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experiments/cover_classifier/poetry.lock | 13 ++++++++++++- experiments/cover_classifier/pyproject.toml | 1 + 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/experiments/cover_classifier/poetry.lock b/experiments/cover_classifier/poetry.lock index 100c01df..3f17270e 100644 --- a/experiments/cover_classifier/poetry.lock +++ b/experiments/cover_classifier/poetry.lock @@ -876,6 +876,17 @@ MarkupSafe = ">=2.0" [package.extras] i18n = ["Babel (>=2.7)"] +[[package]] +name = "jmespath" +version = "1.0.1" +description = "JSON Matching Expressions" +optional = false +python-versions = ">=3.7" +files = [ + {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"}, + {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"}, +] + [[package]] name = "json5" version = "0.9.14" @@ -3142,4 +3153,4 @@ test = ["websockets"] [metadata] lock-version = "2.0" 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(>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"] +tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.0.272)", "scikit-image (>=0.16.2)"] + [[package]] name = "scipy" version = "1.11.4" @@ -2943,6 +3001,17 @@ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"] typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"] +[[package]] +name = "threadpoolctl" +version = "3.2.0" +description = "threadpoolctl" +optional = false +python-versions = ">=3.8" +files = [ + {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"}, + {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"}, +] + [[package]] name = "tinycss2" version = "1.2.1" @@ -3284,4 +3353,4 @@ test = ["websockets"] [metadata] lock-version = "2.0" python-versions = "~3.11" -content-hash = "f6df90d8fd1f7fd172e5922b4a430743277efc0d1ca68a6f521aaf5cf96435e8" +content-hash = "7dcf95f0d45d564ffc0934e54b059e42b368ab6546cf132daa5465967a37fdff" diff --git a/experiments/cover_classifier/pyproject.toml b/experiments/cover_classifier/pyproject.toml index 98d4ad34..b90a2733 100644 --- a/experiments/cover_classifier/pyproject.toml +++ b/experiments/cover_classifier/pyproject.toml @@ -19,6 +19,7 @@ numpy = "*" pillow = "*" polars = "*" pylint = "*" +scikit-learn = "*" scipy = "*" seaborn = "*" torch = "*" -- GitLab From 0702b6da4e73ed13387f870ef3d8cc698e8bc950 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 13:46:38 +0200 Subject: [PATCH 05/45] first version of BoardGameDataset --- .../cover_classifier/cover_classifier/data.py | 105 ++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 experiments/cover_classifier/cover_classifier/data.py diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py new file mode 100644 index 00000000..d4f5cb51 --- /dev/null +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -0,0 +1,105 @@ +import json +import logging +from pathlib import Path +from typing import Any, Callable, Union +import jmespath +import polars as pl +from PIL import Image +from sklearn.preprocessing import MultiLabelBinarizer +import torch +from torch.utils.data import Dataset, DataLoader +from torchvision import transforms + +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, + ): + self.types_mlb = self.read_types_file(types_file) + self.game_data = self.read_games_file(games_file) + self.image_root_dir = Path(image_root_dir).resolve() + self.transform = transform + + def read_types_file(self, types_file: str | Path) -> MultiLabelBinarizer: + 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]) -> pl.DataFrame: + games_file = Path(games_file).resolve() + LOGGER.info("Reading games from file <%s>", games_file) + with games_file.open() as file: + games = (self._parse_game(json.loads(line)) for line in file) + return pl.DataFrame( + data=filter(None, games), + schema=["bgg_id", "image_path", "types"], + orient="row", + ) + + def _parse_game(self, game: dict[str, Any]) -> tuple | None: + bgg_id: str = self.JMESPATH_BGG_ID.search(game) + image_path = self.JMESPATH_IMAGE_PATH.search(game) + game_types = self.JMESPATH_GAME_TYPES.search(game) + if not bgg_id or not image_path or not game_types: + return None + game_types = self.types_mlb.transform([[t.split(":")[0] for t in game_types]])[ + 0 + ] + return bgg_id, image_path, game_types + + def __len__(self) -> int: + return len(self.game_data) + + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: + image_path = self.image_root_dir / self.game_data[idx, "image_path"] + image = Image.open(image_path).convert("RGB") + + labels = torch.tensor( + self.game_data[idx, "types"], + dtype=torch.bool, + ) + + if self.transform: + image = self.transform(image) + + return image, labels + + +# # Example usage: +# csv_file_path = "path/to/your/csv/file.csv" +# image_root_dir = "path/to/your/image/directory" + +# # Define the data transformation +# transform = transforms.Compose( +# [ +# transforms.Resize((224, 224)), +# transforms.ToTensor(), +# ] +# ) + +# # Create an instance of your custom dataset +# board_game_dataset = BoardGameDataset( +# csv_file=csv_file_path, root_dir=image_root_dir, transform=transform +# ) + +# # Create a DataLoader to iterate over your dataset +# batch_size = 32 +# data_loader = DataLoader(board_game_dataset, batch_size=batch_size, shuffle=True) + +# # Example of accessing data in the DataLoader +# for inputs, labels in data_loader: +# # Your training/validation loop here +# pass -- GitLab From 1972a27637c8873ca67cecab097d60a9374eef27 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 15:05:23 +0200 Subject: [PATCH 06/45] first version of train() --- .../cover_classifier/cover_classifier/data.py | 1 + .../cover_classifier/model.py | 38 +++++++++++++++++++ 2 files changed, 39 insertions(+) create mode 100644 experiments/cover_classifier/cover_classifier/model.py diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index d4f5cb51..231bb30a 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -28,6 +28,7 @@ class BoardGameDataset(Dataset): transform: Callable | None = None, ): self.types_mlb = self.read_types_file(types_file) + self.classes = self.types_mlb.classes self.game_data = self.read_games_file(games_file) self.image_root_dir = Path(image_root_dir).resolve() self.transform = transform diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py new file mode 100644 index 00000000..026ef5ca --- /dev/null +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -0,0 +1,38 @@ +from pathlib import Path +from torch import nn +from torch import optim +from torch.utils.data import DataLoader +from torchvision.models import resnet50, ResNet50_Weights + +from cover_classifier.data import BoardGameDataset + + +def train(data_dir: str | Path, images_dir: str | Path): + weights = ResNet50_Weights.DEFAULT + model = resnet50(weights=weights) + + data_dir = Path(data_dir).resolve() + images_dir = Path(images_dir).resolve() + + 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, + ) + dataloader = DataLoader(dataset, batch_size=64, shuffle=True) + + num_classes = len(dataset.classes) + model.fc = nn.Linear(model.fc.in_features, num_classes) + + criterion = nn.BCEWithLogitsLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + + num_epochs = 10 + for epoch in range(num_epochs): + for inputs, labels in dataloader: + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels.int()) + loss.backward() + optimizer.step() -- GitLab From 7ccd8c64e8dd5b76ebb643cd29486983e8295e96 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 15:22:01 +0200 Subject: [PATCH 07/45] added cover_classifier/__main__.py --- .../cover_classifier/__main__.py | 0 .../cover_classifier/cover_classifier/data.py | 38 ++++--------------- .../cover_classifier/model.py | 9 ++++- 3 files changed, 16 insertions(+), 31 deletions(-) create mode 100644 experiments/cover_classifier/cover_classifier/__main__.py diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py new file mode 100644 index 00000000..e69de29b diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 231bb30a..aeb41f49 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -1,3 +1,5 @@ +"""Board game dataset.""" + import json import logging from pathlib import Path @@ -7,8 +9,7 @@ import polars as pl from PIL import Image from sklearn.preprocessing import MultiLabelBinarizer import torch -from torch.utils.data import Dataset, DataLoader -from torchvision import transforms +from torch.utils.data import Dataset LOGGER = logging.getLogger(__name__) @@ -34,15 +35,19 @@ class BoardGameDataset(Dataset): self.transform = transform 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]) -> pl.DataFrame: + """Read games from file.""" + games_file = Path(games_file).resolve() LOGGER.info("Reading games from file <%s>", games_file) - with games_file.open() as file: + with games_file.open(encoding="utf-8") as file: games = (self._parse_game(json.loads(line)) for line in file) return pl.DataFrame( data=filter(None, games), @@ -77,30 +82,3 @@ class BoardGameDataset(Dataset): image = self.transform(image) return image, labels - - -# # Example usage: -# csv_file_path = "path/to/your/csv/file.csv" -# image_root_dir = "path/to/your/image/directory" - -# # Define the data transformation -# transform = transforms.Compose( -# [ -# transforms.Resize((224, 224)), -# transforms.ToTensor(), -# ] -# ) - -# # Create an instance of your custom dataset -# board_game_dataset = BoardGameDataset( -# csv_file=csv_file_path, root_dir=image_root_dir, transform=transform -# ) - -# # Create a DataLoader to iterate over your dataset -# batch_size = 32 -# data_loader = DataLoader(board_game_dataset, batch_size=batch_size, shuffle=True) - -# # Example of accessing data in the DataLoader -# for inputs, labels in data_loader: -# # Your training/validation loop here -# pass diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 026ef5ca..ad802ccb 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -1,3 +1,5 @@ +"""Train a model to classify board game covers.""" + from pathlib import Path from torch import nn from torch import optim @@ -7,7 +9,9 @@ from torchvision.models import resnet50, ResNet50_Weights from cover_classifier.data import BoardGameDataset -def train(data_dir: str | Path, images_dir: str | Path): +def train(data_dir: str | Path, images_dir: str | Path) -> nn.Module: + """Train a model to classify board game covers.""" + weights = ResNet50_Weights.DEFAULT model = resnet50(weights=weights) @@ -30,9 +34,12 @@ def train(data_dir: str | Path, images_dir: str | Path): num_epochs = 10 for epoch in range(num_epochs): + print(f"Epoch {epoch+1}/{num_epochs}") for inputs, labels in dataloader: optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels.int()) loss.backward() optimizer.step() + + return model -- GitLab From fe22ba7446bb9c03faee380451e06f0760cd3021 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 16:00:07 +0200 Subject: [PATCH 08/45] added types dependencies --- experiments/cover_classifier/poetry.lock | 24 ++++++++++++++++++++- experiments/cover_classifier/pyproject.toml | 14 ++++++++++++ 2 files changed, 37 insertions(+), 1 deletion(-) diff --git a/experiments/cover_classifier/poetry.lock b/experiments/cover_classifier/poetry.lock index 62758fae..fd35397c 100644 --- a/experiments/cover_classifier/poetry.lock +++ b/experiments/cover_classifier/poetry.lock @@ -3234,6 +3234,28 @@ build = ["cmake (>=3.18)", "lit"] tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"] tutorials = ["matplotlib", "pandas", "tabulate"] +[[package]] +name = "types-jmespath" +version = "1.0.2.20240106" +description = "Typing stubs for jmespath" +optional = false +python-versions = ">=3.8" +files = [ + {file = "types-jmespath-1.0.2.20240106.tar.gz", hash = "sha256:b4a65a116bfc1c700a4fd9d24e2e397f4a431122e0320a77b7f1989a6b5d819e"}, + {file = "types_jmespath-1.0.2.20240106-py3-none-any.whl", hash = "sha256:c3e715fcaae9e5f8d74e14328fdedc4f2b3f0e18df17f3e457ae0a18e245bde0"}, +] + +[[package]] +name = "types-pillow" +version = "10.2.0.20240111" +description = "Typing stubs for Pillow" +optional = false +python-versions = ">=3.8" +files = [ + {file = "types-Pillow-10.2.0.20240111.tar.gz", hash = "sha256:e8d359bfdc5a149a3c90a7e153cb2d0750ddf7fc3508a20dfadabd8a9435e354"}, + {file = "types_Pillow-10.2.0.20240111-py3-none-any.whl", hash = "sha256:1f4243b30c143b56b0646626f052e4269123e550f9096cdfb5fbd999daee7dbb"}, +] + [[package]] name = "types-python-dateutil" version = "2.8.19.20240106" @@ -3353,4 +3375,4 @@ test = ["websockets"] [metadata] lock-version = "2.0" python-versions = "~3.11" -content-hash = "7dcf95f0d45d564ffc0934e54b059e42b368ab6546cf132daa5465967a37fdff" +content-hash = "2622811cc84dbd69df61948447532c2e4f5193fef2081cd4885c5ee86549c631" diff --git a/experiments/cover_classifier/pyproject.toml b/experiments/cover_classifier/pyproject.toml index b90a2733..fe13bae5 100644 --- a/experiments/cover_classifier/pyproject.toml +++ b/experiments/cover_classifier/pyproject.toml @@ -26,6 +26,20 @@ torch = "*" torchvision = "*" tqdm = "*" +[tool.poetry.group.dev.dependencies] +types-jmespath = "*" +types-pillow = "*" + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" + +[tool.mypy] +python_version = "3.11" + +[[tool.mypy.overrides]] +module = [ + "sklearn", + "torchvision" +] +ignore_missing_imports = true -- GitLab From 1875094286eb6407828afda3420485d62d3fe2c8 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 16:00:55 +0200 Subject: [PATCH 09/45] Added main module plus bug fixes --- .../cover_classifier/__main__.py | 28 ++++++++++++++++++ .../cover_classifier/cover_classifier/data.py | 29 +++++++++++++------ .../cover_classifier/model.py | 5 ++-- 3 files changed, 51 insertions(+), 11 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index e69de29b..48cc32b1 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -0,0 +1,28 @@ +"""Train the cover classifier model.""" + +import logging +from pathlib import Path +import sys + +from cover_classifier.model import train + +BASE_DIR = Path(__file__).resolve().parent.parent.parent.parent + + +def main(): + """Train the cover classifier model.""" + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(message)s", + stream=sys.stdout, + ) + + train( + data_dir=BASE_DIR.parent / "board-game-data", + images_dir=BASE_DIR.parent / "board-game-scraper" / "images", + ) + + +if __name__ == "__main__": + main() diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index aeb41f49..0acd2633 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -6,10 +6,10 @@ from pathlib import Path from typing import Any, Callable, Union import jmespath import polars as pl -from PIL import Image from sklearn.preprocessing import MultiLabelBinarizer import torch from torch.utils.data import Dataset +from torchvision.io import read_image LOGGER = logging.getLogger(__name__) @@ -28,10 +28,10 @@ class BoardGameDataset(Dataset): image_root_dir: str | Path, transform: Callable | None = None, ): + image_root_dir = Path(image_root_dir).resolve() self.types_mlb = self.read_types_file(types_file) self.classes = self.types_mlb.classes - self.game_data = self.read_games_file(games_file) - self.image_root_dir = Path(image_root_dir).resolve() + self.game_data = self.read_games_file(games_file, image_root_dir) self.transform = transform def read_types_file(self, types_file: str | Path) -> MultiLabelBinarizer: @@ -42,36 +42,47 @@ class BoardGameDataset(Dataset): types = pl.read_csv(types_file)["name"].to_list() return MultiLabelBinarizer(classes=types).fit([]) - def read_games_file(self, games_file: Union[str, Path]) -> pl.DataFrame: + def read_games_file( + self, + games_file: Union[str, Path], + image_dir: Path, + ) -> pl.DataFrame: """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: - games = (self._parse_game(json.loads(line)) for line in file) + games = (self._parse_game(json.loads(line), image_dir) for line in file) return pl.DataFrame( data=filter(None, games), schema=["bgg_id", "image_path", "types"], orient="row", ) - def _parse_game(self, game: dict[str, Any]) -> tuple | None: + def _parse_game(self, game: dict[str, Any], image_dir: Path) -> tuple | None: bgg_id: str = self.JMESPATH_BGG_ID.search(game) image_path = self.JMESPATH_IMAGE_PATH.search(game) game_types = self.JMESPATH_GAME_TYPES.search(game) + if not bgg_id or not image_path or not game_types: return None + + image_path = Path(image_dir / image_path).resolve() + if not image_path.exists(): + LOGGER.warning("Image file <%s> does not exist", image_path) + return None + game_types = self.types_mlb.transform([[t.split(":")[0] for t in game_types]])[ 0 ] - return bgg_id, image_path, game_types + return bgg_id, str(image_path), game_types def __len__(self) -> int: return len(self.game_data) def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: - image_path = self.image_root_dir / self.game_data[idx, "image_path"] - image = Image.open(image_path).convert("RGB") + image_path = self.game_data[idx, "image_path"] + image = read_image(image_path) labels = torch.tensor( self.game_data[idx, "types"], diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index ad802ccb..5b231802 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -22,7 +22,7 @@ def train(data_dir: str | Path, images_dir: str | Path) -> nn.Module: games_file=data_dir / "scraped" / "bgg_GameItem.jl", types_file=data_dir / "scraped" / "bgg_GameType.csv", image_root_dir=images_dir, - transform=weights.transforms, + transform=weights.transforms(), ) dataloader = DataLoader(dataset, batch_size=64, shuffle=True) @@ -38,8 +38,9 @@ def train(data_dir: str | Path, images_dir: str | Path) -> nn.Module: for inputs, labels in dataloader: optimizer.zero_grad() outputs = model(inputs) - loss = criterion(outputs, labels.int()) + loss = criterion(outputs, labels.float()) loss.backward() optimizer.step() + print(f"Loss: {loss.item():>7.4f}") return model -- GitLab From ae9c226ce21dc02f56043a89db5cff3a7840cde6 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 16:07:52 +0200 Subject: [PATCH 10/45] no need for pillow --- experiments/cover_classifier/poetry.lock | 13 +------------ experiments/cover_classifier/pyproject.toml | 2 -- 2 files changed, 1 insertion(+), 14 deletions(-) diff --git a/experiments/cover_classifier/poetry.lock b/experiments/cover_classifier/poetry.lock index fd35397c..7061f2da 100644 --- a/experiments/cover_classifier/poetry.lock +++ b/experiments/cover_classifier/poetry.lock @@ -3245,17 +3245,6 @@ files = [ {file = "types_jmespath-1.0.2.20240106-py3-none-any.whl", hash = "sha256:c3e715fcaae9e5f8d74e14328fdedc4f2b3f0e18df17f3e457ae0a18e245bde0"}, ] -[[package]] -name = "types-pillow" -version = "10.2.0.20240111" -description = "Typing stubs for Pillow" -optional = false -python-versions = ">=3.8" -files = [ - {file = "types-Pillow-10.2.0.20240111.tar.gz", hash = "sha256:e8d359bfdc5a149a3c90a7e153cb2d0750ddf7fc3508a20dfadabd8a9435e354"}, - {file = "types_Pillow-10.2.0.20240111-py3-none-any.whl", hash = "sha256:1f4243b30c143b56b0646626f052e4269123e550f9096cdfb5fbd999daee7dbb"}, -] - [[package]] name = "types-python-dateutil" version = "2.8.19.20240106" @@ -3375,4 +3364,4 @@ test = ["websockets"] [metadata] lock-version = "2.0" python-versions = "~3.11" -content-hash = "2622811cc84dbd69df61948447532c2e4f5193fef2081cd4885c5ee86549c631" +content-hash = "bbb4b217d49341616a52b37bb068d96ba40f8344a71c142362f8c88638e043f7" diff --git a/experiments/cover_classifier/pyproject.toml b/experiments/cover_classifier/pyproject.toml index fe13bae5..e27afb34 100644 --- a/experiments/cover_classifier/pyproject.toml +++ b/experiments/cover_classifier/pyproject.toml @@ -16,7 +16,6 @@ jupytext = "*" matplotlib = "*" mypy = "*" numpy = "*" -pillow = "*" polars = "*" pylint = "*" scikit-learn = "*" @@ -28,7 +27,6 @@ tqdm = "*" [tool.poetry.group.dev.dependencies] types-jmespath = "*" -types-pillow = "*" [build-system] requires = ["poetry-core"] -- GitLab From 1b16f12ccf1ad07d4f759a029da5a88c3dc54fee Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 16:19:00 +0200 Subject: [PATCH 11/45] added back some types --- experiments/cover_classifier/poetry.lock | 24 ++++++++++++++++++++- experiments/cover_classifier/pyproject.toml | 2 ++ 2 files changed, 25 insertions(+), 1 deletion(-) diff --git a/experiments/cover_classifier/poetry.lock b/experiments/cover_classifier/poetry.lock index 7061f2da..db5a1264 100644 --- a/experiments/cover_classifier/poetry.lock +++ b/experiments/cover_classifier/poetry.lock @@ -3245,6 +3245,17 @@ files = [ {file = "types_jmespath-1.0.2.20240106-py3-none-any.whl", hash = "sha256:c3e715fcaae9e5f8d74e14328fdedc4f2b3f0e18df17f3e457ae0a18e245bde0"}, ] +[[package]] +name = "types-pillow" +version = "10.2.0.20240111" +description = "Typing stubs for Pillow" +optional = false +python-versions = ">=3.8" +files = [ + {file = "types-Pillow-10.2.0.20240111.tar.gz", hash = "sha256:e8d359bfdc5a149a3c90a7e153cb2d0750ddf7fc3508a20dfadabd8a9435e354"}, + {file = "types_Pillow-10.2.0.20240111-py3-none-any.whl", hash = "sha256:1f4243b30c143b56b0646626f052e4269123e550f9096cdfb5fbd999daee7dbb"}, +] + [[package]] name = "types-python-dateutil" version = "2.8.19.20240106" @@ -3256,6 +3267,17 @@ files = [ {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"}, ] +[[package]] +name = "types-tqdm" +version = "4.66.0.20240106" +description = "Typing stubs for tqdm" +optional = false +python-versions = ">=3.8" +files = [ + {file = "types-tqdm-4.66.0.20240106.tar.gz", hash = "sha256:7acf4aade5bad3ded76eb829783f9961b1c2187948eaa6dd1ae8644dff95a938"}, + {file = "types_tqdm-4.66.0.20240106-py3-none-any.whl", hash = "sha256:7459b0f441b969735685645a5d8480f7912b10d05ab45f99a2db8a8e45cb550b"}, +] + [[package]] name = "typing-extensions" version = "4.9.0" @@ -3364,4 +3386,4 @@ test = ["websockets"] [metadata] lock-version = "2.0" python-versions = "~3.11" -content-hash = "bbb4b217d49341616a52b37bb068d96ba40f8344a71c142362f8c88638e043f7" +content-hash = "8c5f2c2146005b487d7787a66e4d9ec1218e64fc6da525efb0ed41020062b43a" diff --git a/experiments/cover_classifier/pyproject.toml b/experiments/cover_classifier/pyproject.toml index e27afb34..c9697dbc 100644 --- a/experiments/cover_classifier/pyproject.toml +++ b/experiments/cover_classifier/pyproject.toml @@ -27,6 +27,8 @@ tqdm = "*" [tool.poetry.group.dev.dependencies] types-jmespath = "*" +types-pillow = "*" +types-tqdm = "*" [build-system] requires = ["poetry-core"] -- GitLab From d059b0921ec955e36b966e0877be189d88f0465b Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 16:25:01 +0200 Subject: [PATCH 12/45] train/test split and crude evaluation --- .../cover_classifier/model.py | 45 +++++++++++++++---- 1 file changed, 37 insertions(+), 8 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 5b231802..469a8e2c 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -1,30 +1,45 @@ """Train a model to classify board game covers.""" from pathlib import Path + +import torch from torch import nn from torch import optim -from torch.utils.data import DataLoader +from torch.utils.data import DataLoader, random_split from torchvision.models import resnet50, ResNet50_Weights +from tqdm import tqdm + from cover_classifier.data import BoardGameDataset -def train(data_dir: str | Path, images_dir: str | Path) -> nn.Module: +def train( + data_dir: str | Path, + images_dir: str | Path, + test_size: float = 0.1, + batch_size: int = 128, +) -> nn.Module: """Train a model to classify board game covers.""" - weights = ResNet50_Weights.DEFAULT - model = resnet50(weights=weights) - data_dir = Path(data_dir).resolve() images_dir = Path(images_dir).resolve() + 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}" + + weights = ResNet50_Weights.DEFAULT + model = resnet50(weights=weights) + 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(), ) - dataloader = DataLoader(dataset, batch_size=64, shuffle=True) + train_dataset, test_dataset = random_split(dataset, (1 - test_size, test_size)) + train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True) num_classes = len(dataset.classes) model.fc = nn.Linear(model.fc.in_features, num_classes) @@ -35,12 +50,26 @@ def train(data_dir: str | Path, images_dir: str | Path) -> nn.Module: num_epochs = 10 for epoch in range(num_epochs): print(f"Epoch {epoch+1}/{num_epochs}") - for inputs, labels in dataloader: + + model.train() + for inputs, labels in tqdm(train_dataloader): optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels.float()) loss.backward() optimizer.step() - print(f"Loss: {loss.item():>7.4f}") + break + + model.eval() + with torch.no_grad(): + losses = torch.tensor( + [ + criterion(model(inputs), labels.float()) + for inputs, labels in tqdm(test_dataloader) + ] + ) + print(f"Loss: {losses.mean().item():>7.4f} +/- {losses.std().item():>7.4f}") + + # TODO save model return model -- GitLab From 4ee8d02995ac27217528e6fa6bbb3a7aa9406c79 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 18:30:00 +0200 Subject: [PATCH 13/45] better game type processing --- .../cover_classifier/cover_classifier/data.py | 37 ++++++++++++++----- .../cover_classifier/model.py | 14 +++++-- 2 files changed, 38 insertions(+), 13 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 0acd2633..a5ed8dcf 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -10,6 +10,7 @@ from sklearn.preprocessing import MultiLabelBinarizer import torch from torch.utils.data import Dataset from torchvision.io import read_image +from tqdm import tqdm LOGGER = logging.getLogger(__name__) @@ -31,6 +32,7 @@ class BoardGameDataset(Dataset): 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) self.game_data = self.read_games_file(games_file, image_root_dir) self.transform = transform @@ -52,29 +54,44 @@ class BoardGameDataset(Dataset): games_file = Path(games_file).resolve() LOGGER.info("Reading games from file <%s>", games_file) with games_file.open(encoding="utf-8") as file: - games = (self._parse_game(json.loads(line), image_dir) for line in file) + games = ( + self._parse_game(json.loads(line), image_dir) for line in tqdm(file) + ) return pl.DataFrame( data=filter(None, games), schema=["bgg_id", "image_path", "types"], orient="row", ) - def _parse_game(self, game: dict[str, Any], image_dir: Path) -> tuple | None: - bgg_id: str = self.JMESPATH_BGG_ID.search(game) - image_path = self.JMESPATH_IMAGE_PATH.search(game) - game_types = self.JMESPATH_GAME_TYPES.search(game) + def _parse_game( + self, + game: dict[str, Any], + image_dir: Path, + ) -> tuple[int, str, list[int]] | 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 or not game_types: + if not bgg_id or not image_path_str or not game_types_raw: return None - image_path = Path(image_dir / image_path).resolve() + image_path = Path(image_dir / image_path_str).resolve() if not image_path.exists(): - LOGGER.warning("Image file <%s> does not exist", image_path) + LOGGER.debug( + "Image file <%s> for game <%s> does not exist", + image_path, + bgg_id, + ) return None - game_types = self.types_mlb.transform([[t.split(":")[0] for t in game_types]])[ - 0 + game_type_labels = [ + t for s in game_types_raw if (t := s.split(":")[0]) in self.classes_set ] + game_types = self.types_mlb.transform([game_type_labels])[0] + if not any(game_types): + LOGGER.debug("No valid game types for game <%s>", bgg_id) + return None + return bgg_id, str(image_path), game_types def __len__(self) -> int: diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 469a8e2c..6839c292 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -1,5 +1,6 @@ """Train a model to classify board game covers.""" +import logging from pathlib import Path import torch @@ -12,11 +13,13 @@ from tqdm import tqdm from cover_classifier.data import BoardGameDataset +LOGGER = logging.getLogger(__name__) + def train( data_dir: str | Path, images_dir: str | Path, - test_size: float = 0.1, + test_size: float = 0.01, batch_size: int = 128, ) -> nn.Module: """Train a model to classify board game covers.""" @@ -37,7 +40,13 @@ def train( image_root_dir=images_dir, transform=weights.transforms(), ) + LOGGER.info("Loaded %d games and images in total", len(dataset)) train_dataset, test_dataset = random_split(dataset, (1 - test_size, test_size)) + LOGGER.info( + "Split into %d training and %d test samples", + len(train_dataset), + len(test_dataset), + ) train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True) @@ -58,7 +67,6 @@ def train( loss = criterion(outputs, labels.float()) loss.backward() optimizer.step() - break model.eval() with torch.no_grad(): @@ -68,7 +76,7 @@ def train( for inputs, labels in tqdm(test_dataloader) ] ) - print(f"Loss: {losses.mean().item():>7.4f} +/- {losses.std().item():>7.4f}") + print(f"Loss: {losses.mean().item():>7.4f} ± {losses.std().item():>7.4f}") # TODO save model -- GitLab From e93cb7232af8c5c686d0a7a2a6c3fb5d91cba7d1 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sat, 13 Jan 2024 22:36:45 +0200 Subject: [PATCH 14/45] load the image tensors eagerly --- .../cover_classifier/cover_classifier/data.py | 59 ++++++++++--------- .../cover_classifier/model.py | 24 ++++++-- 2 files changed, 49 insertions(+), 34 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index a5ed8dcf..742e811c 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -2,6 +2,7 @@ import json import logging +from itertools import islice from pathlib import Path from typing import Any, Callable, Union import jmespath @@ -31,10 +32,15 @@ class BoardGameDataset(Dataset): ): 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) - self.game_data = self.read_games_file(games_file, image_root_dir) + LOGGER.info("Game types: %s", self.classes) + self.transform = transform + self.images, self.labels = self.read_games_file(games_file, image_root_dir) + assert len(self.images) == len(self.labels) + LOGGER.info("Loaded %d games and images in total", len(self.labels)) def read_types_file(self, types_file: str | Path) -> MultiLabelBinarizer: """Read types from file.""" @@ -48,26 +54,24 @@ class BoardGameDataset(Dataset): self, games_file: Union[str, Path], image_dir: Path, - ) -> pl.DataFrame: + ) -> tuple[tuple[torch.Tensor, ...], tuple[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: games = ( - self._parse_game(json.loads(line), image_dir) for line in tqdm(file) - ) - return pl.DataFrame( - data=filter(None, games), - schema=["bgg_id", "image_path", "types"], - orient="row", + self._parse_game(json.loads(line), image_dir) + for line in tqdm(islice(file, 10_000)) ) + images, labels = zip(*filter(None, games)) + return images, labels def _parse_game( self, game: dict[str, Any], image_dir: Path, - ) -> tuple[int, str, list[int]] | None: + ) -> tuple[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) @@ -75,6 +79,14 @@ class BoardGameDataset(Dataset): if not bgg_id or not image_path_str or not game_types_raw: return None + game_type_labels = [ + t for s in game_types_raw if (t := s.split(":")[0]) in self.classes_set + ] + game_types = self.types_mlb.transform([game_type_labels])[0] + if 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( @@ -84,29 +96,18 @@ class BoardGameDataset(Dataset): ) return None - game_type_labels = [ - t for s in game_types_raw if (t := s.split(":")[0]) in self.classes_set - ] - game_types = self.types_mlb.transform([game_type_labels])[0] - if not any(game_types): - LOGGER.debug("No valid game types for game <%s>", bgg_id) - return None - - return bgg_id, str(image_path), game_types + image = self._read_and_transform_image(str(image_path)) - def __len__(self) -> int: - return len(self.game_data) + return image, torch.from_numpy(game_types) - def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: - image_path = self.game_data[idx, "image_path"] + def _read_and_transform_image(self, image_path: str) -> torch.Tensor: image = read_image(image_path) - - labels = torch.tensor( - self.game_data[idx, "types"], - dtype=torch.bool, - ) - if self.transform: image = self.transform(image) + return image - return image, labels + def __len__(self) -> int: + return len(self.labels) + + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: + return self.images[idx], self.labels[idx] diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 6839c292..e7efa7b8 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -6,6 +6,7 @@ from pathlib import Path import torch from torch import nn from torch import optim +from torch.nn import functional as F from torch.utils.data import DataLoader, random_split from torchvision.models import resnet50, ResNet50_Weights @@ -40,15 +41,24 @@ def train( image_root_dir=images_dir, transform=weights.transforms(), ) - LOGGER.info("Loaded %d games and images in total", len(dataset)) train_dataset, test_dataset = random_split(dataset, (1 - test_size, test_size)) LOGGER.info( "Split into %d training and %d test samples", len(train_dataset), len(test_dataset), ) - train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) - test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True) + train_dataloader = DataLoader( + train_dataset, + batch_size=batch_size, + shuffle=True, + # num_workers=8, + ) + test_dataloader = DataLoader( + test_dataset, + batch_size=batch_size, + shuffle=True, + # num_workers=8, + ) num_classes = len(dataset.classes) model.fc = nn.Linear(model.fc.in_features, num_classes) @@ -61,9 +71,9 @@ def train( print(f"Epoch {epoch+1}/{num_epochs}") model.train() - for inputs, labels in tqdm(train_dataloader): + for images, labels in tqdm(train_dataloader): optimizer.zero_grad() - outputs = model(inputs) + outputs = model(images) loss = criterion(outputs, labels.float()) loss.backward() optimizer.step() @@ -78,6 +88,10 @@ def train( ) print(f"Loss: {losses.mean().item():>7.4f} ± {losses.std().item():>7.4f}") + images, labels = next(iter(test_dataloader)) + output = F.sigmoid(model(images[:3, ...])) + print(labels[:3, ...], output) + # TODO save model return model -- GitLab From eac4b748c550c35a719a048bd026d24c3cec5df2 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 00:11:32 +0200 Subject: [PATCH 15/45] trying to learn on a different device --- .../cover_classifier/__main__.py | 12 +++++++++- .../cover_classifier/cover_classifier/data.py | 8 +++---- .../cover_classifier/model.py | 22 +++++++++++-------- 3 files changed, 28 insertions(+), 14 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 48cc32b1..be468057 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -1,8 +1,10 @@ """Train the cover classifier model.""" import logging -from pathlib import Path import sys +from pathlib import Path + +import torch from cover_classifier.model import train @@ -18,9 +20,17 @@ def main(): stream=sys.stdout, ) + device = torch.device( + "cuda" + if torch.cuda.is_available() + else "mps" + if torch.backends.mps.is_available() + else "cpu" + ) train( data_dir=BASE_DIR.parent / "board-game-data", images_dir=BASE_DIR.parent / "board-game-scraper" / "images", + device=device, ) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 742e811c..5055a3e7 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -2,7 +2,6 @@ import json import logging -from itertools import islice from pathlib import Path from typing import Any, Callable, Union import jmespath @@ -29,6 +28,7 @@ class BoardGameDataset(Dataset): types_file: str | Path, image_root_dir: str | Path, transform: Callable | None = None, + require_any_type: bool = False, ): image_root_dir = Path(image_root_dir).resolve() self.types_mlb = self.read_types_file(types_file) @@ -38,6 +38,7 @@ class BoardGameDataset(Dataset): LOGGER.info("Game types: %s", self.classes) self.transform = transform + self.require_any_type = require_any_type self.images, self.labels = self.read_games_file(games_file, image_root_dir) assert len(self.images) == len(self.labels) LOGGER.info("Loaded %d games and images in total", len(self.labels)) @@ -61,8 +62,7 @@ class BoardGameDataset(Dataset): LOGGER.info("Reading games from file <%s>", games_file) with games_file.open(encoding="utf-8") as file: games = ( - self._parse_game(json.loads(line), image_dir) - for line in tqdm(islice(file, 10_000)) + self._parse_game(json.loads(line), image_dir) for line in tqdm(file) ) images, labels = zip(*filter(None, games)) return images, labels @@ -83,7 +83,7 @@ class BoardGameDataset(Dataset): t for s in game_types_raw if (t := s.split(":")[0]) in self.classes_set ] game_types = self.types_mlb.transform([game_type_labels])[0] - if not any(game_types): + if self.require_any_type and not any(game_types): LOGGER.debug("No valid game types for game <%s>", bgg_id) return None diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index e7efa7b8..e69a62eb 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -8,7 +8,7 @@ from torch import nn from torch import optim from torch.nn import functional as F from torch.utils.data import DataLoader, random_split -from torchvision.models import resnet50, ResNet50_Weights +from torchvision.models import resnet50, ResNet, ResNet50_Weights from tqdm import tqdm @@ -22,6 +22,7 @@ def train( images_dir: str | Path, test_size: float = 0.01, batch_size: int = 128, + device: str | torch.device = torch.device("cpu"), ) -> nn.Module: """Train a model to classify board game covers.""" @@ -33,7 +34,9 @@ def train( assert 0 < test_size < 1, f"Test size must be between 0 and 1: {test_size}" weights = ResNet50_Weights.DEFAULT - model = resnet50(weights=weights) + model: ResNet = resnet50(weights=weights) + + LOGGER.info("Training model on %s", device) dataset = BoardGameDataset( games_file=data_dir / "scraped" / "bgg_GameItem.jl", @@ -62,19 +65,20 @@ def train( num_classes = len(dataset.classes) model.fc = nn.Linear(model.fc.in_features, num_classes) + model.to(device) criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) num_epochs = 10 for epoch in range(num_epochs): - print(f"Epoch {epoch+1}/{num_epochs}") + print(f"Epoch {epoch+1:>3d}/{num_epochs:>3d}") model.train() for images, labels in tqdm(train_dataloader): optimizer.zero_grad() - outputs = model(images) - loss = criterion(outputs, labels.float()) + outputs = model(images.to(device)) + loss = criterion(outputs, labels.float().to(device)) loss.backward() optimizer.step() @@ -82,15 +86,15 @@ def train( with torch.no_grad(): losses = torch.tensor( [ - criterion(model(inputs), labels.float()) + criterion(model(inputs.to(device)), labels.float().to(device)) for inputs, labels in tqdm(test_dataloader) ] ) - print(f"Loss: {losses.mean().item():>7.4f} ± {losses.std().item():>7.4f}") + print(f"Loss: {losses.mean().item():>7.4f}") images, labels = next(iter(test_dataloader)) - output = F.sigmoid(model(images[:3, ...])) - print(labels[:3, ...], output) + output = F.sigmoid(model(images[:3, ...].to(device))) + print(labels[:3, ...].to(device), output) # TODO save model -- GitLab From 9c73da35602d28a837a577c02535b04e29205f31 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 14:21:21 +0200 Subject: [PATCH 16/45] save and load model --- .../cover_classifier/__main__.py | 5 +++++ .../cover_classifier/model.py | 20 ++++++++++++++----- 2 files changed, 20 insertions(+), 5 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index be468057..73dc425e 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -27,10 +27,15 @@ def main(): if torch.backends.mps.is_available() else "cpu" ) + + model_path = Path().resolve() / "models" / "cover_classifier.pt" + model_path.parent.mkdir(parents=True, exist_ok=True) + train( data_dir=BASE_DIR.parent / "board-game-data", images_dir=BASE_DIR.parent / "board-game-scraper" / "images", device=device, + model_path=model_path, ) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index e69a62eb..b4f393e7 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -23,6 +23,8 @@ def train( test_size: float = 0.01, batch_size: int = 128, device: str | torch.device = torch.device("cpu"), + model_path: str | Path | None = None, + resume: bool = False, ) -> nn.Module: """Train a model to classify board game covers.""" @@ -36,14 +38,14 @@ def train( weights = ResNet50_Weights.DEFAULT model: ResNet = resnet50(weights=weights) - LOGGER.info("Training model on %s", device) - 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(), ) + num_classes = len(dataset.classes) + train_dataset, test_dataset = random_split(dataset, (1 - test_size, test_size)) LOGGER.info( "Split into %d training and %d test samples", @@ -63,8 +65,15 @@ def train( # num_workers=8, ) - num_classes = len(dataset.classes) - model.fc = nn.Linear(model.fc.in_features, num_classes) + model_path = Path(model_path).resolve() if model_path else None + + if resume and model_path and model_path.exists(): + LOGGER.info("Resuming training from %s", model_path) + model.load_state_dict(torch.load(model_path)) + else: + model.fc = nn.Linear(model.fc.in_features, num_classes) + + LOGGER.info("Training model on %s", device) model.to(device) criterion = nn.BCEWithLogitsLoss() @@ -96,6 +105,7 @@ def train( output = F.sigmoid(model(images[:3, ...].to(device))) print(labels[:3, ...].to(device), output) - # TODO save model + if model_path: + torch.save(model.state_dict(), model_path) return model -- GitLab From 20738d56ba9e4792f7880e78caa775d8fd688ec4 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 14:26:43 +0200 Subject: [PATCH 17/45] small fixes --- experiments/cover_classifier/cover_classifier/__main__.py | 1 + experiments/cover_classifier/cover_classifier/data.py | 4 +++- experiments/cover_classifier/cover_classifier/model.py | 4 ++-- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 73dc425e..5a788524 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -36,6 +36,7 @@ def main(): images_dir=BASE_DIR.parent / "board-game-scraper" / "images", device=device, model_path=model_path, + resume=True, ) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 5055a3e7..e97f6eca 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -1,5 +1,6 @@ """Board game dataset.""" +from itertools import islice import json import logging from pathlib import Path @@ -62,7 +63,8 @@ class BoardGameDataset(Dataset): LOGGER.info("Reading games from file <%s>", games_file) with games_file.open(encoding="utf-8") as file: games = ( - self._parse_game(json.loads(line), image_dir) for line in tqdm(file) + self._parse_game(json.loads(line), image_dir) + for line in tqdm(islice(file, 1000)) ) images, labels = zip(*filter(None, games)) return images, labels diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index b4f393e7..ef1c33ed 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -65,13 +65,12 @@ def train( # num_workers=8, ) + model.fc = nn.Linear(model.fc.in_features, num_classes) model_path = Path(model_path).resolve() if model_path else None if resume and model_path and model_path.exists(): LOGGER.info("Resuming training from %s", model_path) model.load_state_dict(torch.load(model_path)) - else: - model.fc = nn.Linear(model.fc.in_features, num_classes) LOGGER.info("Training model on %s", device) model.to(device) @@ -90,6 +89,7 @@ def train( loss = criterion(outputs, labels.float().to(device)) loss.backward() optimizer.step() + break model.eval() with torch.no_grad(): -- GitLab From f4e2c2c8349baafe31666b368cf82a16c5232ec9 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 14:33:07 +0200 Subject: [PATCH 18/45] gitignored models; corrected mypy setting in pyproject.toml --- experiments/cover_classifier/.gitignore | 1 + experiments/cover_classifier/pyproject.toml | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 experiments/cover_classifier/.gitignore diff --git a/experiments/cover_classifier/.gitignore b/experiments/cover_classifier/.gitignore new file mode 100644 index 00000000..2bcdfd92 --- /dev/null +++ b/experiments/cover_classifier/.gitignore @@ -0,0 +1 @@ +models/ diff --git a/experiments/cover_classifier/pyproject.toml b/experiments/cover_classifier/pyproject.toml index c9697dbc..99134e79 100644 --- a/experiments/cover_classifier/pyproject.toml +++ b/experiments/cover_classifier/pyproject.toml @@ -39,7 +39,7 @@ python_version = "3.11" [[tool.mypy.overrides]] module = [ - "sklearn", - "torchvision" + "sklearn.*", + "torchvision.*", ] ignore_missing_imports = true -- GitLab From 16147a1b634223707643fd59897f21eb2603d823 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 14:45:16 +0200 Subject: [PATCH 19/45] keep bgg_ids around; slightly more readable evaluation --- .../cover_classifier/cover_classifier/data.py | 25 +++++++++++-------- .../cover_classifier/model.py | 10 +++++--- 2 files changed, 20 insertions(+), 15 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index e97f6eca..8cdfdd7f 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -40,9 +40,12 @@ class BoardGameDataset(Dataset): self.transform = transform self.require_any_type = require_any_type - self.images, self.labels = self.read_games_file(games_file, image_root_dir) - assert len(self.images) == len(self.labels) - LOGGER.info("Loaded %d games and images in total", len(self.labels)) + self.bgg_ids, self.images, self.labels = self.read_games_file( + games_file, + image_root_dir, + ) + 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.""" @@ -56,7 +59,7 @@ class BoardGameDataset(Dataset): self, games_file: Union[str, Path], image_dir: Path, - ) -> tuple[tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]: + ) -> tuple[tuple[int, ...], tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]: """Read games from file.""" games_file = Path(games_file).resolve() @@ -66,14 +69,14 @@ class BoardGameDataset(Dataset): self._parse_game(json.loads(line), image_dir) for line in tqdm(islice(file, 1000)) ) - images, labels = zip(*filter(None, games)) - return images, labels + bgg_ids, images, labels = zip(*filter(None, games)) + return bgg_ids, images, labels def _parse_game( self, game: dict[str, Any], image_dir: Path, - ) -> tuple[torch.Tensor, torch.Tensor] | None: + ) -> 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) @@ -100,7 +103,7 @@ class BoardGameDataset(Dataset): image = self._read_and_transform_image(str(image_path)) - return image, torch.from_numpy(game_types) + 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) @@ -109,7 +112,7 @@ class BoardGameDataset(Dataset): return image def __len__(self) -> int: - return len(self.labels) + return len(self.bgg_ids) - def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: - return self.images[idx], self.labels[idx] + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor, int]: + 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 index ef1c33ed..ce86d5b0 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -78,12 +78,13 @@ def train( criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) + # TODO: Let Lightning handle training loop num_epochs = 10 for epoch in range(num_epochs): print(f"Epoch {epoch+1:>3d}/{num_epochs:>3d}") model.train() - for images, labels in tqdm(train_dataloader): + for images, labels, _ in tqdm(train_dataloader): optimizer.zero_grad() outputs = model(images.to(device)) loss = criterion(outputs, labels.float().to(device)) @@ -96,14 +97,15 @@ def train( losses = torch.tensor( [ criterion(model(inputs.to(device)), labels.float().to(device)) - for inputs, labels in tqdm(test_dataloader) + for inputs, labels, _ in tqdm(test_dataloader) ] ) print(f"Loss: {losses.mean().item():>7.4f}") - images, labels = next(iter(test_dataloader)) + images, labels, bgg_ids = next(iter(test_dataloader)) output = F.sigmoid(model(images[:3, ...].to(device))) - print(labels[:3, ...].to(device), output) + print(dataset.types_mlb.inverse_transform(labels[:3, ...])) + print(bgg_ids[:3], "\n", labels[:3, ...], "\n", output) if model_path: torch.save(model.state_dict(), model_path) -- GitLab From 61d96f4065b45de82bb244c59b21ec90fb9d05af Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 15:18:32 +0200 Subject: [PATCH 20/45] print better evaluation --- .../cover_classifier/model.py | 28 +++++++++++++++---- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index ce86d5b0..217955d8 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -101,13 +101,31 @@ def train( ] ) print(f"Loss: {losses.mean().item():>7.4f}") - - images, labels, bgg_ids = next(iter(test_dataloader)) - output = F.sigmoid(model(images[:3, ...].to(device))) - print(dataset.types_mlb.inverse_transform(labels[:3, ...])) - print(bgg_ids[:3], "\n", labels[:3, ...], "\n", output) + print_game_results(model, test_dataloader, dataset.classes, max_results=3) if model_path: torch.save(model.state_dict(), model_path) 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], + ) + device = next(model.parameters()).device + model.eval() + prediction_batch = F.sigmoid(model(image_batch.to(device))) + 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, + ): + print(f"\t{class_:15}: {pred:>6.1%} ({label})") -- GitLab From 37ad3cf7f10f6502cf8fe32b1df9d326aa3a0733 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 15:26:16 +0200 Subject: [PATCH 21/45] some more training --- experiments/cover_classifier/cover_classifier/data.py | 2 +- experiments/cover_classifier/cover_classifier/model.