test_center_spatial_crop.py

# Copyright (c) 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.

from __future__ import annotations

import unittest

import numpy as np
import torch
from parameterized import parameterized

from monai.data.meta_obj import get_track_meta, set_track_meta
from monai.transforms import CenterSpatialCrop
from monai.transforms.croppad.array import Crop
from tests.croppers import CropTest
from tests.test_utils import SkipIfBeforePyTorchVersion

TEST_SHAPES = [
    [{"roi_size": [2, 2, -1]}, (3, 3, 3, 3), (3, 2, 2, 3), True],
    [{"roi_size": [2, 2, 2]}, (3, 3, 3, 3), (3, 2, 2, 2), True],
    [{"roi_size": [2, 1, 2]}, (3, 3, 3, 3), (3, 2, 1, 2), False],
    [{"roi_size": [2, 1, 3]}, (3, 3, 1, 3), (3, 2, 1, 3), True],
]

TEST_VALUES = [
    [
        {"roi_size": [2, 2]},
        np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]),
        np.array([[[1, 2], [2, 3]]]),
    ]
]


class TestCenterSpatialCrop(CropTest):
    Cropper = CenterSpatialCrop

    @parameterized.expand(TEST_SHAPES)
    def test_shape(self, input_param, input_shape, expected_shape, _):
        self.crop_test(input_param, input_shape, expected_shape)

    @parameterized.expand(TEST_VALUES)
    def test_value(self, input_param, input_arr, expected_arr):
        self.crop_test_value(input_param, input_arr, expected_arr)

    @parameterized.expand(TEST_SHAPES)
    def test_pending_ops(self, input_param, input_shape, _, align_corners):
        self.crop_test_pending_ops(input_param, input_shape, align_corners)

    def test_compute_slices_broadcast(self):
        self.assertEqual(Crop.compute_slices(roi_center=2, roi_size=(4, 6, 8)), (slice(0, 4), slice(0, 6), slice(0, 8)))
        self.assertEqual(Crop.compute_slices(roi_start=1, roi_end=(3, 5, 7)), (slice(1, 3), slice(1, 5), slice(1, 7)))
        with self.assertRaises(ValueError):
            Crop.compute_slices(roi_center=(2, 3), roi_size=(4, 5, 6))
        with self.assertRaises(ValueError):
            Crop.compute_slices(roi_start=(1, 2), roi_end=(3, 5, 7))
        with self.assertRaises(TypeError):
            Crop.compute_slices(roi_center="10", roi_size=(4, 6))

    @SkipIfBeforePyTorchVersion((2, 1))
    def test_torch_compile(self):
        prev_track_meta = get_track_meta()
        set_track_meta(False)
        try:
            cropper = torch.compile(CenterSpatialCrop(roi_size=(1, 16, 16)))
            img = torch.rand(1, 1, 32, 32, dtype=torch.float32)
            self.assertEqual(tuple(cropper(img).shape), (1, 1, 16, 16))
        finally:
            set_track_meta(prev_track_meta)
            torch._dynamo.reset()


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