Unsqueeze[axes=[0]](%3577)\n", " %3587 : Tensor = onnx::Unsqueeze[axes=[0]](%3578)\n", " %3588 : Tensor = onnx::Unsqueeze[axes=[0]](%3579)\n", " %3589 : Tensor = onnx::Concat[axis=0](%3585, %3586, %3587, %3588)\n", " %3590 : Tensor = onnx::Shape(%3584)\n", " %3591 : Tensor = onnx::ConstantOfShape[value={1}](%3590)\n", " %3592 : Tensor = onnx::Expand(%3576, %3591)\n", " %3593 : Float(3:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%3592, %3589) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3594 : Float(1:393216, 3:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=3, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%3566, %3593) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %3595 : Float(128:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %3596 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %3597 : Float(128:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3595, %3596)\n", " %3598 : Float(128:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %3599 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3598) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3600 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%3134, %3597, %3599) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3601 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0883883}]()\n", " %3602 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Mul(%3600, %3601)\n", " %3603 : Tensor = onnx::Shape(%3462)\n", " %3604 : Tensor = onnx::Constant[value={0}]()\n", " %3605 : Long(device=cpu) = onnx::Gather[axis=0](%3603, %3604) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %3606 : Tensor = onnx::Shape(%b256.torgb.weight)\n", " %3607 : Tensor = onnx::Constant[value={1}]()\n", " %3608 : Long(device=cpu) = onnx::Gather[axis=0](%3606, %3607) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3609 : Tensor = onnx::Shape(%b256.torgb.weight)\n", " %3610 : Tensor = onnx::Constant[value={2}]()\n", " %3611 : Long(device=cpu) = onnx::Gather[axis=0](%3609, %3610) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3612 : Tensor = onnx::Shape(%b256.torgb.weight)\n", " %3613 : Tensor = onnx::Constant[value={3}]()\n", " %3614 : Long(device=cpu) = onnx::Gather[axis=0](%3612, %3613) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3615 : Float(1:384, 3:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b256.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %3616 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3617 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3618 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3619 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3620 : Tensor = onnx::Unsqueeze[axes=[0]](%3605)\n", " %3621 : Tensor = onnx::Unsqueeze[axes=[0]](%3616)\n", " %3622 : Tensor = onnx::Unsqueeze[axes=[0]](%3617)\n", " %3623 : Tensor = onnx::Unsqueeze[axes=[0]](%3618)\n", " %3624 : Tensor = onnx::Unsqueeze[axes=[0]](%3619)\n", " %3625 : Tensor = onnx::Concat[axis=0](%3620, %3621, %3622, %3623, %3624)\n", " %3626 : Float(1:128, 1:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3602, %3625) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3627 : Float(1:384, 3:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%3615, %3626) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3628 : Tensor = onnx::Shape(%3462)\n", " %3629 : Tensor = onnx::Constant[value={2}]()\n", " %3630 : Long(device=cpu) = onnx::Gather[axis=0](%3628, %3629) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3631 : Tensor = onnx::Shape(%3462)\n", " %3632 : Tensor = onnx::Constant[value={3}]()\n", " %3633 : Long(device=cpu) = onnx::Gather[axis=0](%3631, %3632) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3634 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3635 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3636 : Tensor = onnx::Unsqueeze[axes=[0]](%3634)\n", " %3637 : Tensor = onnx::Unsqueeze[axes=[0]](%3635)\n", " %3638 : Tensor = onnx::Unsqueeze[axes=[0]](%3630)\n", " %3639 : Tensor = onnx::Unsqueeze[axes=[0]](%3633)\n", " %3640 : Tensor = onnx::Concat[axis=0](%3636, %3637, %3638, %3639)\n", " %3641 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Reshape(%3462, %3640) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3642 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3643 : Tensor = onnx::Unsqueeze[axes=[0]](%3642)\n", " %3644 : Tensor = onnx::Unsqueeze[axes=[0]](%3608)\n", " %3645 : Tensor = onnx::Unsqueeze[axes=[0]](%3611)\n", " %3646 : Tensor = onnx::Unsqueeze[axes=[0]](%3614)\n", " %3647 : Tensor = onnx::Concat[axis=0](%3643, %3644, %3645, %3646)\n", " %3648 : Float(3:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3627, %3647) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %3649 : Float(3:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3648) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %3650 : Float(1:393216, 3:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%3641, %3649) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %3651 : Tensor = onnx::Shape(%3650)\n", " %3652 : Tensor = onnx::Constant[value={2}]()\n", " %3653 : Long(device=cpu) = onnx::Gather[axis=0](%3651, %3652) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3654 : Tensor = onnx::Shape(%3650)\n", " %3655 : Tensor = onnx::Constant[value={3}]()\n", " %3656 : Long(device=cpu) = onnx::Gather[axis=0](%3654, %3655) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3657 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3658 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3659 : Tensor = onnx::Unsqueeze[axes=[0]](%3657)\n", " %3660 : Tensor = onnx::Unsqueeze[axes=[0]](%3658)\n", " %3661 : Tensor = onnx::Unsqueeze[axes=[0]](%3653)\n", " %3662 : Tensor = onnx::Unsqueeze[axes=[0]](%3656)\n", " %3663 : Tensor = onnx::Concat[axis=0](%3659, %3660, %3661, %3662)\n", " %3664 : Float(1:393216, 3:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Reshape(%3650, %3663) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3665 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b256.