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 8cdfdd7f..18f802b4 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -67,7 +67,7 @@ class BoardGameDataset(Dataset): with games_file.open(encoding="utf-8") as file: games = ( self._parse_game(json.loads(line), image_dir) - for line in tqdm(islice(file, 1000)) + for line in tqdm(islice(file, 10_000)) ) bgg_ids, images, labels = zip(*filter(None, games)) return bgg_ids, images, labels diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 217955d8..c58d13b7 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -90,7 +90,6 @@ def train( loss = criterion(outputs, labels.float().to(device)) loss.backward() optimizer.step() - break model.eval() with torch.no_grad(): -- GitLab From 6c644a0dcbf9519df673082216471b70b25f1220 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 16:25:39 +0200 Subject: [PATCH 22/45] make sure require_any_type is respected --- experiments/cover_classifier/cover_classifier/data.py | 8 +++++--- experiments/cover_classifier/cover_classifier/model.py | 2 ++ 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 18f802b4..fbdb4693 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -67,7 +67,7 @@ class BoardGameDataset(Dataset): with games_file.open(encoding="utf-8") as file: games = ( self._parse_game(json.loads(line), image_dir) - for line in tqdm(islice(file, 10_000)) + for line in tqdm(islice(file, 1_000_000)) ) bgg_ids, images, labels = zip(*filter(None, games)) return bgg_ids, images, labels @@ -81,11 +81,13 @@ class BoardGameDataset(Dataset): 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 or not game_types_raw: + if not bgg_id or not image_path_str: return None game_type_labels = [ - t for s in game_types_raw if (t := s.split(":")[0]) in self.classes_set + 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): diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index c58d13b7..6c96301b 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -43,8 +43,10 @@ def train( types_file=data_dir / "scraped" / "bgg_GameType.csv", image_root_dir=images_dir, transform=weights.transforms(), + require_any_type=False, ) num_classes = len(dataset.classes) + # TODO: games without any type should be in a holdout set meant for human review train_dataset, test_dataset = random_split(dataset, (1 - test_size, test_size)) LOGGER.info( -- GitLab From 7334d11a6c1793353302de37af5e81f73d4e0db4 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 16:38:41 +0200 Subject: [PATCH 23/45] load everything into one large tensor --- .../cover_classifier/cover_classifier/data.py | 16 +++++++++++++--- .../cover_classifier/cover_classifier/model.py | 12 ++++++------ 2 files changed, 19 insertions(+), 9 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index fbdb4693..70e9fb84 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -30,6 +30,7 @@ class BoardGameDataset(Dataset): image_root_dir: str | Path, transform: Callable | None = None, require_any_type: bool = False, + device: str | torch.device | None = None, ): image_root_dir = Path(image_root_dir).resolve() self.types_mlb = self.read_types_file(types_file) @@ -40,10 +41,16 @@ class BoardGameDataset(Dataset): 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, ) + if device: + self.bgg_ids = self.bgg_ids.to(device=device) + self.images = self.images.to(device=device) + self.labels = self.labels.to(device=device) + assert len(self.bgg_ids) == len(self.images) == len(self.labels) LOGGER.info("Loaded %d games and images in total", len(self.bgg_ids)) @@ -59,7 +66,7 @@ class BoardGameDataset(Dataset): self, games_file: Union[str, Path], image_dir: Path, - ) -> tuple[tuple[int, ...], tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]: + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Read games from file.""" games_file = Path(games_file).resolve() @@ -70,7 +77,10 @@ class BoardGameDataset(Dataset): for line in tqdm(islice(file, 1_000_000)) ) bgg_ids, images, labels = zip(*filter(None, games)) - return bgg_ids, images, labels + 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, @@ -116,5 +126,5 @@ class BoardGameDataset(Dataset): def __len__(self) -> int: return len(self.bgg_ids) - def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor, int]: + 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 index 6c96301b..4ebfe3e1 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -43,7 +43,8 @@ def train( types_file=data_dir / "scraped" / "bgg_GameType.csv", image_root_dir=images_dir, transform=weights.transforms(), - require_any_type=False, + require_any_type=True, + device=device, ) num_classes = len(dataset.classes) # TODO: games without any type should be in a holdout set meant for human review @@ -88,8 +89,8 @@ def train( model.train() for images, labels, _ in tqdm(train_dataloader): optimizer.zero_grad() - outputs = model(images.to(device)) - loss = criterion(outputs, labels.float().to(device)) + outputs = model(images) + loss = criterion(outputs, labels.float()) loss.backward() optimizer.step() @@ -97,7 +98,7 @@ def train( with torch.no_grad(): losses = torch.tensor( [ - criterion(model(inputs.to(device)), labels.float().to(device)) + criterion(model(inputs), labels.float()) for inputs, labels, _ in tqdm(test_dataloader) ] ) @@ -120,9 +121,8 @@ def print_game_results(model, dataloader, classes, max_results: int | None = Non label_batch[:max_results], bgg_id_batch[:max_results], ) - device = next(model.parameters()).device model.eval() - prediction_batch = F.sigmoid(model(image_batch.to(device))) + 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( -- GitLab From 056657847c205f5fd029d08099c217253fd61435 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 16:47:23 +0200 Subject: [PATCH 24/45] better handling of max_samples --- experiments/cover_classifier/cover_classifier/data.py | 7 +++++-- experiments/cover_classifier/cover_classifier/model.py | 4 +++- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 70e9fb84..981ba98a 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -29,6 +29,7 @@ class BoardGameDataset(Dataset): types_file: str | Path, image_root_dir: str | Path, transform: Callable | None = None, + max_samples: int | None = None, require_any_type: bool = False, device: str | torch.device | None = None, ): @@ -45,6 +46,7 @@ class BoardGameDataset(Dataset): self.bgg_ids, self.images, self.labels = self.read_games_file( games_file, image_root_dir, + max_samples, ) if device: self.bgg_ids = self.bgg_ids.to(device=device) @@ -66,15 +68,16 @@ class BoardGameDataset(Dataset): 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(islice(file, 1_000_000)) + 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) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 4ebfe3e1..974b2ec9 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -44,6 +44,7 @@ def train( image_root_dir=images_dir, transform=weights.transforms(), require_any_type=True, + max_samples=10_000, device=device, ) num_classes = len(dataset.classes) @@ -129,4 +130,5 @@ def print_game_results(model, dataloader, classes, max_results: int | None = Non zip(predictions, labels, classes), reverse=True, ): - print(f"\t{class_:15}: {pred:>6.1%} ({label})") + error = round(pred) != label + print(f"\t{class_:15}: {pred:>6.1%} ({label} {'❌' if error else '✅'})") -- GitLab From 5dbc92e59c70ddec1056147c406571a9d331d90c Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 14 Jan 2024 16:54:51 +0200 Subject: [PATCH 25/45] we need primitives, not tensors --- experiments/cover_classifier/cover_classifier/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 974b2ec9..d8668e86 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -130,5 +130,5 @@ def print_game_results(model, dataloader, classes, 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[PATCH 27/45] added torchmetrics --- experiments/cover_classifier/poetry.lock | 2 +- experiments/cover_classifier/pyproject.toml | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/experiments/cover_classifier/poetry.lock b/experiments/cover_classifier/poetry.lock index 3453b4bf..8ff55014 100644 --- a/experiments/cover_classifier/poetry.lock +++ b/experiments/cover_classifier/poetry.lock @@ -3927,4 +3927,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "~3.11" -content-hash = "571f48627166030e04dca73d29701432a06e5cdcc4e79d1160d3e0e7cf650c39" +content-hash = "a7129b3b3741618c116566bbc05bf3c6ac58c9c4bfcd2f57583b4c73adeadbd7" diff --git a/experiments/cover_classifier/pyproject.toml b/experiments/cover_classifier/pyproject.toml index 7d4e0384..798e650f 100644 --- a/experiments/cover_classifier/pyproject.toml +++ b/experiments/cover_classifier/pyproject.toml @@ -23,6 +23,7 @@ scikit-learn = "*" scipy = "*" seaborn = "*" torch = "*" +torchmetrics = "*" torchvision = "*" tqdm = "*" -- GitLab From 1bb4d5ee673051568200f264adc39489e6f71e20 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Mon, 15 Jan 2024 22:34:09 +0200 Subject: [PATCH 28/45] first version of LightningModule --- .../cover_classifier/__main__.py | 21 +-- .../cover_classifier/cover_classifier/data.py | 2 +- .../cover_classifier/model.py | 166 +++++++++++++----- 3 files changed, 134 insertions(+), 55 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 5a788524..13449dfa 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -20,13 +20,13 @@ def main(): stream=sys.stdout, ) - device = torch.device( - "cuda" - if torch.cuda.is_available() - else "mps" - if torch.backends.mps.is_available() - else "cpu" - ) + # device = torch.device( + # "cuda" + # if torch.cuda.is_available() + # else "mps" + # if torch.backends.mps.is_available() + # else "cpu" + # ) model_path = Path().resolve() / "models" / "cover_classifier.pt" model_path.parent.mkdir(parents=True, exist_ok=True) @@ -34,9 +34,10 @@ def main(): train( data_dir=BASE_DIR.parent / "board-game-data", images_dir=BASE_DIR.parent / "board-game-scraper" / "images", - device=device, - model_path=model_path, - resume=True, + # device=device, + # model_path=model_path, + model_dir=model_path.parent, + # resume=True, ) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index fbdb4693..ae68f62c 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -67,7 +67,7 @@ class BoardGameDataset(Dataset): with games_file.open(encoding="utf-8") as file: games = ( self._parse_game(json.loads(line), image_dir) - for line in tqdm(islice(file, 1_000_000)) + for line in tqdm(islice(file, 1_000)) ) bgg_ids, images, labels = zip(*filter(None, games)) return bgg_ids, images, labels diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 6c96301b..e165d715 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -3,28 +3,87 @@ import logging from pathlib import Path +import lightning as L import torch from torch import nn from torch import optim from torch.nn import functional as F from torch.utils.data import DataLoader, random_split from torchvision.models import resnet50, ResNet, ResNet50_Weights +import torchmetrics as M -from tqdm import tqdm +# from tqdm import tqdm from cover_classifier.data import BoardGameDataset LOGGER = logging.getLogger(__name__) +class CoverClassifier(L.LightningModule): + """Lightning module for cover classification.""" + + def __init__(self, num_classes: int, weights: ResNet50_Weights): + super().__init__() + self.model: ResNet = resnet50(weights=weights) + self.model.fc = nn.Linear(self.model.fc.in_features, num_classes) + self.criterion = nn.BCEWithLogitsLoss() + # self.accuracy = M.Accuracy(task="multilabel", num_labels=num_classes) + # self.f1 = M.F1Score(task="multilabel", num_labels=num_classes) + self.save_hyperparameters() + + def forward(self, x): + return self.model(x) + + def training_step(self, batch, batch_idx): + images, labels, _ = batch + outputs = self(images) + loss = self.criterion(outputs, labels.float()) + self.log("train_loss", loss) + return loss + + def validation_step(self, batch, batch_idx): + images, labels, _ = batch + outputs = self(images) + loss = self.criterion(outputs, labels.float()) + self.log("val_loss", loss) + # self.accuracy(outputs, labels) + # self.f1(outputs, labels) + return loss + + def test_step(self, batch, batch_idx): + images, labels, _ = batch + outputs = self(images) + loss = self.criterion(outputs, labels.float()) + self.log("test_loss", loss) + # self.accuracy(outputs, labels) + # self.f1(outputs, labels) + return loss + + def configure_optimizers(self): + return optim.Adam(self.parameters(), lr=1e-3) + + # def training_epoch_end(self, outputs): + # self.log("train_acc", self.accuracy.compute()) + # self.log("train_f1", self.f1.compute()) + + # def validation_epoch_end(self, outputs): + # self.log("val_acc", self.accuracy.compute()) + # self.log("val_f1", self.f1.compute()) + + # def test_epoch_end(self, outputs): + # self.log("test_acc", self.accuracy.compute()) + # self.log("test_f1", self.f1.compute()) + + def train( data_dir: str | Path, images_dir: str | Path, - test_size: float = 0.01, + test_size: float = 0.05, + val_size: float = 0.05, batch_size: int = 128, - device: str | torch.device = torch.device("cpu"), - model_path: str | Path | None = None, - resume: bool = False, + # device: str | torch.device = torch.device("cpu"), + model_dir: str | Path | None = None, + # resume: bool = False, ) -> nn.Module: """Train a model to classify board game covers.""" @@ -34,9 +93,12 @@ def train( 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}" weights = ResNet50_Weights.DEFAULT - model: ResNet = resnet50(weights=weights) dataset = BoardGameDataset( games_file=data_dir / "scraped" / "bgg_GameItem.jl", @@ -48,64 +110,80 @@ def train( num_classes = len(dataset.classes) # TODO: games without any type should be in a holdout set meant for human review - train_dataset, test_dataset = random_split(dataset, (1 - test_size, test_size)) + train_dataset, test_dataset, val_dataset = random_split( + dataset, (1 - test_size - val_size, test_size, val_size) + ) LOGGER.info( - "Split into %d training and %d test samples", + "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, - # num_workers=8, ) test_dataloader = DataLoader( test_dataset, batch_size=batch_size, shuffle=True, - # num_workers=8, + ) + val_dataloader = DataLoader( + val_dataset, + batch_size=batch_size, + shuffle=True, ) - model.fc = nn.Linear(model.fc.in_features, num_classes) - model_path = Path(model_path).resolve() if model_path else None + model = CoverClassifier(num_classes, weights) + # model_path = Path(model_path).resolve() if model_path else None - if resume and model_path and model_path.exists(): - LOGGER.info("Resuming training from %s", model_path) - model.load_state_dict(torch.load(model_path)) + # if resume and model_path and model_path.exists(): + # LOGGER.info("Resuming training from %s", model_path) + # model.load_state_dict(torch.load(model_path)) - LOGGER.info("Training model on %s", device) - model.to(device) + # LOGGER.info("Training model on %s", device) + # model.to(device) - criterion = nn.BCEWithLogitsLoss() - optimizer = optim.Adam(model.parameters(), lr=0.001) + # criterion = nn.BCEWithLogitsLoss() + # optimizer = optim.Adam(model.parameters(), lr=0.001) # TODO: Let Lightning handle training loop - num_epochs = 10 - for epoch in range(num_epochs): - print(f"Epoch {epoch+1:>3d}/{num_epochs:>3d}") - - model.train() - for images, labels, _ in tqdm(train_dataloader): - optimizer.zero_grad() - outputs = model(images.to(device)) - loss = criterion(outputs, labels.float().to(device)) - loss.backward() - optimizer.step() - - model.eval() - with torch.no_grad(): - losses = torch.tensor( - [ - criterion(model(inputs.to(device)), labels.float().to(device)) - for inputs, labels, _ in tqdm(test_dataloader) - ] - ) - print(f"Loss: {losses.mean().item():>7.4f}") - print_game_results(model, test_dataloader, dataset.classes, max_results=3) - - if model_path: - torch.save(model.state_dict(), model_path) + # num_epochs = 10 + # for epoch in range(num_epochs): + # print(f"Epoch {epoch+1:>3d}/{num_epochs:>3d}") + + # model.train() + # for images, labels, _ in tqdm(train_dataloader): + # optimizer.zero_grad() + # outputs = model(images.to(device)) + # loss = criterion(outputs, labels.float().to(device)) + # loss.backward() + # optimizer.step() + + # model.eval() + # with torch.no_grad(): + # losses = torch.tensor( + # [ + # criterion(model(inputs.to(device)), labels.float().to(device)) + # for inputs, labels, _ in tqdm(test_dataloader) + # ] + # ) + # print(f"Loss: {losses.mean().item():>7.4f}") + # print_game_results(model, test_dataloader, dataset.classes, max_results=3) + + # if model_path: + # torch.save(model.state_dict(), model_path) + + trainer = L.Trainer( + default_root_dir=Path(model_dir).resolve() if model_dir else None, + ) + trainer.fit( + model=model, + train_dataloaders=train_dataloader, + val_dataloaders=val_dataloader, + ) + trainer.test(model, dataloaders=test_dataloader) return model -- GitLab From 83ca7c6fc10875f66fd6fb79ba2d8fff20772b08 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 16 Jan 2024 08:22:42 +0200 Subject: [PATCH 29/45] introduce sampling from the full dataset and multiple training runs --- .../cover_classifier/__main__.py | 20 ++++++++++++------- .../cover_classifier/cover_classifier/data.py | 5 +++++ .../cover_classifier/model.py | 4 ++-- 3 files changed, 20 insertions(+), 9 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 5a788524..c0708fa7 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -31,13 +31,19 @@ def main(): model_path = Path().resolve() / "models" / "cover_classifier.pt" model_path.parent.mkdir(parents=True, exist_ok=True) - train( - data_dir=BASE_DIR.parent / "board-game-data", - images_dir=BASE_DIR.parent / "board-game-scraper" / "images", - device=device, - model_path=model_path, - resume=True, - ) + training_runs = 10 + + for i in range(training_runs): + print(f"Training run {i+1} of {training_runs}") + train( + data_dir=BASE_DIR.parent / "board-game-data", + images_dir=BASE_DIR.parent / "board-game-scraper" / "images", + batch_size=32, + num_epochs=10, + device=device, + model_path=model_path, + resume=True, + ) if __name__ == "__main__": diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 981ba98a..26babbbc 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -4,6 +4,7 @@ from itertools import islice import json import logging from pathlib import Path +import random from typing import Any, Callable, Union import jmespath import polars as pl @@ -116,6 +117,10 @@ class BoardGameDataset(Dataset): ) return None + if random.random() > 0.15: + # randomly skip 15% of images + return None + image = self._read_and_transform_image(str(image_path)) return bgg_id, image, torch.from_numpy(game_types) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index d8668e86..deccd809 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -22,6 +22,7 @@ def train( images_dir: str | Path, test_size: float = 0.01, batch_size: int = 128, + num_epochs: int = 10, device: str | torch.device = torch.device("cpu"), model_path: str | Path | None = None, resume: bool = False, @@ -44,7 +45,7 @@ def train( image_root_dir=images_dir, transform=weights.transforms(), require_any_type=True, - max_samples=10_000, + max_samples=1_000_000, device=device, ) num_classes = len(dataset.classes) @@ -83,7 +84,6 @@ def train( optimizer = optim.Adam(model.parameters(), lr=0.001) # TODO: Let Lightning handle training loop - num_epochs = 10 for epoch in range(num_epochs): print(f"Epoch {epoch+1:>3d}/{num_epochs:>3d}") -- GitLab From 9dbfd7f44c95b525c25a0cff87d1de3a5405cfab Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Sun, 3 Mar 2024 22:50:03 +0200 Subject: [PATCH 30/45] Updated dependencies --- experiments/cover_classifier/poetry.lock | 1807 ++++++++++--------- experiments/cover_classifier/pyproject.toml | 2 +- 2 files changed, 948 insertions(+), 861 deletions(-) diff --git a/experiments/cover_classifier/poetry.lock b/experiments/cover_classifier/poetry.lock index 8ff55014..73342399 100644 --- a/experiments/cover_classifier/poetry.lock +++ b/experiments/cover_classifier/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.1" +version = "3.9.3" description = "Async http client/server framework 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["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] @@ -3926,5 +4013,5 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" -python-versions = "~3.11" -content-hash = "a7129b3b3741618c116566bbc05bf3c6ac58c9c4bfcd2f57583b4c73adeadbd7" +python-versions = "3.11.*" +content-hash = "37c9f7ace0f903a3d2455cdb60c41d744c4a663c51c6ff1f5b7ce57c976891d1" diff --git a/experiments/cover_classifier/pyproject.toml b/experiments/cover_classifier/pyproject.toml index 798e650f..a51261e0 100644 --- a/experiments/cover_classifier/pyproject.toml +++ b/experiments/cover_classifier/pyproject.toml @@ -6,7 +6,7 @@ authors = ["Markus Shepherd "] readme = "README.md" [tool.poetry.dependencies] -python = "~3.11" +python = "3.11.*" black = "*" lightning = "*" -- GitLab From d2c9af06969af73b4374807c87b715d8d8c73c84 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Mon, 4 Mar 2024 09:59:29 +0200 Subject: [PATCH 31/45] Remove outer training loop; actually respect num_episodes --- .../cover_classifier/__main__.py | 24 ++++++++----------- .../cover_classifier/model.py | 1 + 2 files changed, 11 insertions(+), 14 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 5b44dd4d..3489c0c5 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -31,20 +31,16 @@ def main(): model_path = Path().resolve() / "models" / "cover_classifier.pt" model_path.parent.mkdir(parents=True, exist_ok=True) - training_runs = 10 - - for i in range(training_runs): - print(f"Training run {i+1} of {training_runs}") - train( - data_dir=BASE_DIR.parent / "board-game-data", - images_dir=BASE_DIR.parent / "board-game-scraper" / "images", - batch_size=32, - num_epochs=10, - # device=device, - # model_path=model_path, - model_dir=model_path.parent, - # resume=True, - ) + train( + data_dir=BASE_DIR.parent / "board-game-data", + images_dir=BASE_DIR.parent / "board-game-scraper" / "images", + batch_size=32, + num_epochs=100, + # device=device, + # model_path=model_path, + model_dir=model_path.parent, + # resume=True, + ) if __name__ == "__main__": diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 006b76e2..dd72ff00 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -179,6 +179,7 @@ def train( # torch.save(model.state_dict(), model_path) trainer = L.Trainer( + max_epochs=num_epochs, default_root_dir=Path(model_dir).resolve() if model_dir else None, ) trainer.fit( -- GitLab From 7cf9acd917d85c16a5f008b6a138bf2f5381d4d6 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Mon, 4 Mar 2024 22:35:26 +0200 Subject: [PATCH 32/45] Freeze resnet layers; no more manual handling of devices; better metrics --- .../cover_classifier/__main__.py | 9 -- .../cover_classifier/cover_classifier/data.py | 5 - .../cover_classifier/model.py | 107 ++++++++++++------ 3 files changed, 73 insertions(+), 48 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 3489c0c5..10b7fff5 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -20,14 +20,6 @@ def main(): stream=sys.stdout, ) - # device = torch.device( - # "cuda" - # if torch.cuda.is_available() - # else "mps" - # if torch.backends.mps.is_available() - # else "cpu" - # ) - model_path = Path().resolve() / "models" / "cover_classifier.pt" model_path.parent.mkdir(parents=True, exist_ok=True) @@ -36,7 +28,6 @@ def main(): images_dir=BASE_DIR.parent / "board-game-scraper" / "images", batch_size=32, num_epochs=100, - # device=device, # model_path=model_path, model_dir=model_path.parent, # resume=True, diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 3d0ee845..6cf676b3 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -32,7 +32,6 @@ class BoardGameDataset(Dataset): transform: Callable | None = None, max_samples: int | None = None, require_any_type: bool = False, - device: str | torch.device | None = None, ): image_root_dir = Path(image_root_dir).resolve() self.types_mlb = self.read_types_file(types_file) @@ -49,10 +48,6 @@ class BoardGameDataset(Dataset): image_root_dir, max_samples, ) - if device: - self.bgg_ids = self.bgg_ids.to(device=device) - self.images = self.images.to(device=device) - self.labels = self.labels.to(device=device) assert len(self.bgg_ids) == len(self.images) == len(self.labels) LOGGER.info("Loaded %d games and images in total", len(self.bgg_ids)) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index dd72ff00..c34f6290 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -22,57 +22,98 @@ LOGGER = logging.getLogger(__name__) class CoverClassifier(L.LightningModule): """Lightning module for cover classification.""" - def __init__(self, num_classes: int, weights: ResNet50_Weights): + def __init__( + self, + *, + num_classes: int, + weights: ResNet50_Weights, + learning_rate: float = 1e-3, + ): super().