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# coding=utf-8 |
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""" |
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Tests for deepreg/dataset/util.py in |
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pytest style |
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""" |
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from os.path import join |
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import numpy as np |
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from deepreg.dataset.loader.h5_loader import H5FileLoader |
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from deepreg.dataset.loader.nifti_loader import NiftiFileLoader |
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from deepreg.dataset.loader.unpaired_loader import UnpairedDataLoader |
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FileLoaderDict = dict(nifti=NiftiFileLoader, h5=H5FileLoader) |
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DataPaths = dict(nifti="data/test/nifti/unpaired", h5="data/test/h5/unpaired") |
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def test_sample_index_generator(): |
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""" |
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Test to check the randomness and deterministic index generator |
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for train/test respectively. |
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""" |
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image_shape = (64, 64, 60) |
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for key_file_loader, file_loader in FileLoaderDict.items(): |
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for split in ["train", "test"]: |
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data_dir_path = [join(DataPaths[key_file_loader], split)] |
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indices_to_compare = [] |
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for seed in [0, 1, 0]: |
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data_loader = UnpairedDataLoader( |
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data_dir_paths=data_dir_path, |
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image_shape=image_shape, |
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file_loader=file_loader, |
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labeled=True, |
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sample_label="all", |
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seed=seed, |
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) |
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data_indices = [] |
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for ( |
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moving_index, |
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fixed_index, |
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indices, |
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) in data_loader.sample_index_generator(): |
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assert isinstance(moving_index, int) |
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assert isinstance(fixed_index, int) |
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assert isinstance(indices, list) |
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data_indices += indices |
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indices_to_compare.append(data_indices) |
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# test different seeds give different indices |
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assert not np.allclose(indices_to_compare[0], indices_to_compare[1]) |
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# test same seeds give the same indices |
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assert np.allclose(indices_to_compare[0], indices_to_compare[2]) |
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def test_validate_data_files(): |
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""" |
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Test the validate_data_files functions that looks for inconsistencies |
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in the fixed/moving image and label lists. |
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If there is any issue it will raise an error, otherwise it returns None. |
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""" |
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for key_file_loader, file_loader in FileLoaderDict.items(): |
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for split in ["train", "test"]: |
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data_dir_path = [join(DataPaths[key_file_loader], split)] |
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image_shape = (64, 64, 60) |
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common_args = dict( |
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file_loader=file_loader, |
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labeled=True, |
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sample_label="all", |
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seed=None if split == "train" else 0, |
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) |
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data_loader = UnpairedDataLoader( |
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data_dir_paths=data_dir_path, image_shape=image_shape, **common_args |
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) |
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assert data_loader.validate_data_files() is None |
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View Code Duplication |
def test_close(): |
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""" |
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Test the close function. Only needed for H5 data loaders for now. |
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Since fixed/moving loaders are the same for |
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unpaired data loader, only need to test the moving. |
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""" |
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for key_file_loader, file_loader in FileLoaderDict.items(): |
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for split in ["train", "test"]: |
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data_dir_path = [join(DataPaths[key_file_loader], split)] |
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image_shape = (64, 64, 60) |
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common_args = dict( |
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file_loader=file_loader, |
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labeled=True, |
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sample_label="all", |
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seed=None if split == "train" else 0, |
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) |
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data_loader = UnpairedDataLoader( |
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data_dir_paths=data_dir_path, image_shape=image_shape, **common_args |
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) |
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if key_file_loader == "h5": |
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data_loader.close() |
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for f in data_loader.loader_moving_image.h5_files.values(): |
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assert not f.__bool__() |
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DeepRegNet /
DeepReg
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