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ShivaKothuru/yolo_tflite.ipynb

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yolo_tflite.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled9.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyN1a4ZTgzOBjDmqGKKROPBQ",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ShivaKothuru/12c5ffed7a9fdde8af2852dc953d239e/untitled9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H8o7Y9O9JbC0",
"outputId": "fe32a94d-9807-41c1-f3a4-030148fbc87f"
},
"source": [
"!git clone https://github.com/zldrobit/onnx_tflite_yolov3"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'onnx_tflite_yolov3'...\n",
"remote: Enumerating objects: 6898, done.\u001b[K\n",
"remote: Total 6898 (delta 0), reused 0 (delta 0), pack-reused 6898\u001b[K\n",
"Receiving objects: 100% (6898/6898), 8.57 MiB | 19.12 MiB/s, done.\n",
"Resolving deltas: 100% (4731/4731), done.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SkPhie4zJfBm",
"outputId": "1a40deba-8a16-483a-d522-484855715a92"
},
"source": [
"!pip install torchvision==0.5.0\n",
"!pip install torch==1.4.0\n",
"!pip install onnx==1.6.0\n",
"!pip install onnx-tf==1.5.0\n",
"!pip install onnxruntime-gpu==1.0.0\n",
"!pip install tensorflow-gpu==1.15.0\n",
"!pip install pillow"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: torchvision==0.5.0 in /usr/local/lib/python3.6/dist-packages (0.5.0)\n",
"Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.5.0) (7.0.0)\n",
"Collecting torch==1.4.0\n",
" Using cached https://files.pythonhosted.org/packages/24/19/4804aea17cd136f1705a5e98a00618cb8f6ccc375ad8bfa437408e09d058/torch-1.4.0-cp36-cp36m-manylinux1_x86_64.whl\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision==0.5.0) (1.15.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision==0.5.0) (1.19.5)\n",
"Installing collected packages: torch\n",
" Found existing installation: torch 1.3.1\n",
" Uninstalling torch-1.3.1:\n",
" Successfully uninstalled torch-1.3.1\n",
"Successfully installed torch-1.4.0\n",
"Requirement already satisfied: torch==1.4.0 in /usr/local/lib/python3.6/dist-packages (1.4.0)\n",
"Requirement already satisfied: onnx==1.6.0 in /usr/local/lib/python3.6/dist-packages (1.6.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from onnx==1.6.0) (1.19.5)\n",
"Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.6/dist-packages (from onnx==1.6.0) (3.7.4.3)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from onnx==1.6.0) (3.12.4)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from onnx==1.6.0) (1.15.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->onnx==1.6.0) (51.1.1)\n",
"Requirement already satisfied: onnx-tf==1.5.0 in /usr/local/lib/python3.6/dist-packages (1.5.0)\n",
"Requirement already satisfied: onnx>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from onnx-tf==1.5.0) (1.6.0)\n",
"Requirement already satisfied: PyYAML in /usr/local/lib/python3.6/dist-packages (from onnx-tf==1.5.0) (3.13)\n",
"Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.6/dist-packages (from onnx>=1.5.0->onnx-tf==1.5.0) (3.7.4.3)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from onnx>=1.5.0->onnx-tf==1.5.0) (1.19.5)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from onnx>=1.5.0->onnx-tf==1.5.0) (3.12.4)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from onnx>=1.5.0->onnx-tf==1.5.0) (1.15.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->onnx>=1.5.0->onnx-tf==1.5.0) (51.1.1)\n",
"Requirement already satisfied: onnxruntime-gpu==1.0.0 in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
"Requirement already satisfied: tensorflow-gpu==1.15.0 in /usr/local/lib/python3.6/dist-packages (1.15.0)\n",
"Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.12.1)\n",
"Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (0.2.2)\n",
"Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (0.8.1)\n",
"Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (0.10.0)\n",
"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.1.2)\n",
"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.1.0)\n",
"Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.32.0)\n",
"Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.15.1)\n",
"Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.15.0)\n",
"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (3.3.0)\n",
"Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.19.5)\n",
"Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (0.36.2)\n",
"Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (0.2.0)\n",
"Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.0.8)\n",
"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (1.15.0)\n",
"Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15.0) (3.12.4)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (1.0.1)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (3.3.3)\n",
"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (51.1.1)\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu==1.15.0) (2.10.0)\n",
"Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (3.3.0)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (3.4.0)\n",
"Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15.0) (3.7.4.3)\n",
"Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (7.0.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WmFqiO9oJ1xD",
"outputId": "e33a8e35-2cb5-4d98-98c0-bd3599905c9a"
},
"source": [
"%cd /content/onnx_tflite_yolov3"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/onnx_tflite_yolov3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O-LW9YTnK25l",
"outputId": "a1572cf9-b818-45eb-924d-98f036f7767b"
},
"source": [
"%cd weights\n",
"!wget https://pjreddie.com/media/files/yolov3.weights "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/onnx_tflite_yolov3/weights\n",
"--2021-01-19 15:03:28-- https://pjreddie.com/media/files/yolov3.weights\n",
"Resolving pjreddie.com (pjreddie.com)... 128.208.4.108\n",
"Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 248007048 (237M) [application/octet-stream]\n",
"Saving to: ‘yolov3.weights’\n",
"\n",
"yolov3.weights 100%[===================>] 236.52M 4.45MB/s in 79s \n",
"\n",
"2021-01-19 15:04:48 (2.98 MB/s) - ‘yolov3.weights’ saved [248007048/248007048]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d-NPzu5tK6gk",
"outputId": "b8d9f6fe-48b9-4f4d-ddac-03af18786ca6"
},
"source": [
"%cd /content/onnx_tflite_yolov3"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/onnx_tflite_yolov3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "u7O_xUK0LVV2",
"outputId": "309a6b5b-a82a-4e0f-b9b4-6a6377ef6f4f"
},
"source": [
"!python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Namespace(cfg='cfg/yolov3.cfg', conf_thres=0.3, data='data/coco.data', device='', fourcc='mp4v', half=False, img_size=416, nms_thres=0.5, output='output', source='data/samples', view_img=False, weights='weights/yolov3.weights')\n",
"Using CPU\n",
"\n",
"/content/onnx_tflite_yolov3/models.py:260: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" print(\"_io.shape\", _io.shape)\n",
"_io.shape torch.Size([1, 507, 85])\n",
"_io.shape torch.Size([1, 2028, 85])\n",
"_io.shape torch.Size([1, 8112, 85])\n",
"/usr/local/lib/python3.6/dist-packages/torch/onnx/symbolic_helper.py:246: UserWarning: You are trying to export the model with onnx:Upsample for ONNX opset version 9. This operator might cause results to not match the expected results by PyTorch.\n",
"ONNX's Upsample/Resize operator did not match Pytorch's Interpolation until opset 11. Attributes to determine how to transform the input were added in onnx:Resize in opset 11 to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).\n",
"We recommend using opset 11 and above for models using this operator. \n",
" \"\" + str(_export_onnx_opset_version) + \". \"\n",
"graph(%input.1 : Float(1, 3, 416, 416),\n",
" %module_list.0.Conv2d.weight : Float(32, 3, 3, 3),\n",
" %module_list.0.BatchNorm2d.weight : Float(32),\n",
" %module_list.0.BatchNorm2d.bias : Float(32),\n",
" %module_list.0.BatchNorm2d.running_mean : Float(32),\n",
" %module_list.0.BatchNorm2d.running_var : Float(32),\n",
" %module_list.0.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.1.Conv2d.weight : Float(64, 32, 3, 3),\n",
" %module_list.1.BatchNorm2d.weight : Float(64),\n",
" %module_list.1.BatchNorm2d.bias : Float(64),\n",
" %module_list.1.BatchNorm2d.running_mean : Float(64),\n",
" %module_list.1.BatchNorm2d.running_var : Float(64),\n",
" %module_list.1.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.2.Conv2d.weight : Float(32, 64, 1, 1),\n",
" %module_list.2.BatchNorm2d.weight : Float(32),\n",
" %module_list.2.BatchNorm2d.bias : Float(32),\n",
" %module_list.2.BatchNorm2d.running_mean : Float(32),\n",
" %module_list.2.BatchNorm2d.running_var : Float(32),\n",
" %module_list.2.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.3.Conv2d.weight : Float(64, 32, 3, 3),\n",
" %module_list.3.BatchNorm2d.weight : Float(64),\n",
" %module_list.3.BatchNorm2d.bias : Float(64),\n",
" %module_list.3.BatchNorm2d.running_mean : Float(64),\n",
" %module_list.3.BatchNorm2d.running_var : Float(64),\n",
