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# Copyright (c) MONAI Consortium
# 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 __future__ import annotations
import contextlib
from .box_utils import (
box_area,
box_centers,
box_giou,
box_iou,
box_pair_giou,
boxes_center_distance,
centers_in_boxes,
convert_box_mode,
convert_box_to_standard_mode,
)
from .csv_saver import CSVSaver
from .dataloader import DataLoader
from .dataset import (
ArrayDataset,
CacheDataset,
CacheNTransDataset,
CSVDataset,
Dataset,
DatasetFunc,
GDSDataset,
LMDBDataset,
NPZDictItemDataset,
PersistentDataset,
SmartCacheDataset,
ZipDataset,
)
from .dataset_summary import DatasetSummary
from .decathlon_datalist import (
check_missing_files,
create_cross_validation_datalist,
load_decathlon_datalist,
load_decathlon_properties,
)
from .folder_layout import FolderLayout, FolderLayoutBase
from .grid_dataset import GridPatchDataset, PatchDataset, PatchIter, PatchIterd
from .image_dataset import ImageDataset
from .image_reader import ImageReader, ITKReader, NibabelReader, NrrdReader, NumpyReader, PILReader, PydicomReader
from .image_writer import (
SUPPORTED_WRITERS,
ImageWriter,
ITKWriter,
NibabelWriter,
PILWriter,
logger,
register_writer,
resolve_writer,
)
from .iterable_dataset import CSVIterableDataset, IterableDataset, ShuffleBuffer
from .itk_torch_bridge import (
get_itk_image_center,
itk_image_to_metatensor,
itk_to_monai_affine,
metatensor_to_itk_image,
monai_to_itk_affine,
monai_to_itk_ddf,
)
from .meta_obj import MetaObj, get_track_meta, set_track_meta
from .meta_tensor import MetaTensor
from .samplers import DistributedSampler, DistributedWeightedRandomSampler
from .synthetic import create_test_image_2d, create_test_image_3d
from .test_time_augmentation import TestTimeAugmentation
from .thread_buffer import ThreadBuffer, ThreadDataLoader
from .torchscript_utils import load_net_with_metadata, save_net_with_metadata
from .utils import (
PICKLE_KEY_SUFFIX,
affine_to_spacing,
compute_importance_map,
compute_shape_offset,
convert_tables_to_dicts,
correct_nifti_header_if_necessary,
create_file_basename,
decollate_batch,
dense_patch_slices,
get_extra_metadata_keys,
get_random_patch,
get_valid_patch_size,
is_supported_format,
iter_patch,
iter_patch_position,
iter_patch_slices,
json_hashing,
list_data_collate,
orientation_ras_lps,
pad_list_data_collate,
partition_dataset,
partition_dataset_classes,
pickle_hashing,
rectify_header_sform_qform,
remove_extra_metadata,
remove_keys,
reorient_spatial_axes,
resample_datalist,
select_cross_validation_folds,
set_rnd,
sorted_dict,
to_affine_nd,
worker_init_fn,
zoom_affine,
)
# FIXME: workaround for https://github.com/Project-MONAI/MONAI/issues/5291
# from .video_dataset import CameraDataset, VideoDataset, VideoFileDataset
from .wsi_datasets import MaskedPatchWSIDataset, PatchWSIDataset, SlidingPatchWSIDataset
from .wsi_reader import BaseWSIReader, CuCIMWSIReader, OpenSlideWSIReader, TiffFileWSIReader, WSIReader
with contextlib.suppress(BaseException):
from multiprocessing.reduction import ForkingPickler
def _rebuild_meta(cls, storage, dtype, metadata):
storage_offset, size, stride, requires_grad, meta_dict = metadata
storage = storage._untyped_storage if hasattr(storage, "_untyped_storage") else storage
t = cls([], dtype=dtype, device=storage.device)
t.set_(storage, storage_offset, size, stride)
t.requires_grad = requires_grad
t.__dict__ = meta_dict
return t
def reduce_meta_tensor(meta_tensor):
if hasattr(meta_tensor, "untyped_storage"):
storage = meta_tensor.untyped_storage()
elif hasattr(meta_tensor, "_typed_storage"): # gh pytorch 44dac51/torch/_tensor.py#L231-L233
storage = meta_tensor._typed_storage()
else:
storage = meta_tensor.storage()
dtype = meta_tensor.dtype
if meta_tensor.is_cuda:
raise NotImplementedError("sharing CUDA metatensor across processes not implemented")
metadata = (
meta_tensor.storage_offset(),
meta_tensor.size(),
meta_tensor.stride(),
meta_tensor.requires_grad,
meta_tensor.__dict__,
)
return _rebuild_meta, (type(meta_tensor), storage, dtype, metadata)
ForkingPickler.register(MetaTensor, reduce_meta_tensor)
from .ultrasound_confidence_map import UltrasoundConfidenceMap
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