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Ticket Name: TDA2: ssd model import problem
Query Text:
Part Number: TDA2 Hi All, I have already confirmed that it works well on TDA2p EVM as below when running tidl usecase using the already converted model bin(NET_OD/PRM_OD) -. bbox : ok, display fps : ok(16fps) However, I confirmed that the bin(NET_OD, PRM_OD) changed through tild_model_import.out does not work as it did before. -. bbox: no, display fps : fail(8.4fps) Below is the problem log and import file file. Can you help me with what is wrong? modelImportIssue.zip My development environment is as follows. VSDK : 3.3.0.0 TIDLSRC: 1.1.1.0 bootmode ; SDBOOT EVM: tda2p protobuf ver : 3.2.0rc2 model: JDetNet Thank you in advance. BR, Khethan
Responses:
Hi Khethan, Can you please check with new vision SDK release version 03.05.00.00, if you still face the issue please share the generated NET.bin and PARAM.bin from import tool for verification. Thanks, Praveen
Hi all I am sorry, It's take a long time to write back. I downloaded the version(3.5.0.0) you mentioned, but which is does not have a TIDL. The stats_tool_out.bin file created using my import tool has been confirmed to work normally as below. please refer my environment file. I would appreciate any help on what is wrong. modelImportIssue_2.zip BR, Khethan
Hi Khethan, I did not see any issue with your import tool files, because I just now imported and verified that it is detecting objects properly. I used the import executable (tidl_model_import.out.exe) from latest TIDL 01.01.02.00 release. Can you please check with latest TIDL release? Thanks, Praveen
Hi Praveen, I've just checked with the new version(1.1.2.0) you mentioned, but I still have problems. please refer my log file. import_tidl_1_1_2_0_log.txt Microsoft Windows [Version 6.1.7601]
Copyright (c) 2009 Microsoft Corporation. All rights reserved.
c:\PROCESSOR_SDK_VISION_03_03_00_00\ti_components\algorithms\REL.TIDLSRC.01.01.02.00\modules\ti_dl\utils\tidlModel
Import>tidl_model_import.out.exe c:\PROCESSOR_SDK_VISION_03_03_00_00\ti_components\algorithms\REL.TIDLSRC.01.01.02
.00\modules\ti_dl\test\testvecs\config\import\tidl_import_JDetNet.txt
Caffe Network File : d:\work\adas\TI\work_space\tidl\model\caffe_jacinto_models\trained\object_detection\voc0712\J
DetNet\ssd768x320_ds_PSP_dsFac_32_hdDS8_0\sparse\deploy.prototxt
Caffe Model File : d:\work\adas\TI\work_space\tidl\model\caffe_jacinto_models\trained\object_detection\voc0712\J
DetNet\ssd768x320_ds_PSP_dsFac_32_hdDS8_0\sparse\voc0712_ssdJacintoNetV2_iter_120000.caffemodel
TIDL Network File : ..\..\test\testvecs\config\tidl_models\jdetnet\tidl_net_jdetNet_ssd.bin
TIDL Model File : ..\..\test\testvecs\config\tidl_models\jdetnet\tidl_param_jdetNet_ssd.bin
Name of the Network : ssdJacintoNetV2_deploy
Num Inputs : 1
Could not find detection_out Params
Num of Layer Detected : 50
0, TIDL_DataLayer , data 0, -1 , 1 , x , x , x , x ,
x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 320 , 768 , 0
,
1, TIDL_BatchNormLayer , data/bias 1, 1 , 1 , 0 , x , x , x ,
