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/conv2d.h"
20 #include "ir/dtype/type.h"
21 #include "abstract/dshape.h"
22 #include "utils/tensor_construct_utils.h"
23 namespace mindspore {
24 namespace ops {
25 class TestConv2d : public UT::Common {
26 public:
TestConv2d()27 TestConv2d() {}
SetUp()28 void SetUp() {}
TearDown()29 void TearDown() {}
30 };
31
TEST_F(TestConv2d,test_ops_conv2d)32 TEST_F(TestConv2d, test_ops_conv2d) {
33 auto conv_2d = std::make_shared<Conv2D>();
34 conv_2d->Init(64, {7, 7});
35 std::vector<int64_t> kernel_size = conv_2d->get_kernel_size();
36 for (auto item : kernel_size) {
37 EXPECT_EQ(item, 7);
38 }
39 std::vector<int64_t> stride = conv_2d->get_stride();
40 for (auto item : stride) {
41 EXPECT_EQ(item, 1);
42 }
43 std::vector<int64_t> dilation = conv_2d->get_dilation();
44 for (auto item : dilation) {
45 EXPECT_EQ(item, 1);
46 }
47 EXPECT_EQ(conv_2d->get_pad_mode(), VALID);
48 std::vector<int64_t> pad = conv_2d->get_pad();
49 for (auto item : pad) {
50 EXPECT_EQ(item, 0);
51 }
52 EXPECT_EQ(conv_2d->get_mode(), 1);
53 EXPECT_EQ(conv_2d->get_group(), 1);
54 EXPECT_EQ(conv_2d->get_out_channel(), 64);
55 EXPECT_EQ(conv_2d->get_format(), NCHW);
56 auto tensor_x = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{32, 3, 224, 224});
57 auto tensor_w = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{64, 3, 7, 7});
58 MS_EXCEPTION_IF_NULL(tensor_x);
59 MS_EXCEPTION_IF_NULL(tensor_w);
60 auto conv_abstract = conv_2d->Infer({tensor_x->ToAbstract(), tensor_w->ToAbstract()});
61 MS_EXCEPTION_IF_NULL(conv_abstract);
62 EXPECT_EQ(conv_abstract->isa<abstract::AbstractTensor>(), true);
63 auto shape_ptr = conv_abstract->BuildShape();
64 MS_EXCEPTION_IF_NULL(shape_ptr);
65 EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
66 auto conv_shape = shape_ptr->cast<abstract::ShapePtr>();
67 MS_EXCEPTION_IF_NULL(conv_shape);
68 auto shape_vec = conv_shape->shape();
69 auto type = conv_abstract->BuildType();
70 MS_EXCEPTION_IF_NULL(type);
71 EXPECT_EQ(type->isa<TensorType>(), true);
72 auto tensor_type = type->cast<TensorTypePtr>();
73 MS_EXCEPTION_IF_NULL(tensor_type);
74 auto elem_type = tensor_type->element();
75 EXPECT_EQ(elem_type->type_id(), kNumberTypeFloat32);
76 EXPECT_EQ(shape_vec.size(), 4);
77 EXPECT_EQ(shape_vec[0], 32);
78 EXPECT_EQ(shape_vec[1], 64);
79 EXPECT_EQ(shape_vec[2], 218);
80 EXPECT_EQ(shape_vec[3], 218);
81 }
82
83 } // namespace ops
84 } // namespace mindspore
85