def test__transform():
"""
Test _transform by confirming that it generates
appropriate transform output sizes.
"""
dims = (3, 3, 3)
batch_size = 2
moving_image = np.random.uniform(size=dims)
fixed_image = np.random.uniform(size=dims)
affine_transform_3d = preprocess.AffineTransformation3D(
moving_image.shape, fixed_image.shape, batch_size)
moving_image_batched = np.float32(
np.repeat(moving_image[np.newaxis, :, :, :], batch_size, axis=0))
fixed_image_batched = np.float32(
np.repeat(fixed_image[np.newaxis, :, :, :], batch_size, axis=0))
moving_transforms = affine_transform_3d._gen_transforms()
transformed_moving_image = affine_transform_3d._transform(
moving_image_batched, affine_transform_3d._moving_grid_ref,
moving_transforms)
assert transformed_moving_image.shape == moving_image_batched.shape
fixed_transforms = affine_transform_3d._gen_transforms()
transformed_fixed_image = affine_transform_3d._transform(
fixed_image_batched, affine_transform_3d._fixed_grid_ref,
fixed_transforms)
assert transformed_fixed_image.shape == fixed_image_batched.shape
def test__gen_transforms():
"""
Test _gen_transforms by confirming that it generates
appropriate transform output sizes.
"""
dims = (3, 3, 3)
batch_size = 2
moving_image = np.random.uniform(size=dims)
fixed_image = np.random.uniform(size=dims)
affine_transform_3d = preprocess.AffineTransformation3D(
moving_image.shape, fixed_image.shape, batch_size)
transforms = affine_transform_3d._gen_transforms()
assert transforms.shape == (batch_size, 4, 3)
def test_transform():
"""
Test transform by comfirming that it transforms
images and labels only as necessary and when provided.
"""
# Common test setup.
dims = (3, 3, 3)
batch_size = 2
moving_image = np.random.uniform(size=dims)
fixed_image = np.random.uniform(size=dims)
affine_transform_3d = preprocess.AffineTransformation3D(
moving_image.shape, fixed_image.shape, batch_size)
moving_image_batched = np.float32(
np.repeat(moving_image[np.newaxis, :, :, :], batch_size, axis=0))
fixed_image_batched = np.float32(
np.repeat(fixed_image[np.newaxis, :, :, :], batch_size, axis=0))
indices = range(batch_size)
# Test with no labels provided.
inputs = {
"moving_image": moving_image_batched,
"fixed_image": fixed_image_batched,
"indices": indices,
}
tf.config.experimental_run_functions_eagerly(True)
outputs = affine_transform_3d.transform(inputs)
assert outputs.get("moving_image") is not None
assert outputs.get("fixed_image") is not None
assert outputs.get("indices") is not None
assert outputs.get("moving_image").shape == moving_image_batched.shape
assert outputs.get("fixed_image").shape == fixed_image_batched.shape
assert len(outputs.get("indices")) == len(indices)
# Test with labels provided.
moving_label = np.round(np.random.uniform(size=dims))
fixed_label = np.round(np.random.uniform(size=dims))
moving_label_batched = np.float32(
np.repeat(moving_label[np.newaxis, :, :, :], batch_size, axis=0))
fixed_label_batched = np.float32(
np.repeat(fixed_label[np.newaxis, :, :, :], batch_size, axis=0))
inputs = {
"moving_image": moving_image_batched,
"fixed_image": fixed_image_batched,
"moving_label": moving_label_batched,
"fixed_label": fixed_label_batched,
"indices": indices,
}
tf.config.experimental_run_functions_eagerly(True)
outputs = affine_transform_3d.transform(inputs)
assert outputs.get("moving_image") is not None
assert outputs.get("fixed_image") is not None
assert outputs.get("moving_label") is not None
assert outputs.get("fixed_label") is not None
assert outputs.get("indices") is not None
assert outputs.get("moving_image").shape == moving_image_batched.shape
assert outputs.get("fixed_image").shape == fixed_image_batched.shape
assert outputs.get("moving_label").shape == moving_image_batched.shape
assert outputs.get("fixed_label").shape == fixed_image_batched.shape
assert len(outputs.get("indices")) == len(indices)