Tool/software:
Hi,
I trained a custom JDetNet model using the training scripts provided in caffe-jacinto on a subset of the FLIR-ADAS dataset but it runs slower than expected.
Specifically, I am comparing two model imports:
First is the already trained model provided under caffe-jacinto-models/trained/object_detection/voc0712/JDetNet/ssd512x512_ds_PSP_dsFac_32_fc_0_hdDS8_1_kerMbox_3_1stHdSameOpCh_1/sparse/voc0712_ssdJacintoNetV2_iter_104000.caffemodel
Second model is the one I trained on custom dataset for 94000 iterations.
First model takes about 1-3 second to execute per frame but my model takes 5 seconds per frame. Also this stat didn't even change during the 94000 sparse iterations. Even when I use a model snapshot before the sparse training it still executes in similar time so i suspect i must be doing something wrong while importing.
I am providing the tidl_model_import.out outputs and my config.txt
=============================== TIDL import - parsing ===============================
Caffe Network File : deploy.prototxt
Caffe Model File : weights.caffemodel
TIDL Network File : ./out/tidl_net_jdetNet_ssd_512x512.bin
TIDL Model File : ./out/tidl_param_jdetNet_ssd_512x512.bin
Name of the Network : ssdJacintoNetV2_deploy
Num Inputs : 1
Error in DetectionOutput layer: could not find parameters for detection_out!
Num of Layer Detected : 57
0, TIDL_DataLayer , data 0, -1 , 1 , x , x , x , x , x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 512 , 512 , 0 ,
1, TIDL_BatchNormLayer , data/bias 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 512 , 512 , 1 , 3 , 512 , 512 , 786432 ,
2, TIDL_ConvolutionLayer , conv1a 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 512 , 512 , 1 , 32 , 256 , 256 , 157286400 ,
3, TIDL_ConvolutionLayer , conv1b 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 256 , 256 , 1 , 32 , 128 , 128 , 150994944 ,
4, TIDL_ConvolutionLayer , res2a_branch2a 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 128 , 128 , 1 , 64 , 128 , 128 , 301989888 ,
5, TIDL_ConvolutionLayer , res2a_branch2b 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 128 , 128 , 1 , 64 , 64 , 64 , 150994944 ,
6, TIDL_ConvolutionLayer , res3a_branch2a 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 64 , 64 , 1 , 128 , 64 , 64 , 301989888 ,
7, TIDL_ConvolutionLayer , res3a_branch2b 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 64 , 64 , 1 , 128 , 64 , 64 , 150994944 ,
8, TIDL_PoolingLayer , pool3 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 64 , 64 , 1 , 128 , 32 , 32 , 524288 ,
9, TIDL_ConvolutionLayer , res4a_branch2a 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 32 , 32 , 1 , 256 , 32 , 32 , 301989888 ,
10, TIDL_ConvolutionLayer , res4a_branch2b 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 32 , 32 , 1 , 256 , 16 , 16 , 150994944 ,
11, TIDL_ConvolutionLayer , res5a_branch2a 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 16 , 16 , 1 , 512 , 16 , 16 , 301989888 ,
12, TIDL_ConvolutionLayer , res5a_branch2b 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 16 , 16 , 1 , 512 , 16 , 16 , 150994944 ,
13, TIDL_PoolingLayer , pool6 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 16 , 16 , 1 , 512 , 8 , 8 , 131072 ,
14, TIDL_PoolingLayer , pool7 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 8 , 8 , 1 , 512 , 4 , 4 , 32768 ,
15, TIDL_PoolingLayer , pool8 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 4 , 4 , 1 , 512 , 2 , 2 , 8192 ,
16, TIDL_PoolingLayer , pool9 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 16 , 1 , 512 , 2 , 2 , 1 , 512 , 1 , 1 , 2048 ,
17, TIDL_ConvolutionLayer , ctx_output1 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 17 , 1 , 128 , 64 , 64 , 1 , 256 , 64 , 64 , 134217728 ,
18, TIDL_ConvolutionLayer , ctx_output2 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 18 , 1 , 512 , 16 , 16 , 1 , 256 , 16 , 16 , 33554432 ,
19, TIDL_ConvolutionLayer , ctx_output3 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 19 , 1 , 512 , 8 , 8 , 1 , 256 , 8 , 8 , 8388608 ,
20, TIDL_ConvolutionLayer , ctx_output4 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 20 , 1 , 512 , 4 , 4 , 1 , 256 , 4 , 4 , 2097152 ,
21, TIDL_ConvolutionLayer , ctx_output5 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 21 , 1 , 512 , 2 , 2 , 1 , 256 , 2 , 2 , 524288 ,
22, TIDL_ConvolutionLayer , ctx_output6 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 22 , 1 , 512 , 1 , 1 , 1 , 256 , 1 , 1 , 131072 ,
23, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_loc 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 23 , 1 , 256 , 64 , 64 , 1 , 16 , 64 , 64 , 150994944 ,
24, TIDL_FlattenLayer , ctx_output1/relu_mbox_loc_perm 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 16 , 64 , 64 , 1 , 1 , 1 , 65536 , 1 ,
25, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_conf 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 25 , 1 , 256 , 64 , 64 , 1 , 12 , 64 , 64 , 113246208 ,
26, TIDL_FlattenLayer , ctx_output1/relu_mbox_conf_perm 1, 1 , 1 , 25 , x , x , x , x , x , x , x , 26 , 1 , 12 , 64 , 64 , 1 , 1 , 1 , 49152 , 1 ,
28, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_loc 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 28 , 1 , 256 , 16 , 16 , 1 , 24 , 16 , 16 , 14155776 ,
29, TIDL_FlattenLayer , ctx_output2/relu_mbox_loc_perm 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 24 , 16 , 16 , 1 , 1 , 1 , 6144 , 1 ,
30, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_conf 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 30 , 1 , 256 , 16 , 16 , 1 , 18 , 16 , 16 , 10616832 ,
31, TIDL_FlattenLayer , ctx_output2/relu_mbox_conf_perm 1, 1 , 1 , 30 , x , x , x , x , x , x , x , 31 , 1 , 18 , 16 , 16 , 1 , 1 , 1 , 4608 , 1 ,
33, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_loc 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 33 , 1 , 256 , 8 , 8 , 1 , 24 , 8 , 8 , 3538944 ,
34, TIDL_FlattenLayer , ctx_output3/relu_mbox_loc_perm 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 24 , 8 , 8 , 1 , 1 , 1 , 1536 , 1 ,
35, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_conf 1, 1 , 1
Processing config file ./tempDir/qunat_stats_config.txt !
