How to run bpnet in tao toolkit?

AK51 · August 10, 2022, 9:01am

HI,
Can I see it in real-time? Thx

root@ubuntu:/home/nvidia/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app# ls -rlt
total 1704
-rw-rw-r-- 1 nvidia nvidia   4008 Aug  3 15:59 README.md
-rw-rw-r-- 1 nvidia nvidia   3029 Aug  3 15:59 Makefile
-rw-rw-r-- 1 nvidia nvidia   1982 Aug  3 15:59 ds_bodypose2d_meta.h
-rw-rw-r-- 1 nvidia nvidia   4129 Aug  3 15:59 deepstream_bodypose2d_meta.cpp
-rw-rw-r-- 1 nvidia nvidia  46293 Aug  3 15:59 deepstream_bodypose2d_app.cpp
-rw-rw-r-- 1 nvidia nvidia   6569 Aug  3 15:59 bodypose2d_pipeline.png
-rw-rw-r-- 1 nvidia nvidia    712 Aug  3 15:59 bodypose2d_app_config.yml
-rw-rw-r-- 1 nvidia nvidia 744320 Aug  3 16:28 deepstream_bodypose2d_app.o
-rw-rw-r-- 1 nvidia nvidia  62184 Aug  3 16:28 deepstream_bodypose2d_meta.o
-rw-rw-r-- 1 nvidia nvidia 425528 Aug  3 16:28 ds_yml_parse.o
-rwxrwxr-x 1 nvidia nvidia 425936 Aug  3 16:28 deepstream-bodypose2d-app
-rw-rw-r-- 1 nvidia nvidia      0 Aug  3 17:03 body2dout.jpg
-rw-r--r-- 1 root   root        0 Aug 10 16:41 test.264
root@ubuntu:/home/nvidia/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app#

Morganh · August 10, 2022, 9:03am

Can you clean env “DISPLAY” and run again?

AK51 · August 10, 2022, 9:29am

HI,

I have unset DISPLAY and the test.h264 is created.
Still trying to figure out how to display the test.h264…
VLC crash when I tried to open it.
video-viewer test.h264 does not work (from dusty-nv)

Btw, can I view it in real-time? Thx

Morganh · August 10, 2022, 9:40am

You can download “file view plus” to view h264 files.

AK51 · August 22, 2022, 3:33am

Hi,

The test.264 is 12.2Mb, right? But I cannot open it in “file view plus”… and this application is only 14 days trial…
I tried VLC, it crashed… :<
I wonder if the file is corrupted…

Thx

Here is the print out

root@ubuntu:/home/nvidia/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app# ./deepstream-bodypose2d-app bodypose2d_app_config.yml 
Request sink_0 pad from streammux
Request sink_1 pad from streammux
!! [WARNING] Unknown param found : type
!! [WARNING] Unknown param found : enc
!! [WARNING] Unknown param found : udpport
!! [WARNING] Unknown param found : rtspport
!! [WARNING] Unknown param found : filename
total 1 item
group model-config found 0
Now playing: SUDO_GID=1000
Opening in BLOCKING MODE 
0:00:05.586896914 11542 0xaaaae64b6670 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-infer-engine1> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1900> [UID = 1]: deserialized trt engine from :/home/nvidia/deepstream_tao_apps/models/bodypose2d/model.etlt_b32_gpu0_fp16.engine
INFO: [FullDims Engine Info]: layers num: 3
0   INPUT  kFLOAT input_1:0       288x384x3       min: 1x288x384x3     opt: 32x288x384x3    Max: 32x288x384x3    
1   OUTPUT kFLOAT heatmap_out/BiasAdd:0 36x48x19        min: 0               opt: 0               Max: 0               
2   OUTPUT kFLOAT conv2d_transpose_1/BiasAdd:0 144x192x38      min: 0               opt: 0               Max: 0               