" %module_list.3.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.5.Conv2d.weight : Float(128, 64, 3, 3),\n",
" %module_list.5.BatchNorm2d.weight : Float(128),\n",
" %module_list.5.BatchNorm2d.bias : Float(128),\n",
" %module_list.5.BatchNorm2d.running_mean : Float(128),\n",
" %module_list.5.BatchNorm2d.running_var : Float(128),\n",
" %module_list.5.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.6.Conv2d.weight : Float(64, 128, 1, 1),\n",
" %module_list.6.BatchNorm2d.weight : Float(64),\n",
" %module_list.6.BatchNorm2d.bias : Float(64),\n",
" %module_list.6.BatchNorm2d.running_mean : Float(64),\n",
" %module_list.6.BatchNorm2d.running_var : Float(64),\n",
" %module_list.6.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.7.Conv2d.weight : Float(128, 64, 3, 3),\n",
" %module_list.7.BatchNorm2d.weight : Float(128),\n",
" %module_list.7.BatchNorm2d.bias : Float(128),\n",
" %module_list.7.BatchNorm2d.running_mean : Float(128),\n",
" %module_list.7.BatchNorm2d.running_var : Float(128),\n",
" %module_list.7.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.9.Conv2d.weight : Float(64, 128, 1, 1),\n",
" %module_list.9.BatchNorm2d.weight : Float(64),\n",
" %module_list.9.BatchNorm2d.bias : Float(64),\n",
" %module_list.9.BatchNorm2d.running_mean : Float(64),\n",
" %module_list.9.BatchNorm2d.running_var : Float(64),\n",
" %module_list.9.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.10.Conv2d.weight : Float(128, 64, 3, 3),\n",
" %module_list.10.BatchNorm2d.weight : Float(128),\n",
" %module_list.10.BatchNorm2d.bias : Float(128),\n",
" %module_list.10.BatchNorm2d.running_mean : Float(128),\n",
" %module_list.10.BatchNorm2d.running_var : Float(128),\n",
" %module_list.10.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.12.Conv2d.weight : Float(256, 128, 3, 3),\n",
" %module_list.12.BatchNorm2d.weight : Float(256),\n",
" %module_list.12.BatchNorm2d.bias : Float(256),\n",
" %module_list.12.BatchNorm2d.running_mean : Float(256),\n",
" %module_list.12.BatchNorm2d.running_var : Float(256),\n",
" %module_list.12.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.13.Conv2d.weight : Float(128, 256, 1, 1),\n",
" %module_list.13.BatchNorm2d.weight : Float(128),\n",
" %module_list.13.BatchNorm2d.bias : Float(128),\n",
" %module_list.13.BatchNorm2d.running_mean : Float(128),\n",
" %module_list.13.BatchNorm2d.running_var : Float(128),\n",
" %module_list.13.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.14.Conv2d.weight : Float(256, 128, 3, 3),\n",
" %module_list.14.BatchNorm2d.weight : Float(256),\n",
" %module_list.14.BatchNorm2d.bias : Float(256),\n",
" %module_list.14.BatchNorm2d.running_mean : Float(256),\n",
" %module_list.14.BatchNorm2d.running_var : Float(256),\n",
" %module_list.14.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.16.Conv2d.weight : Float(128, 256, 1, 1),\n",
" %module_list.16.BatchNorm2d.weight : Float(128),\n",
" %module_list.16.BatchNorm2d.bias : Float(128),\n",
" %module_list.16.BatchNorm2d.running_mean : Float(128),\n",
" %module_list.16.BatchNorm2d.running_var : Float(128),\n",
" %module_list.16.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.17.Conv2d.weight : Float(256, 128, 3, 3),\n",
" %module_list.17.BatchNorm2d.weight : Float(256),\n",
" %module_list.17.BatchNorm2d.bias : Float(256),\n",
" %module_list.17.BatchNorm2d.running_mean : Float(256),\n",
" %module_list.17.BatchNorm2d.running_var : Float(256),\n",
" %module_list.17.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.19.Conv2d.weight : Float(128, 256, 1, 1),\n",
" %module_list.19.BatchNorm2d.weight : Float(128),\n",
" %module_list.19.BatchNorm2d.bias : Float(128),\n",
" %module_list.19.BatchNorm2d.running_mean : Float(128),\n",
" %module_list.19.BatchNorm2d.running_var : Float(128),\n",
" %module_list.19.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.20.Conv2d.weight : Float(256, 128, 3, 3),\n",
" %module_list.20.BatchNorm2d.weight : Float(256),\n",
" %module_list.20.BatchNorm2d.bias : Float(256),\n",
" %module_list.20.BatchNorm2d.running_mean : Float(256),\n",
" %module_list.20.BatchNorm2d.running_var : Float(256),\n",
" %module_list.20.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.22.Conv2d.weight : Float(128, 256, 1, 1),\n",
" %module_list.22.BatchNorm2d.weight : Float(128),\n",
" %module_list.22.BatchNorm2d.bias : Float(128),\n",
" %module_list.22.BatchNorm2d.running_mean : Float(128),\n",
" %module_list.22.BatchNorm2d.running_var : Float(128),\n",
" %module_list.22.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.23.Conv2d.weight : Float(256, 128, 3, 3),\n",
" %module_list.23.BatchNorm2d.weight : Float(256),\n",
" %module_list.23.BatchNorm2d.bias : Float(256),\n",
" %module_list.23.BatchNorm2d.running_mean : Float(256),\n",
" %module_list.23.BatchNorm2d.running_var : Float(256),\n",
" %module_list.23.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.25.Conv2d.weight : Float(128, 256, 1, 1),\n",
" %module_list.25.BatchNorm2d.weight : Float(128),\n",
" %module_list.25.BatchNorm2d.bias : Float(128),\n",
" %module_list.25.BatchNorm2d.running_mean : Float(128),\n",
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" %module_list.104.BatchNorm2d.running_mean : Float(256),\n",
" %module_list.104.BatchNorm2d.running_var : Float(256),\n",
" %module_list.104.BatchNorm2d.num_batches_tracked : Long(),\n",
" %module_list.105.Conv2d.weight : Float(255, 256, 1, 1),\n",
" %module_list.105.Conv2d.bias : Float(255)):\n",
" %439 : Float(1, 32, 416, 416) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%input.1, %module_list.0.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %440 : Float(1, 32, 416, 416) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%439, %module_list.0.BatchNorm2d.weight, %module_list.0.BatchNorm2d.bias, %module_list.0.BatchNorm2d.running_mean, %module_list.0.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %441 : Float(1, 32, 416, 416) = onnx::LeakyRelu[alpha=0.1](%440) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %442 : Float(1, 64, 208, 208) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%441, %module_list.1.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %443 : Float(1, 64, 208, 208) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%442, %module_list.1.BatchNorm2d.weight, %module_list.1.BatchNorm2d.bias, %module_list.1.BatchNorm2d.running_mean, %module_list.1.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %444 : Float(1, 64, 208, 208) = onnx::LeakyRelu[alpha=0.1](%443) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %445 : Float(1, 32, 208, 208) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%444, %module_list.2.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %446 : Float(1, 32, 208, 208) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%445, %module_list.2.BatchNorm2d.weight, %module_list.2.BatchNorm2d.bias, %module_list.2.BatchNorm2d.running_mean, %module_list.2.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %447 : Float(1, 32, 208, 208) = onnx::LeakyRelu[alpha=0.1](%446) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %448 : Float(1, 64, 208, 208) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%447, %module_list.3.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %449 : Float(1, 64, 208, 208) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%448, %module_list.3.BatchNorm2d.weight, %module_list.3.BatchNorm2d.bias, %module_list.3.BatchNorm2d.running_mean, %module_list.3.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %450 : Float(1, 64, 208, 208) = onnx::LeakyRelu[alpha=0.1](%449) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %451 : Float(1, 64, 208, 208) = onnx::Add(%450, %444) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %452 : Float(1, 128, 104, 104) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%451, %module_list.5.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %453 : Float(1, 128, 104, 104) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%452, %module_list.5.BatchNorm2d.weight, %module_list.5.BatchNorm2d.bias, %module_list.5.BatchNorm2d.running_mean, %module_list.5.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %454 : Float(1, 128, 104, 104) = onnx::LeakyRelu[alpha=0.1](%453) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %455 : Float(1, 64, 104, 104) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%454, %module_list.6.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %456 : Float(1, 64, 104, 104) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%455, %module_list.6.BatchNorm2d.weight, %module_list.6.BatchNorm2d.bias, %module_list.6.BatchNorm2d.running_mean, %module_list.6.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %457 : Float(1, 64, 104, 104) = onnx::LeakyRelu[alpha=0.1](%456) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %458 : Float(1, 128, 104, 104) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%457, %module_list.7.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %459 : Float(1, 128, 104, 104) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%458, %module_list.7.BatchNorm2d.weight, %module_list.7.BatchNorm2d.bias, %module_list.7.BatchNorm2d.running_mean, %module_list.7.