x , x , x , x , 1 , 1 , 3 , 320 , 768 , 1 , 3 , 320 , 768 , 737280
,
2, TIDL_ConvolutionLayer , conv1a 1, 1 , 1 , 1 , x , x , x ,
x , x , x , x , 2 , 1 , 3 , 320 , 768 , 1 , 32 , 160 , 384 , 147456000
,
3, TIDL_ConvolutionLayer , conv1b 1, 1 , 1 , 2 , x , x , x ,
x , x , x , x , 3 , 1 , 32 , 160 , 384 , 1 , 32 , 80 , 192 , 141557760
,
4, TIDL_ConvolutionLayer , res2a_branch2a 1, 1 , 1 , 3 , x , x , x ,
x , x , x , x , 4 , 1 , 32 , 80 , 192 , 1 , 64 , 80 , 192 , 283115520
,
5, TIDL_ConvolutionLayer , res2a_branch2b 1, 1 , 1 , 4 , x , x , x ,
x , x , x , x , 5 , 1 , 64 , 80 , 192 , 1 , 64 , 40 , 96 , 141557760
,
6, TIDL_ConvolutionLayer , res3a_branch2a 1, 1 , 1 , 5 , x , x , x ,
x , x , x , x , 6 , 1 , 64 , 40 , 96 , 1 , 128 , 40 , 96 , 283115520
,
7, TIDL_ConvolutionLayer , res3a_branch2b 1, 1 , 1 , 6 , x , x , x ,
x , x , x , x , 7 , 1 , 128 , 40 , 96 , 1 , 128 , 20 , 48 , 141557760
,
8, TIDL_ConvolutionLayer , res4a_branch2a 1, 1 , 1 , 7 , x , x , x ,
x , x , x , x , 8 , 1 , 128 , 20 , 48 , 1 , 256 , 20 , 48 , 283115520
,
9, TIDL_ConvolutionLayer , res4a_branch2b 1, 1 , 1 , 8 , x , x , x ,
x , x , x , x , 9 , 1 , 256 , 20 , 48 , 1 , 256 , 20 , 48 , 141557760
,
10, TIDL_PoolingLayer , pool4 1, 1 , 1 , 9 , x , x , x ,
x , x , x , x , 10 , 1 , 256 , 20 , 48 , 1 , 256 , 10 , 24 , 245760
,
11, TIDL_ConvolutionLayer , res5a_branch2a 1, 1 , 1 , 10 , x , x , x ,
x , x , x , x , 11 , 1 , 256 , 10 , 24 , 1 , 512 , 10 , 24 , 283115520
,
12, TIDL_ConvolutionLayer , res5a_branch2b 1, 1 , 1 , 11 , x , x , x ,
x , x , x , x , 12 , 1 , 512 , 10 , 24 , 1 , 512 , 10 , 24 , 141557760
,
13, TIDL_PoolingLayer , pool6 1, 1 , 1 , 12 , x , x , x ,
x , x , x , x , 13 , 1 , 512 , 10 , 24 , 1 , 512 , 5 , 12 , 122880
,
14, TIDL_PoolingLayer , pool7 1, 1 , 1 , 13 , x , x , x ,
x , x , x , x , 14 , 1 , 512 , 5 , 12 , 1 , 512 , 3 , 6 , 36864
,
15, TIDL_PoolingLayer , pool8 1, 1 , 1 , 14 , x , x , x ,
x , x , x , x , 15 , 1 , 512 , 3 , 6 , 1 , 512 , 2 , 3 , 12288
,
16, TIDL_ConvolutionLayer , ctx_output1 1, 1 , 1 , 9 , x , x , x ,
x , x , x , x , 16 , 1 , 256 , 20 , 48 , 1 , 256 , 20 , 48 , 62914560
,
17, TIDL_ConvolutionLayer , ctx_output2 1, 1 , 1 , 12 , x , x , x ,
x , x , x , x , 17 , 1 , 512 , 10 , 24 , 1 , 256 , 10 , 24 , 31457280
,
18, TIDL_ConvolutionLayer , ctx_output3 1, 1 , 1 , 13 , x , x , x ,
x , x , x , x , 18 , 1 , 512 , 5 , 12 , 1 , 256 , 5 , 12 , 7864320
,
19, TIDL_ConvolutionLayer , ctx_output4 1, 1 , 1 , 14 , x , x , x ,
x , x , x , x , 19 , 1 , 512 , 3 , 6 , 1 , 256 , 3 , 6 , 2359296
,
20, TIDL_ConvolutionLayer , ctx_output5 1, 1 , 1 , 15 , x , x , x ,
x , x , x , x , 20 , 1 , 512 , 2 , 3 , 1 , 256 , 2 , 3 , 786432
,
21, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_loc 1, 1 , 1 , 16 , x , x , x ,
x , x , x , x , 21 , 1 , 256 , 20 , 48 , 1 , 16 , 20 , 48 , 3932160
,
22, TIDL_FlattenLayer , ctx_output1/relu_mbox_loc_perm 1, 1 , 1 , 21 , x , x , x ,
x , x , x , x , 22 , 1 , 16 , 20 , 48 , 1 , 1 , 1 , 15360 , 1
,
23, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_conf 1, 1 , 1 , 16 , x , x , x ,
