05182022_FasterRCNN.ipynb

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  "metadata": {
    "colab": {
      "name": "05182022_FasterRCNN.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNoLKQ0jE4cJdzMagb2ErAM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TomasMendozaHN/ComputerVision_Networks/blob/master/05182022_FasterRCNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xmltodict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHNYX8bqfkAb",
        "outputId": "797176b5-e247-4908-ee30-7f0b09001cbb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting xmltodict\n",
            "  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
            "Installing collected packages: xmltodict\n",
            "Successfully installed xmltodict-0.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq):\n",
        "    model.train()\n",
        "    metric_logger = utils.MetricLogger(delimiter=\"  \")\n",
        "    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))\n",
        "    header = 'Epoch: [{}]'.format(epoch)\n",
        "\n",
        "    lr_scheduler = None\n",
        "    if epoch == 0:\n",
        "        warmup_factor = 1. / 1000\n",
        "        warmup_iters = min(1000, len(data_loader) - 1)\n",
        "\n",
        "        lr_scheduler = utils.warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor)\n",
        "\n",
        "    for images, targets in metric_logger.log_every(data_loader, print_freq, header):\n",
        "        images = list(image.to(device) for image in images)\n",
        "        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n",
        "\n",
        "        loss_dict = model(images, targets)\n",
        "        losses = sum(loss for loss in loss_dict.values())\n",
        "        \n",
        "        # reduce losses over all GPUs for logging purposes\n",
        "        loss_dict_reduced = utils.reduce_dict(loss_dict)\n",
        "        losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n",
        "\n",
        "        loss_value = losses_reduced.item()   \n",
        "        \n",
        "        if not math.isfinite(loss_value):\n",
        "            print(\"Loss is {}, stopping training\".format(loss_value))\n",
        "            sys.exit(1)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        losses.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if lr_scheduler is not None:\n",
        "            lr_scheduler.step()\n",
        "\n",
        "        metric_logger.update(loss=losses_reduced, **loss_dict_reduced)\n",
        "        metric_logger.update(lr=optimizer.param_groups[0][\"lr\"])"
      ],
      "metadata": {
        "id": "V0MvhMQ_hHVl"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_3NikV0XhJvt"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "bM7SLcM4fOW_",
        "outputId": "b4323b59-9e68-4dfc-e031-76584b29551a"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-82bbf65e9802>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_one_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'engine'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "#Libraries\n",
        "from scipy.io import loadmat\n",
        "import numpy as np\n",
        "import torchvision\n",
        "import os\n",
        "import xmltodict\n",
        "from sklearn.utils import shuffle\n",
        "\n",
        "from scipy.io import loadmat\n",
        "import numpy as np\n",
        "import torchvision\n",
        "import os\n",
        "from engine import train_one_epoch, evaluate\n",
        "import utils\n",
        "import torchvision\n",
        "import transforms as T\n",
        "import torch\n",
        "\n",
        "import utils\n",
        "from PIL import Image\n",
        "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
        "\n",
        "#Check if you will use GPU or CPU for training.\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "device = torch.device(\"cuda:0\")\n",
        "print('You will train using your ', device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "rni4siZafeTk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yYYci2GPfi3G"
      },
      "execution_count": null,
      "outputs": []
    }
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}