diff --git a/experiments/cover_classifier/.gitignore b/experiments/cover_classifier/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..2bcdfd92bacb5e2c34ba4e08b63112abfedaf538 --- /dev/null +++ b/experiments/cover_classifier/.gitignore @@ -0,0 +1 @@ +models/ diff --git a/experiments/cover_classifier/README.md b/experiments/cover_classifier/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/experiments/cover_classifier/cover_classifier/__init__.py b/experiments/cover_classifier/cover_classifier/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..0af86cfaa7850daa5ac484cdcb4d820192bc4e4a --- /dev/null +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -0,0 +1,37 @@ +"""Train the cover classifier model.""" + +import logging +import sys +from pathlib import Path + +from cover_classifier.training import train + +PROJECT_DIR = Path(__file__).resolve().parent.parent +BASE_DIR = PROJECT_DIR.parent.parent + + +def main(): + """Train the cover classifier model.""" + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(message)s", + stream=sys.stdout, + ) + + save_dir = PROJECT_DIR / "models" + model_path = save_dir / "cover_classifier.ckpt" + + train( + data_dir=BASE_DIR.parent / "board-game-data", + images_dir=BASE_DIR.parent / "board-game-scraper" / "images", + batch_size=128, + num_epochs=100, + save_dir=save_dir, + model_path=model_path, + fast_dev_run=False, + ) + + +if __name__ == "__main__": + main() diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py new file mode 100644 index 0000000000000000000000000000000000000000..a2b23c47b70426399837651c2b083fd867ce6286 --- /dev/null +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -0,0 +1,134 @@ +"""Board game dataset.""" + +import json +import logging +import random +from itertools import islice +from pathlib import Path +from typing import Any, Callable, Union + +import jmespath +import polars as pl +import torch +from sklearn.preprocessing import MultiLabelBinarizer +from torch.utils.data import Dataset +from torchvision.io import read_image +from tqdm import tqdm + +LOGGER = logging.getLogger(__name__) + + +class BoardGameDataset(Dataset): + """Board game dataset.""" + + JMESPATH_BGG_ID = jmespath.compile("bgg_id") + JMESPATH_IMAGE_PATH = jmespath.compile("image_file[0].path") + JMESPATH_GAME_TYPES = jmespath.compile("game_type") + + def __init__( + self, + games_file: str | Path, + types_file: str | Path, + image_root_dir: str | Path, + transform: Callable | None = None, + max_samples: int | None = None, + require_any_type: bool = False, + ): + image_root_dir = Path(image_root_dir).resolve() + self.types_mlb = self.read_types_file(types_file) + + self.classes = self.types_mlb.classes + self.classes_set = frozenset(self.classes) + LOGGER.info("Game types: %s", self.classes) + + self.transform = transform + self.require_any_type = require_any_type + + self.bgg_ids, self.images, self.labels = self.read_games_file( + games_file, + image_root_dir, + max_samples, + ) + + assert len(self.bgg_ids) == len(self.images) == len(self.labels) + LOGGER.info("Loaded %d games and images in total", len(self.bgg_ids)) + + def read_types_file(self, types_file: str | Path) -> MultiLabelBinarizer: + """Read types from file.""" + + types_file = Path(types_file).resolve() + LOGGER.info("Reading types from file <%s>", types_file) + types = pl.read_csv(types_file)["name"].to_list() + return MultiLabelBinarizer(classes=types).fit([]) + + def read_games_file( + self, + games_file: Union[str, Path], + image_dir: Path, + max_samples: int | None = None, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Read games from file.""" + + games_file = Path(games_file).resolve() + LOGGER.info("Reading games from file <%s>", games_file) + with games_file.open(encoding="utf-8") as file: + lines = islice(file, max_samples) if max_samples else file + games = ( + self._parse_game(json.loads(line), image_dir) for line in tqdm(lines) + ) + bgg_ids, images, labels = zip(*filter(None, games)) + bgg_ids_tensor = torch.tensor(bgg_ids, dtype=torch.int32) + images_tensor = torch.stack(images) + labels_tensor = torch.stack(labels) + return bgg_ids_tensor, images_tensor, labels_tensor + + def _parse_game( + self, + game: dict[str, Any], + image_dir: Path, + ) -> tuple[int, torch.Tensor, torch.Tensor] | None: + bgg_id: int | None = self.JMESPATH_BGG_ID.search(game) + image_path_str: str | None = self.JMESPATH_IMAGE_PATH.search(game) + game_types_raw: list[str] | None = self.JMESPATH_GAME_TYPES.search(game) + + if not bgg_id or not image_path_str: + return None + + game_type_labels = [ + t + for s in game_types_raw or () + if (t := s.split(":")[0]) in self.classes_set + ] + game_types = self.types_mlb.transform([game_type_labels])[0] + if