1# Copyright 2020 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore.context as context 20from mindspore.common.tensor import Tensor 21from mindspore.nn import Cell 22from mindspore.ops import operations as P 23 24class Net(Cell): 25 def __init__(self, axis=0, epsilon=1e-4): 26 super(Net, self).__init__() 27 self.norm = P.L2Normalize(axis=axis, epsilon=epsilon) 28 29 def construct(self, x): 30 return self.norm(x) 31 32 33@pytest.mark.level0 34@pytest.mark.platform_x86_gpu_training 35@pytest.mark.env_onecard 36def test_l2normalize(): 37 x = np.random.randint(1, 10, (2, 3, 4, 4)).astype(np.float32) 38 expect = x / np.sqrt(np.sum(x**2, axis=0, keepdims=True)) 39 x = Tensor(x) 40 error = np.ones(shape=[2, 3, 4, 4]) * 1.0e-5 41 42 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 43 norm_op = Net(axis=0) 44 output = norm_op(x) 45 diff = output.asnumpy() - expect 46 assert np.all(diff < error) 47 assert np.all(-diff < error) 48