YOLOER_V1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HassanBinHaroon/YOLOER_V1/blob/master/YOLOER_V1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dywCfpbFwq8G",
        "outputId": "40a145be-c3a0-4558-adde-03980accbcc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'YOLOER_V1'...\n",
            "remote: Enumerating objects: 179, done.\u001b[K\n",
            "remote: Counting objects: 100% (35/35), done.\u001b[K\n",
            "remote: Compressing objects: 100% (34/34), done.\u001b[K\n",
            "remote: Total 179 (delta 13), reused 3 (delta 0), pack-reused 144\u001b[K\n",
            "Receiving objects: 100% (179/179), 58.81 MiB | 41.13 MiB/s, done.\n",
            "Resolving deltas: 100% (32/32), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/HassanBinHaroon/YOLOER_V1.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTTDsOkXJ5DH",
        "outputId": "1beb5e7d-b5ed-4888-92af-7289dcfa599a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/YOLOER_V1/object-detector_PLUS_distance-estimator\n"
          ]
        }
      ],
      "source": [
        "%cd YOLOER_V1/object-detector_PLUS_distance-estimator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsQ-euUnKO9_",
        "outputId": "94436ce7-2157-444b-b32f-b5eeb893d0a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading QtPy-2.2.0-py3-none-any.whl (82 kB)\n",
            "\u001b[K     |████████████████████████████████| 82 kB 605 kB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from qtpy>=2.0.1->qtconsole->jupyter->hyperas->-r requirements.txt (line 46)) (21.3)\n",
            "Building wheels for collected packages: tree, asposebarcode, asposestorage\n",
            "  Building wheel for tree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tree: filename=Tree-0.2.4-py3-none-any.whl size=7873 sha256=23528d32cb7cf897a8eed6cdc71efcbeeff73bead3adc7fab95eac5a627234c1\n",
            "  Stored in directory: /root/.cache/pip/wheels/64/46/53/3a413f321c09b0df8d4a26edd5fc3501c6c1b238497d166638\n",
            "  Building wheel for asposebarcode (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for asposebarcode: filename=asposebarcode-1.0.0-py3-none-any.whl size=5479 sha256=f8536b25efb748a0a5628581e69d8b13b714270411484ab2ad07be860b1d972d\n",
            "  Stored in directory: /root/.cache/pip/wheels/37/30/a8/1f8bec5722b2339860160e18c3f63b5f4b7da257037f054c17\n",
            "  Building wheel for asposestorage (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for asposestorage: filename=asposestorage-1.0.2-py3-none-any.whl size=9031 sha256=649c48ada9511740b81b5ea5482893d2bdaf59f7ca3634b52d34184718f23553\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/75/b6/1129a451c6dbc329dc890867e5005d03756cac7485029176e9\n",
            "Successfully built tree asposebarcode asposestorage\n",
            "Installing collected packages: jedi, qtpy, qtconsole, svgwrite, jupyter, asposestorage, xlsxwriter, tree, thop, hyperas, asposebarcode\n",
            "Successfully installed asposebarcode-1.0.0 asposestorage-1.0.2 hyperas-0.4.1 jedi-0.18.1 jupyter-1.0.0 qtconsole-5.3.2 qtpy-2.2.0 svgwrite-1.4.3 thop-0.1.1.post2207130030 tree-0.2.4 xlsxwriter-3.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6h_Dft2rKTD_",
        "outputId": "1ba072e1-55ca-41a9-8051-d92dda83a648"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking setup...\n",
            "YOLOv5 🚀 2022-8-29 Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 37.5/78.2 GB disk)\n"
          ]
        }
      ],
      "source": [
        "!python setup.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TQ8OSYSO0H5",
        "outputId": "fb13eac4-4d81-47a6-f8eb-b214183e6a23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=/content/YOLOER_V1/videos/car.jpg, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.4, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=True, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, model_dist=model@1535470106.json, weights_dist=model@1535470106.h5\n",
            "YOLOv5 🚀 2022-8-29 Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
            "here 1st\n",
            "here 2nd\n",
            "[0, 199, 301, 332, 361, 257.45625, 117.578125, 429.525, 141.015625]\n",
            "2022-08-29 07:28:12.263067: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "Loaded model from disk\n",
            "[[     7.2416]]\n",
            "here 3rd\n",
            "here 2nd\n",
            "[0, 433, 357, 920, 557, 560.19375, 139.453125, 1190.25, 217.578125]\n",
            "Loaded model from disk\n",
            "[[     29.917]\n",
            " [      8.587]]\n",
            "here 3rd\n",
            "here 2nd\n",
            "[0, 1, 318, 41, 367, 1.29375, 124.21875, 53.04375, 143.359375]\n",
            "Loaded model from disk\n",
            "[[     23.766]\n",
            " [     4.4339]\n",
            " [     16.792]]\n",
            "here 3rd\n",
            "here 2nd\n",
            "[0, 630, 344, 948, 486, 815.0625, 134.375, 1226.4750000000001, 189.84375]\n",
            "Loaded model from disk\n",
            "[[     26.555]\n",
            " [     6.6995]\n",
            " [     23.171]\n",
            " [     7.7815]]\n",
            "here 3rd\n",
            "here 2nd\n",
            "[0, 68, 381, 801, 669, 87.975, 148.828125, 1036.29375, 261.328125]\n",
            "Loaded model from disk\n",
            "5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f70c0051f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "[[     20.233]\n",
            " [     9.7043]\n",
            " [     26.914]\n",
            " [     11.473]\n",
            " [     8.0093]]\n",
            "here 3rd\n",
            "here 2nd\n",
            "[0, 119, 308, 220, 361, 153.95625, 120.31250000000001, 284.625, 141.015625]\n",
            "Loaded model from disk\n",
            "6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f70c00368c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "[[     17.053]\n",
            " [     7.2476]\n",
            " [     20.154]\n",
            " [     7.6246]\n",
            " [     5.5863]\n",
            " [     18.568]]\n",
            "here 3rd\n",
            "here 2nd\n",
            "[0, 34, 312, 140, 367, 43.9875, 121.875, 181.125, 143.359375]\n",
            "Loaded model from disk\n",
            "[[     14.265]\n",
            " [     5.7178]\n",
            " [     27.913]\n",
            " [     6.0485]\n",
            " [     4.0251]\n",
            " [     15.779]\n",
            " [     28.094]]\n",
            "here 3rd\n",
            "image 1/1 /content/YOLOER_V1/videos/car.jpg: 640x640 7 cars, Done. (0.013s)\n",
            "Speed: 0.6ms pre-process, 13.4ms inference, 23.0ms NMS per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n",
            "1 labels saved to runs/detect/exp/labels\n"
          ]
        }
      ],
      "source": [
        "!python detect.py --save-txt --weights yolov5s.pt --conf 0.4 --source /content/YOLOER_V1/videos/car.jpg --model_dist model@1535470106.json --weights_dist model@1535470106.h5"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "YOLOER_V1.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}