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %460 : Float(1, 128, 104, 104) = onnx::LeakyRelu[alpha=0.1](%459) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %461 : Float(1, 128, 104, 104) = onnx::Add(%460, %454) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %462 : Float(1, 64, 104, 104) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%461, %module_list.9.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %463 : Float(1, 64, 104, 104) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%462, %module_list.9.BatchNorm2d.weight, %module_list.9.BatchNorm2d.bias, %module_list.9.BatchNorm2d.running_mean, %module_list.9.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %464 : Float(1, 64, 104, 104) = onnx::LeakyRelu[alpha=0.1](%463) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %465 : Float(1, 128, 104, 104) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%464, %module_list.10.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %466 : Float(1, 128, 104, 104) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%465, %module_list.10.BatchNorm2d.weight, %module_list.10.BatchNorm2d.bias, %module_list.10.BatchNorm2d.running_mean, %module_list.10.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %467 : Float(1, 128, 104, 104) = onnx::LeakyRelu[alpha=0.1](%466) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %468 : Float(1, 128, 104, 104) = onnx::Add(%467, %461) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %469 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%468, %module_list.12.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %470 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%469, %module_list.12.BatchNorm2d.weight, %module_list.12.BatchNorm2d.bias, %module_list.12.BatchNorm2d.running_mean, %module_list.12.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %471 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%470) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %472 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%471, %module_list.13.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %473 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%472, %module_list.13.BatchNorm2d.weight, %module_list.13.BatchNorm2d.bias, %module_list.13.BatchNorm2d.running_mean, %module_list.13.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %474 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%473) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %475 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%474, %module_list.14.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %476 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%475, %module_list.14.BatchNorm2d.weight, %module_list.14.BatchNorm2d.bias, %module_list.14.BatchNorm2d.running_mean, %module_list.14.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %477 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%476) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %478 : Float(1, 256, 52, 52) = onnx::Add(%477, %471) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %479 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%478, %module_list.16.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %480 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%479, %module_list.16.BatchNorm2d.weight, %module_list.16.BatchNorm2d.bias, %module_list.16.BatchNorm2d.running_mean, %module_list.16.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %481 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%480) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %482 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%481, %module_list.17.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %483 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%482, %module_list.17.BatchNorm2d.weight, %module_list.17.BatchNorm2d.bias, %module_list.17.BatchNorm2d.running_mean, %module_list.17.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %484 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%483) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %485 : Float(1, 256, 52, 52) = onnx::Add(%484, %478) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %486 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%485, %module_list.19.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %487 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%486, %module_list.19.BatchNorm2d.weight, %module_list.19.BatchNorm2d.bias, %module_list.19.BatchNorm2d.running_mean, %module_list.19.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %488 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%487) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %489 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%488, %module_list.20.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %490 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%489, %module_list.20.BatchNorm2d.weight, %module_list.20.BatchNorm2d.bias, %module_list.20.BatchNorm2d.running_mean, %module_list.20.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %491 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%490) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %492 : Float(1, 256, 52, 52) = onnx::Add(%491, %485) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %493 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%492, %module_list.22.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %494 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%493, %module_list.22.BatchNorm2d.weight, %module_list.22.BatchNorm2d.bias, %module_list.22.BatchNorm2d.running_mean, %module_list.22.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %495 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%494) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %496 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%495, %module_list.23.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %497 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%496, %module_list.23.BatchNorm2d.weight, %module_list.23.BatchNorm2d.bias, %module_list.23.BatchNorm2d.running_mean, %module_list.23.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %498 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%497) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %499 : Float(1, 256, 52, 52) = onnx::Add(%498, %492) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %500 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%499, %module_list.25.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %501 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%500, %module_list.25.BatchNorm2d.weight, %module_list.25.BatchNorm2d.bias, %module_list.25.BatchNorm2d.running_mean, %module_list.25.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %502 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%501) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %503 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%502, %module_list.26.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %504 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%503, %module_list.26.BatchNorm2d.weight, %module_list.26.BatchNorm2d.bias, %module_list.26.BatchNorm2d.running_mean, %module_list.26.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %505 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%504) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %506 : Float(1, 256, 52, 52) = onnx::Add(%505, %499) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %507 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%506, %module_list.28.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %508 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%507, %module_list.28.BatchNorm2d.weight, %module_list.28.BatchNorm2d.bias, %module_list.28.BatchNorm2d.running_mean, %module_list.28.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %509 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%508) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %510 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%509, %module_list.29.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %511 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%510, %module_list.29.BatchNorm2d.weight, %module_list.29.BatchNorm2d.bias, %module_list.29.BatchNorm2d.running_mean, %module_list.29.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %512 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%511) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %513 : Float(1, 256, 52, 52) = onnx::Add(%512, %506) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %514 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%513, %module_list.31.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %515 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%514, %module_list.31.BatchNorm2d.weight, %module_list.31.BatchNorm2d.bias, %module_list.31.BatchNorm2d.running_mean, %module_list.31.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %516 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%515) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %517 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%516, %module_list.32.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %518 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%517, %module_list.32.BatchNorm2d.weight, %module_list.32.BatchNorm2d.bias, %module_list.32.BatchNorm2d.running_mean, %module_list.32.