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %3666 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %3667 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3665, %3666) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3668 : Float(1:393216, 3:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%3664, %3667) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3669 : Float(1:393216, 3:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3668) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %3670 : Float(1:393216, 3:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Add(%3594, %3669)\n", " %3671 : Tensor, %3672 : Tensor, %3673 : Tensor = onnx::Split[axis=1, split=[1, 1, 1]](%220)\n", " %3674 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%3671) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %3675 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%3672) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %3676 : Float(1:8192, 512:1, requires_grad=0, device=cpu) = onnx::Squeeze[axes=[1]](%3673) # /kaggle/working/stylegan3/training/networks_stylegan2.py:437:0\n", " %3677 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3462) # /kaggle/working/stylegan3/training/networks_stylegan2.py:453:0\n", " %3678 : Float(128:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.conv0.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %3679 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %3680 : Float(128:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3678, %3679)\n", " %3681 : Float(128:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.conv0.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %3682 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3681) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3683 : Float(1:128, 128:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%3674, %3680, %3682) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3684 : Float(1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%b512.conv0.noise_const, %b512.conv0.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3685 : Float(1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3684, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3686 : Tensor = onnx::Shape(%3677)\n", " %3687 : Tensor = onnx::Constant[value={0}]()\n", " %3688 : Long(device=cpu) = onnx::Gather[axis=0](%3686, %3687) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %3689 : Tensor = onnx::Shape(%b512.conv0.weight)\n", " %3690 : Tensor = onnx::Constant[value={1}]()\n", " %3691 : Long(device=cpu) = onnx::Gather[axis=0](%3689, %3690) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3692 : Tensor = onnx::Shape(%b512.conv0.weight)\n", " %3693 : Tensor = onnx::Constant[value={2}]()\n", " %3694 : Long(device=cpu) = onnx::Gather[axis=0](%3692, %3693) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3695 : Tensor = onnx::Shape(%b512.conv0.weight)\n", " %3696 : Tensor = onnx::Constant[value={3}]()\n", " %3697 : Long(device=cpu) = onnx::Gather[axis=0](%3695, %3696) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3698 : Float(1:73728, 64:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b512.conv0.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %3699 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3700 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3701 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3702 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3703 : Tensor = onnx::Unsqueeze[axes=[0]](%3688)\n", " %3704 : Tensor = onnx::Unsqueeze[axes=[0]](%3699)\n", " %3705 : Tensor = onnx::Unsqueeze[axes=[0]](%3700)\n", " %3706 : Tensor = onnx::Unsqueeze[axes=[0]](%3701)\n", " %3707 : Tensor = onnx::Unsqueeze[axes=[0]](%3702)\n", " %3708 : Tensor = onnx::Concat[axis=0](%3703, %3704, %3705, %3706, %3707)\n", " %3709 : Float(1:128, 1:128, 128:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3683, %3708) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3710 : Float(1:73728, 64:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3698, %3709) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3711 : Float(1:73728, 64:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3710, %3710) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3712 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%3711) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3713 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %3714 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%3712, %3713)\n", " %3715 : Tensor = onnx::Sqrt(%3714)\n", " %3716 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3717 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Div(%3716, %3715) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3718 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3719 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3720 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3721 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3722 : Tensor = onnx::Unsqueeze[axes=[0]](%3688)\n", " %3723 : Tensor = onnx::Unsqueeze[axes=[0]](%3718)\n", " %3724 : Tensor = onnx::Unsqueeze[axes=[0]](%3719)\n", " %3725 : Tensor = onnx::Unsqueeze[axes=[0]](%3720)\n", " %3726 : Tensor = onnx::Unsqueeze[axes=[0]](%3721)\n", " %3727 : Tensor = onnx::Concat[axis=0](%3722, %3723, %3724, %3725, %3726)\n", " %3728 : Float(1:64, 64:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3717, %3727) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3729 : Float(1:73728, 64:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3710, %3728) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3730 : Tensor = onnx::Shape(%3677)\n", " %3731 : Tensor = onnx::Constant[value={2}]()\n", " %3732 : Long(device=cpu) = onnx::Gather[axis=0](%3730, %3731) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3733 : Tensor = onnx::Shape(%3677)\n", " %3734 : Tensor = onnx::Constant[value={3}]()\n", " %3735 : Long(device=cpu) = onnx::Gather[axis=0](%3733, %3734) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3736 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3737 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3738 : Tensor = onnx::Unsqueeze[axes=[0]](%3736)\n", " %3739 : Tensor = onnx::Unsqueeze[axes=[0]](%3737)\n", " %3740 : Tensor = onnx::Unsqueeze[axes=[0]](%3732)\n", " %3741 : Tensor = onnx::Unsqueeze[axes=[0]](%3735)\n", " %3742 : Tensor = onnx::Concat[axis=0](%3738, %3739, %3740, %3741)\n", " %3743 : Float(1:16777216, 128:131072, 512:256, 256:1, requires_grad=0, device=cpu) = onnx::Reshape(%3677, %3742) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3744 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3745 : Tensor = onnx::Unsqueeze[axes=[0]](%3744)\n", " %3746 : Tensor = onnx::Unsqueeze[axes=[0]](%3691)\n", " %3747 : Tensor = onnx::Unsqueeze[axes=[0]](%3694)\n", " %3748 : Tensor = onnx::Unsqueeze[axes=[0]](%3697)\n", " %3749 : Tensor = onnx::Concat[axis=0](%3745, %3746, %3747, %3748)\n", " %3750 : Float(64:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%3729, %3749) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %3751 : Float(64:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3750) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %3752 : Float(128:9, 