60-load-onnxruntime-gpu.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip -q install onnxruntime-gpu"
      ],
      "metadata": {
        "id": "knIyvnkPLxnC",
        "outputId": "48e09beb-fd8f-467d-c8b9-ada6f2a8042f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.1/157.1 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q https://github.com/Alireza-Akhavan/deeplearning-tensorflow2-notebooks/raw/master/export.onnx"
      ],
      "metadata": {
        "id": "LF72I3B4tITY"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import onnxruntime as rt\n",
        "import cv2"
      ],
      "metadata": {
        "id": "OUdaC1zvvOMd"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rt.get_device()"
      ],
      "metadata": {
        "id": "UEH8nUi5x0Ts",
        "outputId": "e089403b-86ed-4397-8641-5e202a9d4526",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'GPU'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rt.get_available_providers()"
      ],
      "metadata": {
        "id": "CtPGvofxyHdc",
        "outputId": "fd432907-0665-4d9c-de5d-f7a8752d60eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['TensorrtExecutionProvider',\n",
              " 'CUDAExecutionProvider',\n",
              " 'AzureExecutionProvider',\n",
              " 'CPUExecutionProvider']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread('adad.png', 0)\n",
        "img = cv2.resize(img, (28,28))\n",
        "x = img.reshape([1,28,28,1])\n",
        "x = x.astype('float32')"
      ],
      "metadata": {
        "id": "BYE3eq_TvT9b"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "providers = ['CUDAExecutionProvider']\n",
        "m = rt.InferenceSession(\"export.onnx\", providers=providers)\n",
        "onnx_pred = m.run(['dense'], {\"input\": x})"
      ],
      "metadata": {
        "id": "A1jFZGr0MYse"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit onnx_pred = m.run(['dense'], {\"input\": x})"
      ],
      "metadata": {
        "id": "QZI1i-NfNmiB",
        "outputId": "4ed6c20f-6b4f-49b0-8636-7f4611207e8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "434 µs ± 8.48 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## test on Hoda (Persian digits)"
      ],
      "metadata": {
        "id": "BrKPY20qzOKi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -qq https://raw.githubusercontent.com/Alireza-Akhavan/deeplearning-tensorflow2-notebooks/master/dataset.py\n",
        "!wget -qq https://raw.githubusercontent.com/Alireza-Akhavan/deeplearning-tensorflow2-notebooks/master/dataset/Data_hoda_full.mat -P dataset"
      ],
      "metadata": {
        "id": "U-cqyS7NzQia"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dataset import load_hoda\n",
        "\n",
        "_, _, X_test, y_test = load_hoda(\n",
        "                                            training_sample_size=5500,\n",
        "                                            test_sample_size=400,size=28)\n",
        "X_test = X_test.reshape(-1,28,28,1).astype('float32')"
      ],
      "metadata": {
        "id": "nXWwDyPbzUCT"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit onnx_pred = m.run(['dense'], {\"input\": X_test})"
      ],
      "metadata": {
        "id": "_s36Nka8zblz",
        "outputId": "75151be6-d6e6-4dfd-97dd-228c5360e2f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.46 ms ± 18.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "tf2.5",
      "language": "python",
      "name": "tensorflow_25"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    },
    "nbpresent": {
      "slides": {
        "300ee14f-a043-486e-b274-7ff253907cd7": {
          "id": "300ee14f-a043-486e-b274-7ff253907cd7",
          "prev": "cb74e0bc-4513-4d13-b7f1-14c3078a7927",
          "regions": {
            "26dc3f39-a230-447c-af4c-f5e5b2fb7835": {
              "attrs": {
                "height": 0.8,
                "width": 0.8,
                "x": 0.1,
                "y": 0.1
              },
              "content": {
                "cell": "c58440a5-3f8f-4f37-9c79-6bf766209406",
                "part": "whole"
              },
              "id": "26dc3f39-a230-447c-af4c-f5e5b2fb7835"
            }
          }
        },
        "878aa53a-1444-4100-8f50-7a408191c579": {
          "id": "878aa53a-1444-4100-8f50-7a408191c579",
          "prev": null,
          "regions": {
            "a6c6843a-5ea6-4fbc-b890-3b4b8ae475b3": {
              "attrs": {
                "height": 0.8,
                "width": 0.8,
                "x": 0.1,
                "y": 0.1
              },
              "content": {
                "cell": "588ee1fa-64b5-453b-ade7-8e6b2515821c",
                "part": "whole"
              },
              "id": "a6c6843a-5ea6-4fbc-b890-3b4b8ae475b3"
            }
          }
        },
        "96ffe88e-7b50-43de-afdd-942e564f4e3e": {
          "id": "96ffe88e-7b50-43de-afdd-942e564f4e3e",
          "prev": "878aa53a-1444-4100-8f50-7a408191c579",
          "regions": {
            "b7e52e12-489a-468d-b10c-af2024fd2856": {
              "attrs": {
                "height": 0.8,
                "width": 0.8,
                "x": 0.1,
                "y": 0.1
              },
              "content": {
                "cell": "de829a92-1fb6-44ad-a2c6-fc1001e1f6e1",
                "part": "whole"
              },
              "id": "b7e52e12-489a-468d-b10c-af2024fd2856"
            }
          }
        },
        "cb74e0bc-4513-4d13-b7f1-14c3078a7927": {
          "id": "cb74e0bc-4513-4d13-b7f1-14c3078a7927",
          "prev": "96ffe88e-7b50-43de-afdd-942e564f4e3e",
          "regions": {
            "444878ee-68f3-4abb-acff-a7079b21e86d": {
              "attrs": {
                "height": 0.8,
                "width": 0.8,
                "x": 0.1,
                "y": 0.1
              },
              "content": {
                "cell": "25f3f538-1ee8-4d98-a6bb-14cbeb7a702d",
                "part": "whole"
              },
              "id": "444878ee-68f3-4abb-acff-a7079b21e86d"
            }
          }
        }
      },
      "themes": {}
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}