1# Copyright 2022 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# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore.context as context 20import mindspore.nn as nn 21from mindspore import Tensor 22from mindspore.ops import operations as P 23 24 25class NetParallelConcat(nn.Cell): 26 27 def __init__(self): 28 super(NetParallelConcat, self).__init__() 29 self.parallelconcat = P.ParallelConcat() 30 31 def construct(self, x): 32 return self.parallelconcat(x) 33 34 35@pytest.mark.level1 36@pytest.mark.platform_x86_gpu_training 37@pytest.mark.platform_arm_ascend_training 38@pytest.mark.platform_x86_ascend_training 39@pytest.mark.env_onecard 40def test_parallelconcat_1d(): 41 """ 42 Feature: ParallelConcat TEST. 43 Description: 1d test case for ParallelConcat 44 Expectation: the result match to numpy 45 """ 46 context.set_context(mode=context.GRAPH_MODE) 47 x_np = (np.array([[3]])).astype(np.int8) 48 y_np = (np.array([[5]])).astype(np.int8) 49 z_np = np.concatenate([x_np, y_np], axis=0) 50 51 x_ms = Tensor(x_np) 52 y_ms = Tensor(y_np) 53 net = NetParallelConcat() 54 z_ms = net([x_ms, y_ms]) 55 56 assert np.allclose(z_np, z_ms.asnumpy()) 57 58 59@pytest.mark.level1 60@pytest.mark.platform_x86_gpu_training 61@pytest.mark.platform_arm_ascend_training 62@pytest.mark.platform_x86_ascend_training 63@pytest.mark.env_onecard 64def test_parallelconcat_2d(): 65 """ 66 Feature: ParallelConcat TEST. 67 Description: 2d test case for ParallelConcat 68 Expectation: the result match to numpy 69 """ 70 context.set_context(mode=context.PYNATIVE_MODE) 71 x_np = (np.array([[-1, -5, -3, -14, 64]])).astype(np.int8) 72 y_np = (np.array([[5, 0, 7, 11, 66]])).astype(np.int8) 73 z_np = np.concatenate([x_np, y_np], axis=0) 74 75 x_ms = Tensor(x_np) 76 y_ms = Tensor(y_np) 77 net = NetParallelConcat() 78 z_ms = net([x_ms, y_ms]) 79 80 assert np.allclose(z_np, z_ms.asnumpy()) 81