model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install torch torchvision matplotlib grad-cam\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6gvhL69tDEE",
        "outputId": "8f1d5db3-8c9a-4a39-f75b-c2804f009876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f'Using device: {device}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7i7K7AYit4Og",
        "outputId": "8e1779ad-5e68-4587-a414-3a3e303fd2f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "from torchvision import datasets, models, transforms\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "from google.colab import drive\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "# Set device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f'Using device: {device}')\n",
        "\n",
        "# Data preparation\n",
        "data_dir = '/content/drive/My Drive/final'\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'test': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}\n",
        "\n",
        "# Custom Dataset class for test data without subfolders\n",
        "class TestDataset(Dataset):\n",
        "    def __init__(self, test_dir, transform=None):\n",
        "        self.test_dir = test_dir\n",
        "        self.transform = transform\n",
        "        self.image_paths = [os.path.join(test_dir, img) for img in os.listdir(test_dir)]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_paths)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = self.image_paths[idx]\n",
        "        image = Image.open(img_path).convert('RGB')\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "        return image, img_path  # No label\n",
        "\n",
        "image_datasets = {\n",
        "    'train': datasets.ImageFolder(os.path.join(data_dir, 'train'), data_transforms['train']),\n",
        "    'val': datasets.ImageFolder(os.path.join(data_dir, 'validation'), data_transforms['val']),\n",
        "    'test': TestDataset(os.path.join(data_dir, 'test'), data_transforms['test'])\n",
        "}\n",
        "\n",
        "dataloaders = {\n",
        "    'train': DataLoader(image_datasets['train'], batch_size=32, shuffle=True, num_workers=0),\n",
        "    'val': DataLoader(image_datasets['val'], batch_size=32, shuffle=False, num_workers=0),\n",
        "    'test': DataLoader(image_datasets['test'], batch_size=32, shuffle=False, num_workers=0)\n",
        "}\n",
        "\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}\n",
        "class_names = image_datasets['train'].classes\n",
        "\n",
        "print(f'Classes: {class_names}')\n",
        "\n",
        "# Define the CNN Model\n",
        "model_ft = models.resnet18(pretrained=True)\n",
        "num_ftrs = model_ft.fc.in_features\n",
        "model_ft.fc = nn.Linear(num_ftrs, len(class_names))\n",
        "\n",
        "model_ft = model_ft.to(device)\n",
        "\n",
        "# Define the Loss Function and Optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n",
        "\n",
        "# Training Function with Debugging Outputs\n",
        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'Epoch {epoch}/{num_epochs - 1}')\n",
        "        print('-' * 10)\n",
        "\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()\n",
        "            else:\n",
        "                model.eval()\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            phase_start_time = time.time()\n",
        "\n",
        "            for i, (inputs, labels) in enumerate(dataloaders[phase]):\n",
        "                batch_start_time = time.time()\n",
        "\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "                batch_end_time = time.time()\n",
        "                print(f'Batch {i+1}/{len(dataloaders[phase])} - Time: {batch_end_time - batch_start_time:.4f}s')\n",
        "\n",
        "            if phase == 'train':\n",
        "                scheduler.step()\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "\n",
        "            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
        "            print(f'{phase} Phase Time: {time.time() - phase_start_time:.4f}s')\n",
        "\n",
        "            if phase == 'val' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
        "    print(f'Best val Acc: {best_acc:.4f}')\n",
        "\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model\n",
        "\n",
        "# Train the model\n",
        "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)\n",
        "\n",
        "# Save the model\n",
        "torch.save(model_ft.state_dict(), '/content/drive/My Drive/dance_model.pth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Fnv1H7-zoxj",
        "outputId": "065d62a1-303f-4676-c499-3824d1681c7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "train Loss: 1.1483 Acc: 0.6258\n",
            "train Phase Time: 2086.6562s\n",
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            "val Loss: 0.3320 Acc: 0.9231\n",
            "val Phase Time: 218.2677s\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
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            "train Loss: 0.1646 Acc: 0.9507\n",
            "train Phase Time: 58.8442s\n",
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            "val Loss: 0.0077 Acc: 1.0000\n",
            "val Phase Time: 3.3675s\n",
            "\n",
            "Training complete in 63m 16s\n",
            "Best val Acc: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RsJl4UEDOpVi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7ROyLfNO6ye",
        "outputId": "4d3c4402-c2e4-4eb2-b942-0f37219d6afb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = '/content/drive/MyDrive/dance_model.pth'"
      ],
      "metadata": {
