pytorch_mobilenet_to_mlmodel.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pytorch_mobilenet_to_mlmodel.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWn9T09ALpH3"
      },
      "source": [
        "# coremltools\n",
        "Apple 에서 제공하는 coremltools의 convert를 이용해 pytorch 모델을 mlmodel로 변형가능하다. 이를 사용하기위해 설치해준다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2EN7hjTtICHZ",
        "outputId": "32cf5d51-423c-4c7a-b355-9e5d6e6b5140"
      },
      "source": [
        "!pip install coremltools"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: coremltools in /usr/local/lib/python3.6/dist-packages (4.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.4.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.1.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from coremltools) (20.8)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.1.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (3.12.4)\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.6/dist-packages (from coremltools) (20.3.0)\n",
            "Requirement already satisfied: attr in /usr/local/lib/python3.6/dist-packages (from coremltools) (0.3.1)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.19.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from coremltools) (4.41.1)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.6/dist-packages (from sympy->coremltools) (1.1.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->coremltools) (2.4.7)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.1.0->coremltools) (51.1.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NB3H2QwOO_tY"
      },
      "source": [
        "# Pytorch 버전 선택\n",
        "mlmodel convert 과정에서 `ValueError: unexpected number of inputs for node input.2 (_convolution): 13` 에러가 발생했다.\n",
        "https://github.com/apple/coremltools/issues/1012\n",
        "위 글을 참고하여 Pytorch 버전을 1.6.0으로 변경해준다. \n",
        "https://stackoverflow.com/questions/61273244/how-to-downgrade-torch-version-for-google-colab\n",
        "버전 변경방법은 위 글을 참조했다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuFnWMunPij1",
        "outputId": "67d1e033-ae74-49ea-c962-de0fd2524152"
      },
      "source": [
        "!pip install torch==1.6.0 torchvision==0.7.0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch==1.6.0 in /usr/local/lib/python3.6/dist-packages (1.6.0)\n",
            "Requirement already satisfied: torchvision==0.7.0 in /usr/local/lib/python3.6/dist-packages (0.7.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.6.0) (1.19.5)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch==1.6.0) (0.16.0)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.7.0) (7.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaHsizuyIWcp",
        "outputId": "547ffeec-df80-4f21-e202-71a6d8c95f12"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import coremltools as ct"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:scikit-learn version 0.22.2.post1 is not supported. Minimum required version: 0.17. Maximum required version: 0.19.2. Disabling scikit-learn conversion API.\n",
            "WARNING:root:TensorFlow version 2.4.0 detected. Last version known to be fully compatible is 2.3.1 .\n",
            "WARNING:root:Keras version 2.4.3 detected. Last version known to be fully compatible of Keras is 2.2.4 .\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZIjKmKDQJEj",
        "outputId": "7d842adb-4e13-4877-9c22-300478eaa33e"
      },
      "source": [
        "print(torch.__version__)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbBo6TLiMPxd"
      },
      "source": [
        "# mobilenet\n",
        "Pytorch에서 제공하는 mobilenet을 바로 불러왔다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RadR_PbWJIjR"
      },
      "source": [
        "torch_model = torchvision.models.mobilenet_v2()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMJMKZBKOaZ2",
        "outputId": "65e286ec-0e57-4eff-925c-89e5d0db011c"
      },
      "source": [
        "print(torch_model)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MobileNetV2(\n",
            "  (features): Sequential(\n",
            "    (0): ConvBNReLU(\n",
            "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU6(inplace=True)\n",
            "    )\n",
            "    (1): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
            "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (2): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
            "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (3): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
            "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (4): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
            "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (5): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (6): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
            "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (7): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
            "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (8): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (9): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (10): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (11): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
            "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (12): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (13): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
            "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (14): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
            "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (15): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (16): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (17): InvertedResidual(\n",
            "      (conv): Sequential(\n",
            "        (0): ConvBNReLU(\n",
            "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (1): ConvBNReLU(\n",
            "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
            "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU6(inplace=True)\n",
            "        )\n",
            "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (18): ConvBNReLU(\n",
            "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU6(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (classifier): Sequential(\n",
            "    (0): Dropout(p=0.2, inplace=False)\n",
            "    (1): Linear(in_features=1280, out_features=1000, bias=True)\n",
            "  )\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzi_NH4gLgH9"
      },
      "source": [
        "# Tracing\n",
        "Pytorch모델을 mlmodel로 변환하기 위해선 Script를 얻어내기위해 Tracing해주는 과정이 필요하다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AbeegsrZJjpJ"
      },
      "source": [
        "torch_model.eval()\n",
        "example_input = torch.rand(1, 3, 256, 256)\n",
        "traced_model = torch.jit.trace(torch_model, example_input)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvVvRCqfNVjI",
        "outputId": "0c6b3c9a-c883-4672-9026-2284472c33c1"
      },
      "source": [
        "mlmodel = ct.convert(traced_model, inputs=[ct.TensorType(shape=example_input.shape)])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Converting Frontend ==> MIL Ops:  79%|███████▉  | 310/391 [00:00<00:00, 1027.37 ops/s]\n",
            "Running MIL optimization passes: 100%|██████████| 17/17 [00:00<00:00, 83.05 passes/s]\n",
            "Translating MIL ==> MLModel Ops: 100%|██████████| 706/706 [00:00<00:00, 1176.60 ops/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9G1mqzDCN6kf",
        "outputId": "1165b40b-381f-4b25-9999-d71781ae8531"
      },
      "source": [
        "print(mlmodel)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input {\n",
            "  name: \"input.1\"\n",
            "  type {\n",
            "    multiArrayType {\n",
            "      shape: 1\n",
            "      shape: 3\n",
            "      shape: 256\n",
            "      shape: 256\n",
            "      dataType: FLOAT32\n",
            "    }\n",
            "  }\n",
            "}\n",
            "output {\n",
            "  name: \"653\"\n",
            "  type {\n",
            "    multiArrayType {\n",
            "      dataType: FLOAT32\n",
            "    }\n",
            "  }\n",
            "}\n",
            "metadata {\n",
            "  userDefined {\n",
            "    key: \"com.github.apple.coremltools.source\"\n",
            "    value: \"torch==1.6.0\"\n",
            "  }\n",
            "  userDefined {\n",
            "    key: \"com.github.apple.coremltools.version\"\n",
            "    value: \"4.0\"\n",
            "  }\n",
            "}\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64gmnVgmS5-Y"
      },
      "source": [
        "mlmodel.save(\"mobilenet.mlmodel\")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLJ55PMoTT2Q"
      },
      "source": [
        "# 다운로드\n",
        "mlmodel로 convert가 완료되었다. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "QaqjJne4TUWW",
        "outputId": "cc1ccae2-0d5b-418e-bb74-3745eab386bf"
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"mobilenet.mlmodel\") "
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_f025daa7-36bb-4082-bd7c-4bba04243ac8\", \"mobilenet.mlmodel\", 14165952)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}