__init__() - self.model: ResNet = resnet50(weights=weights) - self.model.fc = nn.Linear(self.model.fc.in_features, num_classes) - self.criterion = nn.BCEWithLogitsLoss() - # self.accuracy = M.Accuracy(task="multilabel", num_labels=num_classes) - # self.f1 = M.F1Score(task="multilabel", num_labels=num_classes) + + 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.learning_rate = learning_rate + + self.train_accuracy = M.MultilabelAccuracy(num_labels=num_classes) + self.train_f1 = M.MultilabelF1Score(num_labels=num_classes) + + self.val_accuracy = M.MultilabelAccuracy(num_labels=num_classes) + self.val_f1 = M.MultilabelF1Score(num_labels=num_classes) + + self.test_accuracy = M.MultilabelAccuracy(num_labels=num_classes) + self.test_f1 = M.MultilabelF1Score(num_labels=num_classes) + self.save_hyperparameters() def forward(self, x): - return self.model(x) + return self.layers(self.res_net(x)) - def training_step(self, batch, batch_idx): + def training_step(self, batch, batch_idx=0, dataloader_idx=0): images, labels, _ = batch outputs = self(images) - loss = self.criterion(outputs, labels.float()) + + loss = self.loss_fn(outputs, labels.float()) self.log("train_loss", loss) + + 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): + def validation_step(self, batch, batch_idx=0, dataloader_idx=0): images, labels, _ = batch outputs = self(images) - loss = self.criterion(outputs, labels.float()) + + loss = self.loss_fn(outputs, labels.float()) self.log("val_loss", loss) - # self.accuracy(outputs, labels) - # self.f1(outputs, labels) + + 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): + def test_step(self, batch, batch_idx=0, dataloader_idx=0): images, labels, _ = batch outputs = self(images) - loss = self.criterion(outputs, labels.float()) + + loss = self.loss_fn(outputs, labels.float()) self.log("test_loss", loss) - # self.accuracy(outputs, labels) - # self.f1(outputs, labels) - return loss - def configure_optimizers(self): - return optim.Adam(self.parameters(), lr=1e-3) + self.test_accuracy(outputs, labels) + self.log("test_accuracy", self.test_accuracy) - # def training_epoch_end(self, outputs): - # self.log("train_acc", self.accuracy.compute()) - # self.log("train_f1", self.f1.compute()) + self.test_f1(outputs, labels) + self.log("test_f1", self.test_f1) - # def validation_epoch_end(self, outputs): - # self.log("val_acc", self.accuracy.compute()) - # self.log("val_f1", self.f1.compute()) + return loss - # def test_epoch_end(self, outputs): - # self.log("test_acc", self.accuracy.compute()) - # self.log("test_f1", self.f1.compute()) + def configure_optimizers(self): + return optim.Adam(self.parameters(), lr=self.learning_rate) def train( @@ -82,7 +123,6 @@ def train( val_size: float = 0.05, batch_size: int = 128, num_epochs: int = 10, - # device: str | torch.device = torch.device("cpu"), model_dir: str | Path | None = None, # resume: bool = False, ) -> nn.Module: @@ -108,7 +148,6 @@ def train( transform=weights.transforms(), require_any_type=True, max_samples=1_000_000, - # device=device, ) num_classes = len(dataset.classes) # TODO: games without any type should be in a holdout set meant for human review @@ -130,15 +169,15 @@ def train( test_dataloader = DataLoader( test_dataset, batch_size=batch_size, - shuffle=True, + shuffle=False, ) val_dataloader = DataLoader( val_dataset, batch_size=batch_size, - shuffle=True, + shuffle=False, ) - model = CoverClassifier(num_classes, weights) + model = CoverClassifier(num_classes=num_classes, weights=weights) # model_path = Path(model_path).resolve() if model_path else None # if resume and model_path and model_path.exists(): -- GitLab From 0af67a2814455ecd89e1e98ae5cf940521174902 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Mon, 4 Mar 2024 22:48:24 +0200 Subject: [PATCH 33/45] Corrected metrics --- .../cover_classifier/model.py | 30 +++++++++++-------- 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index c34f6290..9d421a66 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -53,14 +53,14 @@ class CoverClassifier(L.LightningModule): self.learning_rate = learning_rate - self.train_accuracy = M.MultilabelAccuracy(num_labels=num_classes) - self.train_f1 = M.MultilabelF1Score(num_labels=num_classes) + self.train_accuracy = M.Accuracy(task="multilabel", num_labels=num_classes) + self.train_f1 = M.F1Score(task="multilabel", num_labels=num_classes) - self.val_accuracy = M.MultilabelAccuracy(num_labels=num_classes) - self.val_f1 = M.MultilabelF1Score(num_labels=num_classes) + self.val_accuracy = M.Accuracy(task="multilabel", num_labels=num_classes) + self.val_f1 = M.F1Score(task="multilabel", num_labels=num_classes) - self.test_accuracy = M.MultilabelAccuracy(num_labels=num_classes) - self.test_f1 = M.MultilabelF1Score(num_labels=num_classes) + self.test_accuracy = M.Accuracy(task="multilabel", num_labels=num_classes) + self.test_f1 = M.F1Score(task="multilabel", num_labels=num_classes) self.save_hyperparameters() @@ -69,11 +69,13 @@ class CoverClassifier(L.LightningModule): def training_step(self, batch, batch_idx=0, dataloader_idx=0): images, labels, _ = batch - outputs = self(images) + logits = self(images) - loss = self.loss_fn(outputs, labels.float()) + 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) @@ -84,11 +86,13 @@ class CoverClassifier(L.LightningModule): def validation_step(self, batch, batch_idx=0, dataloader_idx=0): images, labels, _ = batch - outputs = self(images) + logits = self(images) - loss = self.loss_fn(outputs, labels.float()) + 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) @@ -99,11 +103,13 @@ class CoverClassifier(L.LightningModule): def test_step(self, batch, batch_idx=0, dataloader_idx=0): images, labels, _ = batch - outputs = self(images) + logits = self(images) - loss = self.loss_fn(outputs, labels.float()) + 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) -- GitLab From 3b3a4dee4226ffb196bd169d04ac168716ac1ded Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Mon, 4 Mar 2024 22:58:08 +0200 Subject: [PATCH 34/45] Remove old code --- .../cover_classifier/__main__.py | 4 +- .../cover_classifier/model.py | 41 +------------------ 2 files changed, 3 insertions(+), 42 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 10b7fff5..efd58505 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -27,10 +27,8 @@ def main(): data_dir=BASE_DIR.parent / "board-game-data", images_dir=BASE_DIR.parent / "board-game-scraper" / "images", batch_size=32, - num_epochs=100, - # model_path=model_path, + num_epochs=10, model_dir=model_path.parent, - # resume=True, ) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 9d421a66..0ce7fbc0 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -130,7 +130,6 @@ def train( batch_size: int = 128, num_epochs: int = 10, model_dir: str | Path | None = None, - # resume: bool = False, ) -> nn.Module: """Train a model to classify board game covers.""" @@ -184,44 +183,6 @@ def train( ) model = CoverClassifier(num_classes=num_classes, weights=weights) - # model_path = Path(model_path).resolve() if model_path else None - - # if resume and model_path and model_path.exists(): - # LOGGER.info("Resuming training from %s", model_path) - # model.load_state_dict(torch.load(model_path)) - - # LOGGER.info("Training model on %s", device) - # model.to(device) - - # criterion = nn.BCEWithLogitsLoss() - # optimizer = optim.Adam(model.parameters(), lr=0.001) - - # TODO: Let Lightning handle training loop - # num_epochs = 10 - # for epoch in range(num_epochs): - # print(f"Epoch {epoch+1:>3d}/{num_epochs:>3d}") - - # model.train() - # for images, labels, _ in tqdm(train_dataloader): - # optimizer.zero_grad() - # outputs = model(images.to(device)) - # loss = criterion(outputs, labels.float().to(device)) - # loss.backward() - # optimizer.step() - - # model.eval() - # with torch.no_grad(): - # losses = torch.tensor( - # [ - # criterion(model(inputs.to(device)), labels.float().to(device)) - # for inputs, labels, _ in tqdm(test_dataloader) - # ] - # ) - # print(f"Loss: {losses.mean().item():>7.4f}") - # print_game_results(model, test_dataloader, dataset.classes, max_results=3) - - # if model_path: - # torch.save(model.state_dict(), model_path) trainer = L.Trainer( max_epochs=num_epochs, @@ -234,6 +195,8 @@ def train( ) trainer.test(model, dataloaders=test_dataloader) + print_game_results(model, test_dataloader, dataset.classes, max_results=3) + return model -- GitLab From ad57b5514e599b9b141aa2fc1a3d8302da83d849 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Mon, 4 Mar 2024 23:02:02 +0200 Subject: [PATCH 35/45] Drop a few TODOs --- experiments/cover_classifier/cover_classifier/model.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 0ce7fbc0..fffbc424 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -182,19 +182,27 @@ def train( shuffle=False, ) + # TODO: Load model from best checkpoint if it exists + model = CoverClassifier(num_classes=num_classes, weights=weights) trainer = L.Trainer( max_epochs=num_epochs, default_root_dir=Path(model_dir).resolve() if model_dir else None, ) + + # TODO: Checkpoints, early stopping, logger, tune learning rate etc. + trainer.fit( model=model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, ) + trainer.test(model, dataloaders=test_dataloader) + # TODO: Link best checkpoint + print_game_results(model, test_dataloader, dataset.classes, max_results=3) return model -- GitLab From c354bf02f84cad3e49a991428ee68526a3f372c1 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 21:36:04 +0200 Subject: [PATCH 36/45] Spell out modules; ruff'ed --- .../cover_classifier/model.py | 38 ++++++++++++++----- 1 file changed, 28 insertions(+), 10 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index fffbc424..81a85bbd 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -3,14 +3,14 @@ import logging from pathlib import Path -import lightning as L +import lightning import torch +import torchmetrics from torch import nn from torch import optim from torch.nn import functional as F from torch.utils.data import DataLoader, random_split from torchvision.models import resnet50, ResNet, ResNet50_Weights -import torchmetrics as M # from tqdm import tqdm @@ -19,7 +19,7 @@ from cover_classifier.data import BoardGameDataset LOGGER = logging.getLogger(__name__) -class CoverClassifier(L.LightningModule): +class CoverClassifier(lightning.LightningModule): """Lightning module for cover classification.""" def __init__( @@ -53,14 +53,32 @@ class CoverClassifier(L.LightningModule): self.learning_rate = learning_rate - self.train_accuracy = M.Accuracy(task="multilabel", num_labels=num_classes) - self.train_f1 = M.F1Score(task="multilabel", num_labels=num_classes) + 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 = M.Accuracy(task="multilabel", num_labels=num_classes) - self.val_f1 = M.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 = M.Accuracy(task="multilabel", num_labels=num_classes) - self.test_f1 = M.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() @@ -186,7 +204,7 @@ def train( model = CoverClassifier(num_classes=num_classes, weights=weights) - trainer = L.Trainer( + trainer = lightning.Trainer( max_epochs=num_epochs, default_root_dir=Path(model_dir).resolve() if model_dir else None, ) -- GitLab From 54b75c46758d7c8bdd1598fb771af15453805236 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 21:39:17 +0200 Subject: [PATCH 37/45] Small change --- experiments/cover_classifier/cover_classifier/model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 81a85bbd..06be5ee9 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -176,7 +176,8 @@ def train( # TODO: games without any type should be in a holdout set meant for human review train_dataset, test_dataset, val_dataset = random_split( - dataset, (1 - test_size - val_size, test_size, val_size) + dataset=dataset, + lengths=(1 - test_size - val_size, test_size, val_size), ) LOGGER.info( "Split into %d training, %d test and %d validation samples", -- GitLab From e7997ab77f9faa1454972e218742205624744e81 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 21:44:53 +0200 Subject: [PATCH 38/45] Added ModelCheckpoint, EarlyStopping and CSVLogger to the model training --- .../cover_classifier/model.py | 31 +++++++++++++++++-- 1 file changed, 28 insertions(+), 3 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 06be5ee9..c5c1d450 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -141,6 +141,7 @@ class CoverClassifier(lightning.LightningModule): def train( + *, data_dir: str | Path, images_dir: str | Path, test_size: float = 0.05, @@ -148,11 +149,13 @@ def train( batch_size: int = 128, num_epochs: int = 10, model_dir: 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() + model_dir = Path(model_dir).resolve() if model_dir 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}" @@ -205,13 +208,35 @@ def train( model = CoverClassifier(num_classes=num_classes, weights=weights) + # TODO: Checkpoints, early stopping, logger, tune learning rate etc. + + 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, + ) + + csv_logger = lightning.pytorch.loggers.csv_logs.CSVLogger( + save_dir=model_dir, + ) + trainer = lightning.Trainer( max_epochs=num_epochs, - default_root_dir=Path(model_dir).resolve() if model_dir else