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1# Copyright 2024 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# ============================================================================
15import pytest
16import numpy as np
17
18import mindspore.context as context
19from mindspore import Tensor
20from mindspore import ops
21import tests.st.utils.test_utils as test_utils
22
23
24@test_utils.run_with_cell
25def forward_func(x, indices):
26    return ops.max_unpool3d(x, indices, kernel_size=2, stride=1, padding=0)
27
28
29@test_utils.run_with_cell
30def backward_func(x, indices):
31    return ops.grad(forward_func, (0))(x, indices)
32
33
34@pytest.mark.level0
35@pytest.mark.env_onecard
36@pytest.mark.platform_arm_ascend_training
37@pytest.mark.platform_x86_ascend_training
38@pytest.mark.parametrize("context_mode", [context.GRAPH_MODE, context.PYNATIVE_MODE])
39def test_maxunpool3d_float32(context_mode):
40    """
41    Feature: maxunpool3d
42    Description: test maxunpool3d
43    Expectation: expect correct result.
44    """
45    context.set_context(mode=context_mode, device_target="Ascend")
46    x = Tensor(np.array([[[[[0, 1], [8, 9]]]]]).astype(np.float32))
47    indices = Tensor(np.array([[[[[0, 1], [2, 3]]]]]).astype(np.int64))
48    output = forward_func(x, indices)
49    expected = np.array([[[[[0., 1., 8.],
50                            [9., 0., 0.],
51                            [0., 0., 0.]],
52                           [[0., 0., 0.],
53                            [0., 0., 0.],
54                            [0., 0., 0.]]]]], np.float32)
55    np.testing.assert_allclose(output.asnumpy(), expected, rtol=1e-3)
56
57
58@pytest.mark.level0
59@pytest.mark.env_onecard
60@pytest.mark.platform_arm_ascend_training
61@pytest.mark.platform_x86_ascend_training
62@pytest.mark.parametrize("context_mode", [context.GRAPH_MODE, context.PYNATIVE_MODE])
63def test_maxunpool3dgrad_float32(context_mode):
64    """
65    Feature: maxunpool3dgrad
66    Description: test maxunpool3dgrad
67    Expectation: expect correct result.
68    """
69    context.set_context(mode=context_mode, device_target="Ascend")
70    x = Tensor(np.array([[[[[0, 1], [8, 9]]]]]).astype(np.float32))
71    indices = Tensor(np.array([[[[[0, 1], [2, 3]]]]]).astype(np.int64))
72    x_grad = backward_func(x, indices)
73    assert x_grad.asnumpy().shape == x.shape
74