Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

affine_transform: Remove inconsistencies with ndimage implementation. #205

Merged
merged 3 commits into from
May 4, 2021
Merged

affine_transform: Remove inconsistencies with ndimage implementation. #205

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
09a62d5
affine_transform: Remove inconsistencies with ndimage implementation.
m-albert Apr 20, 2021
c05fdc4
Simplify cupy imports by using pytest.importorskip
GenevieveBuckley Apr 23, 2021
feaa895
These lines aren't necessary when I test this locally, maybe we can r…
GenevieveBuckley Apr 23, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 12 additions & 9 deletions dask_image/ndinterp/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@
def affine_transform(
image,
matrix,
offset=None,
offset=0.0,
output_shape=None,
order=1,
output_chunks=None,
Expand Down Expand Up @@ -54,16 +54,18 @@ def affine_transform(
The image array.
matrix : array (ndim,), (ndim, ndim), (ndim, ndim+1) or (ndim+1, ndim+1)
Transformation matrix.
offset : array (ndim,)
Transformation offset.
output_shape : array (ndim,), optional
The size of the array to be returned.
offset : float or sequence, optional
The offset into the array where the transform is applied. If a float,
`offset` is the same for each axis. If a sequence, `offset` should
contain one value for each axis.
output_shape : tuple of ints, optional
The shape of the array to be returned.
order : int, optional
The order of the spline interpolation. Note that for order>1
scipy's affine_transform applies prefiltering, which is not
yet supported and skipped in this implementation.
output_chunks : array (ndim,), optional
The chunks of the output Dask Array.
output_chunks : tuple of ints, optional
The shape of the chunks of the output Dask Array.

Returns
-------
Expand Down Expand Up @@ -123,7 +125,8 @@ def affine_transform(
image_shape = image.shape

# calculate output array properties
normalized_chunks = da.core.normalize_chunks(output_chunks, output_shape)
normalized_chunks = da.core.normalize_chunks(output_chunks,
tuple(output_shape))
block_indices = product(*(range(len(bds)) for bds in normalized_chunks))
block_offsets = [np.cumsum((0,) + bds[:-1]) for bds in normalized_chunks]

Expand Down Expand Up @@ -223,7 +226,7 @@ def affine_transform(

transformed = da.Array(graph,
output_name,
shape=output_shape,
shape=tuple(output_shape),
# chunks=output_chunks,
chunks=normalized_chunks,
meta=meta)
Expand Down
30 changes: 15 additions & 15 deletions tests/test_dask_image/test_ndinterp/test_affine_transformation.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -115,15 +115,7 @@ def test_affine_transform_cupy(n,
interp_order,
input_output_chunksize_per_dim,
random_seed):

pytest.importorskip("cupy", minversion="6.0.0")

# somehow, these lines are required for the first parametrized
# test to succeed
import cupy as cp
from dask_image.dispatch._dispatch_ndinterp import (
dispatch_affine_transform)
dispatch_affine_transform(cp.asarray([]))
cupy = pytest.importorskip("cupy", minversion="6.0.0")

kwargs = dict()
kwargs['n'] = n
Expand Down Expand Up @@ -180,6 +172,14 @@ def test_affine_transform_numpy_input():
assert (image == image_t).min()


def test_affine_transform_minimal_input():

image = np.ones((3, 3))
image_t = da_ndinterp.affine_transform(np.ones((3, 3)), np.eye(2))

assert image_t.shape == image.shape


def test_affine_transform_type_consistency():

image = da.ones((3, 3))
Expand All @@ -189,15 +189,15 @@ def test_affine_transform_type_consistency():
assert isinstance(image[0, 0].compute(), type(image_t[0, 0].compute()))


@pytest.mark.cupy
def test_affine_transform_type_consistency_gpu():

pytest.importorskip("cupy", minversion="6.0.0")
cupy = pytest.importorskip("cupy", minversion="6.0.0")

image = da.ones((3, 3))
image_t = da_ndinterp.affine_transform(image, np.eye(2), [0, 0])

import cupy as cp
image.map_blocks(cp.asarray)
image.map_blocks(cupy.asarray)

assert isinstance(image, type(image_t))
assert isinstance(image[0, 0].compute(), type(image_t[0, 0].compute()))
Expand Down Expand Up @@ -236,17 +236,17 @@ def test_affine_transform_large_input_small_output_cpu():
image_t[0, 0, 0].compute()


@pytest.mark.cupy
@pytest.mark.timeout(15)
def test_affine_transform_large_input_small_output_gpu():
"""
Make sure input array does not need to be computed entirely
"""
pytest.importorskip("cupy", minversion="6.0.0")
cupy = pytest.importorskip("cupy", minversion="6.0.0")

# this array would occupy more than 24GB on a GPU
image = da.random.random([2000] * 3, chunks=(50, 50, 50))
import cupy as cp
image.map_blocks(cp.asarray)
image.map_blocks(cupy.asarray)

image_t = da_ndinterp.affine_transform(image, np.eye(3), [0, 0, 0],
output_chunks=[1, 1, 1],
Expand Down