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- # Copyright 2021 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.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.ops.operations import _inner_ops as ops
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.stitch = ops.DynamicStitch()
-
- def construct(self, indices, data):
- return self.stitch(indices, data)
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_net_int32():
- """
- Feature: ALL TO ALL
- Description: test cases for dynamicstitch.
- Expectation: the result match expected array.
- """
- x1 = Tensor([6], mindspore.int32)
- x2 = Tensor(np.array([4, 1]), mindspore.int32)
- x3 = Tensor(np.array([[5, 2], [0, 3]]), mindspore.int32)
- y1 = Tensor(np.array([[61, 62]]), mindspore.int32)
- y2 = Tensor(np.array([[41, 42], [11, 12]]), mindspore.int32)
- y3 = Tensor(np.array([[[51, 52], [21, 22]], [[1, 2], [31, 32]]]), mindspore.int32)
- expected = np.array([[1, 2], [11, 12], [21, 22],
- [31, 32], [41, 42], [51, 52], [61, 62]]).astype(np.int32)
-
- indices = [x1, x2, x3]
- data = [y1, y2, y3]
- net = Net()
- output = net(indices, data)
- assert np.array_equal(output.asnumpy(), expected)
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