TDA2EXEVM: converted SSD model too slow

Lee...

Part Number: TDA2EXEVM

Hi:

I convert a TIDL SSD model using the deploy.prototxa and caffemodel from "github.com/tidsp/caffe-jacinto-models/tree/caffe-0.16/trained/object_detection/voc0712/JDetNet/ssd512x512_ds_PSP_dsFac_32_fc_0_hdDS8_1_kerMbox_3_1stHdSameOpCh_1/sparse";

and I change the "keep_top_k: 200" to "keep_top_k:20" in the deploy.prototxt.

but the model converted runs very slow on TDA2x platform:I run the "f: TIDL usecase"、"1:TIDL File I/O Usecase" 、“2： EVE”、“p: Print Statistics”，the time result is very slow,as below:

[IPU1-0]     70.164551 s: ### CPU [ EVE1], LinkID [ 49],
[IPU1-0]     70.164612 s:
[IPU1-0]     70.164673 s: [ ALG_TIDL ] Link Statistics,
[IPU1-0]     70.165192 s: ******************************
[IPU1-0]     70.165283 s:
[IPU1-0]     70.165314 s: Elapsed time       = 10549 msec
[IPU1-0]     70.165588 s:
[IPU1-0]     70.165649 s: New data Recv      =   0.47 fps
[IPU1-0]     70.165710 s:
[IPU1-0]     70.165771 s: Input Statistics,
[IPU1-0]     70.165832 s:
[IPU1-0]     70.165893 s: CH | In Recv | In Drop | In User Drop | In Process
[IPU1-0]     70.166168 s:     | FPS     | FPS     | FPS          | FPS
[IPU1-0]     70.166259 s: --------------------------------------------------
[IPU1-0]     70.166351 s:   0 |   0.28      0. 0      0. 0           0.37
[IPU1-0]     70.166473 s:
[IPU1-0]     70.166687 s: Output Statistics,
[IPU1-0]     70.166748 s:
[IPU1-0]     70.166809 s: CH | Out | Out     | Out Drop | Out User Drop
[IPU1-0]     70.166900 s:     | ID | FPS     | FPS      | FPS
[IPU1-0]     70.167144 s: ---------------------------------------------
[IPU1-0]     70.167236 s:   0 | 0      0.37     0. 0      0. 0
[IPU1-0]     70.167510 s:
[IPU1-0]     70.167571 s: [ ALG_TIDL ] LATENCY,
[IPU1-0]     70.167632 s: ********************
[IPU1-0]     70.167693 s: Local Link Latency     : Avg = 2587537 us, Min = 2488867 us, Max = 2880833 us,
[IPU1-0]     70.168029 s: Source to Link Latency : Avg = 10172909 us, Min = 10031111 us, Max = 10340694 us,
[IPU1-0]     70.168181 s:
[IPU1-0]     70.168303 s:
[IPU1-0]     70.168334 s: ### CPU [ EVE1], LinkID [ 0],

But when I run the SSD model "tidl_param_jdetNet_ssd.bin" release in "PROCESSOR_SDK_VISION_03_03_00_00\ti_components\algorithms\REL.TIDL.01.01.00.00\modules\ti_dl\test\testvecs\config\tidl_models\jdetnet",it's run quick,only need 177236 us.

And my's import file is:

# Default - 0
randParams         = 0

# 0: Caffe, 1: TensorFlow, Default - 0
modelType          = 0

# 0: Fixed quantization By tarininng Framework, 1: Dyanamic quantization by TIDL, Default - 1
quantizationStyle = 1

# quantRoundAdd/100 will be added while rounding to integer, Default - 50
quantRoundAdd      = 25

numParamBits       = 8
# 0 : 8bit Unsigned, 1 : 8bit Signed Default - 1
inElementType      = 0

