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Merged
Nic-Ma merged 13 commits into from
Oct 26, 2021
Merged

3142 Add backend to utility transforms #3186

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42a45e0
Merge pull request #19 from Project-MONAI/master
Nic-Ma Feb 1, 2021
cd16a13
Merge pull request #32 from Project-MONAI/master
Nic-Ma Feb 24, 2021
6f87afd
Merge pull request #180 from Project-MONAI/dev
Nic-Ma Jul 22, 2021
f398298
Merge pull request #214 from Project-MONAI/dev
Nic-Ma Sep 8, 2021
2da08e4
Merge pull request #258 from Project-MONAI/dev
Nic-Ma Oct 25, 2021
590a515
[DLMED] add backend
Nic-Ma Oct 25, 2021
ce7552f
Merge branch 'dev' into add-backend-to-transforms
Nic-Ma Oct 26, 2021
fe739f7
[DLMED] add backend
Nic-Ma Oct 26, 2021
b949493
[DLMED] fix PyTorch 1.5.1 test
Nic-Ma Oct 26, 2021
dbf6e79
[DLMED] add more tests
Nic-Ma Oct 26, 2021
a6f521a
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 26, 2021
a743a2c
[MONAI] python code formatting
monai-bot Oct 26, 2021
e149729
Merge branch 'dev' into add-backend-to-transforms
wyli Oct 26, 2021
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2 changes: 1 addition & 1 deletion monai/transforms/intensity/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -941,7 +941,7 @@ class MaskIntensity(Transform):

"""

backend = [TransformBackends.NUMPY]
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

def __init__(self, mask_data: Optional[NdarrayOrTensor] = None, select_fn: Callable = is_positive) -> None:
self.mask_data = mask_data
Expand Down
66 changes: 37 additions & 29 deletions monai/transforms/utility/array.py
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Expand Up @@ -38,14 +38,13 @@
convert_to_numpy,
convert_to_tensor,
ensure_tuple,
get_equivalent_dtype,
look_up_option,
min_version,
optional_import,
)
from monai.utils.enums import TransformBackends
from monai.utils.misc import is_module_ver_at_least
from monai.utils.type_conversion import convert_to_dst_type
from monai.utils.type_conversion import convert_to_dst_type, get_equivalent_dtype

PILImageImage, has_pil = optional_import("PIL.Image", name="Image")
pil_image_fromarray, _ = optional_import("PIL.Image", name="fromarray")
Expand Down Expand Up @@ -389,11 +388,9 @@ def __call__(self, data: NdarrayOrTensor):
if applicable.

"""
if self.data_type == "tensor":
dtype_ = get_equivalent_dtype(self.dtype, torch.Tensor)
return convert_to_tensor(data, dtype=dtype_, device=self.device)
dtype_ = get_equivalent_dtype(self.dtype, np.ndarray)
return convert_to_numpy(data, dtype=dtype_)
output_type = torch.Tensor if self.data_type == "tensor" else np.ndarray
out, *_ = convert_data_type(data, output_type=output_type, dtype=self.dtype, device=self.device)
return out


class ToNumpy(Transform):
Expand Down Expand Up @@ -880,18 +877,19 @@ class ConvertToMultiChannelBasedOnBratsClasses(Transform):
and ET (Enhancing tumor).
"""

def __call__(self, img: np.ndarray) -> np.ndarray:
img, *_ = convert_data_type(img, np.ndarray) # type: ignore
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
# if img has channel dim, squeeze it
if img.ndim == 4 and img.shape[0] == 1:
img = np.squeeze(img, axis=0)
img = img.squeeze(0)

result = [np.logical_or(img == 1, img == 4)]
result = [(img == 1) | (img == 4)]
# merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT
result.append(np.logical_or(np.logical_or(img == 1, img == 4), img == 2))
result.append((img == 1) | (img == 4) | (img == 2))
# label 4 is ET
result.append(img == 4)
return np.stack(result, axis=0)
return torch.stack(result, dim=0) if isinstance(img, torch.Tensor) else np.stack(result, axis=0)


class AddExtremePointsChannel(Randomizable, Transform):
Expand Down Expand Up @@ -966,6 +964,8 @@ class TorchVision:

"""

backend = [TransformBackends.TORCH]

def __init__(self, name: str, *args, **kwargs) -> None:
"""
Args:
Expand All @@ -978,14 +978,16 @@ def __init__(self, name: str, *args, **kwargs) -> None:
transform, _ = optional_import("torchvision.transforms", "0.8.0", min_version, name=name)
self.trans = transform(*args, **kwargs)

def __call__(self, img: torch.Tensor):
def __call__(self, img: NdarrayOrTensor):
"""
Args:
img: PyTorch Tensor data for the TorchVision transform.