self.require_any_type and not any(game_types): + LOGGER.debug("No valid game types for game <%s>", bgg_id) + return None + + image_path = Path(image_dir / image_path_str).resolve() + if not image_path.exists(): + LOGGER.debug( + "Image file <%s> for game <%s> does not exist", + image_path, + bgg_id, + ) + return None + + if random.random() > 0.15: + # randomly skip 85% of images + return None + + image = self._read_and_transform_image(str(image_path)) + + return bgg_id, image, torch.from_numpy(game_types) + + def _read_and_transform_image(self, image_path: str) -> torch.Tensor: + image = read_image(image_path) + if self.transform: + image = self.transform(image) + return image + + def __len__(self) -> int: + return len(self.bgg_ids) + + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + return self.images[idx], self.labels[idx], self.bgg_ids[idx] diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6a231d1ad3a2e7a6ad28c81c73b42b380aacd735 --- /dev/null +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -0,0 +1,132 @@ +"""Train a model to classify board game covers.""" + +import logging + +import lightning +import torchmetrics +from torch import nn, optim +from torch.nn import functional as F +from torchvision.models import ResNet, ResNet50_Weights, resnet50 + +LOGGER = logging.getLogger(__name__) + + +class CoverClassifier(lightning.LightningModule): + """Lightning module for cover classification.""" + + # Will be tuned later automatically + learning_rate: float = 1e-3 + + def __init__( + self, + *, + num_classes: int, + weights: ResNet50_Weights, + ): + super().__init__() + + self.res_net: ResNet = resnet50(weights=weights) + res_net_out_features = self.res_net.fc.in_features + self.res_net.fc = nn.Identity() + # freeze resnet layers + self.res_net.requires_grad_(False) + + self.layers = nn.Sequential( + nn.Linear(res_net_out_features, 512), + nn.ReLU(), + nn.Linear(512, 256), + nn.ReLU(), + nn.Linear(256, 128), + nn.ReLU(), + nn.Linear(128, 64), + nn.ReLU(), + nn.Linear(64, num_classes), + ) + + self.loss_fn = nn.BCEWithLogitsLoss() + + self.train_accuracy = torchmetrics.Accuracy( + task="multilabel", + num_labels=num_classes, + ) + self.train_f1 = torchmetrics.F1Score( + task="multilabel", + num_labels=num_classes, + ) + + self.val_accuracy = torchmetrics.Accuracy( + task="multilabel", + num_labels=num_classes, + ) + self.val_f1 = torchmetrics.F1Score( + task="multilabel", + num_labels=num_classes, + ) + + self.test_accuracy = torchmetrics.Accuracy( + task="multilabel", + num_labels=num_classes, + ) + self.test_f1 = torchmetrics.F1Score( + task="multilabel", + num_labels=num_classes, + ) + + self.save_hyperparameters() + + def forward(self, x): + return self.layers(self.res_net(x)) + + def training_step(self, batch, batch_idx=0, dataloader_idx=0): + images, labels, _ = batch + logits = self(images) + + loss = self.loss_fn(logits, labels.float()) + self.log("train_loss", loss) + + outputs = F.sigmoid(logits) + + self.train_accuracy(outputs, labels) + self.log("train_accuracy", self.train_accuracy, prog_bar=True) + + self.train_f1(outputs, labels) + self.log("train_f1", self.train_f1) + + return loss + + def validation_step(self, batch, batch_idx=0, dataloader_idx=0): + images, labels, _ = batch + logits = self(images) + + loss = self.loss_fn(logits, labels.float()) + self.log("val_loss", loss) + + outputs = F.sigmoid(logits) + + self.val_accuracy(outputs, labels) + self.log("val_accuracy", self.val_accuracy) + + self.val_f1(outputs, labels) + self.log("val_f1", self.val_f1) + + return loss + + def test_step(self, batch, batch_idx=0, dataloader_idx=0): + images, labels, _ = batch + logits = self(images) + + loss = self.loss_fn(logits, labels.float()) + self.log("test_loss", loss) + + outputs = F.sigmoid(logits) + + self.test_accuracy(outputs, labels) + self.log("test_accuracy", self.test_accuracy) + + self.test_f1(outputs, labels) + self.log("test_f1", self.test_f1) + + return loss + + def configure_optimizers(self): + return optim.Adam(self.parameters(), lr=self.learning_rate) diff --git a/experiments/cover_classifier/cover_classifier/training.py b/experiments/cover_classifier/cover_classifier/training.py new file mode 100644 index 0000000000000000000000000000000000000000..d8e990a66d883a8114897bab08dc163183731f4c --- /dev/null +++ b/experiments/cover_classifier/cover_classifier/training.py @@ -0,0 +1,178 @@ +"""Train a model to classify board game covers.""" + +import logging +import shutil +from pathlib import Path + +import lightning +import torch +from torch import nn +from torch.nn import functional as F +from torch.utils.data import DataLoader, random_split +from torchvision.models import ResNet50_Weights + +from cover_classifier.data import