0:00:05.749166580 11542 0xaaaae64b6670 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-infer-engine1> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2003> [UID = 1]: Use deserialized engine model: /home/nvidia/deepstream_tao_apps/models/bodypose2d/model.etlt_b32_gpu0_fp16.engine
0:00:06.172064227 11542 0xaaaae64b6670 INFO                 nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-infer-engine1> [UID 1]: Load new model:/home/nvidia/deepstream_tao_apps/configs/bodypose2d_tao/bodypose2d_pgie_config.yml sucessfully
Decodebin child added: source
Decodebin child added: decodebin0
Decodebin child added: source
Decodebin child added: decodebin1
Running...
Decodebin child added: qtdemux0
Decodebin child added: qtdemux1
Decodebin child added: multiqueue1
Decodebin child added: multiqueue0
Decodebin child added: h264parse1
Decodebin child added: h264parse0
Decodebin child added: capsfilter1
Decodebin child added: capsfilter0
Decodebin child added: aacparse0
Decodebin child added: aacparse1
Decodebin child added: avdec_aac0
Decodebin child added: avdec_aac1
Decodebin child added: nvv4l2decoder0
Decodebin child added: nvv4l2decoder1
Opening in BLOCKING MODE 
Opening in BLOCKING MODE 
NvMMLiteOpen : Block : BlockType = 261 
NvMMLiteOpen : Block : BlockType = 261 
NVMEDIA: Reading vendor.tegra.display-size : status: 6 
NVMEDIA: Reading vendor.tegra.display-size : status: 6 
NvMMLiteBlockCreate : Block : BlockType = 261 
NvMMLiteBlockCreate : Block : BlockType = 261 
In cb_newpad
In cb_newpad
###Decodebin pick nvidia decoder plugin.
In cb_newpad
###Decodebin pick nvidia decoder plugin.
In cb_newpad
NvMMLiteOpen : Block : BlockType = 4 
===== NVMEDIA: NVENC =====
NvMMLiteBlockCreate : Block : BlockType = 4 
Frame Number = 0 Person Count = 4
Frame Number = 1 Person Count = 4
H264: Profile = 66, Level = 0 
NVMEDIA: Need to set EMC bandwidth : 376000 
NVMEDIA_ENC: bBlitMode is set to TRUE 
Frame Number = 2 Person Count = 4
Frame Number = 3 Person Count = 4
Frame Number = 4 Person Count = 10
Frame Number = 5 Person Count = 4
Frame Number = 6 Person Count = 4
Frame Number = 7 Person Count = 4
Frame Number = 8 Person Count = 4
Frame Number = 9 Person Count = 4
Frame Number = 10 Person Count = 4
Frame Number = 11 Person Count = 4
Frame Number = 12 Person Count = 4
Frame Number = 13 Person Count = 4
Frame Number = 14 Person Count = 4
Frame Number = 15 Person Count = 4
Frame Number = 16 Person Count = 4
Frame Number = 17 Person Count = 4
Frame Number = 18 Person Count = 4
Frame Number = 19 Person Count = 4
Frame Number = 20 Person Count = 6
Frame Number = 21 Person Count = 4
Frame Number = 22 Person Count = 4
Frame Number = 23 Person Count = 4
Frame Number = 24 Person Count = 6
Frame Number = 25 Person Count = 4
Frame Number = 26 Person Count = 6
Frame Number = 27 Person Count = 6
Frame Number = 28 Person Count = 6
Frame Number = 29 Person Count = 6
Frame Number = 30 Person Count = 4
Frame Number = 31 Person Count = 4
Frame Number = 32 Person Count = 4
Frame Number = 33 Person Count = 6
Frame Number = 34 Person Count = 6
Frame Number = 35 Person Count = 8
Frame Number = 36 Person Count = 8
Frame Number = 37 Person Count = 8
Frame Number = 38 Person Count = 8
Frame Number = 39 Person Count = 10
Frame Number = 40 Person Count = 8
Frame Number = 41 Person Count = 6
Frame Number = 42 Person Count = 4
Frame Number = 43 Person Count = 4
Frame Number = 44 Person Count = 8
Frame Number = 45 Person Count = 6
Frame Number = 46 Person Count = 6
Frame Number = 47 Person Count = 4
Frame Number = 48 Person Count = 6
Frame Number = 49 Person Count = 4
Frame Number = 50 Person Count = 4
Frame Number = 51 Person Count = 4
Frame Number = 52 Person Count = 4
Frame Number = 53 Person Count = 6
Frame Number = 54 Person Count = 4
Frame Number = 55 Person Count = 6
Frame Number = 56 Person Count = 6
Frame Number = 57 Person Count = 4
Frame Number = 58 Person Count = 6
Frame Number = 59 Person Count = 6
Frame Number = 60 Person Count = 6
Frame Number = 61 Person Count = 6
Frame Number = 62 Person Count = 10
Frame Number = 63 Person Count = 8
Frame Number = 64 Person Count = 8
Frame Number = 65 Person Count = 10
Frame Number = 66 Person Count = 8
Frame Number = 67 Person Count = 8
Frame Number = 68 Person Count = 6
Frame Number = 69 Person Count = 6
Frame Number = 70 Person Count = 8
Frame Number = 71 Person Count = 6
Frame Number = 72 Person Count = 6
Frame Number = 73 Person Count = 4
Frame Number = 74 Person Count = 6
Frame Number = 75 Person Count = 6
Frame Number = 76 Person Count = 8
Frame Number = 77 Person Count = 6
Frame Number = 78 Person Count = 8
Frame Number = 79 Person Count = 8
Frame Number = 80 Person Count = 6
Frame Number = 81 Person Count = 4
Frame Number = 82 Person Count = 4
Frame Number = 83 Person Count = 6
Frame Number = 84 Person Count = 8
Frame Number = 85 Person Count = 10
Frame Number = 86 Person Count = 6
Frame Number = 87 Person Count = 6
Frame Number = 88 Person Count = 8
Frame Number = 89 Person Count = 8
Frame Number = 90 Person Count = 8
Frame Number = 91 Person Count = 8
Frame Number = 92 Person Count = 14
Frame Number = 93 Person Count = 6
Frame Number = 94 Person Count = 10
Frame Number = 95 Person Count = 8
Frame Number = 96 Person Count = 8
Frame Number = 97 Person Count = 6
Frame Number = 98 Person Count = 10
Frame Number = 99 Person Count = 12
Frame Number = 100 Person Count = 12
Frame Number = 101 Person Count = 10
Frame Number = 102 Person Count = 14
Frame Number = 103 Person Count = 12
Frame Number = 104 Person Count = 14
Frame Number = 105 Person Count = 12
Frame Number = 106 Person Count = 10
Frame Number = 107 Person Count = 12
Frame Number = 108 Person Count = 14
Frame Number = 109 Person Count = 12
Frame Number = 110 Person Count = 10
Frame Number = 111 Person Count = 10
Frame Number = 112 Person Count = 14
Frame Number = 113 Person Count = 14
Frame Number = 114 Person Count = 12
Frame Number = 115 Person Count = 10
Frame Number = 116 Person Count = 12
Frame Number = 117 Person Count = 10
Frame Number = 118 Person Count = 12
Frame Number = 119 Person Count = 12
Frame Number = 120 Person Count = 12
Frame Number = 121 Person Count = 12
Frame Number = 122 Person Count = 10
Frame Number = 123 Person Count = 10
Frame Number = 124 Person Count = 8
Frame Number = 125 Person Count = 8
Frame Number = 126 Person Count = 8
Frame Number = 127 Person Count = 8
Frame Number = 128 Person Count = 14
Frame Number = 129 Person Count = 12
Frame Number = 130 Person Count = 12
Frame Number = 131 Person Count = 8
Frame Number = 132 Person Count = 10
Frame Number = 133 Person Count = 12
Frame Number = 134 Person Count = 12
Frame Number = 135 Person Count = 12
Frame Number = 136 Person Count = 12
Frame Number = 137 Person Count = 8
Frame Number = 138 Person Count = 10
Frame Number = 139 Person Count = 10
Frame Number = 140 Person Count = 6
Frame Number = 141 Person Count = 10
Frame Number = 142 Person Count = 12
Frame Number = 143 Person Count = 10
Frame Number = 144 Person Count = 6
Frame Number = 145 Person Count = 10
Frame Number = 146 Person Count = 8
Frame Number = 147 Person Count = 10
Frame Number = 148 Person Count = 12