inputNetFile      = "..\..\test\testvecs\config\caffe-jacinto-models\trained\image_detection\jdetNet_512x512\deploy.prototxt"
inputParamsFile    = "..\..\test\testvecs\config\caffe-jacinto-models\trained\image_detection\jdetNet_512x512\ti-jdetNet_512x512.caffemodel"
outputNetFile      = "..\..\test\testvecs\config\tidl_models\tidl_net_jdetNet_ssd_512x512.bin"
outputParamsFile   = "..\..\test\testvecs\config\tidl_models\tidl_param_jdetNet_ssd_512x512.bin"

rawSampleInData = 1
preProcType   = 4
sampleInData = "..\..\test\testvecs\input\trace_dump_0_512x512.y"
tidlStatsTool = "..\quantStatsTool\eve_test_dl_algo.out.exe"
layersGroupId = 0   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   2   1   2   1   2   1   2   1   2   1   2   1   2   1   2   1   2   1   2   2   2   2   2   2   2   0
conv2dKernelType = 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1

Am I missing something?Pls help!

Regards

over 7 years ago

0 Praveen Eppa1 over 7 years ago

TI__Genius 17580 points

Hi,

>>>> I change the "keep_top_k: 200" to "keep_top_k:20" in the deploy.prototxt.

Also change confidence_threshold to 0.15 and try?

Thanks,
Praveen

0 Lee... over 7 years ago in reply to Praveen Eppa1

Intellectual 410 points

Hi:
I change confidence_threshold to 0.15 int the deploy.prototxt,But doesn't work!It still runs very slow!

regards

0 Lee... over 7 years ago in reply to Praveen Eppa1

Intellectual 410 points

Hi:
I use the "eve_test_dl_algo.out.exe" tool,found the "tidl_param_jdetNet_ssd.bin" release in VisionSDK has only 45 layers,but converted myself has 50 layers,so Is there different in the deploy.prototxt between training and tidl model import?
first is VisionSDK's model quantstatus:

0, TIDL_DataLayer , 0, -1 , 1 , x , x , x , x , x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 320 , 768 ,
1, TIDL_BatchNormLayer , 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 320 , 768 , 1 , 3 , 320 , 768 ,
2, TIDL_ConvolutionLayer , 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 320 , 768 , 1 , 32 , 160 , 384 ,
3, TIDL_ConvolutionLayer , 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 160 , 384 , 1 , 32 , 80 , 192 ,
4, TIDL_ConvolutionLayer , 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 80 , 192 , 1 , 64 , 80 , 192 ,
5, TIDL_ConvolutionLayer , 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 80 , 192 , 1 , 64 , 40 , 96 ,
6, TIDL_ConvolutionLayer , 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 40 , 96 , 1 , 128 , 40 , 96 ,
7, TIDL_ConvolutionLayer , 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 40 , 96 , 1 , 128 , 40 , 96 ,
8, TIDL_PoolingLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 40 , 96 , 1 , 128 , 20 , 48 ,
9, TIDL_ConvolutionLayer , 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 20 , 48 , 1 , 256 , 20 , 48 ,
10, TIDL_ConvolutionLayer , 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 20 , 48 , 1 , 256 , 10 , 24 ,
11, TIDL_ConvolutionLayer , 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 10 , 24 , 1 , 512 , 10 , 24 ,
12, TIDL_ConvolutionLayer , 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 10 , 24 , 1 , 512 , 10 , 24 ,
13, TIDL_PoolingLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 10 , 24 , 1 , 512 , 5 , 12 ,
14, TIDL_PoolingLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 5 , 12 , 1 , 512 , 3 , 6 ,
15, TIDL_PoolingLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 3 , 6 , 1 , 512 , 2 , 3 ,
16, TIDL_ConvolutionLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 16 , 1 , 128 , 40 , 96 , 1 , 256 , 40 , 96 ,
17, TIDL_ConvolutionLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 17 , 1 , 512 , 10 , 24 , 1 , 256 , 10 , 24 ,
18, TIDL_ConvolutionLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 18 , 1 , 512 , 5 , 12 , 1 , 256 , 5 , 12 ,
19, TIDL_ConvolutionLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 19 , 1 , 512 , 3 , 6 , 1 , 256 , 3 , 6 ,
20, TIDL_ConvolutionLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 20 , 1 , 512 , 2 , 3 , 1 , 256 , 2 , 3 ,
21, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 21 , 1 , 256 , 40 , 96 , 1 , 16 , 40 , 96 ,
22, TIDL_FlattenLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 22 , 1 , 16 , 40 , 96 , 1 , 1 , 1 ,61440 ,
23, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 23 , 1 , 256 , 40 , 96 , 1 , 16 , 40 , 96 ,
24, TIDL_FlattenLayer , 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 16 , 40 , 96 , 1 , 1 , 1 ,61440 ,
25, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 26 , 1 , 256 , 10 , 24 , 1 , 24 , 10 , 24 ,
26, TIDL_FlattenLayer , 1, 1 , 1 , 26 , x , x , x , x , x , x , x , 27 , 1 , 24 , 10 , 24 , 1 , 1 , 1 , 5760 ,
27, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 28 , 1 , 256 , 10 , 24 , 1 , 24 , 10 , 24 ,
28, TIDL_FlattenLayer , 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 24 , 10 , 24 , 1 , 1 , 1 , 5760 ,
29, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 31 , 1 , 256 , 5 , 12 , 1 , 24 , 5 , 12 ,
30, TIDL_FlattenLayer , 1, 1 , 1 , 31 , x , x , x , x , x , x , x , 32 , 1 , 24 , 5 , 12 , 1 , 1 , 1 , 1440 ,
31, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 33 , 1 , 256 , 5 , 12 , 1 , 24 , 5 , 12 ,
32, TIDL_FlattenLayer , 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 24 , 5 , 12 , 1 , 1 , 1 , 1440 ,
33, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 36 , 1 , 256 , 3 , 6 , 1 , 24 , 3 , 6 ,
34, TIDL_FlattenLayer , 1, 1 , 1 , 36 , x , x , x , x , x , x , x , 37 , 1 , 24 , 3 , 6 , 1 , 1 , 1 , 432 ,
35, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 38 , 1 , 256 , 3 , 6 , 1 , 24 , 3 , 6 ,
36, TIDL_FlattenLayer , 1, 1 , 1 , 38 , x , x , x , x , x , x , x , 39 , 1 , 24 , 3 , 6 , 1 , 1 , 1 , 432 ,
37, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 41 , 1 , 256 , 2 , 3 , 1 , 16 , 2 , 3 ,
38, TIDL_FlattenLayer , 1, 1 , 1 , 41 , x , x , x , x , x , x , x , 42 , 1 , 16 , 2 , 3 , 1 , 1 , 1 , 96 ,
39, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 43 , 1 , 256 , 2 , 3 , 1 , 16 , 2 , 3 ,
40, TIDL_FlattenLayer , 1, 1 , 1 , 43 , x , x , x , x , x , x , x , 44 , 1 , 16 , 2 , 3 , 1 , 1 , 1 , 96 ,
41, TIDL_ConcatLayer , 1, 5 , 1 , 22 , 27 , 32 , 37 , 42 , x , x , x , 46 , 1 , 1 , 1 ,61440 , 1 , 1 , 1 ,69168 ,
42, TIDL_ConcatLayer , 1, 5 , 1 , 24 , 29 , 34 , 39 , 44 , x , x , x , 47 , 1 , 1 , 1 ,61440 , 1 , 1 , 1 ,69168 ,
43, TIDL_DetectionOutputLayer , 1, 2 , 1 , 46 , 47 , x , x , x , x , x , x , 48 , 1 , 1 , 1 ,69168 , 1 , 1 , 1 , 560 ,
44, TIDL_DataLayer , 0, 1 , -1 , 48 , x , x , x , x , x , x , x , 0 , 1 , 1 , 1 , 560 , 0 , 0 , 0 , 0 ,

