1# Copyright 2021 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 numpy as np 16import pytest 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20import mindspore.common.dtype as mstype 21from mindspore.ops import operations as P 22 23context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") 24 25class Net(nn.Cell): 26 def __init__(self, axis=0): 27 super(Net, self).__init__() 28 self.unique = P.Unique() 29 self.reshape = P.Reshape() 30 self.concat = P.Concat(axis=axis) 31 32 def construct(self, x1, x2): 33 out1_unique, _ = self.unique(x1) 34 out2_unique, _ = self.unique(x2) 35 out1_shape = self.reshape(out1_unique, (1, -1, 2)) 36 out2_shape = self.reshape(out2_unique, (1, -1, 2)) 37 return self.concat((out1_shape, out2_shape)) 38 39@pytest.mark.level0 40@pytest.mark.platform_arm_ascend_training 41@pytest.mark.platform_x86_ascend_training 42@pytest.mark.env_onecard 43def test_dynamic_concat(): 44 x1 = Tensor(np.array([1, 2, 3, 1, 4, 2]), mstype.int32) 45 x2 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.int32) 46 net = Net(axis=1) 47 output = net(x1, x2) 48 expect = np.array([[[1, 2], [3, 4], [1, 2], [3, 4], [5, 6]]]) 49 assert (output.asnumpy() == expect).all() 50