Frame Number = 149 Person Count = 10
Frame Number = 150 Person Count = 10
Frame Number = 151 Person Count = 10
Frame Number = 152 Person Count = 10
Frame Number = 153 Person Count = 12
Frame Number = 154 Person Count = 12
Frame Number = 155 Person Count = 10
Frame Number = 156 Person Count = 10
Frame Number = 157 Person Count = 10
Frame Number = 158 Person Count = 10
Frame Number = 159 Person Count = 12
Frame Number = 160 Person Count = 12
Frame Number = 161 Person Count = 14
Frame Number = 162 Person Count = 12
Frame Number = 163 Person Count = 10
Frame Number = 164 Person Count = 10
Frame Number = 165 Person Count = 14
Frame Number = 166 Person Count = 10
Frame Number = 167 Person Count = 8
Frame Number = 168 Person Count = 8
Frame Number = 169 Person Count = 12
Frame Number = 170 Person Count = 10
Frame Number = 171 Person Count = 12
Frame Number = 172 Person Count = 10
Frame Number = 173 Person Count = 8
Frame Number = 174 Person Count = 8
Frame Number = 175 Person Count = 8
Frame Number = 176 Person Count = 6
Frame Number = 177 Person Count = 8
Frame Number = 178 Person Count = 8
Frame Number = 179 Person Count = 8
Frame Number = 180 Person Count = 8
Frame Number = 181 Person Count = 8
Frame Number = 182 Person Count = 8
Frame Number = 183 Person Count = 10
Frame Number = 184 Person Count = 10
Frame Number = 185 Person Count = 10
Frame Number = 186 Person Count = 10
Frame Number = 187 Person Count = 8
Frame Number = 188 Person Count = 10
Frame Number = 189 Person Count = 8
Frame Number = 190 Person Count = 10
Frame Number = 191 Person Count = 10
Frame Number = 192 Person Count = 10
Frame Number = 193 Person Count = 14
Frame Number = 194 Person Count = 12
Frame Number = 195 Person Count = 8
Frame Number = 196 Person Count = 8
Frame Number = 197 Person Count = 10
Frame Number = 198 Person Count = 10
Frame Number = 199 Person Count = 12
Frame Number = 200 Person Count = 12
Frame Number = 201 Person Count = 10
Frame Number = 202 Person Count = 10
Frame Number = 203 Person Count = 8
Frame Number = 204 Person Count = 14
Frame Number = 205 Person Count = 8
Frame Number = 206 Person Count = 12
Frame Number = 207 Person Count = 10
Frame Number = 208 Person Count = 10
Frame Number = 209 Person Count = 8
Frame Number = 210 Person Count = 10
Frame Number = 211 Person Count = 10
Frame Number = 212 Person Count = 10
Frame Number = 213 Person Count = 10
Frame Number = 214 Person Count = 10
Frame Number = 215 Person Count = 10
Frame Number = 216 Person Count = 10
Frame Number = 217 Person Count = 10
Frame Number = 218 Person Count = 12
Frame Number = 219 Person Count = 14
Frame Number = 220 Person Count = 10
Frame Number = 221 Person Count = 8
Frame Number = 222 Person Count = 8
Frame Number = 223 Person Count = 8
Frame Number = 224 Person Count = 8
Frame Number = 225 Person Count = 8
Frame Number = 226 Person Count = 10
Frame Number = 227 Person Count = 8
Frame Number = 228 Person Count = 8
Frame Number = 229 Person Count = 6
Frame Number = 230 Person Count = 8
Frame Number = 231 Person Count = 8
Frame Number = 232 Person Count = 8
Frame Number = 233 Person Count = 10
Frame Number = 234 Person Count = 6
Frame Number = 235 Person Count = 8
Frame Number = 236 Person Count = 6
Frame Number = 237 Person Count = 12
Frame Number = 238 Person Count = 18
Frame Number = 239 Person Count = 14
Frame Number = 240 Person Count = 10
Frame Number = 241 Person Count = 8
Frame Number = 242 Person Count = 8
Frame Number = 243 Person Count = 8
Frame Number = 244 Person Count = 8
Frame Number = 245 Person Count = 8
Frame Number = 246 Person Count = 10
Frame Number = 247 Person Count = 8
Frame Number = 248 Person Count = 8
Frame Number = 249 Person Count = 10
Frame Number = 250 Person Count = 8
Frame Number = 251 Person Count = 6
Frame Number = 252 Person Count = 6
Frame Number = 253 Person Count = 6
Frame Number = 254 Person Count = 6
Frame Number = 255 Person Count = 10
Frame Number = 256 Person Count = 10
Frame Number = 257 Person Count = 10
Frame Number = 258 Person Count = 10
Frame Number = 259 Person Count = 6
Frame Number = 260 Person Count = 6
Frame Number = 261 Person Count = 8
Frame Number = 262 Person Count = 8
Frame Number = 263 Person Count = 8
Frame Number = 264 Person Count = 6
Frame Number = 265 Person Count = 8
Frame Number = 266 Person Count = 8
Frame Number = 267 Person Count = 10
Frame Number = 268 Person Count = 8
Frame Number = 269 Person Count = 8
Frame Number = 270 Person Count = 8
Frame Number = 271 Person Count = 10
Frame Number = 272 Person Count = 8
Frame Number = 273 Person Count = 8
Frame Number = 274 Person Count = 6
Frame Number = 275 Person Count = 6
Frame Number = 276 Person Count = 10
Frame Number = 277 Person Count = 6
Frame Number = 278 Person Count = 10
Frame Number = 279 Person Count = 10
Frame Number = 280 Person Count = 10
Frame Number = 281 Person Count = 10
Frame Number = 282 Person Count = 8
Frame Number = 283 Person Count = 10
Frame Number = 284 Person Count = 10
Frame Number = 285 Person Count = 10
Frame Number = 286 Person Count = 10
Frame Number = 287 Person Count = 10
Frame Number = 288 Person Count = 6
Frame Number = 289 Person Count = 6
Frame Number = 290 Person Count = 6
Frame Number = 291 Person Count = 8
Frame Number = 292 Person Count = 8
Frame Number = 293 Person Count = 8
Frame Number = 294 Person Count = 8
Frame Number = 295 Person Count = 10
Frame Number = 296 Person Count = 6
Frame Number = 297 Person Count = 10
Frame Number = 298 Person Count = 12
Frame Number = 299 Person Count = 12
Frame Number = 300 Person Count = 10
Frame Number = 301 Person Count = 10
Frame Number = 302 Person Count = 8
Frame Number = 303 Person Count = 8
Frame Number = 304 Person Count = 6
Frame Number = 305 Person Count = 8
Frame Number = 306 Person Count = 12
Frame Number = 307 Person Count = 8
Frame Number = 308 Person Count = 8
Frame Number = 309 Person Count = 12
Frame Number = 310 Person Count = 8
Frame Number = 311 Person Count = 10
Frame Number = 312 Person Count = 6
Frame Number = 313 Person Count = 6
Frame Number = 314 Person Count = 8
Frame Number = 315 Person Count = 8
Frame Number = 316 Person Count = 6
Frame Number = 317 Person Count = 12
Frame Number = 318 Person Count = 10
Frame Number = 319 Person Count = 12
Frame Number = 320 Person Count = 14
Frame Number = 321 Person Count = 12
Frame Number = 322 Person Count = 18
Frame Number = 323 Person Count = 14
Frame Number = 324 Person Count = 22
Frame Number = 325 Person Count = 10
Frame Number = 326 Person Count = 14
Frame Number = 327 Person Count = 10
Frame Number = 328 Person Count = 8
Frame Number = 329 Person Count = 8
Frame Number = 330 Person Count = 10
Frame Number = 331 Person Count = 8
Frame Number = 332 Person Count = 6
Frame Number = 333 Person Count = 10
Frame Number = 334 Person Count = 10
Frame Number = 335 Person Count = 10
Frame Number = 336 Person Count = 6
Frame Number = 337 Person Count = 10
Frame Number = 338 Person Count = 10
Frame Number = 339 Person Count = 10
Frame Number = 340 Person Count = 12
Frame Number = 341 Person Count = 12
Frame Number = 342 Person Count = 8
Frame Number = 343 Person Count = 8
