Cat_Dog_Classification.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFVCZXntYT2g",
        "outputId": "a2d49755-a943-4947-c307-2b7ee0f296e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
            "Collecting pyarrow>=15.0.0 (from datasets)\n",
            "  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
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            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Downloading datasets-2.21.0-py3-none-any.whl (527 kB)\n",
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            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "\u001b[?25hDownloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB)\n",
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            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: xxhash, pyarrow, dill, multiprocess, datasets\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.21.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from PIL import Image\n",
        "from datasets import load_dataset\n",
        "from torch.utils.data import Dataset , DataLoader\n",
        "from torchvision.models import resnet18\n",
        "from torchvision import transforms"
      ],
      "metadata": {
        "id": "9S5Y61mwYchJ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DATASET_NAME = 'cats_vs_dogs'\n",
        "datasets = load_dataset(DATASET_NAME)\n",
        "datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373,
          "referenced_widgets": [
            "ee0af9097bdf4a669495faec107bf400",
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            "50bb3d495906413781b0862307c7b5cc"
          ]
        },
        "id": "7wqHsa0PYpP-",
        "outputId": "233b57ce-b00d-42a9-ae15-4b44f840867d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee0af9097bdf4a669495faec107bf400"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/330M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e883325067af4cee95262c940c9b890f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/391M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a9d6a9ef5444cb5a24e8033d8ff875d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/23410 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98b9162a136545028fda164f43ffb470"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['image', 'labels'],\n",
              "        num_rows: 23410\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TEST_SIZE = 0.2\n",
        "datasets = datasets ['train'].train_test_split(test_size = TEST_SIZE)\n"
      ],
      "metadata": {
        "id": "HUb-UhUeY0NU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IMG_SIZE = 64\n",
        "img_transforms = transforms.Compose ([\n",
        "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
        "    transforms.Grayscale(num_output_channels =3),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(\n",
        "        [0.485 , 0.456 , 0.406] ,\n",
        "        [0.229 , 0.224 , 0.225]\n",
        "    )\n",
        "])"
      ],
      "metadata": {
        "id": "nBZ7esr4Y998"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CatDogDataset(Dataset):\n",
        "    def __init__(self, data, transform=None):\n",
        "        self.data = data\n",
        "        self.transform = transform\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "    def __getitem__(self, idx):\n",
        "        images = self.data[idx]['image']\n",
        "        labels = self.data[idx]['labels']\n",
        "\n",
        "        if self.transform:\n",
        "            images = self.transform(images)\n",
        "\n",
        "        labels = torch.tensor(labels, dtype=torch.long)\n",
        "\n",
        "        return images, labels\n"
      ],
      "metadata": {
        "id": "kZ6oScwlZg77"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_BATCH_SIZE = 512\n",
        "VAL_BATCH_SIZE = 256\n",
        "\n",
        "train_dataset = CatDogDataset(datasets['train'], transform = img_transforms)\n",
        "test_dataset = CatDogDataset (datasets['test'], transform = img_transforms )\n",
        "\n",
        "train_loader = DataLoader ( train_dataset , batch_size = TRAIN_BATCH_SIZE ,\n",
        "shuffle = True )\n",
        "test_loader = DataLoader ( test_dataset , batch_size = VAL_BATCH_SIZE , shuffle =\n",
        "False )"
      ],
      "metadata": {
        "id": "gSoJi9gvaDsu"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CatDogModel(nn.Module):\n",
        "    def __init__(self, n_classes):\n",
        "        super(CatDogModel, self).__init__()\n",
        "\n",
        "        resnet_model = resnet18(weights='IMAGENET1K_V1')\n",
        "        self.backbone = nn.Sequential(*list(resnet_model.children())[:-1])\n",
        "\n",
        "        for param in self.backbone.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "        in_features = resnet_model.fc.in_features\n",
        "        self.fc = nn.Linear(in_features, n_classes)\n",
        "    def forward(self, x):\n",
        "        x = self.backbone(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.fc(x)\n",
        "\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "p0dob7ruaRCD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available () else 'cpu'\n",
        "N_CLASSES = 2\n",
        "\n",
        "model = CatDogModel(N_CLASSES).to(device)\n",
        "test_input = torch.rand(1 , 3 , 224 , 224).to( device )\n",
        "with torch.no_grad():\n",
        "  output = model ( test_input )\n",
        "  print(output.shape ) # (1 , 2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJCgqBCtavtk",
        "outputId": "5f2365ff-6cd5-47eb-efdd-83bc25cfc74a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 174MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 2])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "# Hyperparameters\n",
        "EPOCHS = 10\n",
        "LR = 1e-3\n",
        "WEIGHT_DECAY = 1e-5\n",
        "\n",
        "# Assuming model, train_loader, test_loader, and device are already defined\n",
        "optimizer = optim.Adam(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    train_losses = []\n",
        "    model.train()\n",
        "    for images, labels in train_loader:\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        outputs = model(images)\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_losses.append(loss.item())\n",
        "\n",
        "    train_loss = sum(train_losses) / len(train_losses)\n",
        "\n",
        "    val_losses = []\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for images, labels in test_loader:\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "            val_losses.append(loss.item())\n",
        "\n",
        "    val_loss = sum(val_losses) / len(val_losses)\n",
        "\n",
        "    print(f'EPOCH {epoch + 1}:\\tTrain loss: {train_loss:.3f}\\tVal loss: {val_loss:.3f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-GFbjgbbRlh",
        "outputId": "2d51581c-990f-493a-c447-95e4c39cce51"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH 1:\tTrain loss: 0.642\tVal loss: 0.599\n",
            "EPOCH 2:\tTrain loss: 0.543\tVal loss: 0.543\n",
            "EPOCH 3:\tTrain loss: 0.526\tVal loss: 0.533\n",
            "EPOCH 4:\tTrain loss: 0.518\tVal loss: 0.531\n",
            "EPOCH 5:\tTrain loss: 0.514\tVal loss: 0.526\n",
            "EPOCH 6:\tTrain loss: 0.513\tVal loss: 0.524\n",
            "EPOCH 7:\tTrain loss: 0.505\tVal loss: 0.529\n",
            "EPOCH 8:\tTrain loss: 0.507\tVal loss: 0.520\n",
            "EPOCH 9:\tTrain loss: 0.504\tVal loss: 0.521\n",
            "EPOCH 10:\tTrain loss: 0.502\tVal loss: 0.521\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SAVE_PATH = 'catdog_weights.pt'\n",
        "torch.save(model.state_dict(), SAVE_PATH)"
      ],
      "metadata": {
        "id": "fpst5KiVbfpC"
      },
      "execution_count": 13,
      "outputs": []
    }
  ]
}