        "id": "6eCd1Q0PPCj0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import datasets, transforms, models\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from google.colab import drive\n",
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "# Set device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f'Using device: {device}')\n",
        "\n",
        "# Data preparation\n",
        "data_transforms = transforms.Compose([\n",
        "    transforms.Resize(256),\n",
        "    transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Custom Dataset class for test data without subfolders\n",
        "class TestDataset(Dataset):\n",
        "    def __init__(self, test_dir, transform=None):\n",
        "        self.test_dir = test_dir\n",
        "        self.transform = transform\n",
        "        self.image_paths = [os.path.join(test_dir, img) for img in os.listdir(test_dir)]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_paths)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = self.image_paths[idx]\n",
        "        image = Image.open(img_path).convert('RGB')\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "        return image\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Prepare test dataset and dataloader\n",
        "test_dir = '/content/drive/My Drive/final/test'\n",
        "test_dataset = TestDataset(test_dir, transform=data_transforms)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=0)\n",
        "\n",
        "# Load the saved model\n",
        "class_names = ['bharatanatyam', 'kathak', 'kathakali', 'kuchipudi', 'manipuri', 'mohiniyattam', 'odissi', 'sattriya']\n",
        "\n",
        "model_ft = models.resnet18(pretrained=False)\n",
        "num_ftrs = model_ft.fc.in_features\n",
        "model_ft.fc = nn.Linear(num_ftrs, len(class_names))\n",
        "\n",
        "model_ft.load_state_dict(torch.load('/content/drive/My Drive/dance_model.pth'))\n",
        "model_ft = model_ft.to(device)\n",
        "model_ft.eval()\n",
        "\n",
        "# Define the testing function\n",
        "def evaluate_model(model, dataloader, class_names):\n",
        "    model.eval()\n",
        "    predictions = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs in dataloader:\n",
        "            inputs = inputs.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            predictions.extend(predicted.cpu().numpy())\n",
        "\n",
        "    # Print predictions (or you could save them to a file)\n",
        "    for idx, pred in enumerate(predictions):\n",
        "        print(f'Image {idx + 1}: Predicted class = {class_names[pred]}')\n",
        "\n",
        "# Evaluate the model\n",
        "evaluate_model(model_ft, test_loader, class_names)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0Cany8zPxXs",
        "outputId": "291c29b5-2a98-4e2f-ff9f-41f692db31cc"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda:0\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Image 1: Predicted class = bharatanatyam\n",
            "Image 2: Predicted class = bharatanatyam\n",
            "Image 3: Predicted class = kathakali\n",
            "Image 4: Predicted class = kathakali\n",
            "Image 5: Predicted class = bharatanatyam\n",
            "Image 6: Predicted class = kathakali\n",
            "Image 7: Predicted class = kathakali\n",
            "Image 8: Predicted class = bharatanatyam\n",
            "Image 9: Predicted class = manipuri\n",
            "Image 10: Predicted class = kathak\n",
            "Image 11: Predicted class = kathakali\n",
            "Image 12: Predicted class = kathakali\n",
            "Image 13: Predicted class = kuchipudi\n",
            "Image 14: Predicted class = kathakali\n",
            "Image 15: Predicted class = kathak\n",
            "Image 16: Predicted class = bharatanatyam\n",
            "Image 17: Predicted class = kathak\n",
            "Image 18: Predicted class = kathakali\n",
            "Image 19: Predicted class = kuchipudi\n",
            "Image 20: Predicted class = kathakali\n",
            "Image 21: Predicted class = manipuri\n",
            "Image 22: Predicted class = kathakali\n",
            "Image 23: Predicted class = bharatanatyam\n",
            "Image 24: Predicted class = bharatanatyam\n",
            "Image 25: Predicted class = kathak\n",
            "Image 26: Predicted class = kathakali\n",
            "Image 27: Predicted class = kathakali\n",
            "Image 28: Predicted class = kathakali\n",
            "Image 29: Predicted class = bharatanatyam\n",
            "Image 30: Predicted class = kathak\n",
            "Image 31: Predicted class = kathakali\n",
            "Image 32: Predicted class = bharatanatyam\n",
            "Image 33: Predicted class = bharatanatyam\n",
            "Image 34: Predicted class = odissi\n",
            "Image 35: Predicted class = bharatanatyam\n",
            "Image 36: Predicted class = kathakali\n",
            "Image 37: Predicted class = kuchipudi\n",
            "Image 38: Predicted class = bharatanatyam\n",
            "Image 39: Predicted class = kathakali\n",
            "Image 40: Predicted class = kathakali\n",
            "Image 41: Predicted class = bharatanatyam\n",
            "Image 42: Predicted class = kathakali\n",
            "Image 43: Predicted class = kathak\n",
            "Image 44: Predicted class = kuchipudi\n",
            "Image 45: Predicted class = mohiniyattam\n",
            "Image 46: Predicted class = kathakali\n",
            "Image 47: Predicted class = mohiniyattam\n",
            "Image 48: Predicted class = sattriya\n",
            "Image 49: Predicted class = kuchipudi\n",
            "Image 50: Predicted class = mohiniyattam\n",
            "Image 51: Predicted class = mohiniyattam\n",
            "Image 52: Predicted class = sattriya\n",