Frame Number = 344 Person Count = 10
Frame Number = 345 Person Count = 8
Frame Number = 346 Person Count = 10
Frame Number = 347 Person Count = 10
Frame Number = 348 Person Count = 8
Frame Number = 349 Person Count = 8
Frame Number = 350 Person Count = 8
Frame Number = 351 Person Count = 10
Frame Number = 352 Person Count = 8
Frame Number = 353 Person Count = 16
Frame Number = 354 Person Count = 14
Frame Number = 355 Person Count = 8
Frame Number = 356 Person Count = 12
Frame Number = 357 Person Count = 18
Frame Number = 358 Person Count = 10
Frame Number = 359 Person Count = 14
Frame Number = 360 Person Count = 16
Frame Number = 361 Person Count = 10
Frame Number = 362 Person Count = 10
Frame Number = 363 Person Count = 14
Frame Number = 364 Person Count = 12
Frame Number = 365 Person Count = 10
Frame Number = 366 Person Count = 8
Frame Number = 367 Person Count = 14
Frame Number = 368 Person Count = 12
Frame Number = 369 Person Count = 8
Frame Number = 370 Person Count = 12
Frame Number = 371 Person Count = 8
Frame Number = 372 Person Count = 10
Frame Number = 373 Person Count = 10
Frame Number = 374 Person Count = 8
Frame Number = 375 Person Count = 12
Frame Number = 376 Person Count = 14
Frame Number = 377 Person Count = 6
Frame Number = 378 Person Count = 12
Frame Number = 379 Person Count = 12
Frame Number = 380 Person Count = 14
Frame Number = 381 Person Count = 10
Frame Number = 382 Person Count = 12
Frame Number = 383 Person Count = 14
Frame Number = 384 Person Count = 12
Frame Number = 385 Person Count = 10
Frame Number = 386 Person Count = 10
Frame Number = 387 Person Count = 8
Frame Number = 388 Person Count = 8
Frame Number = 389 Person Count = 6
Frame Number = 390 Person Count = 6
Frame Number = 391 Person Count = 6
Frame Number = 392 Person Count = 8
Frame Number = 393 Person Count = 8
Frame Number = 394 Person Count = 8
Frame Number = 395 Person Count = 8
Frame Number = 396 Person Count = 10
Frame Number = 397 Person Count = 12
Frame Number = 398 Person Count = 6
Frame Number = 399 Person Count = 8
Frame Number = 400 Person Count = 8
Frame Number = 401 Person Count = 6
Frame Number = 402 Person Count = 8
Frame Number = 403 Person Count = 10
Frame Number = 404 Person Count = 8
Frame Number = 405 Person Count = 8
Frame Number = 406 Person Count = 8
Frame Number = 407 Person Count = 8
Frame Number = 408 Person Count = 10
Frame Number = 409 Person Count = 8
Frame Number = 410 Person Count = 8
Frame Number = 411 Person Count = 8
Frame Number = 412 Person Count = 10
Frame Number = 413 Person Count = 8
Frame Number = 414 Person Count = 8
Frame Number = 415 Person Count = 10
Frame Number = 416 Person Count = 8
Frame Number = 417 Person Count = 10
Frame Number = 418 Person Count = 8
Frame Number = 419 Person Count = 8
Frame Number = 420 Person Count = 8
Frame Number = 421 Person Count = 8
Frame Number = 422 Person Count = 6
Frame Number = 423 Person Count = 8
Frame Number = 424 Person Count = 6
Frame Number = 425 Person Count = 8
Frame Number = 426 Person Count = 8
Frame Number = 427 Person Count = 6
Frame Number = 428 Person Count = 6
Frame Number = 429 Person Count = 8
Frame Number = 430 Person Count = 6
Frame Number = 431 Person Count = 6
Frame Number = 432 Person Count = 8
Frame Number = 433 Person Count = 8
Frame Number = 434 Person Count = 4
Frame Number = 435 Person Count = 8
Frame Number = 436 Person Count = 6
Frame Number = 437 Person Count = 8
Frame Number = 438 Person Count = 6
Frame Number = 439 Person Count = 8
Frame Number = 440 Person Count = 8
Frame Number = 441 Person Count = 10
Frame Number = 442 Person Count = 6
Frame Number = 443 Person Count = 6
Frame Number = 444 Person Count = 6
Frame Number = 445 Person Count = 8
Frame Number = 446 Person Count = 10
Frame Number = 447 Person Count = 6
Frame Number = 448 Person Count = 8
Frame Number = 449 Person Count = 8
Frame Number = 450 Person Count = 8
Frame Number = 451 Person Count = 8
Frame Number = 452 Person Count = 8
Frame Number = 453 Person Count = 8
Frame Number = 454 Person Count = 6
Frame Number = 455 Person Count = 6
Frame Number = 456 Person Count = 6
Frame Number = 457 Person Count = 6
Frame Number = 458 Person Count = 10
Frame Number = 459 Person Count = 6
Frame Number = 460 Person Count = 8
Frame Number = 461 Person Count = 6
Frame Number = 462 Person Count = 6
Frame Number = 463 Person Count = 6
Frame Number = 464 Person Count = 6
Frame Number = 465 Person Count = 6
Frame Number = 466 Person Count = 8
Frame Number = 467 Person Count = 6
Frame Number = 468 Person Count = 4
Frame Number = 469 Person Count = 4
Frame Number = 470 Person Count = 4
Frame Number = 471 Person Count = 4
Frame Number = 472 Person Count = 8
Frame Number = 473 Person Count = 6
Frame Number = 474 Person Count = 4
Frame Number = 475 Person Count = 6
Frame Number = 476 Person Count = 8
Frame Number = 477 Person Count = 6
Frame Number = 478 Person Count = 6
Frame Number = 479 Person Count = 6
Frame Number = 480 Person Count = 10
Frame Number = 481 Person Count = 10
Frame Number = 482 Person Count = 6
Frame Number = 483 Person Count = 8
Frame Number = 484 Person Count = 6
Frame Number = 485 Person Count = 6
Frame Number = 486 Person Count = 6
Frame Number = 487 Person Count = 6
Frame Number = 488 Person Count = 6
Frame Number = 489 Person Count = 8
Frame Number = 490 Person Count = 8
Frame Number = 491 Person Count = 6
Frame Number = 492 Person Count = 6
Frame Number = 493 Person Count = 10
Frame Number = 494 Person Count = 10
Frame Number = 495 Person Count = 10
Frame Number = 496 Person Count = 10
Frame Number = 497 Person Count = 10
Frame Number = 498 Person Count = 14
Frame Number = 499 Person Count = 10
Frame Number = 500 Person Count = 12
Frame Number = 501 Person Count = 10
Frame Number = 502 Person Count = 8
Frame Number = 503 Person Count = 6
Frame Number = 504 Person Count = 8
Frame Number = 505 Person Count = 10
Frame Number = 506 Person Count = 8
Frame Number = 507 Person Count = 10
Frame Number = 508 Person Count = 10
Frame Number = 509 Person Count = 14
Frame Number = 510 Person Count = 10
Frame Number = 511 Person Count = 12
Frame Number = 512 Person Count = 10
Frame Number = 513 Person Count = 10
Frame Number = 514 Person Count = 10
Frame Number = 515 Person Count = 12
Frame Number = 516 Person Count = 12
Frame Number = 517 Person Count = 8
Frame Number = 518 Person Count = 8
Frame Number = 519 Person Count = 10
Frame Number = 520 Person Count = 10
Frame Number = 521 Person Count = 8
Frame Number = 522 Person Count = 6
Frame Number = 523 Person Count = 10
Frame Number = 524 Person Count = 8
Frame Number = 525 Person Count = 6
Frame Number = 526 Person Count = 10
Frame Number = 527 Person Count = 6
Frame Number = 528 Person Count = 10
Frame Number = 529 Person Count = 10
Frame Number = 530 Person Count = 10
Frame Number = 531 Person Count = 10
Frame Number = 532 Person Count = 12
Frame Number = 533 Person Count = 12
Frame Number = 534 Person Count = 12
Frame Number = 535 Person Count = 10
Frame Number = 536 Person Count = 10
Frame Number = 537 Person Count = 10
Frame Number = 538 Person Count = 10
Frame Number = 539 Person Count = 12
Frame Number = 540 Person Count = 8
