forked from mindspore-Ecosystem/mindspore
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.operations import _inner_ops as ops
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.stitch = ops.DynamicStitch()
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def construct(self, indices, data):
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return self.stitch(indices, data)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net_int32():
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"""
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Feature: ALL TO ALL
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Description: test cases for dynamicstitch.
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Expectation: the result match expected array.
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"""
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x1 = Tensor([6], mindspore.int32)
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x2 = Tensor(np.array([4, 1]), mindspore.int32)
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x3 = Tensor(np.array([[5, 2], [0, 3]]), mindspore.int32)
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y1 = Tensor(np.array([[61, 62]]), mindspore.int32)
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y2 = Tensor(np.array([[41, 42], [11, 12]]), mindspore.int32)
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y3 = Tensor(np.array([[[51, 52], [21, 22]], [[1, 2], [31, 32]]]), mindspore.int32)
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expected = np.array([[1, 2], [11, 12], [21, 22],
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[31, 32], [41, 42], [51, 52], [61, 62]]).astype(np.int32)
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indices = [x1, x2, x3]
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data = [y1, y2, y3]
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net = Net()
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output = net(indices, data)
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assert np.array_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net_1():
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"""
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Feature: Test dynamicstitch op.
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Description: An index corresponds to a number
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Expectation: the result match expected array.
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"""
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x1 = Tensor(np.array([0, 1]), mindspore.int32)
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x2 = Tensor([1], mindspore.int32)
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y1 = Tensor(np.array([1, 3]), mindspore.int32)
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y2 = Tensor(np.array([2]), mindspore.int32)
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expected = np.array([1, 2]).astype(np.int32)
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indices = [x1, x2]
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data = [y1, y2]
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net = Net()
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output = net(indices, data)
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assert np.array_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net_2():
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"""
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Feature: Test dynamicstitch op.
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Description: An index corresponds to a multidimensional array.
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Expectation: the result match expected array.
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"""
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x1 = Tensor(np.array([0, 2]), mindspore.int32)
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x2 = Tensor([1], mindspore.int32)
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y1 = Tensor(np.array([[[1, 2, 3], [4, 5, 6]],
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[[13, 14, 15], [16, 17, 18]]]), mindspore.int32)
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y2 = Tensor(np.array([[[7, 8, 9], [10, 11, 12]]]), mindspore.int32)
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expected = np.array([[[1, 2, 3], [4, 5, 6]],
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[[7, 8, 9], [10, 11, 12]],
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[[13, 14, 15], [16, 17, 18]]]).astype(np.int32)
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indices = [x1, x2]
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data = [y1, y2]
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net = Net()
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output = net(indices, data)
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assert np.array_equal(output.asnumpy(), expected)
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