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Merged
Nic-Ma merged 4 commits into from
Sep 30, 2021
Merged

2975 torch fgbgtoindices #3038

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890a0b0
torch fgbgtoindices
wyli Sep 28, 2021
65053ea
unravel indices
wyli Sep 28, 2021
9415e43
simplified
wyli Sep 28, 2021
079eb04
Merge branch 'dev' into 2975-torch-fgbgtoindices
Nic-Ma Sep 29, 2021
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3 changes: 3 additions & 0 deletions docs/source/transforms.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1418,3 +1418,6 @@ Utilities
---------
.. automodule:: monai.transforms.utils
:members:

.. automodule:: monai.transforms.utils_pytorch_numpy_unification
:members:
22 changes: 9 additions & 13 deletions monai/transforms/utility/array.py
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Expand Up @@ -31,7 +31,7 @@
map_binary_to_indices,
map_classes_to_indices,
)
from monai.transforms.utils_pytorch_numpy_unification import in1d, moveaxis
from monai.transforms.utils_pytorch_numpy_unification import in1d, moveaxis, unravel_indices
from monai.utils import (
convert_data_type,
convert_to_cupy,
Expand Down Expand Up @@ -789,16 +789,18 @@ class FgBgToIndices(Transform):

"""

backend = [TransformBackends.NUMPY, TransformBackends.TORCH]

def __init__(self, image_threshold: float = 0.0, output_shape: Optional[Sequence[int]] = None) -> None:
self.image_threshold = image_threshold
self.output_shape = output_shape

def __call__(
self,
label: np.ndarray,
image: Optional[np.ndarray] = None,
label: NdarrayOrTensor,
image: Optional[NdarrayOrTensor] = None,
output_shape: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, np.ndarray]:
) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]:
"""
Args:
label: input data to compute foreground and background indices.
Expand All @@ -807,18 +809,12 @@ def __call__(
output_shape: expected shape of output indices. if None, use `self.output_shape` instead.

"""
fg_indices: np.ndarray
bg_indices: np.ndarray
label, *_ = convert_data_type(label, np.ndarray) # type: ignore
if image is not None:
image, *_ = convert_data_type(image, np.ndarray) # type: ignore
if output_shape is None:
output_shape = self.output_shape
fg_indices, bg_indices = map_binary_to_indices(label, image, self.image_threshold) # type: ignore
fg_indices, bg_indices = map_binary_to_indices(label, image, self.image_threshold)
if output_shape is not None:
fg_indices = np.stack([np.unravel_index(i, output_shape) for i in fg_indices])
bg_indices = np.stack([np.unravel_index(i, output_shape) for i in bg_indices])

fg_indices = unravel_indices(fg_indices, output_shape)
bg_indices = unravel_indices(bg_indices, output_shape)
return fg_indices, bg_indices


Expand Down
4 changes: 3 additions & 1 deletion monai/transforms/utility/dictionary.py
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Expand Up @@ -1119,6 +1119,8 @@ class FgBgToIndicesd(MapTransform):

"""

backend = FgBgToIndices.backend

def __init__(
self,
keys: KeysCollection,
Expand All @@ -1135,7 +1137,7 @@ def __init__(
self.image_key = image_key
self.converter = FgBgToIndices(image_threshold, output_shape)

def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
d = dict(data)
image = d[self.image_key] if self.image_key else None
for key in self.key_iterator(d):
Expand Down
4 changes: 1 addition & 3 deletions monai/transforms/utils.py
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Expand Up @@ -286,9 +286,7 @@ def map_binary_to_indices(
fg_indices = nonzero(label_flat)
if image is not None:
img_flat = ravel(any_np_pt(image > image_threshold, 0))
img_flat, *_ = convert_data_type(
img_flat, type(label), device=label.device if isinstance(label, torch.Tensor) else None
)
img_flat, *_ = convert_to_dst_type(img_flat, label, dtype=img_flat.dtype)
bg_indices = nonzero(img_flat & ~label_flat)
else:
bg_indices = nonzero(~label_flat)
Expand Down
26 changes: 20 additions & 6 deletions monai/transforms/utils_pytorch_numpy_unification.py
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Expand Up @@ -26,6 +26,7 @@
"nonzero",
"floor_divide",
"unravel_index",
"unravel_indices",
"ravel",
"any_np_pt",
"maximum",
Expand Down Expand Up @@ -91,9 +92,8 @@ def percentile(x: NdarrayOrTensor, q) -> Union[NdarrayOrTensor, float, int]:
if np.isscalar(q):
if not 0 <= q <= 100:
raise ValueError
else:
if any(q < 0) or any(q > 100):
raise ValueError
elif any(q < 0) or any(q > 100):
raise ValueError
result: Union[NdarrayOrTensor, float, int]
if isinstance(x, np.ndarray):
result = np.percentile(x, q)
Expand Down Expand Up @@ -167,20 +167,34 @@ def unravel_index(idx, shape):