Running TIDL simulation for calibration.
0, TIDL_DataLayer , 0, -1 , 1 , x , x , x , x , x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 512 , 512 ,
1, TIDL_BatchNormLayer , 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 512 , 512 , 1 , 3 , 512 , 512 ,
2, TIDL_ConvolutionLayer , 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 512 , 512 , 1 , 32 , 256 , 256 ,
3, TIDL_ConvolutionLayer , 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 256 , 256 , 1 , 32 , 128 , 128 ,
4, TIDL_ConvolutionLayer , 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 128 , 128 , 1 , 64 , 128 , 128 ,
5, TIDL_ConvolutionLayer , 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 128 , 128 , 1 , 64 , 64 , 64 ,
6, TIDL_ConvolutionLayer , 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 64 , 64 , 1 , 128 , 64 , 64 ,
7, TIDL_ConvolutionLayer , 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 64 , 64 , 1 , 128 , 64 , 64 ,
8, TIDL_PoolingLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 64 , 64 , 1 , 128 , 32 , 32 ,
9, TIDL_ConvolutionLayer , 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 32 , 32 , 1 , 256 , 32 , 32 ,
10, TIDL_ConvolutionLayer , 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 32 , 32 , 1 , 256 , 16 , 16 ,
11, TIDL_ConvolutionLayer , 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 16 , 16 , 1 , 512 , 16 , 16 ,
12, TIDL_ConvolutionLayer , 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 16 , 16 , 1 , 512 , 16 , 16 ,
13, TIDL_PoolingLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 16 , 16 , 1 , 512 , 8 , 8 ,
14, TIDL_PoolingLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 8 , 8 , 1 , 512 , 4 , 4 ,
15, TIDL_PoolingLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 4 , 4 , 1 , 512 , 2 , 2 ,
16, TIDL_PoolingLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 16 , 1 , 512 , 2 , 2 , 1 , 512 , 1 , 1 ,
17, TIDL_ConvolutionLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 17 , 1 , 128 , 64 , 64 , 1 , 256 , 64 , 64 ,
18, TIDL_ConvolutionLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 18 , 1 , 512 , 16 , 16 , 1 , 256 , 16 , 16 ,
19, TIDL_ConvolutionLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 19 , 1 , 512 , 8 , 8 , 1 , 256 , 8 , 8 ,
20, TIDL_ConvolutionLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 20 , 1 , 512 , 4 , 4 , 1 , 256 , 4 , 4 ,
21, TIDL_ConvolutionLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 21 , 1 , 512 , 2 , 2 , 1 , 256 , 2 , 2 ,
22, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 22 , 1 , 512 , 1 , 1 , 1 , 256 , 1 , 1 ,
23, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 23 , 1 , 256 , 64 , 64 , 1 , 16 , 64 , 64 ,
24, TIDL_FlattenLayer , 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 16 , 64 , 64 , 1 , 1 , 1 ,65536 ,
25, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 25 , 1 , 256 , 64 , 64 , 1 , 12 , 64 , 64 ,
26, TIDL_FlattenLayer , 1, 1 , 1 , 25 , x , x , x , x , x , x , x , 26 , 1 , 12 , 64 , 64 , 1 , 1 , 1 ,49152 ,
27, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 28 , 1 , 256 , 16 , 16 , 1 , 24 , 16 , 16 ,
28, TIDL_FlattenLayer , 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 24 , 16 , 16 , 1 , 1 , 1 , 6144 ,
29, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 30 , 1 , 256 , 16 , 16 , 1 , 18 , 16 , 16 ,
30, TIDL_FlattenLayer , 1, 1 , 1 , 30 , x , x , x , x , x , x , x , 31 , 1 , 18 , 16 , 16 , 1 , 1 , 1 , 4608 ,
31, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 33 , 1 , 256 , 8 , 8 , 1 , 24 , 8 , 8 ,
32, TIDL_FlattenLayer , 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 24 , 8 , 8 , 1 , 1 , 1 , 1536 ,
33, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 35 , 1 , 256 , 8 , 8 , 1 , 18 , 8 , 8 ,
34, TIDL_FlattenLayer , 1, 1 , 1 , 35 , x , x , x , x , x , x , x , 36 , 1 , 18 , 8 , 8 , 1 , 1 , 1 , 1152 ,
35, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 38 , 1 , 256 , 4 , 4 , 1 , 24 , 4 , 4 ,
36, TIDL_FlattenLayer , 1, 1 , 1 , 38 , x , x , x , x , x , x , x , 39 , 1 , 24 , 4 , 4 , 1 , 1 , 1 , 384 ,
37, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 40 , 1 , 256 , 4 , 4 , 1 , 18 , 4 , 4 ,
38, TIDL_FlattenLayer , 1, 1 , 1 , 40 , x , x , x , x , x , x , x , 41 , 1 , 18 , 4 , 4 , 1 , 1 , 1 , 288 ,
39, TIDL_ConvolutionLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 43 , 1 , 256 , 2 , 2 , 1 , 16 , 2 , 2 ,
40, TIDL_FlattenLayer , 1, 1 , 1 , 43 , x , x , x , x , x , x , x , 44 , 1 , 16 , 2 , 2 , 1 , 1 , 1 , 64 ,
41, TIDL_ConvolutionLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 45 , 1 , 256 , 2 , 2 , 1 , 12 , 2 , 2 ,
42, TIDL_FlattenLayer , 1, 1 , 1 , 45 , x , x , x , x , x , x , x , 46 , 1 , 12 , 2 , 2 , 1 , 1 , 1 , 48 ,
43, TIDL_ConvolutionLayer , 1, 1 , 1 , 22 , x , x , x , x , x , x , x , 48 , 1 , 256 , 1 , 1 , 1 , 16 , 1 , 1 ,
44, TIDL_FlattenLayer , 1, 1 , 1 , 48 , x , x , x , x , x , x , x , 49 , 1 , 16 , 1 , 1 , 1 , 1 , 1 , 16 ,
45, TIDL_ConvolutionLayer , 1, 1 , 1 , 22 , x , x , x , x , x , x , x , 50 , 1 , 256 , 1 , 1 , 1 , 12 , 1 , 1 ,
46, TIDL_FlattenLayer , 1, 1 , 1 , 50 , x , x , x , x , x , x , x , 51 , 1 , 12 , 1 , 1 , 1 , 1 , 1 , 12 ,
47, TIDL_ConcatLayer , 1, 6 , 1 , 24 , 29 , 34 , 39 , 44 , 49 , x , x , 53 , 1 , 1 , 1 ,65536 , 1 , 1 , 1 ,73680 ,
48, TIDL_ConcatLayer , 1, 6 , 1 , 26 , 31 , 36 , 41 , 46 , 51 , x , x , 54 , 1 , 1 , 1 ,49152 , 1 , 1 , 1 ,55260 ,
49, TIDL_DetectionOutputLayer , 1, 2 , 1 , 53 , 54 , x , x , x , x , x , x , 56 , 1 , 1 , 1 ,73680 , 1 , 1 , 1 , 5600 ,
50, TIDL_DataLayer , 0, 1 , -1 , 56 , x , x , x , x , x , x , x , 0 , 1 , 1 , 1 , 5600 , 0 , 0 , 0 , 0 ,
Layer ID ,inBlkWidth ,inBlkHeight ,inBlkPitch ,outBlkWidth ,outBlkHeight,outBlkPitch ,numInChs ,numOutChs ,numProcInChs,numLclInChs ,numLclOutChs,numProcItrs ,numAccItrs ,numHorBlock ,numVerBlock ,inBlkChPitch,outBlkChPitc,alignOrNot
2 72 72 72 32 32 32 3 32 3 1 8 1 3 8 8 5184 1024 1
3 40 34 40 32 32 32 8 8 8 4 8 1 2 8 8 1360 1024 1
4 40 34 40 32 32 32 32 64 32 6 8 1 6 4 4 1360 1024 1
5 40 34 40 32 32 32 16 16 16 6 8 1 3 4 4 1360 1024 1
6 40 34 40 32 32 32 64 128 64 6 8 1 11 2 2 1360 1024 1
7 40 34 40 32 32 32 32 32 32 6 8 1 6 2 2 1360 1024 1
9 34 10 34 32 8 32 128 256 128 32 8 1 4 1 4 340 256 1
10 34 10 34 32 8 32 64 64 64 32 8 1 2 1 4 340 256 1
11 18 10 18 16 8 16 256 512 256 16 32 1 16 1 2 180 128 1
12 18 10 18 16 8 16 128 128 128 16 32 1 8 1 2 180 128 1
17 32 32 32 32 32 32 128 256 128 7 8 1 19 2 2 1024 1024 1
18 16 8 16 16 8 16 512 256 512 32 32 1 16 1 2 128 128 1
19 8 8 8 8 8 8 512 256 512 32 32 1 16 1 1 64 64 1
20 4 4 4 4 4 4 512 256 512 32 32 1 16 1 1 16 16 1
21 2 2 2 2 2 2 512 256 512 32 32 1 16 1 1 4 4 1
22 1 1 1 1 1 1 512 256 512 32 32 1 16 1 1 1 1 1
23 40 18 40 32 16 32 256 16 256 8 8 1 32 2 4 720 512 1
25 40 18 40 32 16 32 256 16 256 8 8 1 32 2 4 720 512 1
27 18 10 18 16 8 16 256 24 256 16 24 1 16 1 2 180 128 1
29 18 10 18 16 8 16 256 18 256 16 18 1 16 1 2 180 128 1
31 10 10 10 8 8 8 256 24 256 16 24 1 16 1 1 100 64 1
33 10 10 10 8 8 8 256 18 256 16 18 1 16 1 1 100 64 1
35 6 6 6 4 4 4 256 24 256 16 24 1 16 1 1 36 16 1
37 6 6 6 4 4 4 256 18 256 16 18 1 16 1 1 36 16 1
39 4 4 4 2 2 2 256 16 256 16 16 1 16 1 1 16 4 1
41 4 4 4 2 2 2 256 12 256 16 12 1 16 1 1 16 4 1
43 3 3 3 1 1 1 256 16 256 16 16 1 16 1 1 9 1 1
45 3 3 3 1 1 1 256 12 256 16 12 1 16 1 1 9 1 1
Processing Frame Number : 0
Layer 1 : Out Q : 254 , TIDL_BatchNormLayer , PASSED #MMACs = 0.79, 0.79, Sparsity : 0.00
Layer 2 : Out Q : 7443 , TIDL_ConvolutionLayer, PASSED #MMACs = 157.29, 110.10, Sparsity : 30.00
Layer 3 : Out Q : 4377 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 69.21, Sparsity : 54.17
Layer 4 : Out Q : 7337 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 131.07, Sparsity : 56.60
Layer 5 : Out Q : 6603 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 76.15, Sparsity : 49.57
Layer 6 : Out Q : 10119 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 127.98, Sparsity : 57.62
Layer 7 : Out Q : 10798 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 67.70, Sparsity : 55.16
Layer 8 :TIDL_PoolingLayer, PASSED #MMACs = 0.13, 0.13, Sparsity : 0.00
