wehub-resource-sync
135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
# Copyright (c) 2020 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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import paddle.base.dygraph as dg
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import paddle.nn.functional as F
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from paddle import base
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class LabelSmoothTestCase(unittest.TestCase):
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def __init__(
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self,
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methodName='runTest',
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label_shape=(20, 1),
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prior_dist=None,
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epsilon=0.1,
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dtype="float32",
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):
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super().__init__(methodName)
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self.label_shape = label_shape
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self.prior_dist = prior_dist
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self.dtype = dtype
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self.epsilon = epsilon
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def setUp(self):
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self.label = np.random.randn(*(self.label_shape)).astype(self.dtype)
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def base_layer(self, place):
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paddle.enable_static()
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main = base.Program()
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start = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, start),
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):
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label_var = paddle.static.data(
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"input", self.label_shape, dtype=self.dtype
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)
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y_var = F.label_smooth(
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label_var,
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prior_dist=self.prior_dist,
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epsilon=self.epsilon,
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)
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feed_dict = {"input": self.label}
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exe = base.Executor(place)
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exe.run(start)
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(y_np,) = exe.run(main, feed=feed_dict, fetch_list=[y_var])
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return y_np
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def functional(self, place):
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paddle.enable_static()
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main = base.Program()
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start = base.Program()
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with base.unique_name.guard(), base.program_guard(main, start):
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label_var = paddle.static.data(
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"input", self.label_shape, dtype=self.dtype
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)
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y_var = F.label_smooth(
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label_var, prior_dist=self.prior_dist, epsilon=self.epsilon
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)
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feed_dict = {"input": self.label}
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exe = base.Executor(place)
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exe.run(start)
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(y_np,) = exe.run(main, feed=feed_dict, fetch_list=[y_var])
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return y_np
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def paddle_dygraph_layer(self):
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paddle.disable_static()
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label_var = paddle.to_tensor(self.label)
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y_var = F.label_smooth(
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label_var, prior_dist=self.prior_dist, epsilon=self.epsilon
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)
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y_np = y_var.numpy()
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return y_np
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def _test_equivalence(self, place):
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place = base.CPUPlace()
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result1 = self.base_layer(place)
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result2 = self.functional(place)
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result3 = self.paddle_dygraph_layer()
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np.testing.assert_array_almost_equal(result1, result2)
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np.testing.assert_array_almost_equal(result2, result3)
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def runTest(self):
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place = base.CPUPlace()
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self._test_equivalence(place)
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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self._test_equivalence(place)
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class LabelSmoothErrorTestCase(LabelSmoothTestCase):
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def runTest(self):
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place = base.CPUPlace()
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with dg.guard(place), self.assertRaises(ValueError):
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self.paddle_dygraph_layer()
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def add_cases(suite):
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suite.addTest(LabelSmoothTestCase(methodName='runTest'))
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suite.addTest(
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LabelSmoothTestCase(methodName='runTest', label_shape=[2, 3, 1])
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)
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def add_error_cases(suite):
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suite.addTest(LabelSmoothErrorTestCase(methodName='runTest', epsilon=2))
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def load_tests(loader, standard_tests, pattern):
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suite = unittest.TestSuite()
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add_cases(suite)
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add_error_cases(suite)
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return suite
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if __name__ == '__main__':
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unittest.main()
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