wehub-resource-sync
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import get_places
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import paddle
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from paddle import base
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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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self.data = np.random.random(size=self.data_desc["shape"]).astype(
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"float32"
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)
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def set_program(self, axis, epsilon):
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"""Build the test program."""
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data = paddle.static.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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)
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data.stop_gradient = False
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l2_norm = paddle.nn.functional.normalize(
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data, axis=axis, epsilon=epsilon
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)
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out = paddle.sum(l2_norm, axis=None)
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base.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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for place in get_places():
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self.set_inputs(place)
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exe = base.Executor(place)
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(output,) = exe.run(
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base.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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)
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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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self.inputs = {}
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tensor = base.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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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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)
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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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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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np.testing.assert_allclose(
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self.op_output, expect_output, rtol=1e-05, atol=0.001
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)
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if __name__ == '__main__':
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unittest.main()
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