mindspore/tests/st/nn/test_prelu.py

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2022-12-14 17:22:42 +08:00
# Copyright 2022 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 as ms
import mindspore.nn as nn
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.pool = nn.PReLU(channel=2, w=-0.25)
def construct(self, x):
out = self.pool(x)
return out
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_prelu_normal(mode):
"""
Feature: PReLU
Description: Verify the result of PReLU
Expectation: success
"""
ms.set_context(mode=mode)
x = ms.Tensor([[[0.9192, -0.1487],
[-0.3999, -0.6840]],
[[0.4745, -0.6271],
[-0.6547, -0.5856]],
[[-0.2572, -0.8412],
[0.1918, -0.6117]]])
net = Net()
out = net(x)
expect_out = np.array([[[0.9192, 0.037175],
[0.099975, 0.171]],
[[0.4745, 0.156775],
[0.163675, 0.1464]],
[[0.0643, 0.2103],
[0.1918, 0.152925]]])
assert np.allclose(out.asnumpy().astype(np.float16), expect_out.astype(np.float16))