None, + logger=[csv_logger], + callbacks=[checkpoint_callback, early_stopping_callback], + default_root_dir=model_dir, + fast_dev_run=fast_dev_run, ) - # TODO: Checkpoints, early stopping, logger, tune learning rate etc. - trainer.fit( model=model, train_dataloaders=train_dataloader, -- GitLab From d6f8779bbdf8f0e34aa5b6c9fbe985a6c07c6f64 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 21:57:23 +0200 Subject: [PATCH 39/45] Resume training; save best model --- .../cover_classifier/__main__.py | 2 +- .../cover_classifier/model.py | 40 ++++++++++++++----- 2 files changed, 30 insertions(+), 12 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index efd58505..cc5358b4 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -28,7 +28,7 @@ def main(): images_dir=BASE_DIR.parent / "board-game-scraper" / "images", batch_size=32, num_epochs=10, - model_dir=model_path.parent, + save_dir=model_path.parent, ) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index c5c1d450..ecbc9469 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -2,6 +2,7 @@ import logging from pathlib import Path +import shutil import lightning import torch @@ -148,14 +149,16 @@ def train( val_size: float = 0.05, batch_size: int = 128, num_epochs: int = 10, - model_dir: str | Path | None = None, + 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() - model_dir = Path(model_dir).resolve() if model_dir else None + 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}" @@ -165,6 +168,11 @@ def train( 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( @@ -204,11 +212,15 @@ def train( shuffle=False, ) - # TODO: Load model from best checkpoint if it exists - - model = CoverClassifier(num_classes=num_classes, weights=weights) - - # TODO: Checkpoints, early stopping, logger, tune learning rate etc. + if model_path: + 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", @@ -226,14 +238,14 @@ def train( ) csv_logger = lightning.pytorch.loggers.csv_logs.CSVLogger( - save_dir=model_dir, + save_dir=save_dir, ) trainer = lightning.Trainer( max_epochs=num_epochs, logger=[csv_logger], callbacks=[checkpoint_callback, early_stopping_callback], - default_root_dir=model_dir, + default_root_dir=save_dir, fast_dev_run=fast_dev_run, ) @@ -243,9 +255,15 @@ def train( val_dataloaders=val_dataloader, ) - trainer.test(model, dataloaders=test_dataloader) + trainer.test( + model=model, + dataloaders=test_dataloader, + ) - # TODO: Link best checkpoint + 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) -- GitLab From 3f999956faf2b8fb707f282873e1d3ac50312b50 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 22:04:51 +0200 Subject: [PATCH 40/45] Cleaning up --- .../cover_classifier/cover_classifier/__main__.py | 15 ++++++++------- .../cover_classifier/cover_classifier/data.py | 7 ++++--- .../cover_classifier/cover_classifier/model.py | 9 +++------ 3 files changed, 15 insertions(+), 16 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index cc5358b4..d474d9fa 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -4,11 +4,10 @@ import logging import sys from pathlib import Path -import torch - from cover_classifier.model import train -BASE_DIR = Path(__file__).resolve().parent.parent.parent.parent +PROJECT_DIR = Path(__file__).resolve().parent.parent +BASE_DIR = PROJECT_DIR.parent.parent def main(): @@ -20,15 +19,17 @@ def main(): stream=sys.stdout, ) - model_path = Path().resolve() / "models" / "cover_classifier.pt" - model_path.parent.mkdir(parents=True, exist_ok=True) + 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=32, + batch_size=128, num_epochs=10, - save_dir=model_path.parent, + save_dir=save_dir, + model_path=model_path, + fast_dev_run=False, ) diff --git a/experiments/cover_classifier/cover_classifier/data.py b/experiments/cover_classifier/cover_classifier/data.py index 6cf676b3..a2b23c47 100644 --- a/experiments/cover_classifier/cover_classifier/data.py +++ b/experiments/cover_classifier/cover_classifier/data.py @@ -1,15 +1,16 @@ """Board game dataset.""" -from itertools import islice import json import logging -from pathlib import Path import random +from itertools import islice +from pathlib import Path from typing import Any, Callable, Union + import jmespath import polars as pl -from sklearn.preprocessing import MultiLabelBinarizer import torch +from sklearn.preprocessing import MultiLabelBinarizer from torch.utils.data import Dataset from torchvision.io import read_image from tqdm import tqdm diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index ecbc9469..c5ad66e0 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -1,19 +1,16 @@ """Train a model to classify board game covers.""" import logging -from pathlib import Path import shutil +from pathlib import Path import lightning import torch import torchmetrics -from torch import nn -from torch import optim +from torch import nn, optim from torch.nn import functional as F from torch.utils.data import DataLoader, random_split -from torchvision.models import resnet50, ResNet, ResNet50_Weights - -# from tqdm import tqdm +from torchvision.models import ResNet, ResNet50_Weights, resnet50 from cover_classifier.data import BoardGameDataset -- GitLab From a1dacfeca64c8f8400ed7b34a8eefef75661c5a8 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 22:13:39 +0200 Subject: [PATCH 41/45] Bug fix in model loading --- experiments/cover_classifier/cover_classifier/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index c5ad66e0..b22d5b22 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -209,7 +209,7 @@ def train( shuffle=False, ) - if model_path: + 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, ( -- GitLab From a86eca609e5f543b23bc1542ec68d437c47ee95c Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 22:33:47 +0200 Subject: [PATCH 42/45] Moved train() to its own module --- .../cover_classifier/__main__.py | 2 +- .../cover_classifier/model.py | 157 ----------------- .../cover_classifier/training.py | 166 ++++++++++++++++++ 3 files changed, 167 insertions(+), 158 deletions(-) create mode 100644 experiments/cover_classifier/cover_classifier/training.py diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index d474d9fa..0f6706c8 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -4,7 +4,7 @@ import logging import sys from pathlib import Path -from cover_classifier.model import train +from cover_classifier.training import train PROJECT_DIR = Path(__file__).resolve().parent.parent BASE_DIR = PROJECT_DIR.parent.parent diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index b22d5b22..74622bb6 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -1,19 +1,13 @@ """Train a model to classify board game covers.""" import logging -import shutil -from pathlib import Path import lightning -import torch import torchmetrics from torch import nn, optim from torch.nn import functional as F -from torch.utils.data import DataLoader, random_split from torchvision.models import ResNet, ResNet50_Weights, resnet50 -from cover_classifier.data import BoardGameDataset - LOGGER = logging.getLogger(__name__) @@ -136,154 +130,3 @@ class CoverClassifier(lightning.LightningModule): def configure_optimizers(self): return optim.Adam(self.parameters(), lr=self.learning_rate) - - -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, - max_samples=1_000_000, - ) - 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, - ) - - csv_logger = lightning.pytorch.loggers.csv_logs.CSVLogger( - save_dir=save_dir, - ) - - trainer = lightning.Trainer( - max_epochs=num_epochs, - logger=[csv_logger], - callbacks=[checkpoint_callback, early_stopping_callback], - default_root_dir=save_dir, - fast_dev_run=fast_dev_run, - ) - - 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()) != label.item() - print(f"\t{class_:15}: {pred:>6.1%} ({label} {'❌' if error else '✅'})") diff --git a/experiments/cover_classifier/cover_classifier/training.py b/experiments/cover_classifier/cover_classifier/training.py new file mode 100644 index 00000000..64cb4a9c --- /dev/null +++ b/experiments/cover_classifier/cover_classifier/training.py @@ -0,0 +1,166 @@ +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, + max_samples=1_000_000, + ) + 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, + ) + + csv_logger = lightning.pytorch.loggers.csv_logs.CSVLogger( + save_dir=save_dir, + ) + + trainer = lightning.Trainer( + max_epochs=num_epochs, + logger=[csv_logger], + callbacks=[checkpoint_callback, early_stopping_callback], + default_root_dir=save_dir, + fast_dev_run=fast_dev_run, + ) + + 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()) != label.item() + print(f"\t{class_:15}: {pred:>6.1%} ({label} {'❌' if error else '✅'})") -- GitLab From 1af14210e950b8ad0f2e34a8076283a887a7f395 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 22:36:21 +0200 Subject: [PATCH 43/45] Small changes --- .../cover_classifier/cover_classifier/training.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/training.py b/experiments/cover_classifier/cover_classifier/training.py index 64cb4a9c..4ffc3283 100644 --- a/experiments/cover_classifier/cover_classifier/training.py +++ b/experiments/cover_classifier/cover_classifier/training.py @@ -1,3 +1,5 @@ +"""Train a model to classify board game covers.""" + import logging import shutil from pathlib import Path @@ -111,13 +113,15 @@ def train( verbose=True, ) - csv_logger = lightning.pytorch.loggers.csv_logs.CSVLogger( - save_dir=save_dir, + loggers = ( + [lightning.pytorch.loggers.csv_logs.CSVLogger(save_dir=save_dir)] + if save_dir + else [] ) trainer = lightning.Trainer( max_epochs=num_epochs, - logger=[csv_logger], + logger=loggers, callbacks=[checkpoint_callback, early_stopping_callback], default_root_dir=save_dir, fast_dev_run=fast_dev_run, -- GitLab From eb7a645b30c23bac0891a4725ef644f99f0f5923 Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 22:41:30 +0200 Subject: [PATCH 44/45] Small changes --- experiments/cover_classifier/cover_classifier/__main__.py | 2 +- experiments/cover_classifier/cover_classifier/training.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/__main__.py b/experiments/cover_classifier/cover_classifier/__main__.py index 0f6706c8..0af86cfa 100644 --- a/experiments/cover_classifier/cover_classifier/__main__.py +++ b/experiments/cover_classifier/cover_classifier/__main__.py @@ -26,7 +26,7 @@ def main(): data_dir=BASE_DIR.parent / "board-game-data", images_dir=BASE_DIR.parent / "board-game-scraper" / "images", batch_size=128, - num_epochs=10, + num_epochs=100, save_dir=save_dir, model_path=model_path, fast_dev_run=False, diff --git a/experiments/cover_classifier/cover_classifier/training.py b/experiments/cover_classifier/cover_classifier/training.py index 4ffc3283..cb55bed1 100644 --- a/experiments/cover_classifier/cover_classifier/training.py +++ b/experiments/cover_classifier/cover_classifier/training.py @@ -57,7 +57,6 @@ def train( image_root_dir=images_dir, transform=weights.transforms(), require_any_type=True, - max_samples=1_000_000, ) num_classes = len(dataset.classes) # TODO: games without any type should be in a holdout set meant for human review -- GitLab From ced41192486688639ac91672934824de885c3b9b Mon Sep 17 00:00:00 2001 From: Markus Schepke Date: Tue, 5 Mar 2024 22:52:26 +0200 Subject: [PATCH 45/45] Learning rate tuner --- experiments/cover_classifier/cover_classifier/model.py | 6 +++--- .../cover_classifier/cover_classifier/training.py | 9 +++++++++ 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/experiments/cover_classifier/cover_classifier/model.py b/experiments/cover_classifier/cover_classifier/model.py index 74622bb6..6a231d1a 100644 --- a/experiments/cover_classifier/cover_classifier/model.py +++ b/experiments/cover_classifier/cover_classifier/model.py @@ -14,12 +14,14 @@ 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, - learning_rate: float = 1e-3, ): super().__init__() @@ -43,8 +45,6 @@ class CoverClassifier(lightning.LightningModule): self.loss_fn = nn.BCEWithLogitsLoss() - self.learning_rate = learning_rate - self.train_accuracy = torchmetrics.Accuracy( task="multilabel", num_labels=num_classes, diff --git a/experiments/cover_classifier/cover_classifier/training.py b/experiments/cover_classifier/cover_classifier/training.py index cb55bed1..d8e990a6 100644 --- a/experiments/cover_classifier/cover_classifier/training.py +++ b/experiments/cover_classifier/cover_classifier/training.py @@ -126,6 +126,15 @@ def train( 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, -- GitLab