Layer 9 : Out Q : 13875 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 301.99, Sparsity : 0.00
Layer 10 : Out Q : 15857 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 150.99, Sparsity : 0.00
Layer 11 : Out Q : 16438 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 301.99, Sparsity : 0.00
Layer 12 : Out Q : 8158 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 150.99, Sparsity : 0.00
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 :TIDL_PoolingLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 17 : Out Q : 21009 , TIDL_ConvolutionLayer, PASSED #MMACs = 134.22, 154.21, Sparsity : -14.89
Layer 18 : Out Q : 20622 , TIDL_ConvolutionLayer, PASSED #MMACs = 33.55, 33.55, Sparsity : 0.00
Layer 19 : Out Q : 11750 , TIDL_ConvolutionLayer, PASSED #MMACs = 8.39, 8.39, Sparsity : 0.00
Layer 20 : Out Q : 14449 , TIDL_ConvolutionLayer, PASSED #MMACs = 2.10, 2.10, Sparsity : 0.00
Layer 21 : Out Q : 13614 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.52, 0.52, Sparsity : 0.00
Layer 22 : Out Q : 19473 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.13, 0.13, Sparsity : 0.00
Layer 23 : Out Q : 2110 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 92.73, Sparsity : 38.59
Layer 24 :TIDL_FlattenLayer, PASSED #MMACs = 0.07, 0.07, Sparsity : 0.00
Layer 25 : Out Q : 4680 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 108.30, Sparsity : 28.28
Layer 26 :TIDL_FlattenLayer, PASSED #MMACs = 0.05, 0.05, Sparsity : 0.00
Layer 27 : Out Q : 9099 , TIDL_ConvolutionLayer, PASSED #MMACs = 14.16, 14.16, Sparsity : 0.00
Layer 28 :TIDL_FlattenLayer, PASSED #MMACs = 0.01, 0.01, Sparsity : 0.00
Layer 29 : Out Q : 6283 , TIDL_ConvolutionLayer, PASSED #MMACs = 10.62, 10.62, Sparsity : 0.00
Layer 30 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 31 : Out Q : 8664 , TIDL_ConvolutionLayer, PASSED #MMACs = 3.54, 3.54, Sparsity : 0.00
Layer 32 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 33 : Out Q : 4219 , TIDL_ConvolutionLayer, PASSED #MMACs = 2.65, 2.65, Sparsity : 0.00
Layer 34 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 35 : Out Q : 7931 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.88, 0.88, Sparsity : 0.00
Layer 36 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 37 : Out Q : 5814 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.66, 0.66, Sparsity : 0.00
Layer 38 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 39 : Out Q : 9744 , 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 : 7674 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.11, 0.11, Sparsity : 0.00
Layer 42 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 43 : Out Q : 13062 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.04, 0.04, Sparsity : 0.00
Layer 44 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 45 : Out Q : 8806 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.03, 0.03, Sparsity : 0.00
Layer 46 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 47 : Out Q : 2118 , TIDL_ConcatLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : nan
Layer 48 : Out Q : 4236 , TIDL_ConcatLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : nan
Layer 49 :
Target: number label value xmin ymin xmax ymax
Target: 0.00 2.00 1.00 0.71 0.36 1.01 0.75
Target: 1.00 2.00 0.51 0.39 0.41 0.44 0.46
Target: 2.00 1.00 0.48 0.69 0.28 0.73 0.38
Target: 3.00 1.00 0.43 0.10 0.42 0.12 0.49
Target: 4.00 2.00 0.41 0.36 0.41 0.38 0.44
Target: 5.00 1.00 0.28 0.72 0.40 0.74 0.47
Target: 6.00 2.00 0.27 0.42 0.40 0.44 0.42
Target: 7.00 1.00 0.24 0.74 0.40 0.76 0.45
Target: 8.00 1.00 0.22 0.45 0.38 0.48 0.45
Target: 9.00 2.00 0.22 0.42 0.40 0.43 0.42
Target: 10.00 2.00 0.20 0.37 0.41 0.39 0.44
Target: 11.00 1.00 0.20 0.46 0.38 0.49 0.45
Target: 12.00 2.00 0.20 0.33 0.40 0.35 0.43
Target: 13.00 2.00 0.19 0.16 0.00 0.28 0.10
Target: 14.00 2.00 0.19 0.38 0.66 0.48 0.76
Target: 15.00 2.00 0.19 0.37 0.41 0.39 0.42
Target: 16.00 2.00 0.18 0.34 0.41 0.37 0.44
Target: 17.00 1.00 0.18 0.58 0.41 0.59 0.45
Target: 18.00 2.00 0.17 0.38 0.66 0.45 0.72
Target: 19.00 2.00 0.17 0.38 0.41 0.39 0.42