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %519 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%518) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %520 : Float(1, 256, 52, 52) = onnx::Add(%519, %513) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %521 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%520, %module_list.34.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %522 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%521, %module_list.34.BatchNorm2d.weight, %module_list.34.BatchNorm2d.bias, %module_list.34.BatchNorm2d.running_mean, %module_list.34.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %523 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%522) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %524 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%523, %module_list.35.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %525 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%524, %module_list.35.BatchNorm2d.weight, %module_list.35.BatchNorm2d.bias, %module_list.35.BatchNorm2d.running_mean, %module_list.35.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %526 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%525) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %527 : Float(1, 256, 52, 52) = onnx::Add(%526, %520) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %528 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%527, %module_list.37.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %529 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%528, %module_list.37.BatchNorm2d.weight, %module_list.37.BatchNorm2d.bias, %module_list.37.BatchNorm2d.running_mean, %module_list.37.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %530 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%529) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %531 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%530, %module_list.38.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %532 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%531, %module_list.38.BatchNorm2d.weight, %module_list.38.BatchNorm2d.bias, %module_list.38.BatchNorm2d.running_mean, %module_list.38.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %533 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%532) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %534 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%533, %module_list.39.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %535 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%534, %module_list.39.BatchNorm2d.weight, %module_list.39.BatchNorm2d.bias, %module_list.39.BatchNorm2d.running_mean, %module_list.39.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %536 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%535) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %537 : Float(1, 512, 26, 26) = onnx::Add(%536, %530) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %538 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%537, %module_list.41.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %539 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%538, %module_list.41.BatchNorm2d.weight, %module_list.41.BatchNorm2d.bias, %module_list.41.BatchNorm2d.running_mean, %module_list.41.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %540 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%539) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %541 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%540, %module_list.42.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %542 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%541, %module_list.42.BatchNorm2d.weight, %module_list.42.BatchNorm2d.bias, %module_list.42.BatchNorm2d.running_mean, %module_list.42.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %543 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%542) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %544 : Float(1, 512, 26, 26) = onnx::Add(%543, %537) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %545 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%544, %module_list.44.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %546 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%545, %module_list.44.BatchNorm2d.weight, %module_list.44.BatchNorm2d.bias, %module_list.44.BatchNorm2d.running_mean, %module_list.44.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %547 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%546) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %548 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%547, %module_list.45.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %549 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%548, %module_list.45.BatchNorm2d.weight, %module_list.45.BatchNorm2d.bias, %module_list.45.BatchNorm2d.running_mean, %module_list.45.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %550 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%549) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %551 : Float(1, 512, 26, 26) = onnx::Add(%550, %544) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %552 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%551, %module_list.47.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %553 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%552, %module_list.47.BatchNorm2d.weight, %module_list.47.BatchNorm2d.bias, %module_list.47.BatchNorm2d.running_mean, %module_list.47.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %554 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%553) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %555 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%554, %module_list.48.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %556 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%555, %module_list.48.BatchNorm2d.weight, %module_list.48.BatchNorm2d.bias, %module_list.48.BatchNorm2d.running_mean, %module_list.48.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %557 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%556) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %558 : Float(1, 512, 26, 26) = onnx::Add(%557, %551) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %559 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%558, %module_list.50.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %560 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%559, %module_list.50.BatchNorm2d.weight, %module_list.50.BatchNorm2d.bias, %module_list.50.BatchNorm2d.running_mean, %module_list.50.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %561 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%560) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %562 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%561, %module_list.51.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %563 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%562, %module_list.51.BatchNorm2d.weight, %module_list.51.BatchNorm2d.bias, %module_list.51.BatchNorm2d.running_mean, %module_list.51.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %564 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%563) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %565 : Float(1, 512, 26, 26) = onnx::Add(%564, %558) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %566 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%565, %module_list.53.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %567 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%566, %module_list.53.BatchNorm2d.weight, %module_list.53.BatchNorm2d.bias, %module_list.53.BatchNorm2d.running_mean, %module_list.53.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %568 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%567) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %569 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%568, %module_list.54.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %570 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%569, %module_list.54.BatchNorm2d.weight, %module_list.54.BatchNorm2d.bias, %module_list.54.BatchNorm2d.running_mean, %module_list.54.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %571 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%570) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %572 : Float(1, 512, 26, 26) = onnx::Add(%571, %565) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %573 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%572, %module_list.56.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %574 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%573, %module_list.56.BatchNorm2d.weight, %module_list.56.BatchNorm2d.bias, %module_list.56.BatchNorm2d.running_mean, %module_list.56.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %575 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%574) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %576 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%575, %module_list.57.