Frame Number = 541 Person Count = 10
Frame Number = 542 Person Count = 10
Frame Number = 543 Person Count = 8
Frame Number = 544 Person Count = 8
Frame Number = 545 Person Count = 12
Frame Number = 546 Person Count = 8
Frame Number = 547 Person Count = 12
Frame Number = 548 Person Count = 10
Frame Number = 549 Person Count = 12
Frame Number = 550 Person Count = 8
Frame Number = 551 Person Count = 8
Frame Number = 552 Person Count = 6
Frame Number = 553 Person Count = 8
Frame Number = 554 Person Count = 6
Frame Number = 555 Person Count = 8
Frame Number = 556 Person Count = 6
Frame Number = 557 Person Count = 10
Frame Number = 558 Person Count = 10
Frame Number = 559 Person Count = 10
Frame Number = 560 Person Count = 10
Frame Number = 561 Person Count = 10
Frame Number = 562 Person Count = 8
Frame Number = 563 Person Count = 12
Frame Number = 564 Person Count = 8
Frame Number = 565 Person Count = 12
Frame Number = 566 Person Count = 12
Frame Number = 567 Person Count = 8
Frame Number = 568 Person Count = 8
Frame Number = 569 Person Count = 10
Frame Number = 570 Person Count = 12
Frame Number = 571 Person Count = 16
Frame Number = 572 Person Count = 12
Frame Number = 573 Person Count = 16
Frame Number = 574 Person Count = 12
Frame Number = 575 Person Count = 6
Frame Number = 576 Person Count = 8
Frame Number = 577 Person Count = 8
Frame Number = 578 Person Count = 8
Frame Number = 579 Person Count = 8
Frame Number = 580 Person Count = 10
Frame Number = 581 Person Count = 8
Frame Number = 582 Person Count = 8
Frame Number = 583 Person Count = 6
Frame Number = 584 Person Count = 8
Frame Number = 585 Person Count = 10
Frame Number = 586 Person Count = 12
Frame Number = 587 Person Count = 10
Frame Number = 588 Person Count = 14
Frame Number = 589 Person Count = 10
Frame Number = 590 Person Count = 10
Frame Number = 591 Person Count = 12
Frame Number = 592 Person Count = 12
Frame Number = 593 Person Count = 10
Frame Number = 594 Person Count = 8
Frame Number = 595 Person Count = 16
Frame Number = 596 Person Count = 10
Frame Number = 597 Person Count = 8
Frame Number = 598 Person Count = 6
Frame Number = 599 Person Count = 10
Frame Number = 600 Person Count = 12
Frame Number = 601 Person Count = 10
Frame Number = 602 Person Count = 10
Frame Number = 603 Person Count = 8
Frame Number = 604 Person Count = 10
Frame Number = 605 Person Count = 14
Frame Number = 606 Person Count = 12
Frame Number = 607 Person Count = 14
Frame Number = 608 Person Count = 10
Frame Number = 609 Person Count = 14
Frame Number = 610 Person Count = 14
Frame Number = 611 Person Count = 14
Frame Number = 612 Person Count = 12
Frame Number = 613 Person Count = 12
Frame Number = 614 Person Count = 14
Frame Number = 615 Person Count = 12
Frame Number = 616 Person Count = 12
Frame Number = 617 Person Count = 12
Frame Number = 618 Person Count = 10
Frame Number = 619 Person Count = 10
Frame Number = 620 Person Count = 14
Frame Number = 621 Person Count = 10
Frame Number = 622 Person Count = 8
Frame Number = 623 Person Count = 12
Frame Number = 624 Person Count = 12
Frame Number = 625 Person Count = 10
Frame Number = 626 Person Count = 10
Frame Number = 627 Person Count = 10
Frame Number = 628 Person Count = 10
Frame Number = 629 Person Count = 14
Frame Number = 630 Person Count = 16
Frame Number = 631 Person Count = 18
Frame Number = 632 Person Count = 14
Frame Number = 633 Person Count = 12
Frame Number = 634 Person Count = 14
Frame Number = 635 Person Count = 10
Frame Number = 636 Person Count = 12
Frame Number = 637 Person Count = 10
Frame Number = 638 Person Count = 10
Frame Number = 639 Person Count = 14
Frame Number = 640 Person Count = 10
Frame Number = 641 Person Count = 10
Frame Number = 642 Person Count = 8
Frame Number = 643 Person Count = 12
Frame Number = 644 Person Count = 6
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Frame Number = 646 Person Count = 8
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Frame Number = 653 Person Count = 6
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Frame Number = 667 Person Count = 12
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Frame Number = 671 Person Count = 10
Frame Number = 672 Person Count = 10
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Frame Number = 1371 Person Count = 4
Frame Number = 1372 Person Count = 6
Frame Number = 1373 Person Count = 6
Frame Number = 1374 Person Count = 4
Frame Number = 1375 Person Count = 4
Frame Number = 1376 Person Count = 6
Frame Number = 1377 Person Count = 6
Frame Number = 1378 Person Count = 6
Frame Number = 1379 Person Count = 6
Frame Number = 1380 Person Count = 4
Frame Number = 1381 Person Count = 4
Frame Number = 1382 Person Count = 4
Frame Number = 1383 Person Count = 4
Frame Number = 1384 Person Count = 2
Frame Number = 1385 Person Count = 4
Frame Number = 1386 Person Count = 2
Frame Number = 1387 Person Count = 4
Frame Number = 1388 Person Count = 2
Frame Number = 1389 Person Count = 4
Frame Number = 1390 Person Count = 6
Frame Number = 1391 Person Count = 6
Frame Number = 1392 Person Count = 2
Frame Number = 1393 Person Count = 2
Frame Number = 1394 Person Count = 2
Frame Number = 1395 Person Count = 2
Frame Number = 1396 Person Count = 4
Frame Number = 1397 Person Count = 4
Frame Number = 1398 Person Count = 2
Frame Number = 1399 Person Count = 2
Frame Number = 1400 Person Count = 4
Frame Number = 1401 Person Count = 4
Frame Number = 1402 Person Count = 2
Frame Number = 1403 Person Count = 2
Frame Number = 1404 Person Count = 4
Frame Number = 1405 Person Count = 2
Frame Number = 1406 Person Count = 4
Frame Number = 1407 Person Count = 2
Frame Number = 1408 Person Count = 2
Frame Number = 1409 Person Count = 4
Frame Number = 1410 Person Count = 4
Frame Number = 1411 Person Count = 4
Frame Number = 1412 Person Count = 4
Frame Number = 1413 Person Count = 2
Frame Number = 1414 Person Count = 2
Frame Number = 1415 Person Count = 4
Frame Number = 1416 Person Count = 2
Frame Number = 1417 Person Count = 2
Frame Number = 1418 Person Count = 2
Frame Number = 1419 Person Count = 4
Frame Number = 1420 Person Count = 2
Frame Number = 1421 Person Count = 2
Frame Number = 1422 Person Count = 2
Frame Number = 1423 Person Count = 2
Frame Number = 1424 Person Count = 2
Frame Number = 1425 Person Count = 2
Frame Number = 1426 Person Count = 2
Frame Number = 1427 Person Count = 2
Frame Number = 1428 Person Count = 2
Frame Number = 1429 Person Count = 2
Frame Number = 1430 Person Count = 4
Frame Number = 1431 Person Count = 6
Frame Number = 1432 Person Count = 4
Frame Number = 1433 Person Count = 6
Frame Number = 1434 Person Count = 12
Frame Number = 1435 Person Count = 6
Frame Number = 1436 Person Count = 12
Frame Number = 1437 Person Count = 12
Frame Number = 1438 Person Count = 10
Frame Number = 1439 Person Count = 8
Frame Number = 1440 Person Count = 6
Frame Number = 1441 Person Count = 6
Frame Number = 1442 Person Count = 0
End of stream
Returned, stopping playback
Average fps 59.435753
Totally 9732 persons are inferred
Deleting pipeline
root@ubuntu:/home/nvidia/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app#