"""
img, *_ = convert_data_type(img, torch.Tensor) # type: ignore
return self.trans(img)
img_t, *_ = convert_data_type(img, torch.Tensor) # type: ignore
out = self.trans(img_t)
out, *_ = convert_to_dst_type(src=out, dst=img)
return out


class MapLabelValue:
Expand All @@ -997,6 +999,8 @@ class MapLabelValue:

"""

backend = [TransformBackends.NUMPY]

def __init__(self, orig_labels: Sequence, target_labels: Sequence, dtype: DtypeLike = np.float32) -> None:
"""
Args:
Expand All @@ -1012,11 +1016,11 @@ def __init__(self, orig_labels: Sequence, target_labels: Sequence, dtype: DtypeL

self.orig_labels = orig_labels
self.target_labels = target_labels
self.dtype = dtype
self.dtype = get_equivalent_dtype(dtype, data_type=np.ndarray)

def __call__(self, img: np.ndarray):
img, *_ = convert_data_type(img, np.ndarray) # type: ignore
img_flat = img.flatten()
def __call__(self, img: NdarrayOrTensor):
img_np, *_ = convert_data_type(img, np.ndarray)
img_flat = img_np.flatten()
try:
out_flat = np.copy(img_flat).astype(self.dtype)
except ValueError:
Expand All @@ -1028,7 +1032,9 @@ def __call__(self, img: np.ndarray):
continue
np.place(out_flat, img_flat == o, t)

return out_flat.reshape(img.shape)
out = out_flat.reshape(img_np.shape)
out, *_ = convert_to_dst_type(src=out, dst=img, dtype=self.dtype)
return out


class IntensityStats(Transform):
Expand All @@ -1050,14 +1056,16 @@ class IntensityStats(Transform):

"""

backend = [TransformBackends.NUMPY]

def __init__(self, ops: Sequence[Union[str, Callable]], key_prefix: str, channel_wise: bool = False) -> None:
self.ops = ensure_tuple(ops)
self.key_prefix = key_prefix
self.channel_wise = channel_wise

def __call__(
self, img: np.ndarray, meta_data: Optional[Dict] = None, mask: Optional[np.ndarray] = None
) -> Tuple[np.ndarray, Dict]:
self, img: NdarrayOrTensor, meta_data: Optional[Dict] = None, mask: Optional[np.ndarray] = None
) -> Tuple[NdarrayOrTensor, Dict]:
"""
Compute statistics for the intensity of input image.

Expand All @@ -1068,15 +1076,15 @@ def __call__(
mask must have the same shape as input `img`.

"""
img, *_ = convert_data_type(img, np.ndarray) # type: ignore
img_np: np.ndarray
img_np, *_ = convert_data_type(img, np.ndarray) # type: ignore
if meta_data is None:
meta_data = {}

img_: np.ndarray = img
if mask is not None:
if mask.shape != img.shape or mask.dtype != bool:
if mask.shape != img_np.shape or mask.dtype != bool:
raise TypeError("mask must be bool array with the same shape as input `img`.")
img_ = img[mask]
img_np = img_np[mask]

supported_ops = {
"mean": np.nanmean,
Expand All @@ -1095,9 +1103,9 @@ def _compute(op: Callable, data: np.ndarray):
for o in self.ops:
if isinstance(o, str):
o = look_up_option(o, supported_ops.keys())
meta_data[self.key_prefix + "_" + o] = _compute(supported_ops[o], img_) # type: ignore
meta_data[self.key_prefix + "_" + o] = _compute(supported_ops[o], img_np) # type: ignore
elif callable(o):
meta_data[self.key_prefix + "_custom_" + str(custom_index)] = _compute(o, img_)
meta_data[self.key_prefix + "_custom_" + str(custom_index)] = _compute(o, img_np)
custom_index += 1
else:
raise ValueError("ops must be key string for predefined operations or callable function.")
Expand Down
22 changes: 16 additions & 6 deletions monai/transforms/utility/dictionary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1192,11 +1192,13 @@ class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
and ET (Enhancing tumor).
"""

backend = ConvertToMultiChannelBasedOnBratsClasses.backend

def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
super().__init__(keys, allow_missing_keys)
self.converter = ConvertToMultiChannelBasedOnBratsClasses()

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)
for key in self.key_iterator(d):
d[key] = self.converter(d[key])
Expand Down Expand Up @@ -1280,6 +1282,8 @@ class TorchVisiond(MapTransform):
data to be dict of PyTorch Tensors, users can easily call `ToTensord` transform to convert Numpy to Tensor.
"""

backend = TorchVision.backend

def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = False, *args, **kwargs) -> None:
"""
Args:
Expand All @@ -1294,7 +1298,7 @@ def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = F
super().__init__(keys, allow_missing_keys)
self.trans = TorchVision(name, *args, **kwargs)

def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]:
def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
d = dict(data)
for key in self.key_iterator(d):
d[key] = self.trans(d[key])
Expand All @@ -1317,6 +1321,8 @@ class RandTorchVisiond(Randomizable, MapTransform):

