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
215
216
217
218
219
220
221
222
223
224
225
226
227
|
/*
* SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "DetectorPostProcessing.hpp"
#include "PlatformMath.hpp"
#include <cmath>
namespace arm {
namespace app {
DetectorPostProcess::DetectorPostProcess(
TfLiteTensor* modelOutput0,
TfLiteTensor* modelOutput1,
std::vector<object_detection::DetectionResult>& results,
const object_detection::PostProcessParams& postProcessParams)
: m_outputTensor0{modelOutput0},
m_outputTensor1{modelOutput1},
m_results{results},
m_postProcessParams{postProcessParams}
{
/* Init PostProcessing */
this->m_net = object_detection::Network{
.inputWidth = postProcessParams.inputImgCols,
.inputHeight = postProcessParams.inputImgRows,
.numClasses = postProcessParams.numClasses,
.branches =
{object_detection::Branch{.resolution = postProcessParams.inputImgCols / 32,
.numBox = 3,
.anchor = postProcessParams.anchor1,
.modelOutput = this->m_outputTensor0->data.int8,
.scale = (static_cast<TfLiteAffineQuantization*>(
this->m_outputTensor0->quantization.params))
->scale->data[0],
.zeroPoint = (static_cast<TfLiteAffineQuantization*>(
this->m_outputTensor0->quantization.params))
->zero_point->data[0],
.size = this->m_outputTensor0->bytes},
object_detection::Branch{.resolution = postProcessParams.inputImgCols / 16,
.numBox = 3,
.anchor = postProcessParams.anchor2,
.modelOutput = this->m_outputTensor1->data.int8,
.scale = (static_cast<TfLiteAffineQuantization*>(
this->m_outputTensor1->quantization.params))
->scale->data[0],
.zeroPoint = (static_cast<TfLiteAffineQuantization*>(
this->m_outputTensor1->quantization.params))
->zero_point->data[0],
.size = this->m_outputTensor1->bytes}},
.topN = postProcessParams.topN};
/* End init */
}
bool DetectorPostProcess::DoPostProcess()
{
/* Start postprocessing */
int originalImageWidth = m_postProcessParams.originalImageSize;
int originalImageHeight = m_postProcessParams.originalImageSize;
std::forward_list<image::Detection> detections;
GetNetworkBoxes(this->m_net, originalImageWidth, originalImageHeight, m_postProcessParams.threshold, detections);
/* Do nms */
CalculateNMS(detections, this->m_net.numClasses, this->m_postProcessParams.nms);
for (auto& it: detections) {
float xMin = it.bbox.x - it.bbox.w / 2.0f;
float xMax = it.bbox.x + it.bbox.w / 2.0f;
float yMin = it.bbox.y - it.bbox.h / 2.0f;
float yMax = it.bbox.y + it.bbox.h / 2.0f;
if (xMin < 0) {
xMin = 0;
}
if (yMin < 0) {
yMin = 0;
}
if (xMax > originalImageWidth) {
xMax = originalImageWidth;
}
if (yMax > originalImageHeight) {
yMax = originalImageHeight;
}
float boxX = xMin;
float boxY = yMin;
float boxWidth = xMax - xMin;
float boxHeight = yMax - yMin;
for (int j = 0; j < this->m_net.numClasses; ++j) {
if (it.prob[j] > 0) {
object_detection::DetectionResult tmpResult = {};
tmpResult.m_normalisedVal = it.prob[j];
tmpResult.m_x0 = boxX;
tmpResult.m_y0 = boxY;
tmpResult.m_w = boxWidth;
tmpResult.m_h = boxHeight;
this->m_results.push_back(tmpResult);
}
}
}
return true;
}
void DetectorPostProcess::InsertTopNDetections(std::forward_list<image::Detection>& detections, image::Detection& det)
{
std::forward_list<image::Detection>::iterator it;
std::forward_list<image::Detection>::iterator last_it;