BoardGameDataset +from cover_classifier.model import CoverClassifier + +LOGGER = logging.getLogger(__name__) + + +def train( + *, + data_dir: str | Path, + images_dir: str | Path, + test_size: float = 0.05, + val_size: float = 0.05, + batch_size: int = 128, + num_epochs: int = 10, + save_dir: str | Path | None = None, + model_path: str | Path | None = None, + fast_dev_run: bool = False, +) -> nn.Module: + """Train a model to classify board game covers.""" + + data_dir = Path(data_dir).resolve() + images_dir = Path(images_dir).resolve() + save_dir = Path(save_dir).resolve() if save_dir else None + model_path = Path(model_path).resolve() if model_path else None + + assert data_dir.is_dir(), f"Data directory does not exist: {data_dir}" + assert images_dir.is_dir(), f"Images directory does not exist: {images_dir}" + assert 0 < test_size < 1, f"Test size must be between 0 and 1: {test_size}" + assert 0 < val_size < 1, f"Validation size must be between 0 and 1: {val_size}" + assert ( + 0 < test_size + val_size < 1 + ), f"Test and validation sizes must sum to less than 1: {test_size + val_size}" + + if save_dir: + save_dir.mkdir(parents=True, exist_ok=True) + if model_path: + model_path.parent.mkdir(parents=True, exist_ok=True) + + weights = ResNet50_Weights.DEFAULT + + dataset = BoardGameDataset( + games_file=data_dir / "scraped" / "bgg_GameItem.jl", + types_file=data_dir / "scraped" / "bgg_GameType.csv", + image_root_dir=images_dir, + transform=weights.transforms(), + require_any_type=True, + ) + num_classes = len(dataset.classes) + # TODO: games without any type should be in a holdout set meant for human review + + train_dataset, test_dataset, val_dataset = random_split( + dataset=dataset, + lengths=(1 - test_size - val_size, test_size, val_size), + ) + LOGGER.info( + "Split into %d training, %d test and %d validation samples", + len(train_dataset), + len(test_dataset), + len(val_dataset), + ) + train_dataloader = DataLoader( + train_dataset, + batch_size=batch_size, + shuffle=True, + ) + test_dataloader = DataLoader( + test_dataset, + batch_size=batch_size, + shuffle=False, + ) + val_dataloader = DataLoader( + val_dataset, + batch_size=batch_size, + shuffle=False, + ) + + if model_path and model_path.exists(): + LOGGER.info("Resuming model training from %s", model_path) + model = CoverClassifier.load_from_checkpoint(model_path) + assert model.hparams.num_classes == num_classes, ( + f"Model classes ({model.hparams.num_classes}) do not match " + f"dataset classes ({num_classes})" + ) + else: + model = CoverClassifier(num_classes=num_classes, weights=weights) + + checkpoint_callback = lightning.pytorch.callbacks.model_checkpoint.ModelCheckpoint( + monitor="val_loss", + mode="min", + save_top_k=3, + save_last=True, + ) + + early_stopping_callback = lightning.pytorch.callbacks.early_stopping.EarlyStopping( + monitor="val_loss", + mode="min", + min_delta=0.0, + patience=5, + verbose=True, + ) + + loggers = ( + [lightning.pytorch.loggers.csv_logs.CSVLogger(save_dir=save_dir)] + if save_dir + else [] + ) + + trainer = lightning.Trainer( + max_epochs=num_epochs, + logger=loggers, + callbacks=[checkpoint_callback, early_stopping_callback], + default_root_dir=save_dir, + fast_dev_run=fast_dev_run, + ) + + tuner = lightning.pytorch.tuner.tuning.Tuner(trainer=trainer) + tuner.lr_find( + model=model, + train_dataloaders=train_dataloader, + max_lr=0.1, + num_training=100, + ) + LOGGER.info("Using learning rate: %f", model.learning_rate) + + trainer.fit( + model=model, + train_dataloaders=train_dataloader, + val_dataloaders=val_dataloader, + ) + + trainer.test( + model=model, + dataloaders=test_dataloader, + ) + + if model_path: + best_model_path = Path(checkpoint_callback.best_model_path).resolve() + LOGGER.info("Copying best model from <%s> to <%s>", best_model_path, model_path) + shutil.copyfile(src=best_model_path, dst=model_path) + + print_game_results(model, test_dataloader, dataset.classes, max_results=3) + + return model + + +@torch.no_grad() +def print_game_results(model, dataloader, classes, max_results: int | None = None): + """Print results for a batch of games.""" + image_batch, label_batch, bgg_id_batch = next(iter(dataloader)) + if max_results: + image_batch, label_batch, bgg_id_batch = ( + image_batch[:max_results], + label_batch[:max_results], + bgg_id_batch[:max_results], + ) + model.eval() + prediction_batch = F.sigmoid(model(image_batch)) + for bgg_id, labels, predictions in zip(bgg_id_batch, label_batch, prediction_batch): + print(f"https://boardgamegeek.com/boardgame/{bgg_id.item()}") + for pred, label, class_ in sorted( + zip(predictions, labels, classes), + reverse=True, + ): + error = round(pred.item()) 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