64:1152, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Transpose[perm=[1, 0, 2, 3]](%3751) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_resample.py:114:0\n", " %3753 : Float(1:33652800, 64:525825, 1025:513, 513:1, requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[2, 2]](%3743, %3752) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:45:0\n", " %3754 : Tensor = onnx::Shape(%3753)\n", " %3755 : Tensor = onnx::Constant[value={0}]()\n", " %3756 : Long(device=cpu) = onnx::Gather[axis=0](%3754, %3755) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3757 : Tensor = onnx::Shape(%3753)\n", " %3758 : Tensor = onnx::Constant[value={1}]()\n", " %3759 : Long(device=cpu) = onnx::Gather[axis=0](%3757, %3758) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3760 : Tensor = onnx::Shape(%3753)\n", " %3761 : Tensor = onnx::Constant[value={2}]()\n", " %3762 : Long(device=cpu) = onnx::Gather[axis=0](%3760, %3761) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3763 : Tensor = onnx::Shape(%3753)\n", " %3764 : Tensor = onnx::Constant[value={3}]()\n", " %3765 : Long(device=cpu) = onnx::Gather[axis=0](%3763, %3764) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %3766 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3767 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3768 : Tensor = onnx::Unsqueeze[axes=[0]](%3756)\n", " %3769 : Tensor = onnx::Unsqueeze[axes=[0]](%3759)\n", " %3770 : Tensor = onnx::Unsqueeze[axes=[0]](%3762)\n", " %3771 : Tensor = onnx::Unsqueeze[axes=[0]](%3766)\n", " %3772 : Tensor = onnx::Unsqueeze[axes=[0]](%3765)\n", " %3773 : Tensor = onnx::Unsqueeze[axes=[0]](%3767)\n", " %3774 : Tensor = onnx::Concat[axis=0](%3768, %3769, %3770, %3771, %3772, %3773)\n", " %3775 : Float(1:33652800, 64:525825, 1025:513, 1:513, 513:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3753, %3774) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %3776 : int[] = onnx::Constant[value= 0 0 0 0 0 0 [ CPULongType{6} ]]()\n", " %3777 : Tensor = onnx::Constant[value={0}]()\n", " %3778 : Tensor = onnx::Shape(%3776)\n", " %3779 : Tensor = onnx::Gather[axis=0](%3778, %3777)\n", " %3780 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %3781 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3782 : LongTensor = onnx::Mul(%3780, %3781)\n", " %3783 : LongTensor = onnx::Sub(%3782, %3779)\n", " %3784 : Tensor = onnx::Cast[to=7](%3776)\n", " %3785 : Tensor = onnx::ConstantOfShape[value={0}](%3783)\n", " %3786 : Tensor = onnx::Concat[axis=0](%3784, %3785)\n", " %3787 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %3788 : Tensor = onnx::Reshape(%3786, %3787)\n", " %3789 : Tensor = onnx::Constant[value={0}]()\n", " %3790 : Tensor = onnx::Constant[value={-1}]()\n", " %3791 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %3792 : Tensor = onnx::Constant[value={-1}]()\n", " %3793 : Tensor = onnx::Slice(%3788, %3790, %3791, %3789, %3792)\n", " %3794 : Tensor = onnx::Transpose[perm=[1, 0]](%3793)\n", " %3795 : Tensor = onnx::Constant[value={-1}]()\n", " %3796 : Tensor = onnx::Reshape(%3794, %3795)\n", " %3797 : Tensor = onnx::Cast[to=7](%3796)\n", " %3798 : Tensor = onnx::Constant[value={0}]()\n", " %3799 : Float(1:33652800, 64:525825, 1025:513, 1:513, 513:1, 1:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%3775, %3797, %3798) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %3800 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3801 : Long(requires_grad=0, device=cpu) = onnx::Mul(%3762, %3800)\n", " %3802 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3803 : Long(requires_grad=0, device=cpu) = onnx::Mul(%3765, %3802)\n", " %3804 : Tensor = onnx::Unsqueeze[axes=[0]](%3756)\n", " %3805 : Tensor = onnx::Unsqueeze[axes=[0]](%3759)\n", " %3806 : Tensor = onnx::Unsqueeze[axes=[0]](%3801)\n", " %3807 : Tensor = onnx::Unsqueeze[axes=[0]](%3803)\n", " %3808 : Tensor = onnx::Concat[axis=0](%3804, %3805, %3806, %3807)\n", " %3809 : Float(1:33652800, 64:525825, 1025:513, 513:1, requires_grad=0, device=cpu) = onnx::Reshape(%3799, %3808) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %3810 : int[] = onnx::Constant[value= 1 1 1 1 [ CPULongType{4} ]]()\n", " %3811 : Tensor = onnx::Constant[value={0}]()\n", " %3812 : Tensor = onnx::Shape(%3810)\n", " %3813 : Tensor = onnx::Gather[axis=0](%3812, %3811)\n", " %3814 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %3815 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3816 : LongTensor = onnx::Mul(%3814, %3815)\n", " %3817 : LongTensor = onnx::Sub(%3816, %3813)\n", " %3818 : Tensor = onnx::Cast[to=7](%3810)\n", " %3819 : Tensor = onnx::ConstantOfShape[value={0}](%3817)\n", " %3820 : Tensor = onnx::Concat[axis=0](%3818, %3819)\n", " %3821 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %3822 : Tensor = onnx::Reshape(%3820, %3821)\n", " %3823 : Tensor = onnx::Constant[value={0}]()\n", " %3824 : Tensor = onnx::Constant[value={-1}]()\n", " %3825 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %3826 : Tensor = onnx::Constant[value={-1}]()\n", " %3827 : Tensor = onnx::Slice(%3822, %3824, %3825, %3823, %3826)\n", " %3828 : Tensor = onnx::Transpose[perm=[1, 0]](%3827)\n", " %3829 : Tensor = onnx::Constant[value={-1}]()\n", " %3830 : Tensor = onnx::Reshape(%3828, %3829)\n", " %3831 : Tensor = onnx::Cast[to=7](%3830)\n", " %3832 : Tensor = onnx::Constant[value={0}]()\n", " %3833 : Float(1:33849920, 64:528905, 1027:515, 515:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%3809, %3831, %3832) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3834 : Tensor = onnx::Shape(%3833)\n", " %3835 : Tensor = onnx::Constant[value={2}]()\n", " %3836 : Long(device=cpu) = onnx::Gather[axis=0](%3834, %3835) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3837 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3838 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3836, %3837)\n", " %3839 : Tensor = onnx::Shape(%3833)\n", " %3840 : Tensor = onnx::Constant[value={3}]()\n", " %3841 : Long(device=cpu) = onnx::Gather[axis=0](%3839, %3840) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3842 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3843 : Long(requires_grad=0, device=cpu) = onnx::Sub(%3841, %3842)\n", " %3844 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %3845 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3846 : Tensor = onnx::Unsqueeze[axes=[0]](%3845)\n", " %3847 : Tensor = onnx::Unsqueeze[axes=[0]](%3838)\n", " %3848 : Tensor = onnx::Unsqueeze[axes=[0]](%3844)\n", " %3849 : Tensor = onnx::Constant[value={1}]()\n", " %3850 : Float(1:33849920, 64:528905, 1027:515, 