Target: 20.00 1.00 0.16 0.80 0.52 0.86 0.67
Target: 21.00 2.00 0.16 0.27 0.42 0.34 0.47
Target: 22.00 2.00 0.16 0.31 0.42 0.34 0.45
Target: 23.00 1.00 0.16 0.50 0.39 0.52 0.44
Target: 24.00 1.00 0.16 0.49 0.38 0.51 0.44
Target: 25.00 1.00 0.16 0.58 0.42 0.59 0.45
Target: 26.00 2.00 0.15 0.40 0.40 0.41 0.42
Target: 27.00 2.00 0.15 0.92 0.40 1.01 0.52
Target: 28.00 2.00 0.15 0.66 0.47 0.77 0.57
Target: 29.00 2.00 0.15 0.40 0.66 0.49 0.71
Target: 30.00 2.00 0.15 0.54 0.45 0.62 0.49
Target: 31.00 1.00 0.15 0.54 0.42 0.55 0.45
Target: 32.00 1.00 0.15 0.72 0.28 0.74 0.35
Target: 33.00 1.00 0.15 0.56 0.42 0.57 0.45
Target: 34.00 1.00 0.15 0.81 0.60 0.85 0.72
Target: 35.00 2.00 0.15 0.40 0.41 0.42 0.43
Target: 36.00 2.00 0.15 0.42 0.40 0.43 0.41
Target: 37.00 2.00 0.15 0.40 0.40 0.41 0.41
Target: 38.00 2.00 0.14 0.92 0.62 0.99 0.67
Target: 39.00 2.00 0.14 0.01 0.32 0.07 0.38
Target: 40.00 2.00 0.14 0.57 0.23 0.59 0.24
Target: 41.00 2.00 0.14 0.81 0.37 0.92 0.42
Target: 42.00 1.00 0.14 0.75 0.23 0.80 0.35
Target: 43.00 1.00 0.14 0.52 0.41 0.54 0.45
Target: 44.00 2.00 0.14 0.49 0.43 0.54 0.47
Target: 45.00 2.00 0.14 0.42 0.40 0.43 0.42
Target: 46.00 2.00 0.14 0.05 0.33 0.10 0.38
Target: 47.00 2.00 0.14 0.43 0.40 0.43 0.41
Target: 48.00 2.00 0.14 0.39 0.41 0.41 0.44
Target: 49.00 2.00 0.13 0.79 0.36 1.00 0.45
Target: 50.00 1.00 0.13 0.73 0.40 0.76 0.48
Target: 51.00 2.00 0.13 0.89 0.53 1.00 0.65
Target: 52.00 1.00 0.13 0.72 0.41 0.74 0.44
Target: 53.00 2.00 0.13 0.15 0.00 0.25 0.05
Target: 54.00 2.00 0.13 0.92 0.43 1.00 0.49
Target: 55.00 2.00 0.13 -0.00 0.36 0.10 0.48
Target: 56.00 2.00 0.13 0.73 0.60 0.85 0.71
Target: 57.00 2.00 0.13 0.36 0.41 0.38 0.43
Target: 58.00 1.00 0.13 0.52 0.41 0.53 0.44
Target: 59.00 2.00 0.13 0.01 0.47 0.09 0.51
Target: 60.00 2.00 0.13 0.50 0.47 0.59 0.51
Target: 61.00 2.00 0.13 0.74 0.23 0.82 0.29
Target: 62.00 2.00 0.13 0.63 0.51 0.70 0.56
Target: 63.00 2.00 0.13 0.92 0.39 1.00 0.44
Target: 64.00 2.00 0.13 0.10 0.33 0.17 0.38
Target: 65.00 2.00 0.13 0.84 0.36 0.96 0.41
Target: 66.00 2.00 0.13 0.83 0.37 0.90 0.40
Target: 67.00 2.00 0.13 0.22 0.34 0.29 0.38
Target: 68.00 1.00 0.13 0.58 0.39 0.60 0.44
Target: 69.00 2.00 0.13 0.68 0.45 0.72 0.48
Target: 70.00 1.00 0.13 0.44 0.40 0.46 0.45
Target: 71.00 2.00 0.13 0.39 0.40 0.40 0.42
Target: 72.00 1.00 0.13 0.53 0.42 0.55 0.45
Target: 73.00 2.00 0.13 0.84 0.17 0.93 0.33
Target: 74.00 2.00 0.13 0.89 0.43 1.00 0.57
Target: 75.00 2.00 0.13 0.38 0.66 0.43 0.69
Target: 76.00 2.00 0.13 0.80 0.93 0.76 0.83
Target: 77.00 2.00 0.13 0.40 0.50 0.47 0.54
Target: 78.00 2.00 0.13 0.43 0.67 0.52 0.78
Target: 79.00 2.00 0.13 0.66 0.71 0.78 0.82
Target: 80.00 2.00 0.13 0.53 0.67 0.63 0.81
Target: 81.00 2.00 0.13 0.94 0.09 1.00 0.18
Target: 82.00 1.00 0.13 0.75 0.41 0.76 0.44
Target: 83.00 2.00 0.13 0.45 0.66 0.51 0.74
Target: 84.00 2.00 0.13 0.90 0.60 1.00 0.70
Target: 85.00 2.00 0.13 0.11 -0.00 0.22 0.10
Target: 86.00 2.00 0.13 0.43 -0.00 0.51 0.03
Target: 87.00 2.00 0.13 0.82 0.17 0.90 0.30
Target: 88.00 2.00 0.13 0.37 0.50 0.44 0.53
Target: 89.00 2.00 0.13 0.41 0.48 0.49 0.52
Target: 90.00 2.00 0.13 0.41 0.50 0.48 0.52
Target: 91.00 2.00 0.13 0.93 0.78 0.83 0.83
Target: 92.00 2.00 0.13 0.41 0.65 0.50 0.69
Target: 93.00 2.00 0.13 0.16 0.47 0.24 0.51
Target: 94.00 2.00 0.13 0.20 0.46 0.26 0.49
Target: 95.00 2.00 0.13 0.25 0.43 0.33 0.48
Target: 96.00 2.00 0.13 0.77 0.27 0.86 0.39
Target: 97.00 2.00 0.13 0.85 0.22 1.00 0.34
Target: 98.00 2.00 0.13 0.61 0.66 0.71 0.78
Target: 99.00 2.00 0.13 0.55 0.46 0.68 0.54
Target: 100.00 2.00 0.13 0.62 0.51 0.73 0.62
Target: 101.00 2.00 0.13 0.41 0.50 0.43 0.51
Target: 102.00 2.00 0.13 0.33 0.41 0.35 0.44
Target: 103.00 2.00 0.13 0.92 0.00 1.00 0.12
Target: 104.00 1.00 0.13 0.54 0.41 0.55 0.44
Target: 105.00 2.00 0.13 0.58 0.22 0.60 0.23
Target: 106.00 2.00 0.13 0.19 0.46 0.23 0.49
Target: 107.00 2.00 0.13 0.35 0.41 0.37 0.44
Target: 108.00 2.00 0.13 0.86 -0.00 0.96 0.05
Target: 109.00 2.00 0.13 0.82 0.00 0.89 0.04
Target: 110.00 2.00 0.13 0.87 -0.00 1.00 0.08
