116 lines
4.1 KiB
Python
116 lines
4.1 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
|
|
from mindspore import Tensor
|
|
import mindspore.context as context
|
|
import mindspore.ops as ops
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, kernel_size, stride=0, padding=0):
|
|
super(Net, self).__init__()
|
|
self.max_unpool3d = nn.MaxUnpool3d(kernel_size, stride, padding)
|
|
|
|
def construct(self, x, indices, output_size=()):
|
|
return self.max_unpool3d(x, indices, output_size)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_arm_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_max_unpool3d_normal(mode):
|
|
"""
|
|
Feature: max_unpool3d
|
|
Description: Verify the result of MaxUnpool3d
|
|
Expectation: success
|
|
"""
|
|
context.set_context(mode=mode)
|
|
x = Tensor(np.array([[[[[7.]]]], [[[[15.]]]]]), mindspore.float32)
|
|
incices = Tensor(np.array([[[[[7]]]], [[[[7]]]]]), mindspore.int64)
|
|
net = Net(kernel_size=2, stride=1, padding=0)
|
|
output = net(x, incices).asnumpy()
|
|
expect = np.array([[[[[0., 0.],
|
|
[0., 0.]],
|
|
[[0., 0.],
|
|
[0., 7.]]]],
|
|
[[[[0., 0.],
|
|
[0., 0.]],
|
|
[[0., 0.],
|
|
[0., 15.]]]]]).astype(np.float32)
|
|
assert np.allclose(output, expect, rtol=0.0001)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_arm_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_max_unpool3d_normal_output_size(mode):
|
|
"""
|
|
Feature: max_unpool3d
|
|
Description: Verify the result of MaxUnpool3d
|
|
Expectation: success
|
|
"""
|
|
context.set_context(mode=mode)
|
|
x = Tensor(np.array([[[[[7.]]]], [[[[15.]]]]]), mindspore.float32)
|
|
incices = Tensor(np.array([[[[[7]]]], [[[[7]]]]]), mindspore.int64)
|
|
net = Net(kernel_size=2, stride=1, padding=0)
|
|
output_size = (2, 1, 2, 2, 2)
|
|
output = net(x, incices, output_size).asnumpy()
|
|
expect = np.array([[[[[0., 0.],
|
|
[0., 0.]],
|
|
[[0., 0.],
|
|
[0., 7.]]]],
|
|
[[[[0., 0.],
|
|
[0., 0.]],
|
|
[[0., 0.],
|
|
[0., 15.]]]]]).astype(np.float32)
|
|
assert np.allclose(output, expect, rtol=0.0001)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_arm_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_f_max_unpool3d_normal(mode):
|
|
"""
|
|
Feature: max_unpool3d
|
|
Description: Verify the result of MaxUnpool3d
|
|
Expectation: success
|
|
"""
|
|
context.set_context(mode=mode)
|
|
x = Tensor(np.array([[[[[7.]]]], [[[[15.]]]]]), mindspore.float32)
|
|
indices = Tensor(np.array([[[[[7]]]], [[[[7]]]]]), mindspore.int64)
|
|
output = ops.max_unpool3d(x, indices, 2, stride=1, padding=0)
|
|
output = output.asnumpy()
|
|
expect = np.array([[[[[0., 0.],
|
|
[0., 0.]],
|
|
[[0., 0.],
|
|
[0., 7.]]]],
|
|
[[[[0., 0.],
|
|
[0., 0.]],
|
|
[[0., 0.],
|
|
[0., 15.]]]]]).astype(np.float32)
|
|
assert np.allclose(output, expect, rtol=0.0001)
|