ML/techveri.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "scratchpad",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIYdn1woOS1n",
        "outputId": "12513f88-d55e-4116-9eaf-fc37804f491c"
      },
      "source": [
        "!pip3 install onnxruntime\n",
        "import onnxruntime as ort\n",
        "from PIL import Image\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting onnxruntime\n",
            "  Downloading onnxruntime-1.9.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.8 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.19.5)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.17.3)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (2.0)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (1.15.0)\n",
            "Installing collected packages: onnxruntime\n",
            "Successfully installed onnxruntime-1.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFhhWfndtoUG"
      },
      "source": [
        "!pip install facenet_pytorch\n",
        "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import torchvision"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYPRHEh-ujl0",
        "outputId": "41dbc168-cad7-4d33-d95e-5731d2f57114"
      },
      "source": [
        "import torch\n",
        "resnet = InceptionResnetV1(pretrained='vggface2').eval()\n",
        "dummy_input = torch.tensor(np.array(Image.open(\"sample.jpg\"))).reshape(1,3,160,160).float()\n",
        "print(dummy_input.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 3, 160, 160])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mHYb3sW3vyN2",
        "outputId": "9073709f-4abb-477b-954d-96a3c4f02e11"
      },
      "source": [
        "print(resnet(dummy_input).shape)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 512])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAwBtlo-wyHK"
      },
      "source": [
        "torch.onnx.export(resnet, dummy_input, \"model2.onnx\",input_names=[\"input\"], output_names=[\"ooutput\"])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeamIx1gxXNX"
      },
      "source": [
        "ort_sess = ort.InferenceSession('model2.onnx')"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHdrxFPJx5Pg"
      },
      "source": [
        "outputs = ort_sess.run(None, {'input': dummy_input.detach().cpu().numpy()})"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EY3AIRcyWw4",
        "outputId": "5b6c8f21-dbd8-4024-b8d9-ed83fed91405"
      },
      "source": [
        "len(outputs[0][0])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWIILTzNrnfm",
        "outputId": "fd2c4016-d78e-4b5b-9339-8ba6b311bcdc"
      },
      "source": [
        "def run(url):\n",
        "    x=np.array(Image.open(url)).reshape(1,3,160,160).astype(np.float32)\n",
        "    ort_sess = ort.InferenceSession('model2.onnx')\n",
        "    outputs = ort_sess.run(None, {'input': x})\n",
        "    return outputs[0][0]\n",
        "run(\"sample.jpg\")"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       -4.73056138e-02, -4.00595032e-02, -4.79760813e-03,  2.86444719e-03,\n",
              "        8.47126693e-02,  5.77528514e-02, -5.48702106e-02,  4.19333205e-02,\n",
              "        8.28500278e-03, -9.33771655e-02,  2.14149226e-02,  4.44050618e-02,\n",
              "        5.80906356e-03, -9.24711209e-03, -6.27235249e-02,  3.31275873e-02,\n",
              "        1.52165145e-02,  3.82696278e-03,  9.95835513e-02, -4.97710183e-02,\n",
              "       -7.19652846e-02,  4.91658561e-02,  2.23294813e-02, -3.21180485e-02],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    }
  ]
}