forked from OAID/Tengine
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtm_segformer.cpp
265 lines (233 loc) · 8.19 KB
/
tm_segformer.cpp
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
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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.
*/
/*
* Copyright (c) 2020, OPEN AI LAB
* Author: [email protected]
* original model: https://github.com/NVlabs/SegFormer
* other demo: https://github.com/FeiGeChuanShu/segformer-tengine
*/
#include <iostream>
#include <functional>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "common.h"
#include "tengine/c_api.h"
#include "tengine_operations.h"
#define DEFAULT_REPEAT_COUNT 1
#define DEFAULT_THREAD_COUNT 1
void get_input_fp32_data(const char* image_file, float* input_data, int letterbox_rows, int letterbox_cols, const float* mean, const float* scale)
{
cv::Mat sample = cv::imread(image_file, 1);
cv::Mat img;
if (sample.channels() == 1)
cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
else
cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);
cv::resize(img, img, cv::Size(letterbox_cols, letterbox_rows));
cv::Mat img_new;
img.convertTo(img_new, CV_32FC3);
float* img_data = (float*)img_new.data;
/* nhwc to nchw */
for (int h = 0; h < letterbox_rows; h++)
{
for (int w = 0; w < letterbox_cols; w++)
{
for (int c = 0; c < 3; c++)
{
int in_index = h * letterbox_cols * 3 + w * 3 + c;
int out_index = c * letterbox_rows * letterbox_cols + h * letterbox_cols + w;
input_data[out_index] = (img_data[in_index] - mean[c]) * scale[c];
}
}
}
}
static void draw_result(const cv::Mat& bgr, const float* data, int target_size_h, int target_size_w)
{
const int cityscapes_palette[][3] = {{128, 64, 128}, {244, 35, 232}, {70, 70, 70}, {102, 102, 156}, {190, 153, 153}, {153, 153, 153}, {250, 170, 30}, {220, 220, 0}, {107, 142, 35}, {152, 251, 152}, {70, 130, 180}, {220, 20, 60}, {255, 0, 0}, {0, 0, 142}, {0, 0, 70}, {0, 60, 100}, {0, 80, 100}, {0, 0, 230}, {119, 11, 32}};
cv::Mat image = bgr.clone();
/* get class index */
cv::Mat segidx = cv::Mat::zeros(target_size_h, target_size_w, CV_8UC1);
for (int i = 0; i < target_size_h; i++)
{
unsigned char* segidx_data = segidx.ptr(i);
for (int j = 0; j < target_size_w; j++)
{
int maxk = 0;
float tmp = data[0 * target_size_w * target_size_h + i * target_size_w + j];
for (int k = 0; k < 19; k++) //cityscapes_dataset
{
if (tmp < data[k * target_size_w * target_size_h + i * target_size_w + j])
{
tmp = data[k * target_size_w * target_size_h + i * target_size_w + j];
maxk = k;
}
}
segidx_data[j] = maxk;
}
}
cv::Mat maskResize;
cv::resize(segidx, maskResize, cv::Size(image.cols, image.rows), 0, 0, cv::INTER_NEAREST);
for (int h = 0; h < image.rows; h++)
{
cv::Vec3b* pRgb = image.ptr<cv::Vec3b>(h);
for (int w = 0; w < image.cols; w++)
{
int index = maskResize.at<uchar>(h, w);
pRgb[w] = cv::Vec3b(cityscapes_palette[index][2] * 0.6 + pRgb[w][2] * 0.4, cityscapes_palette[index][1] * 0.6 + pRgb[w][1] * 0.4, cityscapes_palette[index][0] * 0.6 + pRgb[w][0] * 0.4);
}
}
cv::imwrite("segformer_cityscapes_result.jpg", image);
}
void show_usage()
{
fprintf(stderr, "[Usage]: [-h]\n [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count]\n");
}
int main(int argc, char* argv[])
{