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %577 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%576, %module_list.57.BatchNorm2d.weight, %module_list.57.BatchNorm2d.bias, %module_list.57.BatchNorm2d.running_mean, %module_list.57.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %578 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%577) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %579 : Float(1, 512, 26, 26) = onnx::Add(%578, %572) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %580 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%579, %module_list.59.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %581 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%580, %module_list.59.BatchNorm2d.weight, %module_list.59.BatchNorm2d.bias, %module_list.59.BatchNorm2d.running_mean, %module_list.59.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %582 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%581) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %583 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%582, %module_list.60.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %584 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%583, %module_list.60.BatchNorm2d.weight, %module_list.60.BatchNorm2d.bias, %module_list.60.BatchNorm2d.running_mean, %module_list.60.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %585 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%584) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %586 : Float(1, 512, 26, 26) = onnx::Add(%585, %579) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %587 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%586, %module_list.62.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %588 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%587, %module_list.62.BatchNorm2d.weight, %module_list.62.BatchNorm2d.bias, %module_list.62.BatchNorm2d.running_mean, %module_list.62.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %589 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%588) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %590 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%589, %module_list.63.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %591 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%590, %module_list.63.BatchNorm2d.weight, %module_list.63.BatchNorm2d.bias, %module_list.63.BatchNorm2d.running_mean, %module_list.63.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %592 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%591) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %593 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%592, %module_list.64.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %594 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%593, %module_list.64.BatchNorm2d.weight, %module_list.64.BatchNorm2d.bias, %module_list.64.BatchNorm2d.running_mean, %module_list.64.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %595 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%594) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %596 : Float(1, 1024, 13, 13) = onnx::Add(%595, %589) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %597 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%596, %module_list.66.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %598 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%597, %module_list.66.BatchNorm2d.weight, %module_list.66.BatchNorm2d.bias, %module_list.66.BatchNorm2d.running_mean, %module_list.66.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %599 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%598) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %600 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%599, %module_list.67.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %601 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%600, %module_list.67.BatchNorm2d.weight, %module_list.67.BatchNorm2d.bias, %module_list.67.BatchNorm2d.running_mean, %module_list.67.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %602 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%601) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %603 : Float(1, 1024, 13, 13) = onnx::Add(%602, %596) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %604 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%603, %module_list.69.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %605 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%604, %module_list.69.BatchNorm2d.weight, %module_list.69.BatchNorm2d.bias, %module_list.69.BatchNorm2d.running_mean, %module_list.69.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %606 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%605) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %607 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%606, %module_list.70.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %608 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%607, %module_list.70.BatchNorm2d.weight, %module_list.70.BatchNorm2d.bias, %module_list.70.BatchNorm2d.running_mean, %module_list.70.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %609 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%608) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %610 : Float(1, 1024, 13, 13) = onnx::Add(%609, %603) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %611 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%610, %module_list.72.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %612 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%611, %module_list.72.BatchNorm2d.weight, %module_list.72.BatchNorm2d.bias, %module_list.72.BatchNorm2d.running_mean, %module_list.72.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %613 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%612) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %614 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%613, %module_list.73.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %615 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%614, %module_list.73.BatchNorm2d.weight, %module_list.73.BatchNorm2d.bias, %module_list.73.BatchNorm2d.running_mean, %module_list.73.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %616 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%615) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %617 : Float(1, 1024, 13, 13) = onnx::Add(%616, %610) # /content/onnx_tflite_yolov3/models.py:247:0\n",
" %618 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%617, %module_list.75.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %619 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%618, %module_list.75.BatchNorm2d.weight, %module_list.75.BatchNorm2d.bias, %module_list.75.BatchNorm2d.running_mean, %module_list.75.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %620 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%619) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %621 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%620, %module_list.76.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %622 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%621, %module_list.76.BatchNorm2d.weight, %module_list.76.BatchNorm2d.bias, %module_list.76.BatchNorm2d.running_mean, %module_list.76.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %623 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%622) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %624 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%623, %module_list.77.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %625 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%624, %module_list.77.BatchNorm2d.weight, %module_list.77.BatchNorm2d.bias, %module_list.77.BatchNorm2d.running_mean, %module_list.77.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %626 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%625) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %627 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%626, %module_list.78.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %628 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%627, %module_list.78.BatchNorm2d.weight, %module_list.78.BatchNorm2d.bias, %module_list.78.BatchNorm2d.running_mean, %module_list.78.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %629 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%628) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %630 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%629, %module_list.79.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %631 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%630, %module_list.79.BatchNorm2d.weight, %module_list.79.BatchNorm2d.bias, %module_list.79.BatchNorm2d.running_mean, %module_list.79.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %632 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%631) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %633 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%632, %module_list.80.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %634 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%633, %module_list.80.BatchNorm2d.weight, %module_list.80.BatchNorm2d.bias, %module_list.80.BatchNorm2d.running_mean, %module_list.80.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %635 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%634) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %636 : Float(1, 255, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%635, %module_list.81.Conv2d.weight, %module_list.81.Conv2d.bias) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %637 : Tensor = onnx::Constant[value= 1 3 85 169 [ CPULongType{4} ]]()\n",
