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/softmax.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 TestSoftMax : public UT::Common {
28 public:
TestSoftMax()29 TestSoftMax() {}
SetUp()30 void SetUp() {}
TearDown()31 void TearDown() {}
32 };
33
TEST_F(TestSoftMax,test_ops_softmax1)34 TEST_F(TestSoftMax, test_ops_softmax1) {
35 auto softmax = std::make_shared<Softmax>();
36 std::vector<std::int64_t> init_data = {-1};
37 softmax->Init(-1);
38 EXPECT_EQ(softmax->get_axis(), init_data);
39 softmax->set_axis(init_data);
40 EXPECT_EQ(softmax->get_axis(), init_data);
41 auto input1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{1, 2, 3, 4, 5});
42 MS_EXCEPTION_IF_NULL(input1);
43 auto abstract = softmax->Infer({input1->ToAbstract()});
44 MS_EXCEPTION_IF_NULL(abstract);
45 EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
46 auto shape_ptr = abstract->BuildShape();
47 MS_EXCEPTION_IF_NULL(shape_ptr);
48 EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
49 auto shape = shape_ptr->cast<abstract::ShapePtr>();
50 MS_EXCEPTION_IF_NULL(shape);
51 auto shape_vec = shape->shape();
52 auto type = abstract->BuildType();
53 MS_EXCEPTION_IF_NULL(type);
54 EXPECT_EQ(type->isa<TensorType>(), true);
55 auto tensor_type = type->cast<TensorTypePtr>();
56 MS_EXCEPTION_IF_NULL(tensor_type);
57 auto data_type = tensor_type->element();
58 MS_EXCEPTION_IF_NULL(data_type);
59 EXPECT_EQ(data_type->type_id(), kNumberTypeFloat16);
60 EXPECT_EQ(shape_vec.size(), 5);
61 EXPECT_EQ(shape_vec[0], 1);
62 }
63
TEST_F(TestSoftMax,test_ops_softmax2)64 TEST_F(TestSoftMax, test_ops_softmax2) {
65 auto softmax = std::make_shared<Softmax>();
66 std::vector<std::int64_t> init_data = {-1};
67 softmax->Init(-1);
68 EXPECT_EQ(softmax->get_axis(), init_data);
69 softmax->set_axis(init_data);
70 EXPECT_EQ(softmax->get_axis(), init_data);
71 auto input1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{1, 2, 3, 4, 5});
72 MS_EXCEPTION_IF_NULL(input1);
73 auto abstract = softmax->Infer({input1->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(), kNumberTypeFloat32);
90 EXPECT_EQ(shape_vec.size(), 5);
91 EXPECT_EQ(shape_vec[0], 1);
92 }
93
TEST_F(TestSoftMax,test_ops_softmax3)94 TEST_F(TestSoftMax, test_ops_softmax3) {
95 auto softmax = std::make_shared<Softmax>();
96 std::vector<std::int64_t> init_data = {-1};
97 softmax->Init(-1);
98 EXPECT_EQ(softmax->get_axis(), init_data);
99 softmax->set_axis(init_data);
100 EXPECT_EQ(softmax->get_axis(), init_data);
101 auto input1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat64, std::vector<int64_t>{1, 2, 3, 4, 5});
102 MS_EXCEPTION_IF_NULL(input1);
103 auto abstract = softmax->Infer({input1->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(), kNumberTypeFloat64);
120 EXPECT_EQ(shape_vec.size(), 5);
121 EXPECT_EQ(shape_vec[0], 1);
122 }
123 } // namespace ops
124 } // namespace mindspore
125