515:1, requires_grad=0, device=cpu) = onnx::Slice(%3833, %3846, %3847, %3848, %3849) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3851 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %3852 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %3853 : Tensor = onnx::Unsqueeze[axes=[0]](%3852)\n", " %3854 : Tensor = onnx::Unsqueeze[axes=[0]](%3843)\n", " %3855 : Tensor = onnx::Unsqueeze[axes=[0]](%3851)\n", " %3856 : Tensor = onnx::Constant[value={1}]()\n", " %3857 : Float(1:33849920, 64:528905, 1027:515, 515:1, requires_grad=0, device=cpu) = onnx::Slice(%3850, %3853, %3854, %3855, %3856) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %3858 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %3859 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b512.conv0.resample_filter, %3858)\n", " %3860 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3859) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %3861 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %3862 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3863 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %3864 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %3865 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%3860, %3862, %3863, %3861, %3864) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %3866 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3865) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3867 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%3866) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3868 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3869 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3870 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3871 : Tensor = onnx::Unsqueeze[axes=[0]](%3759)\n", " %3872 : Tensor = onnx::Unsqueeze[axes=[0]](%3868)\n", " %3873 : Tensor = onnx::Unsqueeze[axes=[0]](%3869)\n", " %3874 : Tensor = onnx::Unsqueeze[axes=[0]](%3870)\n", " %3875 : Tensor = onnx::Concat[axis=0](%3871, %3872, %3873, %3874)\n", " %3876 : Tensor = onnx::Unsqueeze[axes=[0]](%3759)\n", " %3877 : Tensor = onnx::Unsqueeze[axes=[0]](%3868)\n", " %3878 : Tensor = onnx::Unsqueeze[axes=[0]](%3869)\n", " %3879 : Tensor = onnx::Unsqueeze[axes=[0]](%3870)\n", " %3880 : Tensor = onnx::Concat[axis=0](%3876, %3877, %3878, %3879)\n", " %3881 : Tensor = onnx::Shape(%3875)\n", " %3882 : Tensor = onnx::ConstantOfShape[value={1}](%3881)\n", " %3883 : Tensor = onnx::Expand(%3867, %3882)\n", " %3884 : Float(64:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%3883, %3880) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %3885 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=64, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%3857, %3884) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %3886 : Tensor = onnx::Shape(%3885)\n", " %3887 : Tensor = onnx::Constant[value={2}]()\n", " %3888 : Long(device=cpu) = onnx::Gather[axis=0](%3886, %3887) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3889 : Tensor = onnx::Shape(%3885)\n", " %3890 : Tensor = onnx::Constant[value={3}]()\n", " %3891 : Long(device=cpu) = onnx::Gather[axis=0](%3889, %3890) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3892 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3893 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3894 : Tensor = onnx::Unsqueeze[axes=[0]](%3892)\n", " %3895 : Tensor = onnx::Unsqueeze[axes=[0]](%3893)\n", " %3896 : Tensor = onnx::Unsqueeze[axes=[0]](%3888)\n", " %3897 : Tensor = onnx::Unsqueeze[axes=[0]](%3891)\n", " %3898 : Tensor = onnx::Concat[axis=0](%3894, %3895, %3896, %3897)\n", " %3899 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%3885, %3898) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3900 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%3899, %3685)\n", " %3901 : Float(64:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.conv0.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %3902 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %3903 : Float(1:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3901, %3902) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3904 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%3900, %3903) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %3905 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%3904) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %3906 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %3907 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3905, %3906)\n", " %3908 : Float(64:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.conv1.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %3909 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %3910 : Float(64:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3908, %3909)\n", " %3911 : Float(64:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.conv1.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %3912 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%3911) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3913 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%3675, %3910, %3912) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %3914 : Float(1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%b512.conv1.noise_const, %b512.conv1.noise_strength) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3915 : Float(1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%3914, %noise) # /kaggle/working/stylegan3/training/networks_stylegan2.py:332:0\n", " %3916 : Tensor = onnx::Shape(%3907)\n", " %3917 : Tensor = onnx::Constant[value={0}]()\n", " %3918 : Long(device=cpu) = onnx::Gather[axis=0](%3916, %3917) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %3919 : Tensor = onnx::Shape(%b512.conv1.weight)\n", " %3920 : Tensor = onnx::Constant[value={1}]()\n", " %3921 : Long(device=cpu) = onnx::Gather[axis=0](%3919, %3920) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3922 : Tensor = onnx::Shape(%b512.conv1.weight)\n", " %3923 : Tensor = onnx::Constant[value={2}]()\n", " %3924 : Long(device=cpu) = onnx::Gather[axis=0](%3922, %3923) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3925 : Tensor = onnx::Shape(%b512.conv1.weight)\n", " %3926 : Tensor = onnx::Constant[value={3}]()\n", " %3927 : Long(device=cpu) = onnx::Gather[axis=0](%3925, %3926) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %3928 : Float(1:36864, 64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b512.conv1.