def test_cropping_dataset_as_data_loader(cropping_dataset: CroppingDataset,
num_dataload_workers: int) -> None:
batch_size = 2
loader = cropping_dataset.as_data_loader(
shuffle=True,
batch_size=batch_size,
num_dataload_workers=num_dataload_workers)
for i, item in enumerate(loader):
item = CroppedSample.from_dict(sample=item)
assert item is not None
assert item.image.shape == \
(batch_size,
cropping_dataset.args.number_of_image_channels) + cropping_dataset.args.crop_size # type: ignore
assert item.mask.shape == (
batch_size, ) + cropping_dataset.args.crop_size # type: ignore
assert item.labels.shape == \
(batch_size, cropping_dataset.args.number_of_classes) + cropping_dataset.args.crop_size # type: ignore
# check the mask center crops are as expected
assert item.mask_center_crop.shape == (
batch_size, ) + cropping_dataset.args.center_size # type: ignore
assert item.labels_center_crop.shape == \
(batch_size, cropping_dataset.args.number_of_classes) + cropping_dataset.args.center_size # type: ignore
# check the contents of the center crops
for b in range(batch_size):
expected = image_util.get_center_crop(
image=item.mask[b],
crop_shape=cropping_dataset.args.center_size)
assert np.array_equal(item.mask_center_crop[b], expected)
for c in range(len(item.labels_center_crop[b])):
expected = image_util.get_center_crop(
image=item.labels[b][c],
crop_shape=cropping_dataset.args.center_size)
assert np.array_equal(item.labels_center_crop[b][c], expected)
def test_cropping_dataset_padding(cropping_dataset: CroppingDataset, num_dataload_workers: int) -> None:
"""
Tests the data type of torch tensors (e.g. image, labels, and mask) created by the dataset generator,
which are provided as input into the computational graph
:return:
"""
cropping_dataset.args.crop_size = (300, 300, 300)
cropping_dataset.args.padding_mode = PaddingMode.Zero
loader = cropping_dataset.as_data_loader(shuffle=True, batch_size=2, num_dataload_workers=1)
for i, item in enumerate(loader):
sample = CroppedSample.from_dict(item)
assert sample.image.shape[-3:] == cropping_dataset.args.crop_size
def test_cropping_dataset_has_reproducible_randomness(cropping_dataset: CroppingDataset,
num_dataload_workers: int) -> None:
cropping_dataset.dataset_indices = [1, 2] * 2
expected_center_indices = None
for k in range(3):
ml_util.set_random_seed(1)
loader = cropping_dataset.as_data_loader(shuffle=True, batch_size=4,
num_dataload_workers=num_dataload_workers)
for i, item in enumerate(loader):
item = CroppedSample.from_dict(sample=item)
if expected_center_indices is None:
expected_center_indices = item.center_indices
else:
assert np.array_equal(expected_center_indices, item.center_indices)
def test_cropping_dataset_sample_dtype(cropping_dataset: CroppingDataset, num_dataload_workers: int) -> None:
"""
Tests the data type of torch tensors (e.g. image, labels, and mask) created by the dataset generator,
which are provided as input into the computational graph
:return:
"""
loader = cropping_dataset.as_data_loader(shuffle=True, batch_size=2,
num_dataload_workers=num_dataload_workers)
for i, item in enumerate(loader):
item = CroppedSample.from_dict(item)
assert item.image.numpy().dtype == ImageDataType.IMAGE.value
assert item.labels.numpy().dtype == ImageDataType.SEGMENTATION.value
assert item.mask.numpy().dtype == ImageDataType.MASK.value
assert item.mask_center_crop.numpy().dtype == ImageDataType.MASK.value
assert item.labels_center_crop.numpy().dtype == ImageDataType.SEGMENTATION.value