then is myself model's status:
0, TIDL_DataLayer , 0, -1 , 1 , x , x , x , x , x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 320 , 768 ,
1, TIDL_BatchNormLayer , 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 320 , 768 , 1 , 3 , 320 , 768 ,
2, TIDL_ConvolutionLayer , 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 320 , 768 , 1 , 32 , 160 , 384 ,
3, TIDL_ConvolutionLayer , 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 160 , 384 , 1 , 32 , 80 , 192 ,
4, TIDL_ConvolutionLayer , 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 80 , 192 , 1 , 64 , 80 , 192 ,
5, TIDL_ConvolutionLayer , 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 80 , 192 , 1 , 64 , 40 , 96 ,
6, TIDL_ConvolutionLayer , 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 40 , 96 , 1 , 128 , 40 , 96 ,
7, TIDL_ConvolutionLayer , 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 40 , 96 , 1 , 128 , 40 , 96 ,
8, TIDL_PoolingLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 40 , 96 , 1 , 128 , 20 , 48 ,
9, TIDL_ConvolutionLayer , 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 20 , 48 , 1 , 256 , 20 , 48 ,
10, TIDL_ConvolutionLayer , 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 20 , 48 , 1 , 256 , 10 , 24 ,
11, TIDL_ConvolutionLayer , 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 10 , 24 , 1 , 512 , 10 , 24 ,
12, TIDL_ConvolutionLayer , 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 10 , 24 , 1 , 512 , 10 , 24 ,
13, TIDL_PoolingLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 10 , 24 , 1 , 512 , 5 , 12 ,
14, TIDL_PoolingLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 5 , 12 , 1 , 512 , 3 , 6 ,
15, TIDL_PoolingLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 3 , 6 , 1 , 512 , 2 , 3 ,
16, TIDL_PoolingLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 16 , 1 , 512 , 2 , 3 , 1 , 512 , 1 , 2 ,
17, TIDL_ConvolutionLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 17 , 1 , 128 , 40 , 96 , 1 , 256 , 40 , 96 ,
18, TIDL_ConvolutionLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 18 , 1 , 512 , 10 , 24 , 1 , 256 , 10 , 24 ,
19, TIDL_ConvolutionLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 19 , 1 , 512 , 5 , 12 , 1 , 256 , 5 , 12 ,
20, TIDL_ConvolutionLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 20 , 1 , 512 , 3 , 6 , 1 , 256 , 3 , 6 ,
21, TIDL_ConvolutionLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 21 , 1 , 512 , 2 , 3 , 1 , 256 , 2 , 3 ,
22, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 22 , 1 , 512 , 1 , 2 , 1 , 256 , 1 , 2 ,
23, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 23 , 1 , 256 , 40 , 96 , 1 , 16 , 40 , 96 ,
24, TIDL_FlattenLayer , 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 16 , 40 , 96 , 1 , 1 , 1 ,61440 ,
25, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 25 , 1 , 256 , 40 , 96 , 1 , 16 , 40 , 96 ,
26, TIDL_FlattenLayer , 1, 1 , 1 , 25 , x , x , x , x , x , x , x , 26 , 1 , 16 , 40 , 96 , 1 , 1 , 1 ,61440 ,
27, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 28 , 1 , 256 , 10 , 24 , 1 , 24 , 10 , 24 ,