x , x , x , x , 23 , 1 , 256 , 20 , 48 , 1 , 84 , 20 , 48 , 20643840
,
24, TIDL_FlattenLayer , ctx_output1/relu_mbox_conf_perm 1, 1 , 1 , 23 , x , x , x ,
x , x , x , x , 24 , 1 , 84 , 20 , 48 , 1 , 1 , 1 , 80640 , 1
,
26, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_loc 1, 1 , 1 , 17 , x , x , x ,
x , x , x , x , 26 , 1 , 256 , 10 , 24 , 1 , 24 , 10 , 24 , 1474560
,
27, TIDL_FlattenLayer , ctx_output2/relu_mbox_loc_perm 1, 1 , 1 , 26 , x , x , x ,
x , x , x , x , 27 , 1 , 24 , 10 , 24 , 1 , 1 , 1 , 5760 , 1
,
28, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_conf 1, 1 , 1 , 17 , x , x , x ,
x , x , x , x , 28 , 1 , 256 , 10 , 24 , 1 , 126 , 10 , 24 , 7741440
,
29, TIDL_FlattenLayer , ctx_output2/relu_mbox_conf_perm 1, 1 , 1 , 28 , x , x , x ,
x , x , x , x , 29 , 1 , 126 , 10 , 24 , 1 , 1 , 1 , 30240 , 1
,
31, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_loc 1, 1 , 1 , 18 , x , x , x ,
x , x , x , x , 31 , 1 , 256 , 5 , 12 , 1 , 24 , 5 , 12 , 368640
,
32, TIDL_FlattenLayer , ctx_output3/relu_mbox_loc_perm 1, 1 , 1 , 31 , x , x , x ,
x , x , x , x , 32 , 1 , 24 , 5 , 12 , 1 , 1 , 1 , 1440 , 1
,
33, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_conf 1, 1 , 1 , 18 , x , x , x ,
x , x , x , x , 33 , 1 , 256 , 5 , 12 , 1 , 126 , 5 , 12 , 1935360
,
34, TIDL_FlattenLayer , ctx_output3/relu_mbox_conf_perm 1, 1 , 1 , 33 , x , x , x ,
x , x , x , x , 34 , 1 , 126 , 5 , 12 , 1 , 1 , 1 , 7560 , 1
,
36, TIDL_ConvolutionLayer , ctx_output4/relu_mbox_loc 1, 1 , 1 , 19 , x , x , x ,
x , x , x , x , 36 , 1 , 256 , 3 , 6 , 1 , 24 , 3 , 6 , 110592
,
37, TIDL_FlattenLayer , ctx_output4/relu_mbox_loc_perm 1, 1 , 1 , 36 , x , x , x ,
x , x , x , x , 37 , 1 , 24 , 3 , 6 , 1 , 1 , 1 , 432 , 1
,
38, TIDL_ConvolutionLayer , ctx_output4/relu_mbox_conf 1, 1 , 1 , 19 , x , x , x ,
x , x , x , x , 38 , 1 , 256 , 3 , 6 , 1 , 126 , 3 , 6 , 580608
,
39, TIDL_FlattenLayer , ctx_output4/relu_mbox_conf_perm 1, 1 , 1 , 38 , x , x , x ,
x , x , x , x , 39 , 1 , 126 , 3 , 6 , 1 , 1 , 1 , 2268 , 1
,
41, TIDL_ConvolutionLayer , ctx_output5/relu_mbox_loc 1, 1 , 1 , 20 , x , x , x ,
x , x , x , x , 41 , 1 , 256 , 2 , 3 , 1 , 16 , 2 , 3 , 24576
,
42, TIDL_FlattenLayer , ctx_output5/relu_mbox_loc_perm 1, 1 , 1 , 41 , x , x , x ,
x , x , x , x , 42 , 1 , 16 , 2 , 3 , 1 , 1 , 1 , 96 , 1
,
43, TIDL_ConvolutionLayer , ctx_output5/relu_mbox_conf 1, 1 , 1 , 20 , x , x , x ,
x , x , x , x , 43 , 1 , 256 , 2 , 3 , 1 , 84 , 2 , 3 , 129024
,
44, TIDL_FlattenLayer , ctx_output5/relu_mbox_conf_perm 1, 1 , 1 , 43 , x , x , x ,
x , x , x , x , 44 , 1 , 84 , 2 , 3 , 1 , 1 , 1 , 504 , 1
,
46, TIDL_ConcatLayer , mbox_loc 1, 5 , 1 , 22 , 27 , 32 , 37 ,
42 , x , x , x , 46 , 1 , 1 , 1 , 15360 , 1 , 1 , 1 , 23088 , 1
,
47, TIDL_ConcatLayer , mbox_conf 1, 5 , 1 , 24 , 29 , 34 , 39 ,
44 , x , x , x , 47 , 1 , 1 , 1 , 80640 , 1 , 1 , 1 , 121212 , 1
,
49, TIDL_DetectionOutputLayer , detection_out 2, 2 , 1 , 46 , 47 , x , x ,
x , x , x , x , 49 , 1 , 1 , 1 , 23088 , 1 , 1 , 1 , 560 , 1
,
Total Giga Macs : 2.1312
1개 파일이 복사되었습니다.