Args:
idx: index to unravel
b: shape of array/tensor
shape: shape of array/tensor

Returns:
Index unravelled for given shape
"""
if isinstance(idx, torch.Tensor):
coord = []
for dim in reversed(shape):
coord.insert(0, idx % dim)
coord.append(idx % dim)
idx = floor_divide(idx, dim)
return torch.stack(coord)
return torch.stack(coord[::-1])
return np.unravel_index(np.asarray(idx, dtype=int), shape)


def unravel_indices(idx, shape):
"""Computing unravel cooridnates from indices.

Args:
idx: a sequence of indices to unravel
shape: shape of array/tensor

Returns:
Stacked indices unravelled for given shape
"""
lib_stack = torch.stack if isinstance(idx[0], torch.Tensor) else np.stack
return lib_stack([unravel_index(i, shape) for i in idx])


def ravel(x: NdarrayOrTensor):
"""`np.ravel` with equivalent implementation for torch.

Expand Down
5 changes: 1 addition & 4 deletions monai/utils/type_conversion.py
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Expand Up @@ -270,10 +270,7 @@ def convert_to_dst_type(
See Also:
:func:`convert_data_type`
"""
device = None
if isinstance(dst, torch.Tensor):
device = dst.device

device = dst.device if isinstance(dst, torch.Tensor) else None
if dtype is None:
dtype = dst.dtype

Expand Down
90 changes: 51 additions & 39 deletions tests/test_fg_bg_to_indices.py
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Expand Up @@ -11,58 +11,70 @@

import unittest

import numpy as np
from parameterized import parameterized

from monai.transforms import FgBgToIndices
from tests.utils import TEST_NDARRAYS, assert_allclose

TEST_CASE_1 = [
{"image_threshold": 0.0, "output_shape": None},
np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),
None,
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 4, 8]),
]
TESTS_CASES = []
for p in TEST_NDARRAYS:
TESTS_CASES.append(
[
{"image_threshold": 0.0, "output_shape": None},
p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),
None,
p([1, 2, 3, 5, 6, 7]),
p([0, 4, 8]),
]
)

TEST_CASE_2 = [
{"image_threshold": 0.0, "output_shape": None},
np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),
np.array([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]),
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 8]),
]
TESTS_CASES.append(
[
{"image_threshold": 0.0, "output_shape": None},
p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),
p([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]),
p([1, 2, 3, 5, 6, 7]),
p([0, 8]),
]
)

TEST_CASE_3 = [
{"image_threshold": 1.0, "output_shape": None},
np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),
np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 8]),
]
TESTS_CASES.append(
[
{"image_threshold": 1.0, "output_shape": None},
p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]),
p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),
p([1, 2, 3, 5, 6, 7]),
p([0, 8]),
]
)

TEST_CASE_4 = [
{"image_threshold": 1.0, "output_shape": None},
np.array([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]),
np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 8]),
]
TESTS_CASES.append(
[
{"image_threshold": 1.0, "output_shape": None},
p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]),
p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),
p([1, 2, 3, 5, 6, 7]),
p([0, 8]),
]
)

