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104 lines
3.6 KiB
104 lines
3.6 KiB
# 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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from __future__ import print_function
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import unittest
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import paddle
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import paddle.nn.functional as F
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import numpy as np
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def p_normalize(x, axis=1, p=2, epsilon=1e-12, keepdims=True):
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xp = np.power(np.abs(x), p)
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s = np.sum(xp, axis=axis, keepdims=keepdims)
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r = np.maximum(np.power(s, 1.0 / p), epsilon)
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return x / r
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class TestNNFunctionalNormalize(unittest.TestCase):
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def setUp(self):
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self.input_np = np.random.random(size=(10, 10)).astype(np.float32)
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self.input_np2 = np.array([0.0, 0.0]).astype(np.float32)
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self.expected0 = p_normalize(self.input_np)
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self.expected1 = p_normalize(self.input_np, p=1.5)
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self.expected2 = p_normalize(self.input_np, axis=0)
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self.expected3 = p_normalize(self.input_np2, axis=0)
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def run_imperative(self):
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x = paddle.to_tensor(self.input_np)
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y = F.normalize(x)
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self.assertTrue(np.allclose(y.numpy(), self.expected0))
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y = F.normalize(x, p=1.5)
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self.assertTrue(np.allclose(y.numpy(), self.expected1))
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y = F.normalize(x, axis=0)
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self.assertTrue(np.allclose(y.numpy(), self.expected2))
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x = paddle.to_tensor(self.input_np2)
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y = F.normalize(x, axis=0)
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self.assertTrue(np.allclose(y.numpy(), self.expected3))
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self.assertRaises(BaseException, F.normalize, x)
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def run_static(self, use_gpu=False):
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x = paddle.fluid.data(name='input', shape=[10, 10], dtype='float32')
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x2 = paddle.fluid.data(name='input2', shape=[2], dtype='float32')
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result0 = F.normalize(x)
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result1 = F.normalize(x, p=1.5)
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result2 = F.normalize(x, axis=0)
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result3 = F.normalize(x, name='aaa')
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result4 = F.normalize(x2, axis=0)
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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static_result = exe.run(
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feed={"input": self.input_np,
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"input2": self.input_np2},
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fetch_list=[result0, result1, result2, result4])
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self.assertTrue(np.allclose(static_result[0], self.expected0))
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self.assertTrue(np.allclose(static_result[1], self.expected1))
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self.assertTrue(np.allclose(static_result[2], self.expected2))
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self.assertTrue('aaa' in result3.name)
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self.assertTrue(np.allclose(static_result[3], self.expected3))
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self.assertRaises(ValueError, F.normalize, x2)
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def test_cpu(self):
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paddle.disable_static(place=paddle.fluid.CPUPlace())
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self.run_imperative()
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paddle.enable_static()
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with fluid.program_guard(fluid.Program()):
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self.run_static()
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def test_gpu(self):
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if not fluid.core.is_compiled_with_cuda():
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return
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paddle.disable_static(place=paddle.fluid.CUDAPlace(0))
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self.run_imperative()
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paddle.enable_static()
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with fluid.program_guard(fluid.Program()):
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self.run_static(use_gpu=True)
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if __name__ == "__main__":
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unittest.main()
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