Target: 111.00 2.00 0.13 0.72 0.60 0.80 0.66
Target: 112.00 2.00 0.13 0.23 0.64 0.33 0.77
Target: 113.00 2.00 0.13 0.12 0.47 0.21 0.51
Target: 114.00 2.00 0.13 0.89 0.37 1.00 0.42
Target: 115.00 2.00 0.13 0.75 0.21 0.85 0.33
Target: 116.00 2.00 0.13 0.81 0.55 0.91 0.62
Target: 117.00 2.00 0.13 0.63 0.48 0.73 0.54
Target: 118.00 2.00 0.13 0.47 0.48 0.56 0.52
Target: 119.00 2.00 0.13 0.36 0.48 0.43 0.50
Target: 120.00 2.00 0.13 0.16 0.46 0.22 0.49
Target: 121.00 2.00 0.13 0.62 0.31 0.69 0.36
Target: 122.00 1.00 0.13 0.63 0.41 0.64 0.45
Target: 123.00 2.00 0.13 0.80 0.38 0.87 0.42
Target: 124.00 2.00 0.13 0.18 0.10 0.26 0.14
Target: 125.00 1.00 0.13 0.63 0.42 0.65 0.46
Target: 126.00 1.00 0.13 0.57 0.39 0.59 0.44
Target: 127.00 2.00 0.13 0.37 0.69 0.45 0.75
Target: 128.00 2.00 0.13 0.55 0.44 0.62 0.48
Target: 129.00 2.00 0.13 0.79 0.38 0.89 0.44
Target: 130.00 2.00 0.13 0.24 0.42 0.36 0.51
Target: 131.00 1.00 0.13 0.53 0.42 0.54 0.44
Target: 132.00 2.00 0.13 -0.01 0.41 0.04 0.46
Target: 133.00 2.00 0.13 0.61 0.49 0.69 0.54
Target: 134.00 2.00 0.13 0.91 0.56 0.99 0.61
Target: 135.00 2.00 0.13 0.19 0.34 0.30 0.40
Target: 136.00 2.00 0.13 0.00 0.33 0.05 0.37
Target: 137.00 2.00 0.13 0.69 0.53 0.78 0.60
Target: 138.00 2.00 0.13 0.93 0.40 0.99 0.45
Target: 139.00 2.00 0.13 0.72 0.56 0.82 0.67
Target: 140.00 2.00 0.13 0.13 0.33 0.18 0.37
Target: 141.00 2.00 0.13 0.42 0.40 0.43 0.41
Target: 142.00 2.00 0.13 0.95 0.45 1.01 0.50
Target: 143.00 2.00 0.13 0.50 0.44 0.56 0.47
Target: 144.00 2.00 0.13 0.33 0.42 0.36 0.45
Target: 145.00 2.00 0.13 0.72 0.31 0.80 0.36
Target: 146.00 2.00 0.13 0.81 0.64 0.89 0.69
Target: 147.00 2.00 0.13 0.63 0.54 0.72 0.60
Target: 148.00 1.00 0.13 0.55 0.42 0.56 0.45
Target: 149.00 2.00 0.13 0.16 0.35 0.22 0.39
Target: 150.00 2.00 0.13 0.21 0.45 0.25 0.47
Target: 151.00 2.00 0.13 0.26 0.43 0.30 0.46
Target: 152.00 2.00 0.13 -0.01 0.30 0.04 0.34
Target: 153.00 2.00 0.13 0.06 0.32 0.13 0.38
Target: 154.00 2.00 0.12 0.27 0.43 0.31 0.46
Target: 155.00 2.00 0.12 0.41 0.41 0.42 0.42
Target: 156.00 2.00 0.12 0.38 0.40 0.39 0.42
Target: 157.00 2.00 0.12 0.18 0.05 0.27 0.16
Target: 158.00 2.00 0.12 0.38 0.41 0.39 0.43
Target: 159.00 2.00 0.12 0.25 0.42 0.30 0.46
Target: 160.00 2.00 0.12 0.89 0.67 1.00 0.79
Target: 161.00 2.00 0.12 0.00 0.52 0.06 0.60
Target: 162.00 2.00 0.12 0.05 0.96 0.01 0.87
Target: 163.00 2.00 0.12 0.54 -0.00 0.63 0.04
Target: 164.00 2.00 0.12 0.00 0.66 0.12 0.76
Target: 165.00 2.00 0.12 -0.01 0.57 0.06 0.65
Target: 166.00 2.00 0.12 0.83 0.22 0.88 0.30
Target: 167.00 2.00 0.12 0.18 0.37 0.37 0.45
Target: 168.00 2.00 0.12 0.79 0.46 0.90 0.52
Target: 169.00 2.00 0.12 0.78 0.44 0.91 0.54
Target: 170.00 2.00 0.12 0.44 0.50 0.46 0.52
Target: 171.00 2.00 0.12 0.15 0.02 0.23 0.08
Target: 172.00 1.00 0.12 0.75 0.40 0.76 0.42
Target: 173.00 1.00 0.12 0.61 0.41 0.63 0.45
Target: 174.00 1.00 0.12 0.80 0.29 0.83 0.40
Target: 175.00 1.00 0.12 0.59 0.42 0.60 0.45
Target: 176.00 1.00 0.12 0.62 0.42 0.63 0.46
Target: 177.00 1.00 0.11 0.38 0.41 0.39 0.45
Target: 178.00 1.00 0.11 0.74 0.41 0.75 0.43
Target: 179.00 1.00 0.11 0.56 0.41 0.57 0.44
Target: 180.00 1.00 0.11 0.58 0.37 0.60 0.43
Target: 181.00 1.00 0.11 0.73 0.41 0.78 0.50
Target: 182.00 1.00 0.11 0.10 0.42 0.12 0.46
Target: 183.00 1.00 0.11 0.57 0.42 0.58 0.45
Target: 184.00 1.00 0.11 0.68 0.45 0.70 0.48
Target: 185.00 1.00 0.11 0.78 0.06 0.83 0.22
Target: 186.00 1.00 0.11 0.74 0.40 0.76 0.43
Target: 187.00 1.00 0.11 0.73 0.40 0.74 0.42
Target: 188.00 1.00 0.11 0.09 0.43 0.11 0.47
Target: 189.00 1.00 0.11 0.70 0.49 0.75 0.66
Target: 190.00 1.00 0.11 0.80 0.03 0.84 0.19
Target: 191.00 1.00 0.11 0.72 0.43 0.74 0.47
Target: 192.00 1.00 0.11 0.75 0.40 0.76 0.43
Target: 193.00 1.00 0.11 0.81 0.30 0.83 0.40
Target: 194.00 1.00 0.11 0.75 0.40 0.79 0.47
Target: 195.00 1.00 0.11 0.50 0.37 0.52 0.41
Target: 196.00 1.00 0.11 0.65 0.42 0.66 0.46
Target: 197.00 1.00 0.11 0.73 0.26 0.76 0.35
Target: 198.00 1.00 0.11 0.74 0.24 0.78 0.35
Target: 199.00 1.00 0.10 0.72 0.41 0.73 0.44
#MMACs = 0.01, 0.01, Sparsity : 0.00
End of config list found !
