chore: import upstream snapshot with attribution

test/legacy_test/test_transforms_static.py

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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+
+import paddle
+from paddle.vision.transforms import transforms
+
+SEED = 2022
+
+
+class TestTransformUnitTestBase(unittest.TestCase):
+    def setUp(self):
+        self.img = (np.random.rand(*self.get_shape()) * 255.0).astype(
+            np.float32
+        )
+        self.set_trans_api()
+        self.init_dy_res()
+
+    def init_dy_res(self):
+        # Obtain the dynamic transform result first before test_transform.
+        self.dy_res = self.dynamic_transform()
+        if isinstance(self.dy_res, paddle.Tensor):
+            self.dy_res = self.dy_res.numpy()
+
+    def get_shape(self):
+        return (3, 64, 64)
+
+    def set_trans_api(self):
+        self.api = transforms.Resize(size=16)
+
+    def dynamic_transform(self):
+        paddle.seed(SEED)
+
+        img_t = paddle.to_tensor(self.img)
+        return self.api(img_t)
+
+    def static_transform(self):
+        paddle.enable_static()
+        paddle.seed(SEED)
+
+        main_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program):
+            x = paddle.static.data(
+                shape=self.get_shape(), dtype=paddle.float32, name='img'
+            )
+            out = self.api(x)
+
+        exe = paddle.static.Executor()
+        res = exe.run(main_program, fetch_list=[out], feed={'img': self.img})
+
+        paddle.disable_static()
+        return res[0]
+
+    def test_transform(self):
+        st_res = self.static_transform()
+        np.testing.assert_almost_equal(self.dy_res, st_res)
+
+
+class TestResize(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.api = transforms.Resize(size=(16, 16))
+
+
+class TestResizeError(TestTransformUnitTestBase):
+    def test_transform(self):
+        pass
+
+    def test_error(self):
+        paddle.enable_static()
+        # Not support while w<=0 or h<=0, but received w=-1, h=-1
+        with self.assertRaises(NotImplementedError):
+            main_program = paddle.static.Program()
+            with paddle.static.program_guard(main_program):
+                x = paddle.static.data(
+                    shape=[-1, -1, -1], dtype=paddle.float32, name='img'
+                )
+                self.api(x)
+
+        paddle.disable_static()
+
+
+class TestRandomVerticalFlip0(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.api = transforms.RandomVerticalFlip(prob=0)
+
+
+class TestRandomVerticalFlip1(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.api = transforms.RandomVerticalFlip(prob=1)
+
+
+class TestRandomHorizontalFlip0(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.api = transforms.RandomHorizontalFlip(0)
+
+
+class TestRandomHorizontalFlip1(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.api = transforms.RandomHorizontalFlip(1)
+
+
+class TestRandomCrop_random(TestTransformUnitTestBase):
+    def get_shape(self):
+        return (3, 240, 240)
+
+    def set_trans_api(self):
+        self.crop_size = (224, 224)
+        self.api = transforms.RandomCrop(self.crop_size)
+
+    def assert_test_random_equal(self, res, eps=1e-4):
+        _, h, w = self.get_shape()
+        c_h, c_w = self.crop_size
+        res_assert = True
+        for y_offset in range(h - c_h + 1):
+            for x_offset in range(w - c_w + 1):
+                diff_abs_sum = np.abs(
+                    self.img[
+                        :, y_offset : y_offset + c_h, x_offset : x_offset + c_w
+                    ]
+                    - res
+                ).sum()
+                if diff_abs_sum < eps:
+                    res_assert = False
+                    break
+            if not res_assert:
+                break
+        assert not res_assert
+
+    def test_transform(self):
+        st_res = self.static_transform()
+
+        self.assert_test_random_equal(self.dy_res)
+        self.assert_test_random_equal(st_res)
+
+
+class TestRandomCrop_same(TestTransformUnitTestBase):
+    def get_shape(self):
+        return (3, 224, 224)
+
+    def set_trans_api(self):
+        self.crop_size = (224, 224)
+        self.api = transforms.RandomCrop(self.crop_size)
+
+
+class FixedAngleRandomRotation(transforms.RandomRotation):
+    # Keep the rotation path under test, but bypass separate dy/static RNGs.
+    def __init__(self, angle, **kwargs):
+        self.angle = float(np.float32(angle))
+        super().__init__((self.angle, self.angle), **kwargs)
+
+    def _get_param(self, degrees):
+        del degrees
+        if paddle.in_dynamic_mode():
+            return self.angle
+        return paddle.full([1], self.angle, dtype='float32')
+
+
+class TestRandomRotation(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.api = FixedAngleRandomRotation(33.0)
+
+
+class TestRandomRotation_expand_True(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.api = FixedAngleRandomRotation(33.0, expand=True, fill=3)
+
+
+class TestRandomErasing(TestTransformUnitTestBase):
+    def set_trans_api(self):
+        self.value = 100
+        self.scale = (0.02, 0.33)
+        self.ratio = (0.3, 3.3)
+        self.api = transforms.RandomErasing(
+            prob=1, value=self.value, scale=self.scale, ratio=self.ratio
+        )
+
+    def test_transform(self):
+        st_res = self.static_transform()
+
+        self.assert_test_erasing(self.dy_res)
+        self.assert_test_erasing(st_res)
+
+    def assert_test_erasing(self, arr):
+        _, h, w = arr.shape
+        area = h * w
+
+        height = (arr[2] == self.value).cumsum(1)[:, -1].max()
+        width = (arr[2] == self.value).cumsum(0)[-1].max()
+        erasing_area = height * width
+
+        assert self.ratio[0] < height / width < self.ratio[1]
+        assert self.scale[0] < erasing_area / area < self.scale[1]
+
+
+class TestRandomResizedCrop(TestTransformUnitTestBase):
+    def set_trans_api(self, eps=10e-5):
+        c, h, w = self.get_shape()
+        size = h, w
+        scale = (1 - eps, 1.0)
+        ratio = (1 - eps, 1.0)
+        self.api = transforms.RandomResizedCrop(size, scale=scale, ratio=ratio)
+
+
+if __name__ == "__main__":
+    unittest.main()