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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ff9b1a2a",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-07T14:29:20.525965Z",
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"shell.execute_reply": "2022-08-07T14:29:23.019133Z"
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"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",
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"shell.execute_reply": "2022-08-07T14:29:23.041771Z"
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"duration": 0.018355,
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"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",
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"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",
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"\u001B[?25hCollecting torch==1.7.0\r\n",
" Downloading torch-1.7.0-cp37-cp37m-manylinux1_x86_64.whl (776.7 MB)\r\n",
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"Requirement already satisfied: typing-extensions>=3.6.2.1 in /opt/conda/lib/python3.7/site-packages (from onnx==1.11.0) (4.1.1)\r\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests) (1.26.9)\r\n",
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"\u001B[?25hRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->click) (3.8.0)\r\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->onnxruntime) (3.0.9)\r\n",
"Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /opt/conda/lib/python3.7/site-packages (from rich->onnx-simplifier) (2.12.0)\r\n",
"Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /opt/conda/lib/python3.7/site-packages (from rich->onnx-simplifier) (0.9.1)\r\n",
"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.7/site-packages (from sympy->onnxruntime) (1.2.1)\r\n",
"Installing collected packages: ninja, torch, pyspng, imageio-ffmpeg, humanfriendly, onnx-simplifier, coloredlogs, onnxruntime\r\n",
" Attempting uninstall: torch\r\n",
" Found existing installation: torch 1.11.0+cpu\r\n",
" Uninstalling torch-1.11.0+cpu:\r\n",
" Successfully uninstalled torch-1.11.0+cpu\r\n",
"\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. 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",
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"shell.execute_reply": "2022-08-07T14:31:15.043398Z"
},
"papermill": {
"duration": 10.46267,
"end_time": "2022-08-07T14:31:15.048871",
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"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",
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"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",
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"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",
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"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",
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"duration": 58.034718,
"end_time": "2022-08-07T14:32:18.371238",
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"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
} |