int repeat_count = DEFAULT_REPEAT_COUNT;
int num_thread = DEFAULT_THREAD_COUNT;
char* model_file = nullptr;
char* image_file = nullptr;
//input size
int img_h = 512;
int img_w = 1024;
const float mean[3] = {123.675f, 116.28f, 103.53f};
const float scale[3] = {0.01712475f, 0.0175f, 0.01742919f};
int res;
while ((res = getopt(argc, argv, "m:i:r:t:h:")) != -1)
{
switch (res)
{
case 'm':
model_file = optarg;
break;
case 'i':
image_file = optarg;
break;
case 'r':
repeat_count = atoi(optarg);
break;
case 't':
num_thread = atoi(optarg);
break;
case 'h':
show_usage();
return 0;
default:
break;
}
}
/* check files */
if (model_file == nullptr)
{
fprintf(stderr, "Error: Tengine model file not specified!\n");
show_usage();
return -1;
}
if (image_file == nullptr)
{
fprintf(stderr, "Error: Image file not specified!\n");
show_usage();
return -1;
}
if (!check_file_exist(model_file) || !check_file_exist(image_file))
return -1;
cv::Mat img = cv::imread(image_file, 1);
if (img.empty())
{
fprintf(stderr, "cv::imread %s failed\n", image_file);
return -1;
}
/* set runtime options */
struct options opt;
opt.num_thread = num_thread;
opt.cluster = TENGINE_CLUSTER_ALL;
opt.precision = TENGINE_MODE_FP32;
opt.affinity = 0;
/* inital tengine */
init_tengine();
fprintf(stderr, "tengine-lite library version: %s\n", get_tengine_version());
/* create graph, load tengine model xxx.tmfile */
graph_t graph = create_graph(nullptr, "tengine", model_file);
if (graph == nullptr)
{
std::cout << "Create graph0 failed\n";
return -1;
}
/* set the input shape to initial the graph, and prerun graph to infer shape */
int img_size = img_h * img_w * 3;
int dims[] = {1, 3, img_h, img_w}; // nchw
float* input_data = (float*)malloc(img_size * sizeof(float));
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
if (input_tensor == nullptr)
{
fprintf(stderr, "Get input tensor failed\n");
return -1;
}
if (set_tensor_shape(input_tensor, dims, 4) < 0)
{
fprintf(stderr, "Set input tensor shape failed\n");
return -1;
}
if (set_tensor_buffer(input_tensor, input_data, img_size * sizeof(float)) < 0)
{
fprintf(stderr, "Set input tensor buffer failed\n");
return -1;
}
/* prerun graph, set work options(num_thread, cluster, precision) */
if (prerun_graph_multithread(graph, opt) < 0)
{
fprintf(stderr, "Prerun multithread graph failed.\n");
return -1;
}
/* prepare process input data, set the data mem to input tensor */
get_input_fp32_data(image_file, input_data, img_h, img_w, mean, scale);
/* run graph */
double min_time = DBL_MAX;
double max_time = DBL_MIN;
double total_time = 0.;
for (int i = 0; i < repeat_count; i++)
{
double start = get_current_time();
if (run_graph(graph, 1) < 0)
{
fprintf(stderr, "Run graph failed\n");
return -1;
}
double end = get_current_time();
double cur = end - start;
total_time += cur;
if (min_time > cur)
min_time = cur;
if (max_time < cur)
max_time = cur;
}
printf("Repeat [%d] min %.3f ms, max %.3f ms, avg %.3f ms\n", repeat_count, min_time, max_time,
total_time / repeat_count);
/* get output tensor */
tensor_t output_tensor = get_graph_output_tensor(graph, 0, 0);
float* data = (float*)(get_tensor_buffer(output_tensor));
/* draw result */
draw_result(img, data, img_h, img_w);
postrun_graph(graph);
destroy_graph(graph);
release_tengine();
return 0;
}