Processing config file .\tempDir\qunat_stats_config.txt !
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 , 32
0 , 768 , 1 , 3 , 320 , 768 ,
2, TIDL_ConvolutionLayer , 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 32
0 , 768 , 1 , 32 , 160 , 384 ,
3, TIDL_ConvolutionLayer , 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 16
0 , 384 , 1 , 32 , 80 , 192 ,
4, TIDL_ConvolutionLayer , 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 8
0 , 192 , 1 , 64 , 80 , 192 ,
5, TIDL_ConvolutionLayer , 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 8
0 , 192 , 1 , 64 , 40 , 96 ,
6, TIDL_ConvolutionLayer , 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 4
0 , 96 , 1 , 128 , 40 , 96 ,
7, TIDL_ConvolutionLayer , 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 4
0 , 96 , 1 , 128 , 20 , 48 ,
8, TIDL_ConvolutionLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 2
0 , 48 , 1 , 256 , 20 , 48 ,
9, TIDL_ConvolutionLayer , 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 256 , 2
0 , 48 , 1 , 256 , 20 , 48 ,
10, TIDL_PoolingLayer , 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 2
0 , 48 , 1 , 256 , 10 , 24 ,
11, TIDL_ConvolutionLayer , 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 1
0 , 24 , 1 , 512 , 10 , 24 ,
12, TIDL_ConvolutionLayer , 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 1
0 , 24 , 1 , 512 , 10 , 24 ,
13, TIDL_PoolingLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 1
0 , 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 , 9 , x , x , x , x , x , x , x , 16 , 1 , 256 , 2
0 , 48 , 1 , 256 , 20 , 48 ,
17, TIDL_ConvolutionLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 17 , 1 , 512 , 1
0 , 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 , 2
0 , 48 , 1 , 16 , 20 , 48 ,
22, TIDL_FlattenLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 22 , 1 , 16 , 2
0 , 48 , 1 , 1 , 1 ,15360 ,
23, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 23 , 1 , 256 , 2
0 , 48 , 1 , 84 , 20 , 48 ,
24, TIDL_FlattenLayer , 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 84 , 2
0 , 48 , 1 , 1 , 1 ,80640 ,
25, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 26 , 1 , 256 , 1
0 , 24 , 1 , 24 , 10 , 24 ,
26, TIDL_FlattenLayer , 1, 1 , 1 , 26 , x , x , x , x , x , x , x , 27 , 1 , 24 , 1
0 , 24 , 1 , 1 , 1 , 5760 ,
27, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 28 , 1 , 256 , 1
0 , 24 , 1 , 126 , 10 , 24 ,
28, TIDL_FlattenLayer , 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 126 , 1
0 , 24 , 1 , 1 , 1 ,30240 ,
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 , 126 , 5 , 12 ,
32, TIDL_FlattenLayer , 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 126 ,
5 , 12 , 1 , 1 , 1 , 7560 ,
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 , 126 , 3 , 6 ,
36, TIDL_FlattenLayer , 1, 1 , 1 , 38 , x , x , x , x , x , x , x , 39 , 1 , 126 ,
3 , 6 , 1 , 1 , 1 , 2268 ,
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 , 84 , 2 , 3 ,
