Volo_MAFood121.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PranavKathar/FoodVision/blob/main/Volo_MAFood121.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XB0hKswVeRQS"
      },
      "source": [
        "# VOLO: Vision Outlooker for Visual Recognition\n",
        "\n",
        "This is a demo example to run volo for image classification, from \n",
        "[VOLO: Vision Outlooker for Visual Recognition](https://arxiv.org/abs/2106.13112). \n",
        "\n",
        "More detail can be found in [this repo](https://github.com/sail-sg/volo).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "KL_SaUq41uah"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCQ8zSzVePfm",
        "outputId": "aff94c52-e25d-4178-b800-0d96019e358a"
      },
      "source": [
        "!pip install timm tlt\n",
        "!git clone https://github.com/sail-sg/volo.git\n",
        "!wget https://github.com/sail-sg/volo/releases/download/volo_1/d1_224_84.2.pth.tar\n",
        "# !wget https://raw.githubusercontent.com/shicai/MobileNet-Caffe/master/cat.jpg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting timm\n",
            "  Downloading timm-0.6.11-py3-none-any.whl (548 kB)\n",
            "\u001b[K     |████████████████████████████████| 548 kB 7.0 MB/s \n",
            "\u001b[?25hCollecting tlt\n",
            "  Downloading tlt-0.2.0-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.7/dist-packages (from timm) (1.12.1+cu113)\n",
            "Collecting huggingface-hub\n",
            "  Downloading huggingface_hub-0.10.1-py3-none-any.whl (163 kB)\n",
            "\u001b[K     |████████████████████████████████| 163 kB 55.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from timm) (0.13.1+cu113)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from timm) (6.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7->timm) (4.1.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from tlt) (1.7.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (4.13.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (4.64.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (3.8.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub->timm) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub->timm) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub->timm) (3.10.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub->timm) (2.10)\n",
            "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /usr/local/lib/python3.7/dist-packages (from scipy->tlt) (1.21.6)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (7.1.2)\n",
            "Installing collected packages: huggingface-hub, timm, tlt\n",
            "Successfully installed huggingface-hub-0.10.1 timm-0.6.11 tlt-0.2.0\n",
            "Cloning into 'volo'...\n",
            "remote: Enumerating objects: 80, done.\u001b[K\n",
            "remote: Counting objects: 100% (42/42), done.\u001b[K\n",
            "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
            "remote: Total 80 (delta 36), reused 25 (delta 25), pack-reused 38\u001b[K\n",
            "Unpacking objects: 100% (80/80), done.\n",
            "--2022-11-09 02:39:59--  https://github.com/sail-sg/volo/releases/download/volo_1/d1_224_84.2.pth.tar\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
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            "2022-11-09 02:40:03 (29.3 MB/s) - ‘d1_224_84.2.pth.tar’ saved [106630739/106630739]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBpJmqq9m0tw",
        "outputId": "d11a6e22-b243-461b-d0f5-52aa2a3458bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "eqoE2NSC5B4B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bca1d84-2d71-43a8-8b25-50561d3fccfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from tqdm.notebook import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas import read_excel\n",
        "from torchvision.datasets import VisionDataset\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "plt.style.use('ggplot')"
      ],
      "metadata": {
        "id": "9MgAOA-s1wo0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available else \"cpu\")\n",
        "print(f\"Using device: {device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzPgEIX14cHA",
        "outputId": "cfe7fc2d-96f4-4b1c-f94f-4423b1d26982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bifUxGxchSF0"
      },
      "source": [
        "# Model Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUZjO4SSgHqE",
        "outputId": "10ae603a-d9e5-4244-b1a9-9bfad249a95d"
      },
      "source": [
        "%cd volo"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/volo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLo3HDKThm_q"
      },
      "source": [
        "import models\n",
        "from PIL import Image\n",
        "from tlt.utils import load_pretrained_weights\n",
        "from timm.data import create_transform\n",
        "model = models.volo_d1(img_size=224)\n",
        "load_pretrained_weights(model=model, checkpoint_path='../d1_224_84.2.pth.tar')\n",
        "# model.eval()\n",
        "transform = create_transform(input_size=224, crop_pct=model.default_cfg['crop_pct'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXcLTUm92V7M",
