wehub-resource-sync
162 lines
4.9 KiB
Python
162 lines
4.9 KiB
Python
# Copyright (c) 2018 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
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import paddle
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from paddle import base
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def ref_frac(x):
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return x - np.trunc(x)
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class TestFracAPI(unittest.TestCase):
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"""Test Frac API"""
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def set_dtype(self):
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self.dtype = 'float64'
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def setUp(self):
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self.set_dtype()
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self.x_np = np.random.uniform(-3, 3, [2, 3]).astype(self.dtype)
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self.place = get_device_place()
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def test_api_static(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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input = paddle.static.data('X', self.x_np.shape, self.x_np.dtype)
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out = paddle.frac(input)
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exe = base.Executor(self.place)
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(res,) = exe.run(feed={'X': self.x_np}, fetch_list=[out])
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out_ref = ref_frac(self.x_np)
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np.testing.assert_allclose(out_ref, res, rtol=1e-05)
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def test_api_dygraph(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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out = paddle.frac(x)
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out_ref = ref_frac(self.x_np)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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def test_api_eager(self):
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with paddle.base.dygraph.guard(self.place):
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x_tensor = paddle.to_tensor(self.x_np)
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out = paddle.frac(x_tensor)
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out_ref = ref_frac(self.x_np)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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class TestFracInt32(TestFracAPI):
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"""Test Frac API with data type int32"""
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def set_dtype(self):
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self.dtype = 'int32'
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class TestFracInt64(TestFracAPI):
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"""Test Frac API with data type int64"""
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def set_dtype(self):
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self.dtype = 'int64'
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class TestFracFloat32(TestFracAPI):
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"""Test Frac API with data type float32"""
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def set_dtype(self):
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self.dtype = 'float32'
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class TestFracError(unittest.TestCase):
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"""Test Frac Error"""
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def setUp(self):
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self.x_np = np.random.uniform(-3, 3, [2, 3]).astype('int16')
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self.place = get_device_place()
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def test_static_error(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', [5, 5], 'bool')
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self.assertRaises(TypeError, paddle.frac, x)
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def test_dygraph_error(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np, dtype='int16')
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self.assertRaises(TypeError, paddle.frac, x)
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class TestFracAPI_ZeroSize(unittest.TestCase):
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def set_dtype(self):
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self.dtype = 'float64'
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def setUp(self):
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self.set_dtype()
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self.x_np = np.random.random([0, 3]).astype(self.dtype)
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self.place = get_device_place()
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def test_api_dygraph(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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x.stop_gradient = False
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out = paddle.frac(x)
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out_ref = ref_frac(self.x_np)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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out.sum().backward()
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np.testing.assert_allclose(x.grad.shape, x.shape)
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class TestFracAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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self.shape = [5, 6]
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self.dtype = "float32"
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np.random.seed(2025)
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self.x_np = np.random.rand(*self.shape).astype(self.dtype)
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self.place = get_device_place()
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def test_frac_input_arg(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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out_ref = ref_frac(self.x_np)
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out = paddle.frac(input=x)
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np.testing.assert_allclose(out.numpy(), out_ref, rtol=1e-05)
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paddle.enable_static()
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def test_frac_output_arg(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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out_ref = ref_frac(self.x_np)
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out = paddle.empty([])
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paddle.frac(x, out=out)
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np.testing.assert_allclose(out.numpy(), out_ref, rtol=1e-05)
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paddle.enable_static()
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def test_frac_tensor_output_arg(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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out_ref = ref_frac(self.x_np)
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out1 = paddle.empty([])
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out2 = paddle.frac(x, out=out1)
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np.testing.assert_allclose(out1.numpy(), out_ref, rtol=1e-05)
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np.testing.assert_allclose(out2.numpy(), out_ref, rtol=1e-05)
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paddle.enable_static()
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if __name__ == '__main__':
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unittest.main()
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