40, TIDL_FlattenLayer , 1, 1 , 1 , 43 , x , x , x , x , x , x , x , 44 , 1 , 84 ,
2 , 3 , 1 , 1 , 1 , 504 ,
41, TIDL_ConcatLayer , 1, 5 , 1 , 22 , 27 , 32 , 37 , 42 , x , x , x , 46 , 1 , 1 ,
1 ,15360 , 1 , 1 , 1 ,23088 ,
42, TIDL_ConcatLayer , 1, 5 , 1 , 24 , 29 , 34 , 39 , 44 , x , x , x , 47 , 1 , 1 ,
1 ,80640 , 1 , 1 , 1 ,121212 ,
43, TIDL_DetectionOutputLayer , 1, 2 , 1 , 46 , 47 , x , x , x , x , x , x , 49 , 1 , 1 ,
1 ,23088 , 1 , 1 , 1 , 560 ,
44, TIDL_DataLayer , 0, 1 , -1 , 49 , x , x , x , x , x , x , x , 0 , 1 , 1 ,
1 , 560 , 0 , 0 , 0 , 0 ,
Layer ID ,inBlkWidth ,inBlkHeight ,inBlkPitch ,outBlkWidth ,outBlkHeight,outBlkPitch ,numInChs ,numOutChs
,numProcInChs,numLclInChs ,numLclOutChs,numProcItrs ,numAccItrs ,numHorBlock ,numVerBlock ,inBlkChPitch,outBlkC
hPitc,alignOrNot
2 72 72 72 32 32 32 3 32
3 1 8 1 3 12 5 5184 1024
1
3 40 34 40 32 32 32 8 8
8 4 8 1 2 12 5 1360 1024
1
4 40 22 40 32 20 32 32 64
32 8 8 1 4 6 4 880 640
1
5 40 22 40 32 20 32 16 16
16 8 8 1 2 6 4 880 640
1
6 40 22 40 32 20 32 64 128
64 8 8 1 8 3 2 880 640
1
7 40 22 40 32 20 32 32 32
32 8 8 1 4 3 2 880 640
1
8 56 22 56 48 20 48 128 256
128 7 8 1 19 1 1 1232 960
1
9 56 22 56 48 20 48 64 64
64 7 8 1 10 1 1 1232 960
1
11 40 12 40 32 10 32 256 512
256 8 8 1 32 1 1 480 320
1
12 40 12 40 32 10 32 128 128
128 8 8 1 16 1 1 480 320
1
16 48 4 48 48 4 48 256 256
256 32 8 1 8 1 5 192 192
1
17 24 10 24 24 10 24 512 256
512 32 32 1 16 1 1 240 240
1
18 12 5 12 12 5 12 512 256
512 32 32 1 16 1 1 60 60
1
19 6 3 6 6 3 6 512 256
512 32 32 1 16 1 1 18 18
1
20 3 2 3 3 2 3 512 256
512 32 32 1 16 1 1 6 6
1
21 48 4 48 48 4 48 256 16
256 32 8 1 8 1 5 192 192
1
23 48 4 48 48 4 48 256 88
256 32 8 1 8 1 5 192 192
1
25 24 10 24 24 10 24 256 24
256 32 24 1 8 1 1 240 240
1
27 24 10 24 24 10 24 256 128
256 32 32 1 8 1 1 240 240
1
29 12 5 12 12 5 12 256 24
256 32 24 1 8 1 1 60 60
1
31 12 5 12 12 5 12 256 128
256 32 32 1 8 1 1 60 60
1
33 6 3 6 6 3 6 256 24
256 32 24 1 8 1 1 18 18
1
35 6 3 6 6 3 6 256 128
256 32 32 1 8 1 1 18 18
1
37 3 2 3 3 2 3 256 16
256 32 16 1 8 1 1 6 6
1
39 3 2 3 3 2 3 256 96
256 32 32 1 8 1 1 6 6
1
Processing Frame Number : 0
Layer 1 : Out Q : 254 , TIDL_BatchNormLayer , PASSED #MMACs = 0.74, 0.74, Sparsity : 0.00
Layer 2 : Out Q : 4787 , TIDL_ConvolutionLayer, PASSED #MMACs = 147.46, 92.65, Sparsity : 37.17
Layer 3 : Out Q : 4230 , TIDL_ConvolutionLayer, PASSED #MMACs = 141.56, 53.33, Sparsity : 62.33
Layer 4 : Out Q : 7280 , TIDL_ConvolutionLayer, PASSED #MMACs = 283.12, 83.44, Sparsity : 70.53
Layer 5 : Out Q : 10223 , TIDL_ConvolutionLayer, PASSED #MMACs = 141.56, 66.11, Sparsity : 53.30
Layer 6 : Out Q : 8988 , TIDL_ConvolutionLayer, PASSED #MMACs = 283.12, 91.59, Sparsity : 67.65
Layer 7 : Out Q : 10923 , TIDL_ConvolutionLayer, PASSED #MMACs = 141.56, 57.32, Sparsity : 59.51
Layer 8 : Out Q : 20852 , TIDL_ConvolutionLayer, PASSED #MMACs = 283.12, 96.27, Sparsity : 66.00