        "outputId": "ff66e091-f9a7-4494-e557-93a49bb22b29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Redefining last layers\n",
        "model.aux_head = nn.Linear(384, 121)\n",
        "model.head = nn.Linear(384,121)"
      ],
      "metadata": {
        "id": "_oHkgcJKb6gB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Freezing \n",
        "for c in list(model.children())[:-3]:\n",
        "  for param in c.parameters():\n",
        "    param.requires_grad = False"
      ],
      "metadata": {
        "id": "-csyLCClcRKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(list(model.post_network.children())[0].attn.kv.weight.requires_grad)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yc7TPBD9grL_",
        "outputId": "11400ca8-fc00-4e68-9519-27d957c61397"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.aux_head.weight.requires_grad)\n",
        "print(model.head.weight.requires_grad)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zm6RM4lggZk",
        "outputId": "e077a8a1-1f4c-4ba2-c7b4-fe76ceed20b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(device)"
      ],
      "metadata": {
        "id": "k-mVPlo0L67U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ResNet"
      ],
      "metadata": {
        "id": "SVXgNlee_mDl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models import resnet50\n",
        "model = resnet50(pretrained=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--uIgSlc_ntA",
        "outputId": "5cef43b0-76e6-418a-ad60-a23cee6f0c81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
            "  f\"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, \"\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fc = nn.Linear(2048, 121)"
      ],
      "metadata": {
        "id": "QTDOVwga_88G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "id": "3DusFt-q_yt_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Freezing \n",
        "# for c in list(model.children())[:-1]:\n",
        "#   for param in c.parameters():\n",
        "#     param.requires_grad = False"
      ],
      "metadata": {
        "id": "6jpButuMDbbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for m in model.modules():\n",
        "    if isinstance(m, nn.Conv2d):\n",
        "        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n",
        "    elif isinstance(m, nn.BatchNorm2d):\n",
        "        nn.init.constant_(m.weight, 1)\n",
        "        nn.init.constant_(m.bias, 0)"
      ],
      "metadata": {
        "id": "B92S2cw2Iz5a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.to(device)"
      ],
      "metadata": {
        "id": "xBjGiO_ZAGbt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wd6GHNd4nkFV"
      },
      "source": [
        "# Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1lr3b7cPl_yoBK1N8thDJMYpPNCxDZFPD\n",
        "!unzip MAFood121.zip"
      ],
      "metadata": {
        "id": "HzME9re6lTkl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !gdown --id 1mHD58j0UyiqSjHcJ2GZf50k0-xX2v1wkgEVxIQ8iuJw"
      ],
      "metadata": {
        "id": "cRA42EBL2oyz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iXWURzJvlVoo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.datasets import VisionDataset\n",
        "class MAFood121(VisionDataset):\n",
        "    def __init__(\n",
        "        self,\n",
        "        root: str,\n",
        "        labels_file: str,\n",
        "        split = 'train',\n",
        "        transform = None,\n",
        "        target_transform = None,\n",
        "    ):\n",
        "        super().__init__(\n",
        "            root,\n",
        "            transform=transform,\n",
        "            target_transform=target_transform\n",
        "        )\n",
        "        self.metadata = labels_file\n",
        "        df = read_excel(self.metadata, sheet_name = f\"{split}_combined\")\n",
        "        self.labels = df[[\"imagepath\", \"dish\"]]\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "      path, dish_label = self.labels.iloc[index]\n",
        "      image_path = os.path.join(self.root, path)\n",
        "      with open(image_path, \"rb\") as f:\n",
        "        img = Image.open(f)\n",
        "        image = img.convert(\"RGB\")\n",
        "\n",
        "      if self.transform is not None:\n",
        "        image = self.transform(image)\n",
        "      \n",
        "      return image, torch.tensor(dish_label)\n",
        "\n",
        "    def __len__(self):\n",
        "      return self.labels.__len__()"
      ],
      "metadata": {
        "id": "nnA9isnau-We"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# img_transform = create_transform(input_size=224, crop_pct=model.default_cfg['crop_pct'])"
      ],
      "metadata": {
        "id": "qvkYHwv3z3-n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize\n",
        "img_transform = Compose([\n",
        "    Resize(size=233),\n",
        "    CenterCrop(size=(224, 224)),\n",
        "    ToTensor(),\n",
        "    Normalize(mean=[0.4850, 0.4560, 0.4060], std=[0.2290, 0.2240, 0.2250])\n",
        "])"
      ],
      "metadata": {
        "id": "UsfSdTxWCa6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = MAFood121(\"/content/MAFood121/images\", \"/content/MAFood121 Dataset csv (2).xlsx\", transform = img_transform)\n",
