forked from mindspore-Ecosystem/mindspore
67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
# 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
|
|
import mindspore.nn as nn
|
|
import mindspore.ops as ops
|
|
import mindspore.context as context
|
|
|
|
|
|
class PixelShuffleNet(nn.Cell):
|
|
"""PixelShuffleNet"""
|
|
|
|
def construct(self, x):
|
|
output = ops.pixel_shuffle(x, 2)
|
|
return output
|
|
|
|
|
|
class PixelUnShuffleNet(nn.Cell):
|
|
"""PixelUnShuffleNet"""
|
|
|
|
def construct(self, x):
|
|
output = ops.pixel_unshuffle(x, 2)
|
|
return output
|
|
|
|
|
|
@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', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_compile_max(mode):
|
|
"""
|
|
Feature: Test PixelShuffleAndUnShuffle
|
|
Description: Test the functionality of PixelShuffleAndUnShuffle
|
|
Expectation: Success
|
|
"""
|
|
context.set_context(mode=mode)
|
|
input_x = np.arange(4 * 2 * 2).reshape((4, 2, 2))
|
|
input_x = mindspore.Tensor(input_x, mindspore.dtype.int32)
|
|
shufflenet = PixelShuffleNet()
|
|
unshufflenet = PixelUnShuffleNet()
|
|
output1 = shufflenet(input_x)
|
|
expect_output1 = np.array([[[0, 4, 1, 5],
|
|
[8, 12, 9, 13],
|
|
[2, 6, 3, 7],
|
|
[10, 14, 11, 15]]])
|
|
assert np.allclose(output1.asnumpy(), expect_output1)
|
|
output2 = unshufflenet(output1)
|
|
assert np.allclose(input_x.asnumpy(), output2.asnumpy())
|