wehub-resource-sync
180 lines
6.8 KiB
Python
180 lines
6.8 KiB
Python
# Copyright (c) 2022 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 is_custom_device
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from utils import static_guard
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import paddle
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def np_sgn(x: np.ndarray):
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if x.dtype == 'complex128' or x.dtype == 'complex64':
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x_abs = np.abs(x)
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eps = np.finfo(x.dtype).eps
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x_abs = np.maximum(x_abs, eps)
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out = x / x_abs
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else:
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out = np.sign(x)
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return out
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class TestSgnError(unittest.TestCase):
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def test_errors_dynamic(self):
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# The input dtype of sgn must be float16, float32, float64,complex64,complex128.
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input2 = paddle.to_tensor(
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np.random.randint(-10, 10, size=[12, 20]).astype('int32')
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)
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input3 = paddle.to_tensor(
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np.random.randint(-10, 10, size=[12, 20]).astype('int64')
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)
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self.assertRaises(TypeError, paddle.sgn, input2)
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self.assertRaises(TypeError, paddle.sgn, input3)
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def test_errors_static_and_pir(self):
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paddle.enable_static()
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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# The input dtype of sgn must be float16, float32, float64,complex64,complex128.
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input2 = paddle.to_tensor(
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np.random.randint(-10, 10, size=[12, 20]).astype('int32')
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)
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input3 = paddle.to_tensor(
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np.random.randint(-10, 10, size=[12, 20]).astype('int64')
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)
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self.assertRaises(TypeError, paddle.sgn, input2)
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self.assertRaises(TypeError, paddle.sgn, input3)
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paddle.disable_static()
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class TestSignAPI(unittest.TestCase):
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def test_complex_dynamic(self):
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for dtype in ['complex64', 'complex128']:
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np_x = np.array(
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[[3 + 4j, 7 - 24j, 0, 1 + 2j], [6 + 8j, 3, 0, -2]], dtype=dtype
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)
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x = paddle.to_tensor(np_x)
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z = paddle.sgn(x)
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np_z = z.numpy()
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z_expected = np_sgn(np_x)
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np.testing.assert_allclose(np_z, z_expected, rtol=1e-05)
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def test_complex_static_and_pir(self):
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with static_guard():
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for dtype in ['complex64', 'complex128']:
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exe = paddle.static.Executor()
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train_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(
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train_program, startup_program
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):
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x = paddle.static.data(name='X', shape=[2, 4], dtype=dtype)
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z = paddle.sgn(x)
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# Run the startup program once and only once.
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# Not need to optimize/compile the startup program.
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exe.run(startup_program)
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# Run the main program directly without compile.
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x = np.array(
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[[3 + 4j, 7 - 24j, 0, 1 + 2j], [6 + 8j, 3, 0, -2]],
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dtype=dtype,
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)
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(z,) = exe.run(train_program, feed={"X": x}, fetch_list=[z])
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z_expected = np_sgn(x)
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np.testing.assert_allclose(z, z_expected, rtol=1e-05)
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def test_float_dynamic(self):
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dtype_list = ['float32', 'float64']
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if paddle.is_compiled_with_cuda() or is_custom_device():
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dtype_list.append('float16')
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for dtype in dtype_list:
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np_x = np.random.randint(-10, 10, size=[12, 20, 2]).astype(dtype)
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x = paddle.to_tensor(np_x)
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z = paddle.sgn(x)
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np_z = z.numpy()
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z_expected = np_sgn(np_x)
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np.testing.assert_allclose(np_z, z_expected, rtol=1e-05)
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def test_float_static_and_pir(self):
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dtype_list = ['float32', 'float64']
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if paddle.is_compiled_with_cuda() or is_custom_device():
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dtype_list.append('float16')
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with static_guard():
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for dtype in dtype_list:
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exe = paddle.static.Executor()
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train_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(
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train_program, startup_program
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):
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np_x = np.random.randint(-10, 10, size=[12, 20, 2]).astype(
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dtype
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)
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x = paddle.static.data(
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name='X', shape=[12, 20, 2], dtype=dtype
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)
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z = paddle.sgn(x)
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# Run the startup program once and only once.
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# Not need to optimize/compile the startup program.
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exe.run(startup_program)
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# Run the main program directly without compile.
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(z,) = exe.run(train_program, feed={"X": np_x}, fetch_list=[z])
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z_expected = np_sgn(np_x)
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np.testing.assert_allclose(z, z_expected, rtol=1e-05)
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def test_zero_size_complex_dynamic(self):
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for dtype in ['complex64', 'complex128']:
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np_x = np.empty((0, 4), dtype=dtype) # 空张量 shape=[0, 4]
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x = paddle.to_tensor(np_x)
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z = paddle.sgn(x)
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np_z = z.numpy()
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z_expected = np_sgn(np_x)
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np.testing.assert_allclose(np_z, z_expected, rtol=1e-05)
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np.testing.assert_equal(np_z.shape, (0, 4))
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def test_zero_size_complex_static_and_pir(self):
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with static_guard():
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for dtype in ['complex64', 'complex128']:
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exe = paddle.static.Executor()
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train_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(
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train_program, startup_program
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):
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x = paddle.static.data(name='X', shape=[0, 4], dtype=dtype)
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z = paddle.sgn(x)
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exe.run(startup_program)
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x_np = np.empty((0, 4), dtype=dtype)
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(z_out,) = exe.run(
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train_program, feed={"X": x_np}, fetch_list=[z]
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)
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z_expected = np_sgn(x_np)
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np.testing.assert_allclose(z_out, z_expected, rtol=1e-05)
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np.testing.assert_equal(z_out.shape, (0, 4))
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if __name__ == "__main__":
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unittest.main()
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