        "val_data = MAFood121(\"/content/MAFood121/images\", \"/content/MAFood121 Dataset csv (2).xlsx\", split = \"val\", transform = img_transform)\n",
        "test_data = MAFood121(\"/content/MAFood121/images\", \"/content/MAFood121 Dataset csv (2).xlsx\", split = \"test\", transform = img_transform)"
      ],
      "metadata": {
        "id": "O0_dfgKGy3aB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data"
      ],
      "metadata": {
        "id": "6JmO3gowzId8",
        "outputId": "ecc473d8-6c52-4dd1-da74-d9297b04debb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset MAFood121\n",
              "    Number of datapoints: 15298\n",
              "    Root location: /content/MAFood121/images\n",
              "    StandardTransform\n",
              "Transform: Compose(\n",
              "               Resize(size=233, interpolation=bilinear, max_size=None, antialias=None)\n",
              "               CenterCrop(size=(224, 224))\n",
              "               ToTensor()\n",
              "               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
              "           )"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSP1DUbzdPGH",
        "outputId": "585f4740-cb14-4ec6-bb86-352d7c2c743e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset MAFood121\n",
              "    Number of datapoints: 2700\n",
              "    Root location: /content/MAFood121/images\n",
              "    StandardTransform\n",
              "Transform: Compose(\n",
              "               Resize(size=233, interpolation=bilinear, max_size=None, antialias=None)\n",
              "               CenterCrop(size=(224, 224))\n",
              "               ToTensor()\n",
              "               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
              "           )"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data"
      ],
      "metadata": {
        "id": "TLtd-8aLzNS_",
        "outputId": "b4ba9f91-1915-4dec-b505-7f62cd17768d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset MAFood121\n",
              "    Number of datapoints: 3177\n",
              "    Root location: /content/MAFood121/images\n",
              "    StandardTransform\n",
              "Transform: Compose(\n",
              "               Resize(size=233, interpolation=bilinear, max_size=None, antialias=None)\n",
              "               CenterCrop(size=(224, 224))\n",
              "               ToTensor()\n",
              "               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
              "           )"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[0][0].shape"
      ],
      "metadata": {
        "id": "RizsMo0RzV3m",
        "outputId": "1bb999f8-e692-4c94-f43e-189faf45470b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 224, 224])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Temp Data"
      ],
      "metadata": {
        "id": "GzRmExr5FnQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.datasets import ImageFolder"
      ],
      "metadata": {
        "id": "ai41orH0FmvG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = ImageFolder(\"/content/MAFood121/images\" , transform=img_transform)"
      ],
      "metadata": {
        "id": "AnbFLENAFsBp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "indices = torch.randint(0, 15000, (20,))\n",
        "train_data = torch.utils.data.Subset(dataset, indices)"
      ],
      "metadata": {
        "id": "sGsG1qgeHpeP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PBBY59OGA_1",
        "outputId": "cbd10815-9c6f-46ff-bbf6-9e0f2dd47508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "P3l3W3Fw3j1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = optim.Adam(model.parameters(), 0.001)\n",
        "criterion = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "EBcMF60Czhuv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9I_xGAE0dLUl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Epochs = 53\n",
        "BatchSize = 200"
      ],
      "metadata": {
        "id": "B1oQn4Xp1BC0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = DataLoader(train_data, BatchSize, shuffle = True, num_workers=2)\n",
        "val_loader = DataLoader(val_data, BatchSize, shuffle=True, num_workers=2)\n",
        "test_loader = DataLoader(test_data, BatchSize, num_workers=2)"
      ],
      "metadata": {
        "id": "i48q923j0oBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_one_epoch(model, loader, criterion, optimizer):\n",
        "  model.train()\n",
        "  epoch_loss=0\n",
        "  correct = 0\n",
        "  for step, (images, labels) in tqdm(enumerate(loader), desc=\"Training\", leave=False):\n",
        "      torch.cuda.empty_cache()\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      images = images.to(device)\n",
        "      labels = labels.long().to(device)\n",
        "      # output = model(images)\n",
        "      x_cls, x_aux,_ = model(images)\n",
        "      output = x_cls + 0.5 * x_aux.max(1)[0]\n",
        "      loss = criterion(output, labels)\n",
        "      epoch_loss += loss.item()\n",