Morganh · August 22, 2022, 4:22am

You can install ffmpeg and run.
$ ffmpeg -i xxx.h264

AK51 · August 22, 2022, 6:40am

Hi,

Going back to the bpnet, I have trained the model in my linux machine, then, copy the model.etlt to the jetson Orin.
And then, shall I do the tao-converter? I am looking at the help but it needs encoding key? Thx

nvidia@ubuntu:~/Downloads/files$ ./tao-converter bodyposenet_deployable_v1.0.1/model.etlt 
Error: no encoding key given

Here are my files from Linux machine to Jetson machine

And I tried to use my own API key, it seems wrong.

nvidia@ubuntu:~/Downloads/files$ ./tao-converter bodyposenet_deployable_v1.0.1/model.etlt -k N2hkYWdzdWhlYTdycGE5bDc0MDFrdG0wY3Q6NWI5YTI5MjAtNmFjYy00YTIyLTk0ZGEtZjRlNjxxxxxxxxxx (Hidden)
[INFO] [MemUsageChange] Init CUDA: CPU +303, GPU +0, now: CPU 327, GPU 9990 (MiB)
[INFO] [MemUsageChange] Init builder kernel library: CPU +403, GPU +534, now: CPU 749, GPU 10544 (MiB)
[libprotobuf ERROR google/protobuf/text_format.cc:298] Error parsing text-format onnx2trt_onnx.ModelProto: 1:1: Interpreting non ascii codepoint 190.
[libprotobuf ERROR google/protobuf/text_format.cc:298] Error parsing text-format onnx2trt_onnx.ModelProto: 1:1: Expected identifier, got: �
[ERROR] ModelImporter.cpp:705: Failed to parse ONNX model from file: /tmp/file6LGQIM
[ERROR] Failed to parse the model, please check the encoding key to make sure it's correct
[INFO] Model has no dynamic shape.
[ERROR] 4: [network.cpp::validate::2722] Error Code 4: Internal Error (Network must have at least one output)
[ERROR] Unable to create engine
Segmentation fault (core dumped)

How to view my API key again? I cannot find the button in NGC website… sorry

I did check with my linux machine and I only have one API key. I wonder why it said wrong…

AK51 · August 22, 2022, 6:53am

Btw, can I use model.etlt directly in deepstream without conversion?
Thx

AK51 · August 22, 2022, 7:40am

Hi,

I tried to follow this link but it is for detectnet not bpnet…

Thx

To be short, I just want to try to train the bpnet and run the model in my Jetson Orin. Thx

Morganh · August 23, 2022, 2:15am

Hi,
Several comments here.

Please not follow Using the Nvidia Transfer Learning Toolkit Part 3 - YouTube since it is not for bpnet.
For bpnet running in deepstream, current official github is
deepstream_tao_apps/apps/tao_others at master · NVIDIA-AI-IOT/deepstream_tao_apps · GitHub → deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app at master · NVIDIA-AI-IOT/deepstream_tao_apps · GitHub

./deepstream-bodypose2d-app 2 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt 0 0 file:///usr/data/bodypose2d_test.png ./body2dout

For bpnet running with “tao bpnet inference xxx”, please refer to Body Pose Estimation — TAO Toolkit 3.22.05 documentation

Please use above item2 to run firstly.

AK51 · August 23, 2022, 3:11am

Hi,

I don’t have the bodypose2d_test.png…
I use this jpg instead, but it has segmentation fault. I don’t have error if I use yml file.

Summary, I am still looking for a tutorial of using the trained bpnet model in my Orin…
Thx
https://www.google.com/imgres?imgurl=https%3A%2F%2Ft4.ftcdn.net%2Fjpg%2F02%2F44%2F32%2F85%2F360_F_244328590_RiyvTqK8DUKDX6GSHj3JVMjiuJnwhu0y.jpg&imgrefurl=https%3A%2F%2Fstock.adobe.com%2Fsk%2Fsearch%2Fimages%3Fk%3Dhands%2Bup%2Bsurrender&tbnid=fXXKJYVakOCj6M&vet=12ahUKEwjotcb4-9v5AhVK8ZQKHX-qCjkQMygTegUIARCQAg..i&docid=m2BOn64APV5HoM&w=540&h=360&q=hand%20up&client=ubuntu&ved=2ahUKEwjotcb4-9v5AhVK8ZQKHX-qCjkQMygTegUIARCQAg

nvidia@ubuntu:~/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app$ ./deepstream-bodypose2d-app 2 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt 0 0 file:///home/nvidia/Pictures/hands_up.jpg ./body2dout
Request sink_0 pad from streammux
joint Edges 1 , 8
joint Edges 8 , 9
joint Edges 9 , 10
joint Edges 1 , 11
joint Edges 11 , 12
joint Edges 12 , 13
joint Edges 1 , 2
joint Edges 2 , 3
joint Edges 3 , 4
joint Edges 2 , 16
joint Edges 1 , 5
joint Edges 5 , 6
joint Edges 6 , 7
joint Edges 5 , 17
joint Edges 1 , 0
joint Edges 0 , 14
joint Edges 0 , 15
joint Edges 14 , 16
joint Edges 15 , 17
connections 0 , 1
connections 1 , 2
connections 1 , 5
connections 2 , 3
connections 3 , 4
connections 5 , 6
connections 6 , 7
connections 2 , 8
connections 8 , 9
connections 9 , 10
connections 5 , 11
connections 11 , 12
connections 12 , 13
connections 0 , 14
connections 14 , 16
connections 8 , 11
connections 15 , 17
connections 0 , 15
Now playing: file:///home/nvidia/Pictures/hands_up.jpg
WARNING: [TRT]: Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
0:00:02.566085372  6833 0xaaaabb9b20c0 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-infer-engine1> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1900> [UID = 1]: deserialized trt engine from :/home/nvidia/deepstream_tao_apps/models/bodypose2d/model.etlt_b32_gpu0_fp16.engine
INFO: [FullDims Engine Info]: layers num: 3
0   INPUT  kFLOAT input_1:0       288x384x3       min: 1x288x384x3     opt: 32x288x384x3    Max: 32x288x384x3    
1   OUTPUT kFLOAT heatmap_out/BiasAdd:0 36x48x19        min: 0               opt: 0               Max: 0               
2   OUTPUT kFLOAT conv2d_transpose_1/BiasAdd:0 144x192x38      min: 0               opt: 0               Max: 0               

0:00:02.719753553  6833 0xaaaabb9b20c0 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-infer-engine1> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2003> [UID = 1]: Use deserialized engine model: /home/nvidia/deepstream_tao_apps/models/bodypose2d/model.etlt_b32_gpu0_fp16.engine
0:00:02.813269621  6833 0xaaaabb9b20c0 INFO                 nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-infer-engine1> [UID 1]: Load new model:../../../configs/bodypose2d_tao/bodypose2d_pgie_config.txt sucessfully
Decodebin child added: source
Decodebin child added: decodebin0
Running...
Decodebin child added: nvjpegdec0
In cb_newpad
###Decodebin pick nvidia decoder plugin.
Segmentation fault (core dumped)
nvidia@ubuntu:~/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app

Morganh · August 23, 2022, 3:13am

As mentioned above, can you download below image and if it can run successfully?
$ wget https://developer-blogs.nvidia.com/wp-content/uploads/2021/06/original-image.png

AK51 · August 23, 2022, 3:27am

Hi,

I can’t find the output body2dout file… :<

But my focus is how to use a trained model in bpnet… I still stick at tao-converter. :<

nvidia@ubuntu:~/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app$ ./deepstream-bodypose2d-app 2 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt 0 0 file:///home/nvidia/Pictures/original-image.png ./body2dout
Request sink_0 pad from streammux
joint Edges 1 , 8
joint Edges 8 , 9
joint Edges 9 , 10
joint Edges 1 , 11
joint Edges 11 , 12
joint Edges 12 , 13
joint Edges 1 , 2
joint Edges 2 , 3
joint Edges 3 , 4
joint Edges 2 , 16
joint Edges 1 , 5
joint Edges 5 , 6
joint Edges 6 , 7
joint Edges 5 , 17
joint Edges 1 , 0
joint Edges 0 , 14
joint Edges 0 , 15
joint Edges 14 , 16
joint Edges 15 , 17
connections 0 , 1
connections 1 , 2
connections 1 , 5
connections 2 , 3
connections 3 , 4
connections 5 , 6
connections 6 , 7
connections 2 , 8
connections 8 , 9
connections 9 , 10
connections 5 , 11
connections 11 , 12
connections 12 , 13
connections 0 , 14
connections 14 , 16
connections 8 , 11
connections 15 , 17
connections 0 , 15
Now playing: file:///home/nvidia/Pictures/original-image.png
WARNING: [TRT]: Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
0:00:02.631460108  8003 0xaaaad61d20c0 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-infer-engine1> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1900> [UID = 1]: deserialized trt engine from :/home/nvidia/deepstream_tao_apps/models/bodypose2d/model.etlt_b32_gpu0_fp16.engine
INFO: [FullDims Engine Info]: layers num: 3
0   INPUT  kFLOAT input_1:0       288x384x3       min: 1x288x384x3     opt: 32x288x384x3    Max: 32x288x384x3    
1   OUTPUT kFLOAT heatmap_out/BiasAdd:0 36x48x19        min: 0               opt: 0               Max: 0               
2   OUTPUT kFLOAT conv2d_transpose_1/BiasAdd:0 144x192x38      min: 0               opt: 0               Max: 0               