" %638 : Float(1, 3, 85, 169) = onnx::Reshape(%636, %637) # /content/onnx_tflite_yolov3/models.py:170:0\n",
" %639 : Float(1, 3, 169, 85) = onnx::Transpose[perm=[0, 1, 3, 2]](%638) # /content/onnx_tflite_yolov3/models.py:170:0\n",
" %640 : Tensor = onnx::Constant[value= 1 3 169 85 [ CPULongType{4} ]]()\n",
" %641 : Float(1, 3, 169, 85) = onnx::Reshape(%639, %640) # /content/onnx_tflite_yolov3/models.py:176:0\n",
" %642 : Float(1, 3, 169, 2) = onnx::Slice[axes=[3], ends=[2], starts=[0]](%641) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %643 : Float(1, 3, 169, 2) = onnx::Sigmoid(%642) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %644 : Float(1, 1, 169, 2) = onnx::Constant[value=<Tensor>]()\n",
" %645 : Float(1, 3, 169, 2) = onnx::Add(%643, %644) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %646 : Float() = onnx::Constant[value={32}]()\n",
" %647 : Float(1, 3, 169, 2) = onnx::Mul(%645, %646)\n",
" %648 : Float(1, 3, 169, 2) = onnx::Slice[axes=[3], ends=[4], starts=[2]](%641) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %649 : Float(1, 3, 169, 2) = onnx::Exp(%648) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %650 : Float(1, 3, 1, 2) = onnx::Constant[value=(1,1,.,.) = 3.6250 2.8125 (1,2,.,.) = 4.8750 6.1875 (1,3,.,.) = 11.6562 10.1875 [ CPUFloatType{1,3,1,2} ]]()\n",
" %651 : Float(1, 3, 169, 2) = onnx::Mul(%649, %650) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %652 : Float() = onnx::Constant[value={32}]()\n",
" %653 : Float(1, 3, 169, 2) = onnx::Mul(%651, %652)\n",
" %654 : Float(1, 3, 169, 1) = onnx::Slice[axes=[3], ends=[5], starts=[4]](%641) # /content/onnx_tflite_yolov3/models.py:182:0\n",
" %655 : Float(1, 3, 169, 1) = onnx::Sigmoid(%654) # /content/onnx_tflite_yolov3/models.py:182:0\n",
" %656 : Float(1, 3, 169, 80) = onnx::Slice[axes=[3], ends=[9223372036854775807], starts=[5]](%641) # /content/onnx_tflite_yolov3/models.py:183:0\n",
" %657 : Float(1, 3, 169, 80) = onnx::Sigmoid(%656) # /content/onnx_tflite_yolov3/models.py:183:0\n",
" %658 : Float(1, 3, 169, 85) = onnx::Concat[axis=-1](%647, %653, %655, %657) # /content/onnx_tflite_yolov3/models.py:208:0\n",
" %659 : Tensor = onnx::Constant[value= 1 -1 85 [ CPULongType{3} ]]()\n",
" %660 : Float(1, 507, 85) = onnx::Reshape(%658, %659) # /content/onnx_tflite_yolov3/models.py:209:0\n",
" %661 : Tensor = onnx::Constant[value= 1 3 13 13 85 [ CPULongType{5} ]]()\n",
" %662 : Float(1, 3, 13, 13, 85) = onnx::Reshape(%641, %661) # /content/onnx_tflite_yolov3/models.py:209:0\n",
" %663 : Float(1, 256, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%632, %module_list.84.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %664 : Float(1, 256, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%663, %module_list.84.BatchNorm2d.weight, %module_list.84.BatchNorm2d.bias, %module_list.84.BatchNorm2d.running_mean, %module_list.84.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %665 : Float(1, 256, 13, 13) = onnx::LeakyRelu[alpha=0.1](%664) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %666 : Tensor = onnx::Shape(%665)\n",
" %667 : Tensor = onnx::Constant[value={2}]()\n",
" %668 : Long() = onnx::Gather[axis=0](%666, %667) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %669 : Float() = onnx::Cast[to=1](%668) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %670 : Float() = onnx::Constant[value={2}]()\n",
" %671 : Float() = onnx::Mul(%669, %670)\n",
" %672 : Float() = onnx::Cast[to=1](%671) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %673 : Float() = onnx::Floor(%672) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %674 : Tensor = onnx::Shape(%665)\n",
" %675 : Tensor = onnx::Constant[value={3}]()\n",
" %676 : Long() = onnx::Gather[axis=0](%674, %675) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %677 : Float() = onnx::Cast[to=1](%676) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %678 : Float() = onnx::Constant[value={2}]()\n",
" %679 : Float() = onnx::Mul(%677, %678)\n",
" %680 : Float() = onnx::Cast[to=1](%679) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %681 : Float() = onnx::Floor(%680) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %682 : Tensor = onnx::Unsqueeze[axes=[0]](%673)\n",
" %683 : Tensor = onnx::Unsqueeze[axes=[0]](%681)\n",
" %684 : Tensor = onnx::Concat[axis=0](%682, %683)\n",
" %685 : Tensor = onnx::Constant[value= 1 1 [ CPUFloatType{2} ]]()\n",
" %686 : Tensor = onnx::Cast[to=1](%684)\n",
" %687 : Tensor = onnx::Shape(%665)\n",
" %688 : Tensor = onnx::Slice[axes=[0], ends=[9223372036854775807], starts=[2]](%687)\n",
" %689 : Tensor = onnx::Cast[to=1](%688)\n",
" %690 : Tensor = onnx::Div(%686, %689)\n",
" %691 : Tensor = onnx::Concat[axis=0](%685, %690)\n",
" %692 : Float(1, 256, 26, 26) = onnx::Upsample[mode=\"nearest\"](%665, %691) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2512:0\n",
" %693 : Float(1, 768, 26, 26) = onnx::Concat[axis=1](%692, %586) # /content/onnx_tflite_yolov3/models.py:241:0\n",
" %694 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%693, %module_list.87.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %695 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%694, %module_list.87.BatchNorm2d.weight, %module_list.87.BatchNorm2d.bias, %module_list.87.BatchNorm2d.running_mean, %module_list.87.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %696 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%695) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %697 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%696, %module_list.88.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %698 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%697, %module_list.88.BatchNorm2d.weight, %module_list.88.BatchNorm2d.bias, %module_list.88.BatchNorm2d.running_mean, %module_list.88.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %699 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%698) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %700 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%699, %module_list.89.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %701 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%700, %module_list.89.BatchNorm2d.weight, %module_list.89.BatchNorm2d.bias, %module_list.89.BatchNorm2d.running_mean, %module_list.89.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %702 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%701) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %703 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%702, %module_list.90.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %704 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%703, %module_list.90.BatchNorm2d.weight, %module_list.90.BatchNorm2d.bias, %module_list.90.BatchNorm2d.running_mean, %module_list.90.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %705 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%704) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %706 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%705, %module_list.91.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %707 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%706, %module_list.91.BatchNorm2d.weight, %module_list.91.BatchNorm2d.bias, %module_list.91.BatchNorm2d.running_mean, %module_list.91.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %708 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%707) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %709 : Float(1, 512, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%708, %module_list.92.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %710 : Float(1, 512, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%709, %module_list.92.BatchNorm2d.weight, %module_list.92.BatchNorm2d.bias, %module_list.92.BatchNorm2d.running_mean, %module_list.92.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %711 : Float(1, 512, 26, 26) = onnx::LeakyRelu[alpha=0.1](%710) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %712 : Float(1, 255, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%711, %module_list.93.Conv2d.weight, %module_list.93.Conv2d.bias) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %713 : Tensor = onnx::Constant[value= 1 3 85 676 [ CPULongType{4} ]]()\n",
" %714 : Float(1, 3, 85, 676) = onnx::Reshape(%712, %713) # /content/onnx_tflite_yolov3/models.py:170:0\n",
" %715 : Float(1, 3, 676, 85) = onnx::Transpose[perm=[0, 1, 3, 2]](%714) # /content/onnx_tflite_yolov3/models.py:170:0\n",
" %716 : Tensor = onnx::Constant[value= 1 3 676 85 [ CPULongType{4} ]]()\n",
" %717 : Float(1, 3, 676, 85) = onnx::Reshape(%715, %716) # /content/onnx_tflite_yolov3/models.py:176:0\n",