# 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 = "deploy.prototxt" inputParamsFile = "weights.caffemodel" outputNetFile = "./out/tidl_net_jdetNet_ssd_512x512.bin" outputParamsFile = "./out/tidl_param_jdetNet_ssd_512x512.bin" rawSampleInData = 0 preProcType = 4 sampleInData = "./frame.png" tidlStatsTool = "eve_test_dl_algo_ref.out" 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 1 1 1 2 0
=============================== TIDL import - parsing ===============================
Caffe Network File : deploy.prototxt
Caffe Model File : weights.caffemodel
TIDL Network File : ./out/tidl_net_jdetNet_ssd_512x512.bin
TIDL Model File : ./out/tidl_param_jdetNet_ssd_512x512.bin
Name of the Network : ssdJacintoNetV2_deploy
Num Inputs : 1
Error in DetectionOutput layer: could not find parameters for detection_out!
Num of Layer Detected : 57
0, TIDL_DataLayer , data 0, -1 , 1 , x , x , x , x , x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 512 , 512 , 0 ,
1, TIDL_BatchNormLayer , data/bias 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 512 , 512 , 1 , 3 , 512 , 512 , 786432 ,
2, TIDL_ConvolutionLayer , conv1a 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 512 , 512 , 1 , 32 , 256 , 256 , 157286400 ,
3, TIDL_ConvolutionLayer , conv1b 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 256 , 256 , 1 , 32 , 128 , 128 , 150994944 ,
4, TIDL_ConvolutionLayer , res2a_branch2a 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 128 , 128 , 1 , 64 , 128 , 128 , 301989888 ,
5, TIDL_ConvolutionLayer , res2a_branch2b 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 128 , 128 , 1 , 64 , 64 , 64 , 150994944 ,
6, TIDL_ConvolutionLayer , res3a_branch2a 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 64 , 64 , 1 , 128 , 64 , 64 , 301989888 ,
7, TIDL_ConvolutionLayer , res3a_branch2b 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 64 , 64 , 1 , 128 , 64 , 64 , 150994944 ,
8, TIDL_PoolingLayer , pool3 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 64 , 64 , 1 , 128 , 32 , 32 , 524288 ,
9, TIDL_ConvolutionLayer , res4a_branch2a 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 32 , 32 , 1 , 256 , 32 , 32 , 301989888 ,
10, TIDL_ConvolutionLayer , res4a_branch2b 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 32 , 32 , 1 , 256 , 16 , 16 , 150994944 ,
11, TIDL_ConvolutionLayer , res5a_branch2a 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 16 , 16 , 1 , 512 , 16 , 16 , 301989888 ,
12, TIDL_ConvolutionLayer , res5a_branch2b 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 16 , 16 , 1 , 512 , 16 , 16 , 150994944 ,
13, TIDL_PoolingLayer , pool6 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 16 , 16 , 1 , 512 , 8 , 8 , 131072 ,
14, TIDL_PoolingLayer , pool7 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 8 , 8 , 1 , 512 , 4 , 4 , 32768 ,
15, TIDL_PoolingLayer , pool8 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 4 , 4 , 1 , 512 , 2 , 2 , 8192 ,
16, TIDL_PoolingLayer , pool9 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 16 , 1 , 512 , 2 , 2 , 1 , 512 , 1 , 1 , 2048 ,
17, TIDL_ConvolutionLayer , ctx_output1 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 17 , 1 , 128 , 64 , 64 , 1 , 256 , 64 , 64 , 134217728 ,
18, TIDL_ConvolutionLayer , ctx_output2 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 18 , 1 , 512 , 16 , 16 , 1 , 256 , 16 , 16 , 33554432 ,
19, TIDL_ConvolutionLayer , ctx_output3 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 19 , 1 , 512 , 8 , 8 , 1 , 256 , 8 , 8 , 8388608 ,
20, TIDL_ConvolutionLayer , ctx_output4 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 20 , 1 , 512 , 4 , 4 , 1 , 256 , 4 , 4 , 2097152 ,
21, TIDL_ConvolutionLayer , ctx_output5 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 21 , 1 , 512 , 2 , 2 , 1 , 256 , 2 , 2 , 524288 ,
22, TIDL_ConvolutionLayer , ctx_output6 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 22 , 1 , 512 , 1 , 1 , 1 , 256 , 1 , 1 , 131072 ,
23, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_loc 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 23 , 1 , 256 , 64 , 64 , 1 , 16 , 64 , 64 , 150994944 ,
24, TIDL_FlattenLayer , ctx_output1/relu_mbox_loc_perm 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 16 , 64 , 64 , 1 , 1 , 1 , 65536 , 1 ,
25, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_conf 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 25 , 1 , 256 , 64 , 64 , 1 , 84 , 64 , 64 , 792723456 ,
26, TIDL_FlattenLayer , ctx_output1/relu_mbox_conf_perm 1, 1 , 1 , 25 , x , x , x , x , x , x , x , 26 , 1 , 84 , 64 , 64 , 1 , 1 , 1 , 344064 , 1 ,
28, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_loc 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 28 , 1 , 256 , 16 , 16 , 1 , 24 , 16 , 16 , 14155776 ,
29, TIDL_FlattenLayer , ctx_output2/relu_mbox_loc_perm 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 24 , 16 , 16 , 1 , 1 , 1 , 6144 , 1 ,
30, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_conf 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 30 , 1 , 256 , 16 , 16 , 1 , 126 , 16 , 16 , 74317824 ,
31, TIDL_FlattenLayer , ctx_output2/relu_mbox_conf_perm 1, 1 , 1 , 30 , x , x , x , x , x , x , x , 31 , 1 , 126 , 16 , 16 , 1 , 1 , 1 , 32256 , 1 ,
33, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_loc 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 33 , 1 , 256 , 8 , 8 , 1 , 24 , 8 , 8 , 3538944 ,
34, TIDL_FlattenLayer , ctx_output3/relu_mbox_loc_perm 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 24 , 8 , 8 , 1 , 1 , 1 , 1536 , 1 ,
35, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_conf 1, 1 , 1
Processing config file ./tempDir/qunat_stats_config.txt !