TEST_CASE_5 = [
{"image_threshold": 1.0, "output_shape": [3, 3]},
np.array([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]),
np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),
np.array([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]),
np.array([[0, 0], [2, 2]]),
]
TESTS_CASES.append(
[
{"image_threshold": 1.0, "output_shape": [3, 3]},
p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]),
p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]]),
p([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]),
p([[0, 0], [2, 2]]),
]
)


class TestFgBgToIndices(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])
@parameterized.expand(TESTS_CASES)
def test_type_shape(self, input_data, label, image, expected_fg, expected_bg):
fg_indices, bg_indices = FgBgToIndices(**input_data)(label, image)
np.testing.assert_allclose(fg_indices, expected_fg)
np.testing.assert_allclose(bg_indices, expected_bg)
assert_allclose(fg_indices, expected_fg)
assert_allclose(bg_indices, expected_bg)


if __name__ == "__main__":
Expand Down
81 changes: 47 additions & 34 deletions tests/test_fg_bg_to_indicesd.py
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Expand Up @@ -11,53 +11,66 @@

import unittest

import numpy as np
from parameterized import parameterized

from monai.transforms import FgBgToIndicesd
from tests.utils import TEST_NDARRAYS, assert_allclose

TEST_CASE_1 = [
{"keys": "label", "image_key": None, "image_threshold": 0.0, "output_shape": None},
{"label": np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])},
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 4, 8]),
]
TEST_CASES = []
for p in TEST_NDARRAYS:

TEST_CASE_2 = [
{"keys": "label", "image_key": "image", "image_threshold": 0.0, "output_shape": None},
{"label": np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]), "image": np.array([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]])},
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 8]),
]
TEST_CASES.append(
[
{"keys": "label", "image_key": None, "image_threshold": 0.0, "output_shape": None},
{"label": p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]])},
p([1, 2, 3, 5, 6, 7]),
p([0, 4, 8]),
]
)

TEST_CASE_3 = [
{"keys": "label", "image_key": "image", "image_threshold": 1.0, "output_shape": None},
{"label": np.array([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]), "image": np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 8]),
]
TEST_CASES.append(
[
{"keys": "label", "image_key": "image", "image_threshold": 0.0, "output_shape": None},
{"label": p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]), "image": p([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]])},
p([1, 2, 3, 5, 6, 7]),
p([0, 8]),
]
)

TEST_CASE_4 = [
{"keys": "label", "image_key": "image", "image_threshold": 1.0, "output_shape": None},
{"label": np.array([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]), "image": np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},
np.array([1, 2, 3, 5, 6, 7]),
np.array([0, 8]),
]
TEST_CASES.append(
[
{"keys": "label", "image_key": "image", "image_threshold": 1.0, "output_shape": None},
{"label": p([[[0, 1, 1], [1, 0, 1], [1, 1, 0]]]), "image": p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},
p([1, 2, 3, 5, 6, 7]),
p([0, 8]),
]
)

TEST_CASE_5 = [
{"keys": "label", "image_key": "image", "image_threshold": 1.0, "output_shape": [3, 3]},
{"label": np.array([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]), "image": np.array([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},
np.array([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]),
np.array([[0, 0], [2, 2]]),
]
TEST_CASES.append(
[
{"keys": "label", "image_key": "image", "image_threshold": 1.0, "output_shape": None},
{"label": p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]), "image": p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},
p([1, 2, 3, 5, 6, 7]),
p([0, 8]),
]
)

TEST_CASES.append(
[
{"keys": "label", "image_key": "image", "image_threshold": 1.0, "output_shape": [3, 3]},
{"label": p([[[0, 1, 2], [3, 0, 4], [5, 6, 0]]]), "image": p([[[3, 3, 3], [3, 1, 3], [3, 3, 3]]])},
p([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]]),
p([[0, 0], [2, 2]]),
]
)


class TestFgBgToIndicesd(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])
@parameterized.expand(TEST_CASES)
def test_type_shape(self, input_data, data, expected_fg, expected_bg):
result = FgBgToIndicesd(**input_data)(data)
np.testing.assert_allclose(result["label_fg_indices"], expected_fg)
np.testing.assert_allclose(result["label_bg_indices"], expected_bg)
assert_allclose(result["label_fg_indices"], expected_fg)
assert_allclose(result["label_bg_indices"], expected_bg)


if __name__ == "__main__":
Expand Down