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/crop.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 TestCrop : public UT::Common {
28 public:
TestCrop()29 TestCrop() {}
SetUp()30 void SetUp() {}
TearDown()31 void TearDown() {}
32 };
33
TEST_F(TestCrop,test_ops_crop1)34 TEST_F(TestCrop, test_ops_crop1) {
35 auto crop = std::make_shared<Crop>();
36 crop->Init(1, std::vector<int64_t>{1, 1, 1, 1});
37 std::vector<int64_t> ret = crop->get_offsets();
38 EXPECT_EQ(crop->get_axis(), 1);
39 for (auto item : ret) {
40 EXPECT_EQ(item, 1);
41 }
42 auto tensor_x1 = std::make_shared<tensor::Tensor>(kNumberTypeFloat32, std::vector<int64_t>{2, 2});
43 auto tensor_x2 = std::make_shared<tensor::Tensor>(kNumberTypeInt32, std::vector<int64_t>{1});
44 MS_EXCEPTION_IF_NULL(tensor_x1);
45 MS_EXCEPTION_IF_NULL(tensor_x2);
46 auto tensor_x1_data = reinterpret_cast<float *>(tensor_x1->data_c());
47 *tensor_x1_data = 1.0;
48 tensor_x1_data++;
49 *tensor_x1_data = 2.0;
50 tensor_x1_data++;
51 *tensor_x1_data = 3.0;
52 tensor_x1_data++;
53 *tensor_x1_data = 4.0;
54 tensor_x1_data++;
55 auto tensor_x2_data = reinterpret_cast<int *>(tensor_x2->data_c());
56 *tensor_x2_data = 1;
57 auto abstract = crop->Infer({tensor_x1->ToAbstract(), tensor_x2->ToAbstract()});
58 MS_EXCEPTION_IF_NULL(abstract);
59 EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
60 auto shape_ptr = abstract->BuildShape();
61 MS_EXCEPTION_IF_NULL(shape_ptr);
62 EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
63 auto shape = shape_ptr->cast<abstract::ShapePtr>();
64 MS_EXCEPTION_IF_NULL(shape);
65 auto shape_vec = shape->shape();
66 auto type = abstract->BuildType();
67 MS_EXCEPTION_IF_NULL(type);
68 EXPECT_EQ(type->isa<TensorType>(), true);
69 auto tensor_type = type->cast<TensorTypePtr>();
70 MS_EXCEPTION_IF_NULL(tensor_type);
71 auto data_type = tensor_type->element();
72 MS_EXCEPTION_IF_NULL(data_type);
73 EXPECT_EQ(data_type->type_id(), kNumberTypeFloat32);
74 EXPECT_EQ(shape_vec.size(), 1);
75 EXPECT_EQ(shape_vec[0], 1);
76 }
77 } // namespace ops
78 } // namespace mindspore