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"""Anomalib Datasets."""
# Copyright (C) 2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
from typing import Union
from omegaconf import DictConfig, ListConfig
from pytorch_lightning import LightningDataModule
from .btech import BTechDataModule
from .folder import FolderDataModule
from .inference import InferenceDataset
from .mvtec import MVTecDataModule
def get_datamodule(config: Union[DictConfig, ListConfig]) -> LightningDataModule:
"""Get Anomaly Datamodule.
Args:
config (Union[DictConfig, ListConfig]): Configuration of the anomaly model.
Returns:
PyTorch Lightning DataModule
"""
datamodule: LightningDataModule
if config.dataset.format.lower() == "mvtec":
datamodule = MVTecDataModule(
# TODO: Remove config values. IAAALD-211
root=config.dataset.path,
category=config.dataset.category,
image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
train_batch_size=config.dataset.train_batch_size,
test_batch_size=config.dataset.test_batch_size,
num_workers=config.dataset.num_workers,
seed=config.project.seed,
task=config.dataset.task,
transform_config_train=config.dataset.transform_config.train,
transform_config_val=config.dataset.transform_config.val,
create_validation_set=config.dataset.create_validation_set,
)
elif config.dataset.format.lower() == "btech":
datamodule = BTechDataModule(
# TODO: Remove config values. IAAALD-211
root=config.dataset.path,
category=config.dataset.category,
image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
train_batch_size=config.dataset.train_batch_size,
test_batch_size=config.dataset.test_batch_size,
num_workers=config.dataset.num_workers,
seed=config.project.seed,
task=config.dataset.task,
transform_config_train=config.dataset.transform_config.train,
transform_config_val=config.dataset.transform_config.val,
create_validation_set=config.dataset.create_validation_set,
)
elif config.dataset.format.lower() == "folder":
datamodule = FolderDataModule(
root=config.dataset.path,
normal_dir=config.dataset.normal_dir,
abnormal_dir=config.dataset.abnormal_dir,
task=config.dataset.task,
normal_test_dir=config.dataset.normal_test_dir,
mask_dir=config.dataset.mask,
extensions=config.dataset.extensions,
split_ratio=config.dataset.split_ratio,
seed=config.dataset.seed,
image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
train_batch_size=config.dataset.train_batch_size,
test_batch_size=config.dataset.test_batch_size,
num_workers=config.dataset.num_workers,
transform_config_train=config.dataset.transform_config.train,
transform_config_val=config.dataset.transform_config.val,
create_validation_set=config.dataset.create_validation_set,
)
else:
raise ValueError(
"Unknown dataset! \n"
"If you use a custom dataset make sure you initialize it in"
"`get_datamodule` in `anomalib.data.__init__.py"
)
return datamodule
__all__ = [
"get_datamodule",
"BTechDataModule",
"FolderDataModule",
"InferenceDataset",
"MVTecDataModule",
]
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