        "\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      correct += torch.argmax(output, 1).eq(labels).sum().item()\n",
        "      # print(f\"Loss: {loss.item():.3f} Accuracy: {correct / labels.size(0) * 100}\") \n",
        "\n",
        "  return epoch_loss/len(loader), correct / len(loader.dataset) * 100"
      ],
      "metadata": {
        "id": "fNKpGTv6VzkM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad()\n",
        "def validate(model, loader, criterion):\n",
        "  model.eval()\n",
        "  total_loss = 0\n",
        "  total_correct = 0\n",
        "  for batch in tqdm(loader, desc=\"Validation\"):\n",
        "    image, target = batch\n",
        "    image, target = image.to(device), target.long().to(device)\n",
        "    output = model(image)\n",
        "    loss = criterion(output, target).item()\n",
        "    total_loss += loss\n",
        "    correct = torch.argmax(output, 1).eq(target).sum().item()\n",
        "    total_correct += correct\n",
        "  accuracy = total_correct / len(loader.dataset) * 100\n",
        "  return total_loss / len(loader), accuracy"
      ],
      "metadata": {
        "id": "yXHPPhJvXE20"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.inference_mode()\n",
        "def evaluate(model, loader):\n",
        "  model.eval()\n",
        "  total_correct = 0\n",
        "  for batch in tqdm(loader, \"Evaluation\"):\n",
        "    image, target = batch\n",
        "    image, target = image.to(device), target.to(device)\n",
        "    output = model(image)\n",
        "    correct = torch.argmax(output, 1).eq(target).sum().item()\n",
        "    total_correct += correct\n",
        "  accuracy = total_correct / len(loader.dataset) * 100\n",
        "  return accuracy"
      ],
      "metadata": {
        "id": "h9EhxgWrYpNB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading saved model\n",
        "model.load_state_dict(torch.load(\"/content/drive/MyDrive/VoloCheckpoints/volo_mafood_40epochs_80testacc.pth\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MU-HLiQ6GtId",
        "outputId": "0c95a261-865c-43ff-b92e-8c27960b3fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_acc = evaluate(model, test_loader)\n",
        "print(test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e04035260437402c932f758fecb3a4eb",
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            "f57b061663244aadb1c14996ac0c1fcf"
          ]
        },
        "id": "p_KW8XUhG7uM",
        "outputId": "8b841194-25ec-41a2-fd33-8f959261558f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluation:   0%|          | 0/16 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e04035260437402c932f758fecb3a4eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "80.26440037771482\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_losses, train_accs, val_losses, val_accs = [], [], [], []\n",
        "\n",
        "for epoch in tqdm(range(41, Epochs)):\n",
        "  train_loss, train_acc = train_one_epoch(model, train_loader, criterion, optimizer)\n",
        "  train_losses.append(train_loss)\n",
        "  train_accs.append(train_acc)\n",
        "  val_loss, val_acc = validate(model, val_loader, criterion)\n",
        "  val_losses.append(val_loss)\n",
        "  val_accs.append(val_acc)\n",
        "  \n",
        "  if epoch % 10 == 0:\n",
        "    test_accuracy = evaluate(model, test_loader)\n",
        "    print(f\"Epoch [{epoch:3.0f}]: Train Accuracy - {train_acc:.3f}, Val Accuracy - {val_acc:.3f}, Test Accuracy - {test_accuracy:.3f}\")\n",
        "    path = f\"/content/drive/MyDrive/VoloCheckpoints/volo_mafood_{epoch}epochs_{int(test_accuracy)}testacc.pth\"\n",
        "    torch.save(model.state_dict(), path)\n"
      ],
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          "height": 482,
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            "Epoch [ 50]: Train Accuracy - 93.019, Val Accuracy - 79.963, Test Accuracy - 80.422\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
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        "id": "JvD13lgbQKyQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "plt.figure()\n",
        "plt.plot(train_losses, label=\"Train Loss\")\n",
        "plt.plot(val_losses, label=\"Val Loss\")\n",
        "plt.legend()\n",
        "plt.savefig(\"/content/drive/MyDrive/VoloCheckpoints/Loss Curves\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "4MsLqmykAl7g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "4c87f180-2b33-4155-ddb9-d7022093e61f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.plot(train_accs, label=\"Train Acc\")\n",
        "plt.plot(val_accs, label=\"Val Acc\")\n",
        "plt.legend()\n",
        "plt.savefig(\"/content/drive/MyDrive/VoloCheckpoints/Acc Curves\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bGf1vfyv9LbH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "5d023f21-a8c0-4752-ae67-77b7f6e6d721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), \"/content/drive/MyDrive/VoloCheckpoints/volo_mafood_final.pth\")"
      ],
      "metadata": {
        "id": "HcfkGhoxi5pk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VGpegFMni9TD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}