o  i@sdZddlmZddlmZmZddlmZddlZ ddl Z ddl m Z ddl mZddlmZmZdd lmZdd lmZmZmZd gZd d ZGdd d eZdS)z] A collection of "vanilla" transforms for crop and pad operations acting on batches of data. ) annotations)HashableMapping)AnyN) MetaTensor)list_data_collate)CenterSpatialCrop SpatialPad)InvertibleTransform)MethodPytorchPadMode TraceKeysPadListDataCollatecCsBt||trt||}|||<t|||<|S||||<|SN) isinstancetuplelist) to_replacebatchidx key_or_idxZbatch_idx_listr`/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/transforms/croppad/batch.pyreplace_element s   rc@s<eZdZdZejejfdddZdd d Z e dddZ dS)ra  Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest tensor in each dimension. This transform is useful if some of the applied transforms generate batch data of different sizes. This can be used on both list and dictionary data. Note that in the case of the dictionary data, it may add the transform information to the list of invertible transforms if input batch have different spatial shape, so need to call static method: `inverse` before inverting other transforms. Note that normally, a user won't explicitly use the `__call__` method. Rather this would be passed to the `DataLoader`. This means that `__call__` handles data as it comes out of a `DataLoader`, containing batch dimension. However, the `inverse` operates on dictionaries containing images of shape `C,H,W,[D]`. This asymmetry is necessary so that we can pass the inverse through multiprocessing. Args: method: padding method (see :py:class:`monai.transforms.SpatialPad`) mode: padding mode (see :py:class:`monai.transforms.SpatialPad`) kwargs: other arguments for the `np.pad` or `torch.pad` function. note that `np.pad` treats channel dimension as the first dimension. methodstrmodereturnNonecKs||_||_||_dSr)rrkwargs)selfrrrrrr__init__Cs zPadListDataCollate.__init__rrc Cs>t|dt}|rt|dntt|d}|D]}g}|D]}t||tjtj fs0n | ||j ddq!|s@qt |j dd}tt |jdd|krXqtd ||j|jd|j}t|D]/\} } | |j dd} || |} t| || |}|r|j|| || |j|| |dddqjqt|S) zG Args: batch: batch of data to pad-collate rN)axis) spatial_sizerrF)check) orig_size extra_infor)rdictrkeysrangelentorchTensornpndarrayappendshapearraymaxallminr rrr enumeraterpush_transform pop_transformr) r rZis_list_of_dictsZ batch_itemrZ max_shapeselem max_shapepadderrZbatch_ir&paddedrrr__call__Hs8$ zPadListDataCollate.__call__datar(dict[Hashable, np.ndarray]c Cst|tstdt|dt|}|D]]}d}t||tr&||j}n t|}||vr3||}|rdkey transforms transform_keyxformZcroppingrrrinversets(    zPadListDataCollate.inverseN)rrrrrr)rr)r>r(rr?) rH __module__ __qualname____doc__r SYMMETRICr CONSTANTr!r= staticmethodrQrrrrr,s  ,)rT __future__rcollections.abcrrtypingrnumpyr.r,monai.data.meta_tensorrmonai.data.utilsrZmonai.transforms.croppad.arrayrr monai.transforms.inverser monai.utils.enumsr r r __all__rrrrrrs