wehub-resource-sync
68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
# Copyright (c) 2021 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 EagerOpAPIGenerateTestCase(unittest.TestCase):
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def test_elementwise_add(self):
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paddle.set_device("cpu")
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np_x = np.ones([4, 16, 16, 32]).astype('float32')
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np_y = np.ones([4, 16, 16, 32]).astype('float32')
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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out = paddle.add(x, y)
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out_arr = out.numpy()
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out_arr_expected = np.add(np_x, np_y)
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np.testing.assert_array_equal(out_arr, out_arr_expected)
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def test_sum(self):
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x_data = np.array([[0.2, 0.3, 0.5, 0.9], [0.1, 0.2, 0.6, 0.7]]).astype(
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'float32'
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)
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x = paddle.to_tensor(x_data, 'float32')
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out = paddle.sum(x, axis=0)
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out_arr = out.numpy()
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out_arr_expected = np.sum(x_data, axis=0)
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np.testing.assert_array_equal(out_arr, out_arr_expected)
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def test_mm(self):
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np_input = np.random.random([16, 32]).astype('float32')
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np_mat2 = np.random.random([32, 32]).astype('float32')
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input = paddle.to_tensor(np_input)
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mat2 = paddle.to_tensor(np_mat2)
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out = paddle.mm(input, mat2)
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out_arr = out.numpy()
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out_arr_expected = np.matmul(np_input, np_mat2)
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np.testing.assert_allclose(out_arr, out_arr_expected, rtol=1e-05)
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def test_sigmoid(self):
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np_x = np.array([-0.4, -0.2, 0.1, 0.3]).astype('float32')
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x = paddle.to_tensor(np_x)
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out = paddle.nn.functional.sigmoid(x)
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out_arr = out.numpy()
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out_arr_expected = np.array(
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[0.40131234, 0.450166, 0.52497919, 0.57444252]
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).astype('float32')
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np.testing.assert_allclose(out_arr, out_arr_expected, rtol=1e-05)
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if __name__ == "__main__":
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unittest.main()
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