" %718 : Float(1, 3, 676, 2) = onnx::Slice[axes=[3], ends=[2], starts=[0]](%717) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %719 : Float(1, 3, 676, 2) = onnx::Sigmoid(%718) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %720 : Float(1, 1, 676, 2) = onnx::Constant[value=<Tensor>]()\n",
" %721 : Float(1, 3, 676, 2) = onnx::Add(%719, %720) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %722 : Float() = onnx::Constant[value={16}]()\n",
" %723 : Float(1, 3, 676, 2) = onnx::Mul(%721, %722)\n",
" %724 : Float(1, 3, 676, 2) = onnx::Slice[axes=[3], ends=[4], starts=[2]](%717) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %725 : Float(1, 3, 676, 2) = onnx::Exp(%724) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %726 : Float(1, 3, 1, 2) = onnx::Constant[value=(1,1,.,.) = 1.8750 3.8125 (1,2,.,.) = 3.8750 2.8125 (1,3,.,.) = 3.6875 7.4375 [ CPUFloatType{1,3,1,2} ]]()\n",
" %727 : Float(1, 3, 676, 2) = onnx::Mul(%725, %726) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %728 : Float() = onnx::Constant[value={16}]()\n",
" %729 : Float(1, 3, 676, 2) = onnx::Mul(%727, %728)\n",
" %730 : Float(1, 3, 676, 1) = onnx::Slice[axes=[3], ends=[5], starts=[4]](%717) # /content/onnx_tflite_yolov3/models.py:182:0\n",
" %731 : Float(1, 3, 676, 1) = onnx::Sigmoid(%730) # /content/onnx_tflite_yolov3/models.py:182:0\n",
" %732 : Float(1, 3, 676, 80) = onnx::Slice[axes=[3], ends=[9223372036854775807], starts=[5]](%717) # /content/onnx_tflite_yolov3/models.py:183:0\n",
" %733 : Float(1, 3, 676, 80) = onnx::Sigmoid(%732) # /content/onnx_tflite_yolov3/models.py:183:0\n",
" %734 : Float(1, 3, 676, 85) = onnx::Concat[axis=-1](%723, %729, %731, %733) # /content/onnx_tflite_yolov3/models.py:208:0\n",
" %735 : Tensor = onnx::Constant[value= 1 -1 85 [ CPULongType{3} ]]()\n",
" %736 : Float(1, 2028, 85) = onnx::Reshape(%734, %735) # /content/onnx_tflite_yolov3/models.py:209:0\n",
" %737 : Tensor = onnx::Constant[value= 1 3 26 26 85 [ CPULongType{5} ]]()\n",
" %738 : Float(1, 3, 26, 26, 85) = onnx::Reshape(%717, %737) # /content/onnx_tflite_yolov3/models.py:209:0\n",
" %739 : Float(1, 128, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%708, %module_list.96.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %740 : Float(1, 128, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%739, %module_list.96.BatchNorm2d.weight, %module_list.96.BatchNorm2d.bias, %module_list.96.BatchNorm2d.running_mean, %module_list.96.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %741 : Float(1, 128, 26, 26) = onnx::LeakyRelu[alpha=0.1](%740) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %742 : Tensor = onnx::Shape(%741)\n",
" %743 : Tensor = onnx::Constant[value={2}]()\n",
" %744 : Long() = onnx::Gather[axis=0](%742, %743) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %745 : Float() = onnx::Cast[to=1](%744) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %746 : Float() = onnx::Constant[value={2}]()\n",
" %747 : Float() = onnx::Mul(%745, %746)\n",
" %748 : Float() = onnx::Cast[to=1](%747) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %749 : Float() = onnx::Floor(%748) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %750 : Tensor = onnx::Shape(%741)\n",
" %751 : Tensor = onnx::Constant[value={3}]()\n",
" %752 : Long() = onnx::Gather[axis=0](%750, %751) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %753 : Float() = onnx::Cast[to=1](%752) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %754 : Float() = onnx::Constant[value={2}]()\n",
" %755 : Float() = onnx::Mul(%753, %754)\n",
" %756 : Float() = onnx::Cast[to=1](%755) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %757 : Float() = onnx::Floor(%756) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2493:0\n",
" %758 : Tensor = onnx::Unsqueeze[axes=[0]](%749)\n",
" %759 : Tensor = onnx::Unsqueeze[axes=[0]](%757)\n",
" %760 : Tensor = onnx::Concat[axis=0](%758, %759)\n",
" %761 : Tensor = onnx::Constant[value= 1 1 [ CPUFloatType{2} ]]()\n",
" %762 : Tensor = onnx::Cast[to=1](%760)\n",
" %763 : Tensor = onnx::Shape(%741)\n",
" %764 : Tensor = onnx::Slice[axes=[0], ends=[9223372036854775807], starts=[2]](%763)\n",
" %765 : Tensor = onnx::Cast[to=1](%764)\n",
" %766 : Tensor = onnx::Div(%762, %765)\n",
" %767 : Tensor = onnx::Concat[axis=0](%761, %766)\n",
" %768 : Float(1, 128, 52, 52) = onnx::Upsample[mode=\"nearest\"](%741, %767) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2512:0\n",
" %769 : Float(1, 384, 52, 52) = onnx::Concat[axis=1](%768, %527) # /content/onnx_tflite_yolov3/models.py:241:0\n",
" %770 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%769, %module_list.99.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %771 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%770, %module_list.99.BatchNorm2d.weight, %module_list.99.BatchNorm2d.bias, %module_list.99.BatchNorm2d.running_mean, %module_list.99.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %772 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%771) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %773 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%772, %module_list.100.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %774 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%773, %module_list.100.BatchNorm2d.weight, %module_list.100.BatchNorm2d.bias, %module_list.100.BatchNorm2d.running_mean, %module_list.100.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %775 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%774) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %776 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%775, %module_list.101.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %777 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%776, %module_list.101.BatchNorm2d.weight, %module_list.101.BatchNorm2d.bias, %module_list.101.BatchNorm2d.running_mean, %module_list.101.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %778 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%777) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %779 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%778, %module_list.102.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %780 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%779, %module_list.102.BatchNorm2d.weight, %module_list.102.BatchNorm2d.bias, %module_list.102.BatchNorm2d.running_mean, %module_list.102.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %781 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%780) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %782 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%781, %module_list.103.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %783 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%782, %module_list.103.BatchNorm2d.weight, %module_list.103.BatchNorm2d.bias, %module_list.103.BatchNorm2d.running_mean, %module_list.103.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %784 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%783) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %785 : Float(1, 256, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%784, %module_list.104.Conv2d.weight) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %786 : Float(1, 256, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%785, %module_list.104.BatchNorm2d.weight, %module_list.104.BatchNorm2d.bias, %module_list.104.BatchNorm2d.running_mean, %module_list.104.BatchNorm2d.running_var) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0\n",
" %787 : Float(1, 256, 52, 52) = onnx::LeakyRelu[alpha=0.1](%786) # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0\n",
" %788 : Float(1, 255, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%787, %module_list.105.Conv2d.weight, %module_list.105.Conv2d.bias) # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0\n",
" %789 : Tensor = onnx::Constant[value= 1 3 85 2704 [ CPULongType{4} ]]()\n",
" %790 : Float(1, 3, 85, 2704) = onnx::Reshape(%788, %789) # /content/onnx_tflite_yolov3/models.py:170:0\n",
" %791 : Float(1, 3, 2704, 85) = onnx::Transpose[perm=[0, 1, 3, 2]](%790) # /content/onnx_tflite_yolov3/models.py:170:0\n",
" %792 : Tensor = onnx::Constant[value= 1 3 2704 85 [ CPULongType{4} ]]()\n",
" %793 : Float(1, 3, 2704, 85) = onnx::Reshape(%791, %792) # /content/onnx_tflite_yolov3/models.py:176:0\n",
" %794 : Float(1, 3, 2704, 2) = onnx::Slice[axes=[3], ends=[2], starts=[0]](%793) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %795 : Float(1, 3, 2704, 2) = onnx::Sigmoid(%794) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %796 : Float(1, 1, 2704, 2) = onnx::Constant[value=<Tensor>]()\n",
" %797 : Float(1, 3, 2704, 2) = onnx::Add(%795, %796) # /content/onnx_tflite_yolov3/models.py:177:0\n",
" %798 : Float() = onnx::Constant[value={8}]()\n",
" %799 : Float(1, 3, 2704, 2) = onnx::Mul(%797, %798)\n",
" %800 : Float(1, 3, 2704, 2) = onnx::Slice[axes=[3], ends=[4], starts=[2]](%793) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %801 : Float(1, 3, 2704, 2) = onnx::Exp(%800) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %802 : Float(1, 3, 1, 2) = onnx::Constant[value=(1,1,.,.) = 1.2500 1.6250 (1,2,.,.) = 2.0000 3.7500 (1,3,.,.) = 4.1250 2.8750 [ CPUFloatType{1,3,1,2} ]]()\n",
