1 /**
2 * Copyright 2021 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/fusion/full_connection.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 TestFullConnection : public UT::Common {
28 public:
TestFullConnection()29 TestFullConnection() {}
SetUp()30 void SetUp() {}
TearDown()31 void TearDown() {}
32 };
33
TEST_F(TestFullConnection,test_full_connection_1)34 TEST_F(TestFullConnection, test_full_connection_1) {
35 auto op = std::make_shared<FullConnection>();
36 bool has_bias = false;
37 bool use_axis = false;
38 int64_t axis = 3;
39 op->Init(has_bias, axis, use_axis, NO_ACTIVATION);
40 auto tensor_1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
41 auto tensor_2 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
42 auto abstract = op->Infer({tensor_1->ToAbstract(), tensor_2->ToAbstract()});
43 MS_EXCEPTION_IF_NULL(abstract);
44 EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
45 auto shape_ptr = abstract->BuildShape();
46 MS_EXCEPTION_IF_NULL(shape_ptr);
47 EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
48 auto shape = shape_ptr->cast<abstract::ShapePtr>();
49 MS_EXCEPTION_IF_NULL(shape);
50 auto shape_vec = shape->shape();
51 auto type = abstract->BuildType();
52 MS_EXCEPTION_IF_NULL(type);
53 EXPECT_EQ(type->isa<TensorType>(), true);
54 auto tensor_type = type->cast<TensorTypePtr>();
55 MS_EXCEPTION_IF_NULL(tensor_type);
56 auto data_type = tensor_type->element();
57 MS_EXCEPTION_IF_NULL(data_type);
58 EXPECT_EQ(data_type->type_id(), kNumberTypeFloat16);
59 EXPECT_EQ(shape_vec.size(), 2);
60 EXPECT_EQ(shape_vec[0], 2);
61 EXPECT_EQ(shape_vec[1], 2);
62 }
63
TEST_F(TestFullConnection,test_full_connection_2)64 TEST_F(TestFullConnection, test_full_connection_2) {
65 auto op = std::make_shared<FullConnection>();
66 bool has_bias = true;
67 bool use_axis = false;
68 int64_t axis = 1;
69 op->Init(has_bias, axis, use_axis, NO_ACTIVATION);
70 auto tensor_1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
71 auto tensor_2 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
72 auto tensor_3 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 2});
73 auto abstract = op->Infer({tensor_1->ToAbstract(), tensor_2->ToAbstract(), tensor_3->ToAbstract()});
74 MS_EXCEPTION_IF_NULL(abstract);
75 EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
76 auto shape_ptr = abstract->BuildShape();
77 MS_EXCEPTION_IF_NULL(shape_ptr);
78 EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
79 auto shape = shape_ptr->cast<abstract::ShapePtr>();
80 MS_EXCEPTION_IF_NULL(shape);
81 auto shape_vec = shape->shape();
82 auto type = abstract->BuildType();
83 MS_EXCEPTION_IF_NULL(type);
84 EXPECT_EQ(type->isa<TensorType>(), true);
85 auto tensor_type = type->cast<TensorTypePtr>();
86 MS_EXCEPTION_IF_NULL(tensor_type);
87 auto data_type = tensor_type->element();
88 MS_EXCEPTION_IF_NULL(data_type);
89 EXPECT_EQ(data_type->type_id(), kNumberTypeFloat16);
90 EXPECT_EQ(shape_vec.size(), 2);
91 EXPECT_EQ(shape_vec[0], 2);
92 EXPECT_EQ(shape_vec[1], 2);
93 }
94
TEST_F(TestFullConnection,test_full_connection_3)95 TEST_F(TestFullConnection, test_full_connection_3) {
96 auto op = std::make_shared<FullConnection>();
97 bool has_bias = false;
98 bool use_axis = true;
99 int64_t axis = 1;
100 op->Init(has_bias, axis, use_axis, NO_ACTIVATION);
101 auto tensor_1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
102 auto tensor_2 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
103 auto abstract = op->Infer({tensor_1->ToAbstract(), tensor_2->ToAbstract()});
104 MS_EXCEPTION_IF_NULL(abstract);
105 EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
106 auto shape_ptr = abstract->BuildShape();
107 MS_EXCEPTION_IF_NULL(shape_ptr);
108 EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
109 auto shape = shape_ptr->cast<abstract::ShapePtr>();
110 MS_EXCEPTION_IF_NULL(shape);
111 auto shape_vec = shape->shape();
112 auto type = abstract->BuildType();
113 MS_EXCEPTION_IF_NULL(type);
114 EXPECT_EQ(type->isa<TensorType>(), true);
115 auto tensor_type = type->cast<TensorTypePtr>();
116 MS_EXCEPTION_IF_NULL(tensor_type);
117 auto data_type = tensor_type->element();
118 MS_EXCEPTION_IF_NULL(data_type);
119 EXPECT_EQ(data_type->type_id(), kNumberTypeFloat16);
120 EXPECT_EQ(shape_vec.size(), 2);
121 EXPECT_EQ(shape_vec[0], 2);
122 EXPECT_EQ(shape_vec[1], 2);
123 }
124 } // namespace ops
125 } // namespace mindspore
126