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %3929 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3930 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3931 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3932 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3933 : Tensor = onnx::Unsqueeze[axes=[0]](%3918)\n", " %3934 : Tensor = onnx::Unsqueeze[axes=[0]](%3929)\n", " %3935 : Tensor = onnx::Unsqueeze[axes=[0]](%3930)\n", " %3936 : Tensor = onnx::Unsqueeze[axes=[0]](%3931)\n", " %3937 : Tensor = onnx::Unsqueeze[axes=[0]](%3932)\n", " %3938 : Tensor = onnx::Concat[axis=0](%3933, %3934, %3935, %3936, %3937)\n", " %3939 : Float(1:64, 1:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3913, %3938) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3940 : Float(1:36864, 64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3928, %3939) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %3941 : Float(1:36864, 64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3940, %3940) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3942 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::ReduceSum[axes=[2, 3, 4], keepdims=0](%3941) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3943 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1e-08}]()\n", " %3944 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Add(%3942, %3943)\n", " %3945 : Tensor = onnx::Sqrt(%3944)\n", " %3946 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3947 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Div(%3946, %3945) # /kaggle/working/stylegan3/training/networks_stylegan2.py:67:0\n", " %3948 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3949 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3950 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3951 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3952 : Tensor = onnx::Unsqueeze[axes=[0]](%3918)\n", " %3953 : Tensor = onnx::Unsqueeze[axes=[0]](%3948)\n", " %3954 : Tensor = onnx::Unsqueeze[axes=[0]](%3949)\n", " %3955 : Tensor = onnx::Unsqueeze[axes=[0]](%3950)\n", " %3956 : Tensor = onnx::Unsqueeze[axes=[0]](%3951)\n", " %3957 : Tensor = onnx::Concat[axis=0](%3952, %3953, %3954, %3955, %3956)\n", " %3958 : Float(1:64, 64:1, 1:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3947, %3957) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3959 : Float(1:36864, 64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Mul(%3940, %3958) # /kaggle/working/stylegan3/training/networks_stylegan2.py:69:0\n", " %3960 : Tensor = onnx::Shape(%3907)\n", " %3961 : Tensor = onnx::Constant[value={2}]()\n", " %3962 : Long(device=cpu) = onnx::Gather[axis=0](%3960, %3961) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3963 : Tensor = onnx::Shape(%3907)\n", " %3964 : Tensor = onnx::Constant[value={3}]()\n", " %3965 : Long(device=cpu) = onnx::Gather[axis=0](%3963, %3964) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3966 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3967 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3968 : Tensor = onnx::Unsqueeze[axes=[0]](%3966)\n", " %3969 : Tensor = onnx::Unsqueeze[axes=[0]](%3967)\n", " %3970 : Tensor = onnx::Unsqueeze[axes=[0]](%3962)\n", " %3971 : Tensor = onnx::Unsqueeze[axes=[0]](%3965)\n", " %3972 : Tensor = onnx::Concat[axis=0](%3968, %3969, %3970, %3971)\n", " %3973 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%3907, %3972) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %3974 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3975 : Tensor = onnx::Unsqueeze[axes=[0]](%3974)\n", " %3976 : Tensor = onnx::Unsqueeze[axes=[0]](%3921)\n", " %3977 : Tensor = onnx::Unsqueeze[axes=[0]](%3924)\n", " %3978 : Tensor = onnx::Unsqueeze[axes=[0]](%3927)\n", " %3979 : Tensor = onnx::Concat[axis=0](%3975, %3976, %3977, %3978)\n", " %3980 : Float(64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Reshape(%3959, %3979) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %3981 : Float(64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%3980) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %3982 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%3973, %3981) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %3983 : Tensor = onnx::Shape(%3982)\n", " %3984 : Tensor = onnx::Constant[value={2}]()\n", " %3985 : Long(device=cpu) = onnx::Gather[axis=0](%3983, %3984) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3986 : Tensor = onnx::Shape(%3982)\n", " %3987 : Tensor = onnx::Constant[value={3}]()\n", " %3988 : Long(device=cpu) = onnx::Gather[axis=0](%3986, %3987) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3989 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %3990 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %3991 : Tensor = onnx::Unsqueeze[axes=[0]](%3989)\n", " %3992 : Tensor = onnx::Unsqueeze[axes=[0]](%3990)\n", " %3993 : Tensor = onnx::Unsqueeze[axes=[0]](%3985)\n", " %3994 : Tensor = onnx::Unsqueeze[axes=[0]](%3988)\n", " %3995 : Tensor = onnx::Concat[axis=0](%3991, %3992, %3993, %3994)\n", " %3996 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%3982, %3995) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %3997 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%3996, %3915)\n", " %3998 : Float(64:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.conv1.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:341:0\n", " %3999 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %4000 : Float(1:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3998, %3999) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %4001 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%3997, %4000) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %4002 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::LeakyRelu[alpha=0.20000000000000001](%4001) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1309:0\n", " %4003 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}]()\n", " %4004 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%4002, %4003)\n", " %4005 : Tensor = onnx::Shape(%3670)\n", " %4006 : Tensor = onnx::Constant[value={0}]()\n", " %4007 : Long(device=cpu) = onnx::Gather[axis=0](%4005, %4006) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %4008 : Tensor = onnx::Shape(%3670)\n", " %4009 : Tensor = onnx::Constant[value={1}]()\n", " %4010 : Long(device=cpu) = onnx::Gather[axis=0](%4008, %4009) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %4011 : Tensor = onnx::Shape(%3670)\n", " %4012 : Tensor = onnx::Constant[value={2}]()\n", " %4013 : Long(device=cpu) = onnx::Gather[axis=0](%4011, %4012) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %4014 : Tensor = onnx::Shape(%3670)\n", " %4015 : Tensor = onnx::Constant[value={3}]()\n", " %4016 : Long(device=cpu) = onnx::Gather[axis=0](%4014, %4015) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:176:0\n", " %4017 