" %803 : Float(1, 3, 2704, 2) = onnx::Mul(%801, %802) # /content/onnx_tflite_yolov3/models.py:178:0\n",
" %804 : Float() = onnx::Constant[value={8}]()\n",
" %805 : Float(1, 3, 2704, 2) = onnx::Mul(%803, %804)\n",
" %806 : Float(1, 3, 2704, 1) = onnx::Slice[axes=[3], ends=[5], starts=[4]](%793) # /content/onnx_tflite_yolov3/models.py:182:0\n",
" %807 : Float(1, 3, 2704, 1) = onnx::Sigmoid(%806) # /content/onnx_tflite_yolov3/models.py:182:0\n",
" %808 : Float(1, 3, 2704, 80) = onnx::Slice[axes=[3], ends=[9223372036854775807], starts=[5]](%793) # /content/onnx_tflite_yolov3/models.py:183:0\n",
" %809 : Float(1, 3, 2704, 80) = onnx::Sigmoid(%808) # /content/onnx_tflite_yolov3/models.py:183:0\n",
" %810 : Float(1, 3, 2704, 85) = onnx::Concat[axis=-1](%799, %805, %807, %809) # /content/onnx_tflite_yolov3/models.py:208:0\n",
" %811 : Tensor = onnx::Constant[value= 1 -1 85 [ CPULongType{3} ]]()\n",
" %812 : Float(1, 8112, 85) = onnx::Reshape(%810, %811) # /content/onnx_tflite_yolov3/models.py:209:0\n",
" %813 : Tensor = onnx::Constant[value= 1 3 52 52 85 [ CPULongType{5} ]]()\n",
" %814 : Float(1, 3, 52, 52, 85) = onnx::Reshape(%793, %813) # /content/onnx_tflite_yolov3/models.py:209:0\n",
" %815 : Float(1, 10647, 85) = onnx::Concat[axis=1](%660, %736, %812) # /content/onnx_tflite_yolov3/models.py:262:0\n",
" return (%815, %662, %738, %814)\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nG5UAwv5LYqJ",
"outputId": "0ec3ded5-c64a-4493-b793-2e29f85e20c5"
},
"source": [
"!python3 onnx2tf.py"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/__init__.py:89: UserWarning: onnx_tf.common.get_outputs_names is deprecated. It will be removed in future release. Use TensorflowGraph.get_outputs_names instead.\n",
" warnings.warn(message)\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/ceil.py:10: The name tf.ceil is deprecated. Please use tf.math.ceil instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/depth_to_space.py:12: The name tf.depth_to_space is deprecated. Please use tf.compat.v1.depth_to_space instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/erf.py:9: The name tf.erf is deprecated. Please use tf.math.erf instead.\n",
"\n",
"WARNING:tensorflow:\n",
"The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
"For more information, please see:\n",
" * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
" * https://github.com/tensorflow/addons\n",
" * https://github.com/tensorflow/io (for I/O related ops)\n",
"If you depend on functionality not listed there, please file an issue.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/is_nan.py:9: The name tf.is_nan is deprecated. Please use tf.math.is_nan instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/log.py:10: The name tf.log is deprecated. Please use tf.math.log instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/random_normal.py:9: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/random_uniform.py:9: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/reciprocal.py:10: The name tf.reciprocal is deprecated. Please use tf.math.reciprocal instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/space_to_depth.py:12: The name tf.space_to_depth is deprecated. Please use tf.compat.v1.space_to_depth instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/upsample.py:15: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/xor.py:10: The name tf.logical_xor is deprecated. Please use tf.math.logical_xor instead.\n",
"\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:37: UserWarning: Unknown op ConstantFill in domain `ai.onnx`.\n",
" handler.ONNX_OP, handler.DOMAIN or \"ai.onnx\"))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of ConvInteger in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of DequantizeLinear in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of GatherND in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:37: UserWarning: Unknown op ImageScaler in domain `ai.onnx`.\n",
" handler.ONNX_OP, handler.DOMAIN or \"ai.onnx\"))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of IsInf in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of MatMulInteger in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of Mod in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of NonMaxSuppression in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of QLinearConv in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of QLinearMatMul in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of QuantizeLinear in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of Range in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of Resize in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of ReverseSequence in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of Round in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of ScatterElements in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of ScatterND in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"/usr/local/lib/python3.6/dist-packages/onnx_tf/common/handler_helper.py:34: UserWarning: Fail to get since_version of ThresholdedRelu in domain `` with max_inclusive_version=9. Set to 1.\n",
" handler.ONNX_OP, handler.DOMAIN, version))\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/backend.py:123: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"2021-01-19 15:17:59.144966: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
"2021-01-19 15:17:59.159343: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299995000 Hz\n",
"2021-01-19 15:17:59.159749: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3308ae00 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
"2021-01-19 15:17:59.159791: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n",
"2021-01-19 15:17:59.199025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
"2021-01-19 15:17:59.300348: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
"2021-01-19 15:17:59.300414: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (eb0d8b35a000): /proc/driver/nvidia/version does not exist\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/reshape.py:26: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend/reshape.py:31: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/onnx_tf/handlers/backend_handler.py:182: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Deprecated in favor of operator or tf.math.divide.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4JRjW9NrOHNI",
"outputId": "d00f744a-0965-4f66-c054-45eb1dbdcf84"
},
"source": [
"!python3 prep.py"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From prep.py:8: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From prep.py:9: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.\n",
"\n",
"WARNING:tensorflow:From prep.py:11: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
"\n",
"2021-01-19 15:18:29.825039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
"2021-01-19 15:18:29.834758: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
"2021-01-19 15:18:29.834818: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (eb0d8b35a000): /proc/driver/nvidia/version does not exist\n",
"2021-01-19 15:18:29.835377: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
"2021-01-19 15:18:29.858650: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299995000 Hz\n",
"2021-01-19 15:18:29.858918: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2c5cbc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
"2021-01-19 15:18:29.858959: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n",
"WARNING:tensorflow:From prep.py:13: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
"\n",
"WARNING:tensorflow:From prep.py:42: The name tf.AttrValue is deprecated. Please use tf.compat.v1.AttrValue instead.\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\", line 1607, in _create_c_op\n",
" c_op = c_api.TF_FinishOperation(op_desc)\n",
"tensorflow.python.framework.errors_impl.InvalidArgumentError: Depth of input (418) is not a multiple of input depth of filter (3) for 'convolution_new' (op: 'Conv2D') with input shapes: [1,418,3,418], [3,3,3,32].\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"prep.py\", line 45, in <module>\n",
" op = sess.graph.create_op(op_type=n_org.type, inputs=op_inputs, name=n_org.name+'_new', attrs=atts) \n",
" File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py\", line 507, in new_func\n",
" return func(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\", line 3357, in create_op\n",
" attrs, op_def, compute_device)\n",
" File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\", line 3426, in _create_op_internal\n",
" op_def=op_def)\n",
" File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\", line 1770, in __init__\n",
" control_input_ops)\n",
" File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\", line 1610, in _create_c_op\n",
" raise ValueError(str(e))\n",
"ValueError: Depth of input (418) is not a multiple of input depth of filter (3) for 'convolution_new' (op: 'Conv2D') with input shapes: [1,418,3,418], [3,3,3,32].\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VRBv6kMoOcbp"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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