for ( it = detections.begin(); it != detections.end(); ++it ) {
if(it->objectness > det.objectness)
break;
last_it = it;
}
if(it != detections.begin()) {
detections.emplace_after(last_it, det);
detections.pop_front();
}
}
void DetectorPostProcess::GetNetworkBoxes(
object_detection::Network& net,
int imageWidth,
int imageHeight,
float threshold,
std::forward_list<image::Detection>& detections)
{
int numClasses = net.numClasses;
int num = 0;
auto det_objectness_comparator = [](image::Detection& pa, image::Detection& pb) {
return pa.objectness < pb.objectness;
};
for (size_t i = 0; i < net.branches.size(); ++i) {
int height = net.branches[i].resolution;
int width = net.branches[i].resolution;
int channel = net.branches[i].numBox*(5+numClasses);
for (int h = 0; h < net.branches[i].resolution; h++) {
for (int w = 0; w < net.branches[i].resolution; w++) {
for (int anc = 0; anc < net.branches[i].numBox; anc++) {
/* Objectness score */
int bbox_obj_offset = h * width * channel + w * channel + anc * (numClasses + 5) + 4;
float objectness = math::MathUtils::SigmoidF32(
(static_cast<float>(net.branches[i].modelOutput[bbox_obj_offset])
- net.branches[i].zeroPoint
) * net.branches[i].scale);
if(objectness > threshold) {
image::Detection det;
det.objectness = objectness;
/* Get bbox prediction data for each anchor, each feature point */
int bbox_x_offset = bbox_obj_offset -4;
int bbox_y_offset = bbox_x_offset + 1;
int bbox_w_offset = bbox_x_offset + 2;
int bbox_h_offset = bbox_x_offset + 3;
int bbox_scores_offset = bbox_x_offset + 5;
det.bbox.x = (static_cast<float>(net.branches[i].modelOutput[bbox_x_offset])
- net.branches[i].zeroPoint) * net.branches[i].scale;
det.bbox.y = (static_cast<float>(net.branches[i].modelOutput[bbox_y_offset])
- net.branches[i].zeroPoint) * net.branches[i].scale;
det.bbox.w = (static_cast<float>(net.branches[i].modelOutput[bbox_w_offset])
- net.branches[i].zeroPoint) * net.branches[i].scale;
det.bbox.h = (static_cast<float>(net.branches[i].modelOutput[bbox_h_offset])
- net.branches[i].zeroPoint) * net.branches[i].scale;
float bbox_x, bbox_y;
/* Eliminate grid sensitivity trick involved in YOLOv4 */
bbox_x = math::MathUtils::SigmoidF32(det.bbox.x);
bbox_y = math::MathUtils::SigmoidF32(det.bbox.y);
det.bbox.x = (bbox_x + w) / width;
det.bbox.y = (bbox_y + h) / height;
det.bbox.w = std::exp(det.bbox.w) * net.branches[i].anchor[anc*2] / net.inputWidth;
det.bbox.h = std::exp(det.bbox.h) * net.branches[i].anchor[anc*2+1] / net.inputHeight;
for (int s = 0; s < numClasses; s++) {
float sig = math::MathUtils::SigmoidF32(
(static_cast<float>(net.branches[i].modelOutput[bbox_scores_offset + s]) -
net.branches[i].zeroPoint) * net.branches[i].scale
) * objectness;
det.prob.emplace_back((sig > threshold) ? sig : 0);
}
/* Correct_YOLO_boxes */
det.bbox.x *= imageWidth;
det.bbox.w *= imageWidth;
det.bbox.y *= imageHeight;
det.bbox.h *= imageHeight;
if (num < net.topN || net.topN <=0) {
detections.emplace_front(det);
num += 1;
} else if (num == net.topN) {
detections.sort(det_objectness_comparator);
InsertTopNDetections(detections, det);
num += 1;
} else {
InsertTopNDetections(detections, det);
}
}
}
}
}
}
if(num > net.topN)
num -=1;
}
} /* namespace app */
} /* namespace arm */
|