Layer 9 : Out Q : 18101 , TIDL_ConvolutionLayer, PASSED #MMACs = 141.56, 52.28, Sparsity : 63.07
Layer 10 :TIDL_PoolingLayer, PASSED #MMACs = 0.06, 0.06, Sparsity : 0.00
Layer 11 : Out Q : 27171 , TIDL_ConvolutionLayer, PASSED #MMACs = 283.12, 76.31, Sparsity : 73.04
Layer 12 : Out Q : 5405 , TIDL_ConvolutionLayer, PASSED #MMACs = 141.56, 31.40, Sparsity : 77.82
Layer 13 :TIDL_PoolingLayer, PASSED #MMACs = 0.03, 0.03, Sparsity : 0.00
Layer 14 :TIDL_PoolingLayer, PASSED #MMACs = 0.01, 0.01, Sparsity : 0.00
Layer 15 :TIDL_PoolingLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 16 : Out Q : 15924 , TIDL_ConvolutionLayer, PASSED #MMACs = 62.91, 62.91, Sparsity : 0.00
Layer 17 : Out Q : 10177 , TIDL_ConvolutionLayer, PASSED #MMACs = 31.46, 31.46, Sparsity : 0.00
Layer 18 : Out Q : 14028 , TIDL_ConvolutionLayer, PASSED #MMACs = 7.86, 7.86, Sparsity : 0.00
Layer 19 : Out Q : 17569 , TIDL_ConvolutionLayer, PASSED #MMACs = 2.36, 2.36, Sparsity : 0.00
Layer 20 : Out Q : 26121 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.79, 0.79, Sparsity : 0.00
Layer 21 : Out Q : 4366 , TIDL_ConvolutionLayer, PASSED #MMACs = 3.93, 3.93, Sparsity : 0.00
Layer 22 :TIDL_FlattenLayer, PASSED #MMACs = 0.02, 0.02, Sparsity : 0.00
Layer 23 : Out Q : 3862 , TIDL_ConvolutionLayer, PASSED #MMACs = 21.63, 21.63, Sparsity : 0.00
Layer 24 :TIDL_FlattenLayer, PASSED #MMACs = 0.08, 0.08, Sparsity : 0.00
Layer 25 : Out Q : 5460 , TIDL_ConvolutionLayer, PASSED #MMACs = 1.47, 1.47, Sparsity : 0.00
Layer 26 :TIDL_FlattenLayer, PASSED #MMACs = 0.01, 0.01, Sparsity : 0.00
Layer 27 : Out Q : 2597 , TIDL_ConvolutionLayer, PASSED #MMACs = 7.86, 7.86, Sparsity : 0.00
Layer 28 :TIDL_FlattenLayer, PASSED #MMACs = 0.03, 0.03, Sparsity : 0.00
Layer 29 : Out Q : 6983 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.37, 0.37, Sparsity : 0.00
Layer 30 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 31 : Out Q : 2508 , TIDL_ConvolutionLayer, PASSED #MMACs = 1.97, 1.97, Sparsity : 0.00
Layer 32 :TIDL_FlattenLayer, PASSED #MMACs = 0.01, 0.01, Sparsity : 0.00
Layer 33 : Out Q : 9470 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.11, 0.11, Sparsity : 0.00
Layer 34 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 35 : Out Q : 3264 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.59, 0.59, Sparsity : 0.00
Layer 36 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 37 : Out Q : 8417 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.02, 0.02, Sparsity : 0.00
Layer 38 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 39 : Out Q : 3940 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.15, 0.15, Sparsity : 0.00
Layer 40 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 41 : Out Q : 4383 , TIDL_ConcatLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : -1.#J
Layer 42 : Out Q : 2518 , TIDL_ConcatLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : -1.#J
Layer 43 : #MMACs = 0.00, 0.00, Sparsity : 0.00
End of config list found !
