1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
/*M ///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
namespace cv
{
namespace dnn
{
class ReorgLayerImpl CV_FINAL : public ReorgLayer
{
int reorgStride;
public:
ReorgLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
reorgStride = params.get<int>("reorg_stride", 2);
CV_Assert(reorgStride > 0);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() > 0);
outputs = std::vector<MatShape>(inputs.size(), shape(
inputs[0][0],
inputs[0][1] * reorgStride * reorgStride,
inputs[0][2] / reorgStride,
inputs[0][3] / reorgStride));
CV_Assert(outputs[0][0] > 0 && outputs[0][1] > 0 && outputs[0][2] > 0 && outputs[0][3] > 0);
CV_Assert(total(outputs[0]) == total(inputs[0]));
return false;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
Mat inp = inputs[0];
Mat out = outputs[0];
int batchSize = inp.size[0];
LayerParams permParams;
if (batchSize == 1)
{
int order[] = {1, 3, 0, 2};
permParams.set("order", DictValue::arrayInt(&order[0], 4));
permuteInpShape.resize(4);
permuteInpShape[0] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride); // (channels*height)/(r*r)
permuteInpShape[1] = reorgStride;
permuteInpShape[2] = inp.size[3]; // width
permuteInpShape[3] = reorgStride;
permuteOutShape.resize(4);
for (int i = 0; i < 4; ++i)
permuteOutShape[i] = permuteInpShape[order[i]];
}
else
{
int order[] = {0, 2, 4, 1, 3};
permParams.set("order", DictValue::arrayInt(&order[0], 5));
permuteInpShape.resize(5);
permuteInpShape[0] = batchSize;
permuteInpShape[1] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride); // (channels*height)/(r*r)
permuteInpShape[2] = reorgStride;
permuteInpShape[3] = inp.size[3]; // width
permuteInpShape[4] = reorgStride;
permuteOutShape.resize(5);
for (int i = 0; i < 5; ++i)
permuteOutShape[i] = permuteInpShape[order[i]];
}
permute = PermuteLayer::create(permParams);
std::vector<Mat> permuteInputs(1, inp.reshape(1, permuteInpShape));
std::vector<Mat> permuteOutputs(1, out.reshape(1, permuteOutShape));
permute->finalize(permuteInputs, permuteOutputs);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
inputs[0] = inputs[0].reshape(1, permuteInpShape.size(), &permuteInpShape[0]);
outputs[0] = outputs[0].reshape(1, permuteOutShape.size(), &permuteOutShape[0]);
permute->preferableTarget = preferableTarget;
permute->forward(inputs, outputs, internals);
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
if (inputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
inputs[0] = inputs[0].reshape(1, permuteInpShape);
outputs[0] = outputs[0].reshape(1, permuteOutShape);
permute->forward(inputs, outputs, internals_arr);
}
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ReorgYoloLayer ieLayer(name);
ieLayer.setStride(reorgStride);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_UNUSED(outputs); // suppress unused variable warning
int64 flops = 0;
for(int i = 0; i < inputs.size(); i++)
{
flops += 21*total(inputs[i]);
}
return flops;
}
private:
Ptr<PermuteLayer> permute;
std::vector<int> permuteInpShape, permuteOutShape;
};
Ptr<ReorgLayer> ReorgLayer::create(const LayerParams& params)
{
return Ptr<ReorgLayer>(new ReorgLayerImpl(params));
}
} // namespace dnn
} // namespace cv