            "Image 53: Predicted class = odissi\n",
            "Image 54: Predicted class = mohiniyattam\n",
            "Image 55: Predicted class = kuchipudi\n",
            "Image 56: Predicted class = kathak\n",
            "Image 57: Predicted class = mohiniyattam\n",
            "Image 58: Predicted class = mohiniyattam\n",
            "Image 59: Predicted class = mohiniyattam\n",
            "Image 60: Predicted class = sattriya\n",
            "Image 61: Predicted class = sattriya\n",
            "Image 62: Predicted class = kuchipudi\n",
            "Image 63: Predicted class = sattriya\n",
            "Image 64: Predicted class = mohiniyattam\n",
            "Image 65: Predicted class = sattriya\n",
            "Image 66: Predicted class = kathak\n",
            "Image 67: Predicted class = mohiniyattam\n",
            "Image 68: Predicted class = sattriya\n",
            "Image 69: Predicted class = mohiniyattam\n",
            "Image 70: Predicted class = kuchipudi\n",
            "Image 71: Predicted class = kathak\n",
            "Image 72: Predicted class = sattriya\n",
            "Image 73: Predicted class = manipuri\n",
            "Image 74: Predicted class = kuchipudi\n",
            "Image 75: Predicted class = bharatanatyam\n",
            "Image 76: Predicted class = kathak\n",
            "Image 77: Predicted class = kuchipudi\n",
            "Image 78: Predicted class = mohiniyattam\n",
            "Image 79: Predicted class = mohiniyattam\n",
            "Image 80: Predicted class = kathak\n",
            "Image 81: Predicted class = mohiniyattam\n",
            "Image 82: Predicted class = kuchipudi\n",
            "Image 83: Predicted class = kathak\n",
            "Image 84: Predicted class = mohiniyattam\n",
            "Image 85: Predicted class = sattriya\n",
            "Image 86: Predicted class = kathak\n",
            "Image 87: Predicted class = mohiniyattam\n",
            "Image 88: Predicted class = mohiniyattam\n",
            "Image 89: Predicted class = sattriya\n",
            "Image 90: Predicted class = mohiniyattam\n",
            "Image 91: Predicted class = bharatanatyam\n",
            "Image 92: Predicted class = mohiniyattam\n",
            "Image 93: Predicted class = mohiniyattam\n",
            "Image 94: Predicted class = odissi\n",
            "Image 95: Predicted class = kathak\n",
            "Image 96: Predicted class = sattriya\n",
            "Image 97: Predicted class = sattriya\n",
            "Image 98: Predicted class = kuchipudi\n",
            "Image 99: Predicted class = kuchipudi\n",
            "Image 100: Predicted class = kuchipudi\n",
            "Image 101: Predicted class = kathak\n",
            "Image 102: Predicted class = kuchipudi\n",
            "Image 103: Predicted class = odissi\n",
            "Image 104: Predicted class = odissi\n",
            "Image 105: Predicted class = kathak\n",
            "Image 106: Predicted class = kuchipudi\n",
            "Image 107: Predicted class = kuchipudi\n",
            "Image 108: Predicted class = odissi\n",
            "Image 109: Predicted class = kathak\n",
            "Image 110: Predicted class = odissi\n",
            "Image 111: Predicted class = kuchipudi\n",
            "Image 112: Predicted class = odissi\n",
            "Image 113: Predicted class = kathak\n",
            "Image 114: Predicted class = odissi\n",
            "Image 115: Predicted class = odissi\n",
            "Image 116: Predicted class = kuchipudi\n",
            "Image 117: Predicted class = odissi\n",
            "Image 118: Predicted class = kuchipudi\n",
            "Image 119: Predicted class = kathak\n",
            "Image 120: Predicted class = odissi\n",
            "Image 121: Predicted class = odissi\n",
            "Image 122: Predicted class = odissi\n",
            "Image 123: Predicted class = odissi\n",
            "Image 124: Predicted class = odissi\n",
            "Image 125: Predicted class = odissi\n",
            "Image 126: Predicted class = odissi\n",
            "Image 127: Predicted class = kuchipudi\n",
            "Image 128: Predicted class = odissi\n",
            "Image 129: Predicted class = odissi\n",
            "Image 130: Predicted class = odissi\n",
            "Image 131: Predicted class = odissi\n",
            "Image 132: Predicted class = bharatanatyam\n",
            "Image 133: Predicted class = odissi\n",
            "Image 134: Predicted class = kuchipudi\n",
            "Image 135: Predicted class = kathak\n",
            "Image 136: Predicted class = odissi\n",
            "Image 137: Predicted class = mohiniyattam\n",
            "Image 138: Predicted class = odissi\n",
            "Image 139: Predicted class = odissi\n",
            "Image 140: Predicted class = kuchipudi\n",
            "Image 141: Predicted class = kuchipudi\n",
            "Image 142: Predicted class = bharatanatyam\n",
            "Image 143: Predicted class = odissi\n",
            "Image 144: Predicted class = kathak\n",
            "Image 145: Predicted class = kathak\n",
            "Image 146: Predicted class = odissi\n",
            "Image 147: Predicted class = kathak\n",
            "Image 148: Predicted class = kuchipudi\n",
            "Image 149: Predicted class = odissi\n",
            "Image 150: Predicted class = manipuri\n",
            "Image 151: Predicted class = kathak\n",
            "Image 152: Predicted class = manipuri\n",
            "Image 153: Predicted class = manipuri\n",
            "Image 154: Predicted class = manipuri\n",
            "Image 155: Predicted class = manipuri\n",
            "Image 156: Predicted class = manipuri\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "13bFkwZrTm61"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}