test_norm_op.py

#   Copyright (c) 2018 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.

from __future__ import print_function

import unittest
import numpy as np
from op_test import OpTest


def l2_norm(x, axis, epsilon):
    x2 = x**2
    s = np.sum(x2, axis=axis, keepdims=True)
    r = np.sqrt(s + epsilon)
    y = x / np.broadcast_to(r, x.shape)
    return y, r


class TestNormOp(OpTest):
    def setUp(self):
        self.op_type = "norm"
        self.init_test_case()
        x = np.random.random(self.shape).astype("float64")
        y, norm = l2_norm(x, self.axis, self.epsilon)
        self.inputs = {'X': x}
        self.attrs = {'epsilon': self.epsilon, 'axis': self.axis}
        self.outputs = {'Out': y, 'Norm': norm}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')

    def init_test_case(self):
        self.shape = [2, 3, 4, 4]
        self.axis = 1
        self.epsilon = 1e-8


class TestNormOp2(TestNormOp):
    def init_test_case(self):
        self.shape = [5, 3, 9, 7]
        self.axis = 0
        self.epsilon = 1e-8


class TestNormOp3(TestNormOp):
    def init_test_case(self):
        self.shape = [5, 3, 2, 7]
        self.axis = -1
        self.epsilon = 1e-8


class TestNormOp4(TestNormOp):
    def init_test_case(self):
        self.shape = [128, 1024, 14, 14]
        self.axis = 2
        self.epsilon = 1e-8

    def test_check_grad(self):
        # since the gradient check is very slow in large shape, so skip check_grad
        pass


class TestNormOp5(TestNormOp):
    def init_test_case(self):
        self.shape = [2048, 2048]
        self.axis = 1
        self.epsilon = 1e-8

    def test_check_grad(self):
        # since the gradient check is very slow in large shape, so skip check_grad
        pass


if __name__ == '__main__':
    unittest.main()