0:00:02.791108509  8003 0xaaaad61d20c0 INFO                 nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger:<primary-infer-engine1> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2003> [UID = 1]: Use deserialized engine model: /home/nvidia/deepstream_tao_apps/models/bodypose2d/model.etlt_b32_gpu0_fp16.engine
0:00:03.024526111  8003 0xaaaad61d20c0 INFO                 nvinfer gstnvinfer_impl.cpp:328:notifyLoadModelStatus:<primary-infer-engine1> [UID 1]: Load new model:../../../configs/bodypose2d_tao/bodypose2d_pgie_config.txt sucessfully
Decodebin child added: source
Decodebin child added: decodebin0
Running...
Decodebin child added: pngparse0
Decodebin child added: pngdec0

(deepstream-bodypose2d-app:8003): GStreamer-WARNING **: 11:24:28.567: Name 'source_vidconv' is not unique in bin 'source-bin-00', not adding
In cb_newpad
###Decodebin did not pick nvidia decoder plugin.
Frame Number = 0 Person Count = 3
End of stream
Returned, stopping playback
Average fps 0.000233
Totally 3 persons are inferred
Deleting pipeline
nvidia@ubuntu:~/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app$

AK51 · August 23, 2022, 3:38am

Hi,

Here is where I am stuck at tao-converter
I keep using the same key. What may be the problem? Thx

nvidia@ubuntu:~/Downloads/files$ docker login nvcr.io
Username: $oauthtoken
Password: 
WARNING! Your password will be stored unencrypted in /home/nvidia/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Login Succeeded

Then, I went to that json and found another key…

nvidia@ubuntu:~/Downloads/files$ ./tao-converter -k JG9hdXRodG9rZW46TjJoa1lXZHpkV2hsWVRkeWNHRTViRGMwTURGcmRHMHdZM1E2TldJNVlUSTVNakF0Tm1Gall5MDBZVEl5TFRrMFpHRXRaalJsTmpVM1xxxxxxxxxx(Hidden)  bodyposenet_deployable_v1.0.1/model.etlt
[INFO] [MemUsageChange] Init CUDA: CPU +303, GPU +0, now: CPU 327, GPU 9635 (MiB)
[INFO] [MemUsageChange] Init builder kernel library: CPU +403, GPU +378, now: CPU 749, GPU 10033 (MiB)
[INFO] ----------------------------------------------------------------
[INFO] Input filename:   /tmp/filevMHXE5
[INFO] ONNX IR version:  0.0.0
[INFO] Opset version:    0
[INFO] Producer name:    
[INFO] Producer version: 
[INFO] Domain:           
[INFO] Model version:    0
[INFO] Doc string:       
[INFO] ----------------------------------------------------------------
[INFO] Model has no dynamic shape.
[ERROR] 4: [network.cpp::validate::2722] Error Code 4: Internal Error (Network must have at least one output)
[ERROR] Unable to create engine
Segmentation fault (core dumped)
nvidia@ubuntu:~/Downloads/files$

model.etlt (64.1 MB)

AK51 · August 23, 2022, 3:57am

Hi,

It is kind of working I guess, Still waiting…

nvidia@ubuntu:~/Downloads/files$ # Set dimensions of desired output model for inference/deployment
nvidia@ubuntu:~/Downloads/files$ INPUT_SHAPE=288x384x3
nvidia@ubuntu:~/Downloads/files$ # Set input name
nvidia@ubuntu:~/Downloads/files$ INPUT_NAME=input_1:0
nvidia@ubuntu:~/Downloads/files$ # Set opt profile shapes
nvidia@ubuntu:~/Downloads/files$ MAX_BATCH_SIZE=1
nvidia@ubuntu:~/Downloads/files$ OPT_BATCH_SIZE=1
nvidia@ubuntu:~/Downloads/files$ 
nvidia@ubuntu:~/Downloads/files$ # Convert to TensorRT engine (FP16).
nvidia@ubuntu:~/Downloads/files$ tao converter /home/nvidia/Downloads/files/bodyposenet_deployable_v1.0.1/model.etlt
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
~/.tao_mounts.json wasn't found. Falling back to obtain mount points and docker configs from ~/.tlt_mounts.json.
Please note that this will be deprecated going forward.
2022-08-23 11:55:25,149 [INFO] root: Registry: ['nvcr.io']
2022-08-23 11:55:25,242 [INFO] tlt.components.instance_handler.local_instance: Running command in container: nvcr.io/nvidia/tao/tao-toolkit-tf:v3.22.05-tf1.15.5-py3
2022-08-23 11:55:25,245 [INFO] tlt.components.docker_handler.docker_handler: The required docker doesn't exist locally/the manifest has changed. Pulling a new docker.
2022-08-23 11:55:25,245 [INFO] tlt.components.docker_handler.docker_handler: Pulling the required container. This may take several minutes if you're doing this for the first time. Please wait here.
...
Pulling from repository: nvcr.io/nvidia/tao/tao-toolkit-tf

Morganh · August 23, 2022, 4:13am

Can you find body2dout.jpg in the folder?

AK51 · August 23, 2022, 4:16am

Hi,
I search the whole disk.
I found an empty one.
And I did change the name to body2dout1, cannot find it neither…

nvidia@ubuntu:~/deepstream_tao_apps/apps/tao_others/deepstream-bodypose2d-app$ ls -l
total 35144
-rw-rw-r-- 1 nvidia nvidia        0 Aug  3 17:03 body2dout.jpg
-rw-rw-r-- 1 nvidia nvidia      712 Aug  3 15:59 bodypose2d_app_config.yml
-rw-rw-r-- 1 nvidia nvidia     6569 Aug  3 15:59 bodypose2d_pipeline.png
-rwxrwxr-x 1 nvidia nvidia   425936 Aug  3 16:28 deepstream-bodypose2d-app
-rw-rw-r-- 1 nvidia nvidia    46293 Aug  3 15:59 deepstream_bodypose2d_app.cpp
-rw-rw-r-- 1 nvidia nvidia   744320 Aug  3 16:28 deepstream_bodypose2d_app.o
-rw-rw-r-- 1 nvidia nvidia     4129 Aug  3 15:59 deepstream_bodypose2d_meta.cpp
-rw-rw-r-- 1 nvidia nvidia    62184 Aug  3 16:28 deepstream_bodypose2d_meta.o
-rw-rw-r-- 1 nvidia nvidia     1982 Aug  3 15:59 ds_bodypose2d_meta.h
-rw-rw-r-- 1 nvidia nvidia   425528 Aug  3 16:28 ds_yml_parse.o
-rw-rw-r-- 1 nvidia nvidia     3029 Aug  3 15:59 Makefile
-rw-rw-r-- 1 nvidia nvidia     4008 Aug  3 15:59 README.md
-rw-r--r-- 1 root   root   12180381 Aug 22 11:38 test.264
-rw-r--r-- 1 root   root   22060864 Aug 22 13:09 test.mp4

