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

Make the order of slices row-major #4297

Merged
merged 3 commits into from
May 19, 2022
Merged

Make the order of slices row-major #4297

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
ac18d99
Make the order of slices row-major
bhashemian May 19, 2022
38f905d
Remove a comment
bhashemian May 19, 2022
952ce6c
Merge branch 'dev' into update-slice-order
wyli May 19, 2022
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
4 changes: 2 additions & 2 deletions monai/data/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -145,8 +145,8 @@ def iter_patch_slices(
ranges = tuple(starmap(range, zip(start_pos, dims, patch_size_)))

# choose patches by applying product to the ranges
for position in product(*ranges[::-1]): # reverse ranges order to iterate in index order
yield tuple(slice(s, s + p) for s, p in zip(position[::-1], patch_size_))
for position in product(*ranges):
yield tuple(slice(s, s + p) for s, p in zip(position, patch_size_))


def dense_patch_slices(
Expand Down
16 changes: 8 additions & 8 deletions tests/test_grid_dataset.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -60,20 +60,20 @@ def test_loading_array(self):
np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
np.testing.assert_allclose(
item[0],
np.array([[[[1.4965, 2.4965], [5.4965, 6.4965]]], [[[11.3584, 12.3584], [15.3584, 16.3584]]]]),
np.array([[[[7.4965, 8.4965], [11.4965, 12.4965]]], [[[11.3584, 12.3584], [15.3584, 16.3584]]]]),
rtol=1e-4,
)
np.testing.assert_allclose(item[1], np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5)
np.testing.assert_allclose(item[1], np.array([[[0, 1], [2, 4], [0, 2]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5)
if sys.platform != "win32":
for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=2):
np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
np.testing.assert_allclose(
item[0],
np.array([[[[1.2548, 2.2548], [5.2548, 6.2548]]], [[[9.1106, 10.1106], [13.1106, 14.1106]]]]),
np.array([[[[7.2548, 8.2548], [11.2548, 12.2548]]], [[[9.1106, 10.1106], [13.1106, 14.1106]]]]),
rtol=1e-3,
)
np.testing.assert_allclose(
item[1], np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5
item[1], np.array([[[0, 1], [2, 4], [0, 2]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5
)

def test_loading_dict(self):
Expand Down Expand Up @@ -102,20 +102,20 @@ def test_loading_dict(self):
self.assertListEqual(item[0]["metadata"], ["test string", "test string"])
np.testing.assert_allclose(
item[0]["image"],
np.array([[[[1.4965, 2.4965], [5.4965, 6.4965]]], [[[11.3584, 12.3584], [15.3584, 16.3584]]]]),
np.array([[[[7.4965, 8.4965], [11.4965, 12.4965]]], [[[11.3584, 12.3584], [15.3584, 16.3584]]]]),
rtol=1e-4,
)
np.testing.assert_allclose(item[1], np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5)
np.testing.assert_allclose(item[1], np.array([[[0, 1], [2, 4], [0, 2]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5)
if sys.platform != "win32":
for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=2):
np.testing.assert_equal(item[0]["image"].shape, (2, 1, 2, 2))
np.testing.assert_allclose(
item[0]["image"],
np.array([[[[1.2548, 2.2548], [5.2548, 6.2548]]], [[[9.1106, 10.1106], [13.1106, 14.1106]]]]),
np.array([[[[7.2548, 8.2548], [11.2548, 12.2548]]], [[[9.1106, 10.1106], [13.1106, 14.1106]]]]),
rtol=1e-3,
)
np.testing.assert_allclose(
item[1], np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5
item[1], np.array([[[0, 1], [2, 4], [0, 2]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5
)


Expand Down