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3174 Add support to scale datalist #3175

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Merged
wyli merged 9 commits into from
Oct 25, 2021
Merged

3174 Add support to scale datalist #3175

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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
b886eac
Merge pull request #256 from Project-MONAI/dev
Nic-Ma Oct 22, 2021
0bc7075
[DLMED] add scale_datalist
Nic-Ma Oct 22, 2021
f38525b
Merge branch 'dev' into 3174-scale-datalist
wyli Oct 23, 2021
b481303
Merge branch 'dev' into 3174-scale-datalist
Nic-Ma Oct 24, 2021
239a4e1
[DLMED] update according to comments
Nic-Ma Oct 24, 2021
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1 change: 1 addition & 0 deletions monai/data/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,6 +58,7 @@
pickle_hashing,
rectify_header_sform_qform,
rep_scalar_to_batch,
resample_datalist,
select_cross_validation_folds,
set_rnd,
sorted_dict,
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26 changes: 26 additions & 0 deletions monai/data/utils.py
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Expand Up @@ -66,6 +66,7 @@
"is_supported_format",
"partition_dataset",
"partition_dataset_classes",
"resample_datalist",
"select_cross_validation_folds",
"json_hashing",
"pickle_hashing",
Expand Down Expand Up @@ -991,6 +992,31 @@ def partition_dataset_classes(
return datasets


def resample_datalist(data: Sequence, factor: float, random_pick: bool = False, seed: int = 0):
"""
Utility function to resample the loaded datalist for training, for example:
If factor < 1.0, randomly pick part of the datalist and set to Dataset, useful to quickly test the program.
If factor > 1.0, repeat the datalist to enhance the Dataset.

Args:
data: original datalist to scale.
factor: scale factor for the datalist, for example, factor=4.5, repeat the datalist 4 times and plus
50% of the original datalist.
random_pick: whether to randomly pick data if scale factor has decimal part.
seed: random seed to randomly pick data.

"""
scale, repeats = math.modf(factor)
ret: List = list()

for _ in range(int(repeats)):
ret.extend(list(deepcopy(data)))
if scale > 1e-6:
ret.extend(partition_dataset(data=data, ratios=[scale, 1 - scale], shuffle=random_pick, seed=seed)[0])

return ret


def select_cross_validation_folds(partitions: Sequence[Iterable], folds: Union[Sequence[int], int]) -> List:
"""
Select cross validation data based on data partitions and specified fold index.
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2 changes: 1 addition & 1 deletion tests/test_inverse.py
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Expand Up @@ -450,7 +450,7 @@ def test_inverse_inferred_seg(self, extra_transform):

batch_size = 10
# num workers = 0 for mac
num_workers = 2 if sys.platform != "darwin" else 0
num_workers = 2 if sys.platform == "linux" else 0
transforms = Compose([AddChanneld(KEYS), SpatialPadd(KEYS, (150, 153)), extra_transform])
num_invertible_transforms = sum(1 for i in transforms.transforms if isinstance(i, InvertibleTransform))

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40 changes: 40 additions & 0 deletions tests/test_resample_datalist.py
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@@ -0,0 +1,40 @@
# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import numpy as np
from parameterized import parameterized

from monai.data import resample_datalist

TEST_CASE_1 = [
{"data": [1, 2, 3, 4, 5], "factor": 2.5, "random_pick": True, "seed": 123},
[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 2, 4, 5],
]

TEST_CASE_2 = [
{"data": [1, 2, 3, 4, 5], "factor": 2.5, "random_pick": False, "seed": 0},
[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3],
]

TEST_CASE_3 = [{"data": [1, 2, 3, 4, 5], "factor": 0.6, "random_pick": True, "seed": 123}, [2, 4, 5]]


class TestResampleDatalist(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
def test_value_shape(self, input_param, expected):
result = resample_datalist(**input_param)
np.testing.assert_allclose(result, expected)


if __name__ == "__main__":
unittest.main()