wehub-resource-sync
107 lines
3.2 KiB
Python
107 lines
3.2 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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from op_test import get_device_place, is_custom_device
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import paddle
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from paddle import base, static
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TEST_REAL_DATA = [
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np.array(1.0),
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np.random.randint(-10, 10, (2, 3)),
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np.random.randn(64, 32),
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]
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REAL_TYPE = [
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'float16',
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'float32',
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'float64',
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'bool',
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'int16',
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'int32',
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'int64',
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'uint16',
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]
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TEST_COMPLEX_DATA = [
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np.array(1.0 + 2j),
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np.array(1.0 + 0j),
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np.array([[0.2 + 3j, 3 + 0j, -0.7 - 6j], [-0.4 + 0j, 3.5 - 10j, 2.5 + 0j]]),
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]
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COMPLEX_TYPE = ['complex64', 'complex128']
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def run_dygraph(data, type, use_gpu=False):
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place = paddle.CPUPlace()
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if use_gpu and (base.core.is_compiled_with_cuda() or is_custom_device()):
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place = get_device_place()
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paddle.disable_static(place)
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data = data.astype(type)
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x = paddle.to_tensor(data)
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return paddle.isreal(x)
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def run_static(data, type, use_gpu=False):
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paddle.enable_static()
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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place = paddle.CPUPlace()
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if use_gpu and (base.core.is_compiled_with_cuda() or is_custom_device()):
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place = get_device_place()
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exe = base.Executor(place)
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with static.program_guard(main_program, startup_program):
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data = data.astype(type)
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x = paddle.static.data(name='x', shape=data.shape, dtype=type)
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res = paddle.isreal(x)
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static_result = exe.run(feed={'x': data}, fetch_list=[res])
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return static_result
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def test(data_cases, type_cases, use_gpu=False):
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for data in data_cases:
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for type in type_cases:
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dygraph_result = run_dygraph(data, type, use_gpu).numpy()
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np_result = np.isreal(data.astype(type))
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np.testing.assert_equal(dygraph_result, np_result)
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def test_static_or_pir_mode():
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(static_result,) = run_static(data, type, use_gpu)
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np.testing.assert_equal(static_result, np_result)
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test_static_or_pir_mode()
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class TestIsRealError(unittest.TestCase):
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def test_for_exception(self):
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with self.assertRaises(TypeError):
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paddle.isreal(np.array([1, 2]))
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class TestIsReal(unittest.TestCase):
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def test_for_real_tensor_without_gpu(self):
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test(TEST_REAL_DATA, REAL_TYPE)
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def test_for_real_tensor_with_gpu(self):
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test(TEST_REAL_DATA, REAL_TYPE, True)
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def test_for_complex_tensor_without_gpu(self):
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test(TEST_COMPLEX_DATA, COMPLEX_TYPE)
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def test_for_complex_tensor_with_gpu(self):
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test(TEST_COMPLEX_DATA, COMPLEX_TYPE, True)
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if __name__ == '__main__':
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unittest.main()
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