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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import unittest
-
- import numpy as np
-
- import paddle
- import paddle.nn.functional as F
- from paddle import base
- from paddle.pir_utils import test_with_pir_api
-
-
- def p_normalize(x, axis=1, p=2, epsilon=1e-12, keepdims=True):
- xp = np.power(np.abs(x), p)
- s = np.sum(xp, axis=axis, keepdims=keepdims)
- r = np.maximum(np.power(s, 1.0 / p), epsilon)
- return x / r
-
-
- class TestNNFunctionalNormalize(unittest.TestCase):
- def setUp(self):
- self.input_np = np.random.random(size=(10, 10)).astype(np.float32)
- self.input_np2 = np.array([0.0, 0.0]).astype(np.float32)
- self.expected0 = p_normalize(self.input_np)
- self.expected1 = p_normalize(self.input_np, p=1.5)
- self.expected2 = p_normalize(self.input_np, axis=0)
- self.expected3 = p_normalize(self.input_np2, axis=0)
-
- def run_imperative(self):
- x = paddle.to_tensor(self.input_np)
- y = F.normalize(x)
- np.testing.assert_allclose(y.numpy(), self.expected0, rtol=1e-05)
-
- y = F.normalize(x, p=1.5)
- np.testing.assert_allclose(y.numpy(), self.expected1, rtol=1e-05)
-
- y = F.normalize(x, axis=0)
- np.testing.assert_allclose(y.numpy(), self.expected2, rtol=1e-05)
-
- x = paddle.to_tensor(self.input_np2)
- y = F.normalize(x, axis=0)
- np.testing.assert_allclose(y.numpy(), self.expected3, rtol=1e-05)
-
- self.assertRaises(BaseException, F.normalize, x)
-
- @test_with_pir_api
- def run_static(self, use_gpu=False):
- x = paddle.static.data(name='input', shape=[10, 10], dtype='float32')
- x2 = paddle.static.data(name='input2', shape=[2], dtype='float32')
- result0 = F.normalize(x)
- result1 = F.normalize(x, p=1.5)
- result2 = F.normalize(x, axis=0)
- result3 = F.normalize(x, name='aaa')
- result4 = F.normalize(x2, axis=0)
-
- place = base.CUDAPlace(0) if use_gpu else base.CPUPlace()
- exe = base.Executor(place)
- exe.run(paddle.static.default_startup_program())
- static_result = exe.run(
- feed={"input": self.input_np, "input2": self.input_np2},
- fetch_list=[result0, result1, result2, result4],
- )
-
- np.testing.assert_allclose(static_result[0], self.expected0, rtol=1e-05)
- np.testing.assert_allclose(static_result[1], self.expected1, rtol=1e-05)
- np.testing.assert_allclose(static_result[2], self.expected2, rtol=1e-05)
- np.testing.assert_allclose(static_result[3], self.expected3, rtol=1e-05)
- self.assertRaises(ValueError, F.normalize, x2)
-
- def test_cpu(self):
- paddle.disable_static(place=paddle.base.CPUPlace())
- self.run_imperative()
- paddle.enable_static()
-
- with paddle.static.program_guard(paddle.static.Program()):
- self.run_static()
-
- def test_gpu(self):
- if not base.core.is_compiled_with_cuda():
- return
-
- paddle.disable_static(place=paddle.base.CUDAPlace(0))
- self.run_imperative()
- paddle.enable_static()
-
- with paddle.static.program_guard(paddle.static.Program()):
- self.run_static(use_gpu=True)
-
- def test_errors(self):
- with base.dygraph.guard():
- # The size of input in Normalize should not be 0.
- def test_0_size():
- array = np.array([], dtype=np.float32)
- x = paddle.to_tensor(
- np.reshape(array, [1, 1, 0]), dtype='float32'
- )
- paddle.nn.functional.normalize(x)
-
- self.assertRaises(ValueError, test_0_size)
-
-
- if __name__ == "__main__":
- unittest.main()
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