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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:
29   TestCrop() {}
30   void SetUp() {}
31   void TearDown() {}
32 };
33 
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