1 /** 2 * Copyright 2020 Huawei Technologies Co., Ltd 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * http://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 #include <vector> 17 #include <memory> 18 #include "common/common_test.h" 19 #include "ops/grad/pooling_grad.h" 20 #include "ir/dtype/type.h" 21 #include "ir/value.h" 22 #include "abstract/dshape.h" 23 #include "utils/tensor_construct_utils.h" 24 25 namespace mindspore { 26 namespace ops { 27 class TestPoolingGrad : public UT::Common { 28 public: 29 TestPoolingGrad() {} 30 void SetUp() {} 31 void TearDown() {} 32 }; 33 34 TEST_F(TestPoolingGrad, test_ops_pooling_grad1) { 35 auto pooling_grad = std::make_shared<PoolingGrad>(); 36 pooling_grad->Init(MAX_POOLING, std::vector<int64_t>{1, 1}, std::vector<int64_t>{1, 1}, VALID, 37 std::vector<int64_t>{1, 1, 1, 1}, FLOOR, NCHW, false); 38 EXPECT_EQ(pooling_grad->get_pool_mode(), MAX_POOLING); 39 // EXPECT_EQ(pooling_grad->get_window(), std::vector<int64_t>{1, 1}); 40 EXPECT_EQ(pooling_grad->get_pad_mode(), VALID); 41 // EXPECT_EQ(pooling_grad->get_stride(), std::vector<int64_t>{1, 1}); 42 // EXPECT_EQ(pooling_grad->get_pad_list(), std::vector<int64_t>{1, 1, 1, 1}); 43 EXPECT_EQ(pooling_grad->get_round_mode(), FLOOR); 44 EXPECT_EQ(pooling_grad->get_format(), NCHW); 45 EXPECT_EQ(pooling_grad->get_global(), false); 46 auto input0 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{1}); 47 auto input1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{1}); 48 auto input2 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{3, 3}); 49 MS_EXCEPTION_IF_NULL(input0); 50 MS_EXCEPTION_IF_NULL(input1); 51 MS_EXCEPTION_IF_NULL(input2); 52 auto abstract = pooling_grad->Infer({input0->ToAbstract(), input1->ToAbstract(), input2->ToAbstract()}); 53 MS_EXCEPTION_IF_NULL(abstract); 54 EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true); 55 auto shape_ptr = abstract->BuildShape(); 56 MS_EXCEPTION_IF_NULL(shape_ptr); 57 EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true); 58 auto shape = shape_ptr->cast<abstract::ShapePtr>(); 59 MS_EXCEPTION_IF_NULL(shape); 60 auto shape_vec = shape->shape(); 61 EXPECT_EQ(shape_vec.size(), 2); 62 EXPECT_EQ(shape_vec[0], 3); 63 EXPECT_EQ(shape_vec[1], 3); 64 auto type = abstract->BuildType(); 65 MS_EXCEPTION_IF_NULL(type); 66 EXPECT_EQ(type->isa<TensorType>(), true); 67 auto tensor_type = type->cast<TensorTypePtr>(); 68 MS_EXCEPTION_IF_NULL(tensor_type); 69 auto data_type = tensor_type->element(); 70 MS_EXCEPTION_IF_NULL(data_type); 71 EXPECT_EQ(data_type->type_id(), kNumberTypeFloat32); 72 } 73 } // namespace ops 74 } // namespace mindspore 75