AK51 · August 23, 2022, 4:19am

For the tao-converter, still has error…
I have added KEY=my own key before

nvidia@ubuntu:~/Downloads/files$ # Set dimensions of desired output model for inference/deployment
nvidia@ubuntu:~/Downloads/files$ INPUT_SHAPE=288x384x3
nvidia@ubuntu:~/Downloads/files$ # Set input name
nvidia@ubuntu:~/Downloads/files$ INPUT_NAME=input_1:0
nvidia@ubuntu:~/Downloads/files$ # Set opt profile shapes
nvidia@ubuntu:~/Downloads/files$ MAX_BATCH_SIZE=1
nvidia@ubuntu:~/Downloads/files$ OPT_BATCH_SIZE=1
nvidia@ubuntu:~/Downloads/files$ 
nvidia@ubuntu:~/Downloads/files$ # Convert to TensorRT engine (FP16).
nvidia@ubuntu:~/Downloads/files$ tao converter /workspace/tao-experiments/bpnet/models/exp_m1_final/bpnet_model.deploy.etlt -k $KEY -t fp16 -e /workspace/tao-experiments/bpnet/models/exp_m1_final/bpnet_model.$IN_HEIGHT.$IN_WIDTH.fp16.deploy.engine -p ${INPUT_NAME},1x$INPUT_SHAPE,${OPT_BATCH_SIZE}x$INPUT_SHAPE,${MAX_BATCH_SIZE}x$INPUT_SHAPE
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
~/.tao_mounts.json wasn't found. Falling back to obtain mount points and docker configs from ~/.tlt_mounts.json.
Please note that this will be deprecated going forward.
2022-08-23 12:18:41,400 [INFO] root: Registry: ['nvcr.io']
2022-08-23 12:18:41,486 [INFO] tlt.components.instance_handler.local_instance: Running command in container: nvcr.io/nvidia/tao/tao-toolkit-tf:v3.22.05-tf1.15.5-py3
2022-08-23 12:18:41,496 [INFO] root: No mount points were found in the /home/nvidia/.tlt_mounts.json file.
2022-08-23 12:18:41,496 [WARNING] tlt.components.docker_handler.docker_handler: 
Docker will run the commands as root. If you would like to retain your
local host permissions, please add the "user":"UID:GID" in the
DockerOptions portion of the "/home/nvidia/.tlt_mounts.json" file. You can obtain your
users UID and GID by using the "id -u" and "id -g" commands on the
terminal.
Error response from daemon: Container 30459d860c80fd8976cbd3bb551385b1d9ddca99022a5fd651dd03ea49a98f95 is not running
2022-08-23 12:18:42,269 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.
Traceback (most recent call last):
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/api/client.py", line 259, in _raise_for_status
    response.raise_for_status()
  File "/usr/lib/python3/dist-packages/requests/models.py", line 940, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.41/containers/30459d860c80fd8976cbd3bb551385b1d9ddca99022a5fd651dd03ea49a98f95/stop

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/nvidia/.local/bin/tao", line 8, in <module>
    sys.exit(main())
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/entrypoint/entrypoint.py", line 113, in main
    local_instance.launch_command(
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/components/instance_handler/local_instance.py", line 319, in launch_command
    docker_handler.run_container(command)
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/components/docker_handler/docker_handler.py", line 316, in run_container
    self.stop_container()
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/components/docker_handler/docker_handler.py", line 323, in stop_container
    self._container.stop()
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/models/containers.py", line 436, in stop
    return self.client.api.stop(self.id, **kwargs)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/utils/decorators.py", line 19, in wrapped
    return f(self, resource_id, *args, **kwargs)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/api/container.py", line 1167, in stop
    self._raise_for_status(res)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/api/client.py", line 261, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.NotFound: 404 Client Error: Not Found ("No such container: 30459d860c80fd8976cbd3bb551385b1d9ddca99022a5fd651dd03ea49a98f95")
nvidia@ubuntu:~/Downloads/files$

Morganh · August 23, 2022, 4:24am

For tao-converter , please use below.

tao-converter /tao_models/bpnet_model/bpnet.etlt \
	-k nvidia_tlt \
    -p input_1:0,1x288x384x3,4x288x384x3,16x288x384x3 \
	-t fp16 \
	-m 16 \
	-e trt.engine

AK51 · August 23, 2022, 4:27am

Hi,

Still error…

nvidia@ubuntu:~/Downloads/files$ tao converter /home/nvidia/Downloads/files/bodyposenet_deployable_v1.0.1/bpnet_model.deploy.etlt -k nvidia_tlt     -p input_1:0,1x288x384x3,4x288x384x3,16x288x384x3 -t fp16 -m 16 -e trt.engine
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
~/.tao_mounts.json wasn't found. Falling back to obtain mount points and docker configs from ~/.tlt_mounts.json.
Please note that this will be deprecated going forward.
2022-08-23 12:26:45,634 [INFO] root: Registry: ['nvcr.io']
2022-08-23 12:26:45,721 [INFO] tlt.components.instance_handler.local_instance: Running command in container: nvcr.io/nvidia/tao/tao-toolkit-tf:v3.22.05-tf1.15.5-py3
2022-08-23 12:26:45,740 [INFO] root: No mount points were found in the /home/nvidia/.tlt_mounts.json file.
2022-08-23 12:26:45,740 [WARNING] tlt.components.docker_handler.docker_handler: 
Docker will run the commands as root. If you would like to retain your
local host permissions, please add the "user":"UID:GID" in the
DockerOptions portion of the "/home/nvidia/.tlt_mounts.json" file. You can obtain your
users UID and GID by using the "id -u" and "id -g" commands on the
terminal.
Error response from daemon: Container 27f76784a36be87ef687dcc60628ef011fc19be3a139cf2d69b180441c0d9a8b is not running
2022-08-23 12:26:46,622 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.
Traceback (most recent call last):
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/api/client.py", line 259, in _raise_for_status
    response.raise_for_status()
  File "/usr/lib/python3/dist-packages/requests/models.py", line 940, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.41/containers/27f76784a36be87ef687dcc60628ef011fc19be3a139cf2d69b180441c0d9a8b/stop

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/nvidia/.local/bin/tao", line 8, in <module>
    sys.exit(main())
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/entrypoint/entrypoint.py", line 113, in main
    local_instance.launch_command(
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/components/instance_handler/local_instance.py", line 319, in launch_command
    docker_handler.run_container(command)
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/components/docker_handler/docker_handler.py", line 316, in run_container
    self.stop_container()
  File "/home/nvidia/.local/lib/python3.8/site-packages/tlt/components/docker_handler/docker_handler.py", line 323, in stop_container
    self._container.stop()
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/models/containers.py", line 436, in stop
    return self.client.api.stop(self.id, **kwargs)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/utils/decorators.py", line 19, in wrapped
    return f(self, resource_id, *args, **kwargs)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/api/container.py", line 1167, in stop
    self._raise_for_status(res)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/api/client.py", line 261, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/home/nvidia/.local/lib/python3.8/site-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.NotFound: 404 Client Error: Not Found ("No such container: 27f76784a36be87ef687dcc60628ef011fc19be3a139cf2d69b180441c0d9a8b")
nvidia@ubuntu:~/Downloads/files$