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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"source": [
"In this notebook, we will be discussing about the pytorch lightning datamodule library with images in a folder strutcture with folders as class labels. We will be using the cats and dogs dataset from kaggle. The dataset can be downloaded from [here](https://www.kaggle.com/c/dogs-vs-cats/data). The dataset contains 25000 images of cats and dogs. We will be using 20000 images for training and 5000 images for validation. The images are in a folder structure with folders as class labels."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "IPython.notebook.set_autosave_interval(300000)"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Autosaving every 300 seconds\n"
]
}
],
"source": [
"%autosave 300\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%reload_ext autoreload\n",
"%config Completer.use_jedi = False"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/mnt/batch/tasks/shared/LS_root/mounts/clusters/soutrik-vm-dev/code/Users/Soutrik.Chowdhury/pytorch-template-aws\n"
]
}
],
"source": [
"import os\n",
"\n",
"os.chdir(\"..\")\n",
"print(os.getcwd())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/envs/emlo_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from pathlib import Path\n",
"from typing import Union, Tuple, Optional, List\n",
"import os\n",
"import lightning as L\n",
"from torch.utils.data import DataLoader, random_split\n",
"from torchvision import transforms\n",
"from torchvision.datasets import ImageFolder\n",
"from torchvision.datasets.utils import download_and_extract_archive\n",
"from loguru import logger"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"class CatDogImageDataModule(L.LightningDataModule):\n",
" \"\"\"DataModule for Cat and Dog Image Classification using ImageFolder.\"\"\"\n",
"\n",
" def __init__(\n",
" self,\n",
" data_root: Union[str, Path] = \"data\",\n",
" data_dir: Union[str, Path] = \"cats_and_dogs_filtered\",\n",
" batch_size: int = 32,\n",
" num_workers: int = 4,\n",
" train_val_split: List[float] = [0.8, 0.2],\n",
" pin_memory: bool = False,\n",
" image_size: int = 224,\n",
" url: str = \"https://download.pytorch.org/tutorials/cats_and_dogs_filtered.zip\",\n",
" ):\n",
" super().__init__()\n",
" self.data_root = Path(data_root)\n",
" self.data_dir = data_dir\n",
" self.batch_size = batch_size\n",
" self.num_workers = num_workers\n",
" self.train_val_split = train_val_split\n",
" self.pin_memory = pin_memory\n",
" self.image_size = image_size\n",
" self.url = url\n",
"\n",
" # Initialize variables for datasets\n",
" self.train_dataset = None\n",
" self.val_dataset = None\n",
" self.test_dataset = None\n",
"\n",
" def prepare_data(self):\n",
" \"\"\"Download the dataset if it doesn't exist.\"\"\"\n",
" self.dataset_path = self.data_root / self.data_dir\n",
" if not self.dataset_path.exists():\n",
" logger.info(\"Downloading and extracting dataset.\")\n",
" download_and_extract_archive(\n",
" url=self.url, download_root=self.data_root, remove_finished=True\n",
" )\n",
" logger.info(\"Download completed.\")\n",
"\n",
" def setup(self, stage: Optional[str] = None):\n",
" \"\"\"Set up the train, validation, and test datasets.\"\"\"\n",
"\n",
" train_transform = transforms.Compose(\n",
" [\n",
" transforms.Resize((self.image_size, self.image_size)),\n",
" transforms.RandomHorizontalFlip(0.1),\n",
" transforms.RandomRotation(10),\n",
" transforms.RandomAffine(0, shear=10, scale=(0.8, 1.2)),\n",
" transforms.RandomAutocontrast(0.1),\n",
" transforms.RandomAdjustSharpness(2, 0.1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n",
" ),\n",
" ]\n",
" )\n",
"\n",
" test_transform = transforms.Compose(\n",
" [\n",
" transforms.Resize((self.image_size, self.image_size)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n",
" ),\n",
" ]\n",
" )\n",
"\n",
" train_path = self.dataset_path / \"train\"\n",
" test_path = self.dataset_path / \"test\"\n",
"\n",
" self.prepare_data()\n",
"\n",
" if stage == \"fit\" or stage is None:\n",
" full_train_dataset = ImageFolder(root=train_path, transform=train_transform)\n",
" self.class_names = full_train_dataset.classes\n",
" train_size = int(self.train_val_split[0] * len(full_train_dataset))\n",
" val_size = len(full_train_dataset) - train_size\n",
" self.train_dataset, self.val_dataset = random_split(\n",
" full_train_dataset, [train_size, val_size]\n",
" )\n",
" logger.info(\n",
" f\"Train/Validation split: {len(self.train_dataset)} train, {len(self.val_dataset)} validation images.\"\n",
" )\n",
"\n",
" if stage == \"test\" or stage is None:\n",
" self.test_dataset = ImageFolder(root=test_path, transform=test_transform)\n",
" logger.info(f\"Test dataset size: {len(self.test_dataset)} images.\")\n",
"\n",
" def _create_dataloader(self, dataset, shuffle: bool = False) -> DataLoader:\n",
" \"\"\"Helper function to create a DataLoader.\"\"\"\n",
" return DataLoader(\n",
" dataset=dataset,\n",
" batch_size=self.batch_size,\n",
" num_workers=self.num_workers,\n",
" pin_memory=self.pin_memory,\n",
" shuffle=shuffle,\n",
" )\n",
"\n",
" def train_dataloader(self) -> DataLoader:\n",
" return self._create_dataloader(self.train_dataset, shuffle=True)\n",
"\n",
" def val_dataloader(self) -> DataLoader:\n",
" return self._create_dataloader(self.val_dataset)\n",
"\n",
" def test_dataloader(self) -> DataLoader:\n",
" return self._create_dataloader(self.test_dataset)\n",
"\n",
" def get_class_names(self) -> List[str]:\n",
" return self.class_names"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"datamodule = CatDogImageDataModule(\n",
" data_root=\"data\",\n",
" data_dir=\"cats_and_dogs_filtered\",\n",
" batch_size=32,\n",
" num_workers=4,\n",
" train_val_split=[0.8, 0.2],\n",
" pin_memory=True,\n",
" image_size=224,\n",
" url=\"https://download.pytorch.org/tutorials/cats_and_dogs_filtered.zip\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-11-10 05:37:17.840\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m81\u001b[0m - \u001b[1mTrain/Validation split: 2241 train, 561 validation images.\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-11-10 05:37:17.910\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m87\u001b[0m - \u001b[1mTest dataset size: 198 images.\u001b[0m\n"
]
}
],
"source": [
"datamodule.prepare_data()\n",
"datamodule.setup()\n",
"class_names = datamodule.get_class_names()\n",
"train_dataloader = datamodule.train_dataloader()\n",
"val_dataloader= datamodule.val_dataloader()\n",
"test_dataloader= datamodule.test_dataloader()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['cats', 'dogs']"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "emlo_env",
"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": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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