c:\PROCESSOR_SDK_VISION_03_03_00_00\ti_components\algorithms\REL.TIDLSRC.01.01.02.00\modules\ti_dl\utils\tidlModel
Import> What else should I check? Thank you for your help. BR, Khethan
Hi Khethan, Can you share your output ? Thanks, Praveen
Hi Praveen, Here I attach the output for the input. tempDir.zip BR, Khethan
Hi Praveen, I have tested on TDA2P EVM, but the output data is initialized as below attachment files. evm_output.zip This phenomenon seems to be the same in PC simulation mode. As mentioned earlier, if I use eve_test_dl_algo.out.exe, which is included in the package by default, the output is very good. The TIDL build was referenced in the TIDeepLearningLibrary_UserGuide.pdf document. BR, Khethan
Hi Praveen, I tried to compile eve and dsp again with TIDL_SRC(1.1.2), and confirmed that the generated file (dsp_test_dl_algo.out, eve_test_dl_algo.out) works fine on EVM through CCS as output file(stats_tool_out.bin). However, when I execute the same model file with "tidl od usecase" in EVM(sd_boot), the video is played but the bbox is not displayed. please refer my data. eve_data_log.zip I would appreciate your advice on what went wrong. BR, Khethan
Hi Khethan, Were you able get TIDL Usecase working as is from release package? Thanks, Praveen
Hi Praveen, The package(3.3.0.0) which i use current was not supported TIDL usecase for TDA2P, but I confirmed that it works well after modifying the chain etc with reference to TDA2X. Regarding above issue, It was related to layersGroupId valule in tidl_import_JDetNex.txt. When I values just change as below regardless of version, the problem was solved. before: 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 after: 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 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 But there was another problem. 1. When tidl usecase is executed, bbox is not displayed from the beginning and box is displayed from about 20sec of video. 2. When video(inData and inHeader) are replaced with mine, frame is played at 1 ~ 2fps slowly, resulting in frame reversal. the Problem is not reproduced when using ti demo clip. I made this clip using the program(ffmpeg&ffprobe&sizeBin) and It seem like to have no problem. BR, Khethan
Hi Praveen #1 I have reduced the time to 6sec by adjusting parameters( quantHistoryParam1 / 2 and quantMargin) as below. 20, 5, 0 -> 40, 40, 40 Is there any problem using this value? How can we reduce the lead time further? I have read for the doc(tidl user guide) for parameter but I do not understand it enough. could you please explain about the parameter in detail(unit.etc) #2 The attached log file does not seem to be a problem. revers_sym_log.txt Referencing TIDeepLearningLibrary_UserGuide.pdf 3.7 (Input and Output Data Formats), clip format is below. right? width: 760(video)+4(MAX_PAD), height; 312(video)+4(MAX_PAD) If it is not related to it, What should I do ? BR, Khethan
I am sorry, The clip size should be modified as follows due to my mistake width: 760(video)+2*4(MAX_PAD), height; 312(video)+2*4(MAX_PAD) BR, Khethan
Hi Khethan, #1 The lead time can not be reduced by changing these quant parameters, these parameters are used to improve the accuracy, I explained these parameters in detail below, In TIDL, we use the current computation of min and max to update the quantization parameters for next frame. We don’t directly use it in the next frame, we gradually update. This quantHistoryParam1 / 2 and quantMargin values are used to control, how fast we need update the quantization parameter. If quantHistoryParam1 / 2 are higher than the update will happen faster. QuantMargin controls the margin that would want for max to grow. #2 Yes, this padding is correct Thanks, Praveen
Hi Praveen, My issue was solved Thank you for your support. BR, Khethan
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