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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # 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 numpy as np
- import pytest
-
- import mindspore.context as context
- from mindspore.common.tensor import Tensor
- from mindspore.nn import Cell
- from mindspore.ops import operations as P
-
- class Net(Cell):
- def __init__(self, axis=0, epsilon=1e-4):
- super(Net, self).__init__()
- self.norm = P.L2Normalize(axis=axis, epsilon=epsilon)
-
- def construct(self, x):
- return self.norm(x)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_l2normalize():
- x = np.random.randint(1, 10, (2, 3, 4, 4)).astype(np.float32)
- expect = x / np.sqrt(np.sum(x**2, axis=0, keepdims=True))
- x = Tensor(x)
- error = np.ones(shape=[2, 3, 4, 4]) * 1.0e-5
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
- norm_op = Net(axis=0)
- output = norm_op(x)
- diff = output.asnumpy() - expect
- assert np.all(diff < error)
- assert np.all(-diff < error)
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