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100 lines
3.1 KiB
100 lines
3.1 KiB
# 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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from __future__ import print_function
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import unittest
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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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class TestNormalization(unittest.TestCase):
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data_desc = {"name": "input", "shape": (2, 3, 7)}
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def gen_random_input(self):
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"""Generate random input data.
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"""
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self.data = np.random.random(
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size=self.data_desc["shape"]).astype("float32")
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def set_program(self, axis, epsilon):
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"""Build the test program.
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"""
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data = fluid.layers.data(
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name=self.data_desc["name"],
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shape=self.data_desc["shape"],
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dtype="float32",
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append_batch_size=False)
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data.stop_gradient = False
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l2_norm = fluid.layers.l2_normalize(x=data, axis=axis, epsilon=epsilon)
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out = fluid.layers.reduce_sum(l2_norm, dim=None)
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fluid.backward.append_backward(loss=out)
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self.fetch_list = [l2_norm]
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def run_program(self):
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"""Run the test program.
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"""
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(core.CUDAPlace(0))
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for place in places:
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self.set_inputs(place)
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exe = fluid.Executor(place)
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output = exe.run(fluid.default_main_program(),
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feed=self.inputs,
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fetch_list=self.fetch_list,
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return_numpy=True)
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self.op_output = output
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def set_inputs(self, place):
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"""Set the randomly generated data to the test program.
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"""
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self.inputs = {}
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tensor = fluid.Tensor()
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tensor.set(self.data, place)
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self.inputs[self.data_desc["name"]] = tensor
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def l2_normalize(self, data, axis, epsilon):
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""" Compute the groundtruth.
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"""
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output = data / np.broadcast_to(
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np.sqrt(np.sum(np.square(data), axis=axis, keepdims=True)),
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data.shape)
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return output
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def test_l2_normalize(self):
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""" Test the python wrapper for l2_normalize.
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"""
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axis = 1
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#TODO(caoying) epsilon is not supported due to lack of a maximum_op.
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epsilon = 1e-6
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self.gen_random_input()
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self.set_program(axis, epsilon)
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self.run_program()
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expect_output = self.l2_normalize(self.data, axis, epsilon)
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# check output
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self.assertTrue(np.allclose(self.op_output, expect_output, atol=0.001))
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if __name__ == '__main__':
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unittest.main()
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