28, TIDL_FlattenLayer , 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 24 , 10 , 24 , 1 , 1 , 1 , 5760 ,
29, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 30 , 1 , 256 , 10 , 24 , 1 , 24 , 10 , 24 ,
30, TIDL_FlattenLayer , 1, 1 , 1 , 30 , x , x , x , x , x , x , x , 31 , 1 , 24 , 10 , 24 , 1 , 1 , 1 , 5760 ,
31, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 33 , 1 , 256 , 5 , 12 , 1 , 24 , 5 , 12 ,
32, TIDL_FlattenLayer , 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 24 , 5 , 12 , 1 , 1 , 1 , 1440 ,
33, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 35 , 1 , 256 , 5 , 12 , 1 , 24 , 5 , 12 ,
34, TIDL_FlattenLayer , 1, 1 , 1 , 35 , x , x , x , x , x , x , x , 36 , 1 , 24 , 5 , 12 , 1 , 1 , 1 , 1440 ,
35, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 38 , 1 , 256 , 3 , 6 , 1 , 24 , 3 , 6 ,
36, TIDL_FlattenLayer , 1, 1 , 1 , 38 , x , x , x , x , x , x , x , 39 , 1 , 24 , 3 , 6 , 1 , 1 , 1 , 432 ,
37, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 40 , 1 , 256 , 3 , 6 , 1 , 24 , 3 , 6 ,
38, TIDL_FlattenLayer , 1, 1 , 1 , 40 , x , x , x , x , x , x , x , 41 , 1 , 24 , 3 , 6 , 1 , 1 , 1 , 432 ,
39, TIDL_ConvolutionLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 43 , 1 , 256 , 2 , 3 , 1 , 16 , 2 , 3 ,
40, TIDL_FlattenLayer , 1, 1 , 1 , 43 , x , x , x , x , x , x , x , 44 , 1 , 16 , 2 , 3 , 1 , 1 , 1 , 96 ,
41, TIDL_ConvolutionLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 45 , 1 , 256 , 2 , 3 , 1 , 16 , 2 , 3 ,
42, TIDL_FlattenLayer , 1, 1 , 1 , 45 , x , x , x , x , x , x , x , 46 , 1 , 16 , 2 , 3 , 1 , 1 , 1 , 96 ,
43, TIDL_ConvolutionLayer , 1, 1 , 1 , 22 , x , x , x , x , x , x , x , 48 , 1 , 256 , 1 , 2 , 1 , 16 , 1 , 2 ,
44, TIDL_FlattenLayer , 1, 1 , 1 , 48 , x , x , x , x , x , x , x , 49 , 1 , 16 , 1 , 2 , 1 , 1 , 1 , 32 ,
45, TIDL_ConvolutionLayer , 1, 1 , 1 , 22 , x , x , x , x , x , x , x , 50 , 1 , 256 , 1 , 2 , 1 , 16 , 1 , 2 ,
46, TIDL_FlattenLayer , 1, 1 , 1 , 50 , x , x , x , x , x , x , x , 51 , 1 , 16 , 1 , 2 , 1 , 1 , 1 , 32 ,
47, TIDL_ConcatLayer , 1, 6 , 1 , 24 , 29 , 34 , 39 , 44 , 49 , x , x , 53 , 1 , 1 , 1 ,61440 , 1 , 1 , 1 ,69200 ,
48, TIDL_ConcatLayer , 1, 6 , 1 , 26 , 31 , 36 , 41 , 46 , 51 , x , x , 54 , 1 , 1 , 1 ,61440 , 1 , 1 , 1 ,69200 ,
49, TIDL_DetectionOutputLayer , 1, 2 , 1 , 53 , 54 , x , x , x , x , x , x , 55 , 1 , 1 , 1 ,69200 , 1 , 1 , 1 , 560 ,
50, TIDL_DataLayer , 0, 1 , -1 , 55 , x , x , x , x , x , x , x , 0 , 1 , 1 , 1 , 560 , 0 , 0 , 0 , 0 ,

0 Praveen Eppa1 over 7 years ago in reply to Lee...

TI__Genius 17580 points

Hi,

The difference is "tidl_param_jdetNet_ssd.bin" release in VisionSDK has only 5 heads and what ever you imported deploy.prototxt had 6 heads for detection, so please update your import file (need to add "layersGroupId" and "conv2dKernelType" values for extra layers) with below and profile now.

layersGroupId = 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 2 2 2 0

conv2dKernelType = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Thanks,
Praveen

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TDA2EXEVM: converted SSD model too slow