wehub-resource-sync
148 lines
4.7 KiB
Python
148 lines
4.7 KiB
Python
# Copyright (c) 2025 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 copy
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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, get_places, is_custom_device
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import paddle
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import paddle.base.dygraph as dg
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import paddle.nn.functional as F
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from paddle import base, nn
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def ref_hardsigmoid(x, slope=0.1666667, offset=0.5):
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return np.maximum(np.minimum(x * slope + offset, 1.0), 0.0).astype(x.dtype)
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class TestHardSigmoidOpClass_Inplace(unittest.TestCase):
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def _test_case1_cpu(self):
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slope = 0.1666667
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offset = 0.5
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x = np.random.uniform(-5, 5, [10, 12]).astype("float32")
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y_ref = ref_hardsigmoid(x, slope, offset)
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place = base.CPUPlace()
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with dg.guard(place) as g:
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x_var1 = paddle.to_tensor(x)
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x_var2 = paddle.to_tensor(x)
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y_var1 = F.hardsigmoid(x_var1, slope, offset, True)
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y_test1 = y_var1.numpy()
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func = nn.Hardsigmoid(inplace=True)
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y_var2 = func(x_var2)
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y_test2 = y_var2.numpy()
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np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(
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y_ref, x_var1.numpy(), rtol=1e-05, atol=1e-08
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)
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np.testing.assert_allclose(
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y_ref, x_var2.numpy(), rtol=1e-05, atol=1e-08
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)
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def _test_case1_gpu(self):
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slope = 0.1666667
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offset = 0.5
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x = np.random.uniform(-5, 5, [10, 12]).astype("float32")
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y_ref = ref_hardsigmoid(x, slope, offset)
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place = get_device_place()
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with dg.guard(place) as g:
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x_var1 = paddle.to_tensor(x)
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x_var2 = paddle.to_tensor(x)
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y_var1 = F.hardsigmoid(x_var1, slope, offset, True)
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y_test1 = y_var1.numpy()
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func = nn.Hardsigmoid(inplace=True)
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y_var2 = func(x_var2)
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y_test2 = y_var2.numpy()
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np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(
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y_ref, x_var1.numpy(), rtol=1e-05, atol=1e-08
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)
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np.testing.assert_allclose(
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y_ref, x_var2.numpy(), rtol=1e-05, atol=1e-08
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)
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def test_cases(self):
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self._test_case1_cpu()
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if base.is_compiled_with_cuda() or is_custom_device():
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self._test_case1_gpu()
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class TestHardSigmoidAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(0)
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self.slope = 0.1666667
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self.offset = 0.5
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self.shape = [10, 10]
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self.x_np = np.random.uniform(-5, 5, self.shape).astype("float32")
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self.place = get_places()
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self.x_feed = copy.deepcopy(self.x_np)
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def test_api_static(self):
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paddle.enable_static()
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def run(place, inplace):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.shape)
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out = F.hardsigmoid(x, self.slope, self.offset, inplace)
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exe = paddle.static.Executor(self.place[0])
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res = exe.run(
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feed={
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'X': self.x_feed,
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},
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fetch_list=[out],
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)
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target = copy.deepcopy(self.x_np)
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out_ref = ref_hardsigmoid(target, self.slope, self.offset)
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for out in res:
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np.testing.assert_allclose(out, out_ref, rtol=0.001)
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for place in self.place:
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run(place, True)
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run(place, False)
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def test_api_dygraph(self):
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def run(place, inplace):
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paddle.disable_static(place)
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x_tensor = paddle.to_tensor(self.x_np)
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out = F.hardsigmoid(x_tensor, self.slope, self.offset, inplace)
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target = copy.deepcopy(self.x_np)
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out_ref = ref_hardsigmoid(target, self.slope, self.offset)
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np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.001)
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paddle.enable_static()
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for place in self.place:
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run(place, True)
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run(place, False)
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if __name__ == '__main__':
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unittest.main()
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