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4018 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4019 : Tensor = onnx::Unsqueeze[axes=[0]](%4007)\n", " %4020 : Tensor = onnx::Unsqueeze[axes=[0]](%4010)\n", " %4021 : Tensor = onnx::Unsqueeze[axes=[0]](%4013)\n", " %4022 : Tensor = onnx::Unsqueeze[axes=[0]](%4017)\n", " %4023 : Tensor = onnx::Unsqueeze[axes=[0]](%4016)\n", " %4024 : Tensor = onnx::Unsqueeze[axes=[0]](%4018)\n", " %4025 : Tensor = onnx::Concat[axis=0](%4019, %4020, %4021, %4022, %4023, %4024)\n", " %4026 : Float(1:393216, 3:131072, 512:256, 1:256, 256:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%3670, %4025) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:187:0\n", " %4027 : int[] = onnx::Constant[value= 0 1 0 0 0 1 [ CPULongType{6} ]]()\n", " %4028 : Tensor = onnx::Constant[value={0}]()\n", " %4029 : Tensor = onnx::Shape(%4027)\n", " %4030 : Tensor = onnx::Gather[axis=0](%4029, %4028)\n", " %4031 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={6}]()\n", " %4032 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %4033 : LongTensor = onnx::Mul(%4031, %4032)\n", " %4034 : LongTensor = onnx::Sub(%4033, %4030)\n", " %4035 : Tensor = onnx::Cast[to=7](%4027)\n", " %4036 : Tensor = onnx::ConstantOfShape[value={0}](%4034)\n", " %4037 : Tensor = onnx::Concat[axis=0](%4035, %4036)\n", " %4038 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %4039 : Tensor = onnx::Reshape(%4037, %4038)\n", " %4040 : Tensor = onnx::Constant[value={0}]()\n", " %4041 : Tensor = onnx::Constant[value={-1}]()\n", " %4042 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %4043 : Tensor = onnx::Constant[value={-1}]()\n", " %4044 : Tensor = onnx::Slice(%4039, %4041, %4042, %4040, %4043)\n", " %4045 : Tensor = onnx::Transpose[perm=[1, 0]](%4044)\n", " %4046 : Tensor = onnx::Constant[value={-1}]()\n", " %4047 : Tensor = onnx::Reshape(%4045, %4046)\n", " %4048 : Tensor = onnx::Cast[to=7](%4047)\n", " %4049 : Tensor = onnx::Constant[value={0}]()\n", " %4050 : Float(1:1572864, 3:524288, 512:1024, 2:512, 256:2, 2:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%4026, %4048, %4049) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3553:0\n", " %4051 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %4052 : Long(requires_grad=0, device=cpu) = onnx::Mul(%4013, %4051)\n", " %4053 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %4054 : Long(requires_grad=0, device=cpu) = onnx::Mul(%4016, %4053)\n", " %4055 : Tensor = onnx::Unsqueeze[axes=[0]](%4007)\n", " %4056 : Tensor = onnx::Unsqueeze[axes=[0]](%4010)\n", " %4057 : Tensor = onnx::Unsqueeze[axes=[0]](%4052)\n", " %4058 : Tensor = onnx::Unsqueeze[axes=[0]](%4054)\n", " %4059 : Tensor = onnx::Concat[axis=0](%4055, %4056, %4057, %4058)\n", " %4060 : Float(1:1572864, 3:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%4050, %4059) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:189:0\n", " %4061 : int[] = onnx::Constant[value= 2 1 2 1 [ CPULongType{4} ]]()\n", " %4062 : Tensor = onnx::Constant[value={0}]()\n", " %4063 : Tensor = onnx::Shape(%4061)\n", " %4064 : Tensor = onnx::Gather[axis=0](%4063, %4062)\n", " %4065 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %4066 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %4067 : LongTensor = onnx::Mul(%4065, %4066)\n", " %4068 : LongTensor = onnx::Sub(%4067, %4064)\n", " %4069 : Tensor = onnx::Cast[to=7](%4061)\n", " %4070 : Tensor = onnx::ConstantOfShape[value={0}](%4068)\n", " %4071 : Tensor = onnx::Concat[axis=0](%4069, %4070)\n", " %4072 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()\n", " %4073 : Tensor = onnx::Reshape(%4071, %4072)\n", " %4074 : Tensor = onnx::Constant[value={0}]()\n", " %4075 : Tensor = onnx::Constant[value={-1}]()\n", " %4076 : Tensor = onnx::Constant[value={-9223372036854775807}]()\n", " %4077 : Tensor = onnx::Constant[value={-1}]()\n", " %4078 : Tensor = onnx::Slice(%4073, %4075, %4076, %4074, %4077)\n", " %4079 : Tensor = onnx::Transpose[perm=[1, 0]](%4078)\n", " %4080 : Tensor = onnx::Constant[value={-1}]()\n", " %4081 : Tensor = onnx::Reshape(%4079, %4080)\n", " %4082 : Tensor = onnx::Cast[to=7](%4081)\n", " %4083 : Tensor = onnx::Constant[value={0}]()\n", " %4084 : Float(1:1586715, 3:528905, 1027:515, 515:1, requires_grad=0, device=cpu) = onnx::Pad[mode=\"constant\"](%4060, %4082, %4083) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %4085 : Tensor = onnx::Shape(%4084)\n", " %4086 : Tensor = onnx::Constant[value={2}]()\n", " %4087 : Long(device=cpu) = onnx::Gather[axis=0](%4085, %4086) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %4088 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %4089 : Long(requires_grad=0, device=cpu) = onnx::Sub(%4087, %4088)\n", " %4090 : Tensor = onnx::Shape(%4084)\n", " %4091 : Tensor = onnx::Constant[value={3}]()\n", " %4092 : Long(device=cpu) = onnx::Gather[axis=0](%4090, %4091) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %4093 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %4094 : Long(requires_grad=0, device=cpu) = onnx::Sub(%4092, %4093)\n", " %4095 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}]()\n", " %4096 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %4097 : Tensor = onnx::Unsqueeze[axes=[0]](%4096)\n", " %4098 : Tensor = onnx::Unsqueeze[axes=[0]](%4089)\n", " %4099 : Tensor = onnx::Unsqueeze[axes=[0]](%4095)\n", " %4100 : Tensor = onnx::Constant[value={1}]()\n", " %4101 : Float(1:1586715, 3:528905, 1027:515, 515:1, requires_grad=0, device=cpu) = onnx::Slice(%4084, %4097, %4098, %4099, %4100) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %4102 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={3}]()\n", " %4103 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()\n", " %4104 : Tensor = onnx::Unsqueeze[axes=[0]](%4103)\n", " %4105 : Tensor = onnx::Unsqueeze[axes=[0]](%4094)\n", " %4106 : Tensor = onnx::Unsqueeze[axes=[0]](%4102)\n", " %4107 : Tensor = onnx::Constant[value={1}]()\n", " %4108 : Float(1:1586715, 3:528905, 1027:515, 515:1, requires_grad=0, device=cpu) = onnx::Slice(%4101, %4104, %4105, %4106, %4107) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:193:0\n", " %4109 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={4}]()\n", " %4110 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Mul(%b512.resample_filter, %4109)\n", " %4111 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%4110) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:197:0\n", " %4112 : Tensor = onnx::Constant[value= 0 1 [ CPULongType{2} ]]()\n", " %4113 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %4114 : Tensor = onnx::Constant[value=-9.2234e+18 -9.2234e+18 [ CPULongType{2} ]]()\n", " %4115 : Tensor = onnx::Constant[value=-1 -1 [ CPULongType{2} ]]()\n", " %4116 : Float(4:4, 4:1, requires_grad=0, device=cpu) = onnx::Slice(%4111, %4113, %4114, %4112, %4115) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:199:0\n", " %4117 : Float(1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%4116) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %4118 : Float(1:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[1]](%4117) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %4119 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4120 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4121 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4122 : Tensor = onnx::Unsqueeze[axes=[0]](%4010)\n", " %4123 : Tensor = onnx::Unsqueeze[axes=[0]](%4119)\n", " %4124 : Tensor = onnx::Unsqueeze[axes=[0]](%4120)\n", " %4125 : Tensor = onnx::Unsqueeze[axes=[0]](%4121)\n", " %4126 : Tensor = onnx::Concat[axis=0](%4122, %4123, %4124, %4125)\n", " %4127 : Tensor = onnx::Unsqueeze[axes=[0]](%4010)\n", " %4128 : Tensor = onnx::Unsqueeze[axes=[0]](%4119)\n", " %4129 : Tensor = onnx::Unsqueeze[axes=[0]](%4120)\n", " %4130 : Tensor = onnx::Unsqueeze[axes=[0]](%4121)\n", " %4131 : Tensor = onnx::Concat[axis=0](%4127, %4128, %4129, %4130)\n", " %4132 : Tensor = onnx::Shape(%4126)\n", " %4133 : Tensor = onnx::ConstantOfShape[value={1}](%4132)\n", " %4134 : Tensor = onnx::Expand(%4118, %4133)\n", " %4135 : Float(3:16, 1:16, 4:4, 4:1, requires_grad=0, device=cpu) = onnx::Tile(%4134, %4131) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:202:0\n", " %4136 : Float(1:1572864, 3:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=3, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[1, 1]](%4108, %4135) # /kaggle/working/stylegan3/torch_utils/ops/upfirdn2d.py:210:0\n", " %4137 : Float(64:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.torgb.affine.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:120:0\n", " %4138 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.0441942}]()\n", " %4139 : Float(64:512, 512:1, requires_grad=0, device=cpu) = onnx::Mul(%4137, %4138)\n", " %4140 : Float(64:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.torgb.affine.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:123:0\n", " %4141 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%4140) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %4142 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%3676, %4139, %4141) # /kaggle/working/stylegan3/training/networks_stylegan2.py:128:0\n", " %4143 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.125}]()\n", " %4144 : Float(1:64, 64:1, requires_grad=0, device=cpu) = onnx::Mul(%4142, %4143)\n", " %4145 : Tensor = onnx::Shape(%4004)\n", " %4146 : Tensor = onnx::Constant[value={0}]()\n", " %4147 : Long(device=cpu) = onnx::Gather[axis=0](%4145, %4146) # /kaggle/working/stylegan3/training/networks_stylegan2.py:48:0\n", " %4148 : Tensor = onnx::Shape(%b512.torgb.weight)\n", " %4149 : Tensor = onnx::Constant[value={1}]()\n", " %4150 : Long(device=cpu) = onnx::Gather[axis=0](%4148, %4149) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %4151 : Tensor = onnx::Shape(%b512.torgb.weight)\n", " %4152 : Tensor = onnx::Constant[value={2}]()\n", " %4153 : Long(device=cpu) = onnx::Gather[axis=0](%4151, %4152) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %4154 : Tensor = onnx::Shape(%b512.torgb.weight)\n", " %4155 : Tensor = onnx::Constant[value={3}]()\n", " %4156 : Long(device=cpu) = onnx::Gather[axis=0](%4154, %4155) # /kaggle/working/stylegan3/training/networks_stylegan2.py:49:0\n", " %4157 : Float(1:192, 3:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Unsqueeze[axes=[0]](%b512.torgb.weight) # /kaggle/working/stylegan3/training/networks_stylegan2.py:64:0\n", " %4158 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4159 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %4160 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4161 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4162 : Tensor = onnx::Unsqueeze[axes=[0]](%4147)\n", " %4163 : Tensor = onnx::Unsqueeze[axes=[0]](%4158)\n", " %4164 : Tensor = onnx::Unsqueeze[axes=[0]](%4159)\n", " %4165 : Tensor = onnx::Unsqueeze[axes=[0]](%4160)\n", " %4166 : Tensor = onnx::Unsqueeze[axes=[0]](%4161)\n", " %4167 : Tensor = onnx::Concat[axis=0](%4162, %4163, %4164, %4165, %4166)\n", " %4168 : Float(1:64, 1:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%4144, %4167) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %4169 : Float(1:192, 3:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Mul(%4157, %4168) # /kaggle/working/stylegan3/training/networks_stylegan2.py:65:0\n", " %4170 : Tensor = onnx::Shape(%4004)\n", " %4171 : Tensor = onnx::Constant[value={2}]()\n", " %4172 : Long(device=cpu) = onnx::Gather[axis=0](%4170, %4171) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %4173 : Tensor = onnx::Shape(%4004)\n", " %4174 : Tensor = onnx::Constant[value={3}]()\n", " %4175 : Long(device=cpu) = onnx::Gather[axis=0](%4173, %4174) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %4176 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4177 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %4178 : Tensor = onnx::Unsqueeze[axes=[0]](%4176)\n", " %4179 : Tensor = onnx::Unsqueeze[axes=[0]](%4177)\n", " %4180 : Tensor = onnx::Unsqueeze[axes=[0]](%4172)\n", " %4181 : Tensor = onnx::Unsqueeze[axes=[0]](%4175)\n", " %4182 : Tensor = onnx::Concat[axis=0](%4178, %4179, %4180, %4181)\n", " %4183 : Float(1:33554432, 64:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%4004, %4182) # /kaggle/working/stylegan3/training/networks_stylegan2.py:88:0\n", " %4184 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %4185 : Tensor = onnx::Unsqueeze[axes=[0]](%4184)\n", " %4186 : Tensor = onnx::Unsqueeze[axes=[0]](%4150)\n", " %4187 : Tensor = onnx::Unsqueeze[axes=[0]](%4153)\n", " %4188 : Tensor = onnx::Unsqueeze[axes=[0]](%4156)\n", " %4189 : Tensor = onnx::Concat[axis=0](%4185, %4186, %4187, %4188)\n", " %4190 : Float(3:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%4169, %4189) # /kaggle/working/stylegan3/training/networks_stylegan2.py:89:0\n", " %4191 : Float(3:64, 64:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%4190) # /kaggle/working/stylegan3/training/networks_stylegan2.py:90:0\n", " %4192 : Float(1:1572864, 3:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%4183, %4191) # /kaggle/working/stylegan3/torch_utils/ops/conv2d_gradfix.py:40:0\n", " %4193 : Tensor = onnx::Shape(%4192)\n", " %4194 : Tensor = onnx::Constant[value={2}]()\n", " %4195 : Long(device=cpu) = onnx::Gather[axis=0](%4193, %4194) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %4196 : Tensor = onnx::Shape(%4192)\n", " %4197 : Tensor = onnx::Constant[value={3}]()\n", " %4198 : Long(device=cpu) = onnx::Gather[axis=0](%4196, %4197) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %4199 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()\n", " %4200 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={-1}]()\n", " %4201 : Tensor = onnx::Unsqueeze[axes=[0]](%4199)\n", " %4202 : Tensor = onnx::Unsqueeze[axes=[0]](%4200)\n", " %4203 : Tensor = onnx::Unsqueeze[axes=[0]](%4195)\n", " %4204 : Tensor = onnx::Unsqueeze[axes=[0]](%4198)\n", " %4205 : Tensor = onnx::Concat[axis=0](%4201, %4202, %4203, %4204)\n", " %4206 : Float(1:1572864, 3:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Reshape(%4192, %4205) # /kaggle/working/stylegan3/training/networks_stylegan2.py:92:0\n", " %4207 : Float(3:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%b512.torgb.bias) # /kaggle/working/stylegan3/training/networks_stylegan2.py:370:0\n", " %4208 : Tensor = onnx::Constant[value= 1 -1 1 1 [ CPULongType{4} ]]()\n", " %4209 : Float(1:3, 3:1, 1:1, 1:1, requires_grad=0, device=cpu) = onnx::Reshape(%4207, %4208) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %4210 : Float(1:1572864, 3:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%4206, %4209) # /kaggle/working/stylegan3/torch_utils/ops/bias_act.py:106:0\n", " %4211 : Float(1:1572864, 3:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Cast[to=1](%4210) # /kaggle/working/stylegan3/training/networks_stylegan2.py:473:0\n", " %img : Float(1:1572864, 3:524288, 1024:512, 512:1, requires_grad=0, device=cpu) = onnx::Add(%4136, %4211)\n", " return (%img)\n", "\n", "finished exporting onnx\n" ] } ], "source": [ "convert(g_mapping,(torch.randn((1,g_mapping.z_dim)),[],torch.tensor([0.5,0.5],dtype=torch.float32)),[\"z\",\"psi\"],[\"w\"],\"model/g_mapping.onnx\")\n", "convert(g_synthesis,(torch.randn((1,g_mapping.num_ws,g_mapping.w_dim)), torch.tensor([1.0],dtype=torch.float32)),[\"w\",\"noise\"],[\"img\"],\"model/g_synthesis.onnx\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "f52d85f0", "metadata": { "execution": { "iopub.execute_input": "2022-08-07T14:32:18.468094Z", "iopub.status.busy": "2022-08-07T14:32:18.467633Z", "iopub.status.idle": "2022-08-07T14:32:22.656887Z", "shell.execute_reply": "2022-08-07T14:32:22.655539Z" }, "papermill": { "duration": 4.24102, "end_time": "2022-08-07T14:32:22.659853", "exception": false, "start_time": "2022-08-07T14:32:18.418833", "status": "completed" }, "tags": [], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Converting models with optimization style 'Runtime' and level 'all'\r\n", "Converting optimized ONNX model /kaggle/working/stylegan3/model/g_mapping.onnx to ORT format model /kaggle/working/stylegan3/model/g_mapping.with_runtime_opt.ort\r\n", "Converting optimized ONNX model /kaggle/working/stylegan3/model/g_synthesis.onnx to ORT format model /kaggle/working/stylegan3/model/g_synthesis.with_runtime_opt.ort\r\n", "2022-08-07 14:32:20.654859639 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1573'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654906715 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1554'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654915106 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1535'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654922210 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1471'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654928515 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1470'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654934935 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1644'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654941151 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1642'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654961139 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1572'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:20.654992462 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1452'. It is not used by any node and should be removed from the model.\r\n", "Converted 2/2 models successfully.\r\n", "Converting models again without runtime optimizations to generate a complete config file. These converted models are temporary and will be deleted.\r\n", "Converting optimized ONNX model /kaggle/working/stylegan3/model/g_mapping.onnx to ORT format model /kaggle/working/stylegan3/model/tmp21mjrfoe.without_runtime_opt/g_mapping.ort\r\n", "Converting optimized ONNX model /kaggle/working/stylegan3/model/g_synthesis.onnx to ORT format model /kaggle/working/stylegan3/model/tmp21mjrfoe.without_runtime_opt/g_synthesis.ort\r\n", "2022-08-07 14:32:21.359831927 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1573'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.359896874 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1554'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.359919461 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1535'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.359943497 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1471'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.359957111 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1470'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.359970815 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1644'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.359984932 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1642'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.360006137 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1572'. It is not used by any node and should be removed from the model.\r\n", "2022-08-07 14:32:21.360033299 [W:onnxruntime:, graph.cc:3494 CleanUnusedInitializersAndNodeArgs] Removing initializer '1452'. It is not used by any node and should be removed from the model.\r\n", "Converted 2/2 models successfully.\r\n", "Generating config file from ORT format models with optimization style 'Runtime' and level 'all'\r\n" ] } ], "source": [ "!python -m onnxruntime.tools.convert_onnx_models_to_ort --optimization_style=Runtime ./model" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "papermill": { "default_parameters": {}, "duration": 193.843974, "end_time": "2022-08-07T14:32:23.834550", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2022-08-07T14:29:09.990576", "version": "2.3.4" } }, "nbformat": 4, "nbformat_minor": 5 }