wehub-resource-sync
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle
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class TestNegativeApi(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.shape = [2, 3, 4, 5]
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self.low = -100
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self.high = 100
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def test_negative_int16(self):
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x = np.random.randint(self.low, self.high, self.shape, dtype=np.int16)
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expected_out = np.negative(x)
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x_tensor = paddle.to_tensor(x)
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out = paddle.negative(x_tensor).numpy()
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np.testing.assert_allclose(out, expected_out, atol=1e-5)
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def test_negative_int32(self):
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x = np.random.randint(self.low, self.high, self.shape, dtype=np.int32)
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expected_out = np.negative(x)
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x_tensor = paddle.to_tensor(x)
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out = paddle.negative(x_tensor).numpy()
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np.testing.assert_allclose(out, expected_out, atol=1e-5)
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def test_negative_int64(self):
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x = np.random.randint(self.low, self.high, self.shape, dtype=np.int64)
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expected_out = np.negative(x)
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x_tensor = paddle.to_tensor(x)
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out = paddle.negative(x_tensor).numpy()
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np.testing.assert_allclose(out, expected_out, atol=1e-5)
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def test_negative_float16(self):
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x = np.random.uniform(self.low, self.high, self.shape).astype(
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np.float16
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)
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expected_out = np.negative(x)
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x_tensor = paddle.to_tensor(x)
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out = paddle.negative(x_tensor).numpy()
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np.testing.assert_allclose(out, expected_out, atol=1e-3)
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def test_negative_float32(self):
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x = np.random.uniform(self.low, self.high, self.shape).astype(
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np.float32
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)
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expected_out = np.negative(x)
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x_tensor = paddle.to_tensor(x)
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out = paddle.negative(x_tensor).numpy()
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np.testing.assert_allclose(out, expected_out, atol=1e-3)
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def test_negative_float64(self):
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x = np.random.uniform(self.low, self.high, self.shape).astype(
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np.float64
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)
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expected_out = np.negative(x)
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x_tensor = paddle.to_tensor(x)
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out = paddle.negative(x_tensor).numpy()
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np.testing.assert_allclose(out, expected_out, atol=1e-3)
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def test_negative_bool(self):
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x = np.random.choice([True, False], size=self.shape)
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x_tensor = paddle.to_tensor(x, dtype=paddle.bool)
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with self.assertRaises(TypeError):
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paddle.negative(x_tensor)
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if __name__ == '__main__':
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unittest.main()
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