"""

backend = TorchVision.backend

def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = False, *args, **kwargs) -> None:
"""
Args:
Expand All @@ -1331,7 +1337,7 @@ def __init__(self, keys: KeysCollection, name: str, allow_missing_keys: bool = F
MapTransform.__init__(self, keys, allow_missing_keys)
self.trans = TorchVision(name, *args, **kwargs)

def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]:
def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
d = dict(data)
for key in self.key_iterator(d):
d[key] = self.trans(d[key])
Expand All @@ -1343,6 +1349,8 @@ class MapLabelValued(MapTransform):
Dictionary-based wrapper of :py:class:`monai.transforms.MapLabelValue`.
"""

backend = MapLabelValue.backend

def __init__(
self,
keys: KeysCollection,
Expand All @@ -1364,7 +1372,7 @@ def __init__(
super().__init__(keys, allow_missing_keys)
self.mapper = MapLabelValue(orig_labels=orig_labels, target_labels=target_labels, dtype=dtype)

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)
for key in self.key_iterator(d):
d[key] = self.mapper(d[key])
Expand Down Expand Up @@ -1406,6 +1414,8 @@ class IntensityStatsd(MapTransform):

"""

backend = IntensityStats.backend

def __init__(
self,
keys: KeysCollection,
Expand All @@ -1425,7 +1435,7 @@ def __init__(
raise ValueError("meta_keys should have the same length as keys.")
self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))

def __call__(self, data) -> Dict[Hashable, np.ndarray]:
def __call__(self, data) -> Dict[Hashable, NdarrayOrTensor]:
d = dict(data)
for key, mask_key, meta_key, meta_key_postfix in self.key_iterator(
d, self.mask_keys, self.meta_keys, self.meta_key_postfix
Expand All @@ -1442,7 +1452,7 @@ class ToDeviced(MapTransform):
Dictionary-based wrapper of :py:class:`monai.transforms.ToDevice`.
"""

backend = [TransformBackends.TORCH]
backend = ToDevice.backend

def __init__(
self, keys: KeysCollection, device: Union[torch.device, str], allow_missing_keys: bool = False, **kwargs
Expand Down
2 changes: 1 addition & 1 deletion monai/utils/type_conversion.py
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Expand Up @@ -244,7 +244,7 @@ def convert_data_type(

output_type = output_type or orig_type

dtype_ = get_equivalent_dtype(dtype or get_dtype(data), output_type)
dtype_ = get_equivalent_dtype(dtype, output_type)

if output_type is torch.Tensor:
data = convert_to_tensor(data, dtype=dtype_, device=device, wrap_sequence=wrap_sequence)
Expand Down
5 changes: 0 additions & 5 deletions tests/test_convert_data_type.py
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Expand Up @@ -60,11 +60,6 @@ def test_convert_data_type(self, in_image, im_out):
def test_neg_stride(self):
_ = convert_data_type(np.array((1, 2))[::-1], torch.Tensor)

def test_ill_arg(self):
with self.assertRaises(ValueError):
convert_data_type(None, torch.Tensor)
convert_data_type(None, np.ndarray)

@parameterized.expand(TESTS_LIST)
def test_convert_list(self, in_image, im_out, wrap):
output_type = type(im_out) if wrap else type(im_out[0])
Expand Down
46 changes: 29 additions & 17 deletions tests/test_convert_to_multi_channel.py
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Expand Up @@ -11,34 +11,46 @@

import unittest

import numpy as np
import torch
from parameterized import parameterized

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

TEST_CASE_1 = [
np.array([[0, 1, 2], [1, 2, 4], [0, 1, 4]]),
np.array([[[0, 1, 0], [1, 0, 1], [0, 1, 1]], [[0, 1, 1], [1, 1, 1], [0, 1, 1]], [[0, 0, 0], [0, 0, 1], [0, 0, 1]]]),
]

TEST_CASE_2 = [
np.array([[[[0, 1], [1, 2]], [[2, 4], [4, 4]]]]),
np.array(
TESTS = []
for p in TEST_NDARRAYS:
TESTS.extend(
[
[[[0, 1], [1, 0]], [[0, 1], [1, 1]]],
[[[0, 1], [1, 1]], [[1, 1], [1, 1]]],
[[[0, 0], [0, 0]], [[0, 1], [1, 1]]],
[
p([[0, 1, 2], [1, 2, 4], [0, 1, 4]]),
p(
[
[[0, 1, 0], [1, 0, 1], [0, 1, 1]],
[[0, 1, 1], [1, 1, 1], [0, 1, 1]],
[[0, 0, 0], [0, 0, 1], [0, 0, 1]],
]
),
],
[
p([[[[0, 1], [1, 2]], [[2, 4], [4, 4]]]]),
p(
[
[[[0, 1], [1, 0]], [[0, 1], [1, 1]]],
[[[0, 1], [1, 1]], [[1, 1], [1, 1]]],
[[[0, 0], [0, 0]], [[0, 1], [1, 1]]],
]
),
],
]
),
]
)


class TestConvertToMultiChannel(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2])
@parameterized.expand(TESTS)
def test_type_shape(self, data, expected_result):
result = ConvertToMultiChannelBasedOnBratsClasses()(data)
np.testing.assert_equal(result, expected_result)
self.assertEqual(f"{result.dtype}", "bool")
assert_allclose(result, expected_result)
self.assertTrue(result.dtype in (bool, torch.bool))


if __name__ == "__main__":
Expand Down
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