Running TIDL simulation for calibration.
0, TIDL_DataLayer , 0, -1 , 1 , x , x , x , x , x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 512 , 512 ,
1, TIDL_BatchNormLayer , 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 512 , 512 , 1 , 3 , 512 , 512 ,
2, TIDL_ConvolutionLayer , 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 512 , 512 , 1 , 32 , 256 , 256 ,
3, TIDL_ConvolutionLayer , 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 256 , 256 , 1 , 32 , 128 , 128 ,
4, TIDL_ConvolutionLayer , 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 128 , 128 , 1 , 64 , 128 , 128 ,
5, TIDL_ConvolutionLayer , 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 128 , 128 , 1 , 64 , 64 , 64 ,
6, TIDL_ConvolutionLayer , 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 64 , 64 , 1 , 128 , 64 , 64 ,
7, TIDL_ConvolutionLayer , 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 64 , 64 , 1 , 128 , 64 , 64 ,
8, TIDL_PoolingLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 64 , 64 , 1 , 128 , 32 , 32 ,
9, TIDL_ConvolutionLayer , 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 32 , 32 , 1 , 256 , 32 , 32 ,
10, TIDL_ConvolutionLayer , 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 32 , 32 , 1 , 256 , 16 , 16 ,
11, TIDL_ConvolutionLayer , 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 16 , 16 , 1 , 512 , 16 , 16 ,
12, TIDL_ConvolutionLayer , 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 16 , 16 , 1 , 512 , 16 , 16 ,
13, TIDL_PoolingLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 16 , 16 , 1 , 512 , 8 , 8 ,
14, TIDL_PoolingLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 8 , 8 , 1 , 512 , 4 , 4 ,
15, TIDL_PoolingLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 4 , 4 , 1 , 512 , 2 , 2 ,
16, TIDL_PoolingLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 16 , 1 , 512 , 2 , 2 , 1 , 512 , 1 , 1 ,
17, TIDL_ConvolutionLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 17 , 1 , 128 , 64 , 64 , 1 , 256 , 64 , 64 ,
18, TIDL_ConvolutionLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 18 , 1 , 512 , 16 , 16 , 1 , 256 , 16 , 16 ,
19, TIDL_ConvolutionLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 19 , 1 , 512 , 8 , 8 , 1 , 256 , 8 , 8 ,
20, TIDL_ConvolutionLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 20 , 1 , 512 , 4 , 4 , 1 , 256 , 4 , 4 ,
21, TIDL_ConvolutionLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 21 , 1 , 512 , 2 , 2 , 1 , 256 , 2 , 2 ,
22, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 22 , 1 , 512 , 1 , 1 , 1 , 256 , 1 , 1 ,
23, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 23 , 1 , 256 , 64 , 64 , 1 , 16 , 64 , 64 ,
24, TIDL_FlattenLayer , 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 16 , 64 , 64 , 1 , 1 , 1 ,65536 ,
25, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 25 , 1 , 256 , 64 , 64 , 1 , 84 , 64 , 64 ,
26, TIDL_FlattenLayer , 1, 1 , 1 , 25 , x , x , x , x , x , x , x , 26 , 1 , 84 , 64 , 64 , 1 , 1 , 1 ,344064 ,
27, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 28 , 1 , 256 , 16 , 16 , 1 , 24 , 16 , 16 ,
28, TIDL_FlattenLayer , 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 24 , 16 , 16 , 1 , 1 , 1 , 6144 ,
29, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 30 , 1 , 256 , 16 , 16 , 1 , 126 , 16 , 16 ,
30, TIDL_FlattenLayer , 1, 1 , 1 , 30 , x , x , x , x , x , x , x , 31 , 1 , 126 , 16 , 16 , 1 , 1 , 1 ,32256 ,
31, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 33 , 1 , 256 , 8 , 8 , 1 , 24 , 8 , 8 ,
32, TIDL_FlattenLayer , 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 24 , 8 , 8 , 1 , 1 , 1 , 1536 ,
33, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 35 , 1 , 256 , 8 , 8 , 1 , 126 , 8 , 8 ,
34, TIDL_FlattenLayer , 1, 1 , 1 , 35 , x , x , x , x , x , x , x , 36 , 1 , 126 , 8 , 8 , 1 , 1 , 1 , 8064 ,
35, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 38 , 1 , 256 , 4 , 4 , 1 , 24 , 4 , 4 ,
36, TIDL_FlattenLayer , 1, 1 , 1 , 38 , x , x , x , x , x , x , x , 39 , 1 , 24 , 4 , 4 , 1 , 1 , 1 , 384 ,
37, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 40 , 1 , 256 , 4 , 4 , 1 , 126 , 4 , 4 ,
38, TIDL_FlattenLayer , 1, 1 , 1 , 40 , x , x , x , x , x , x , x , 41 , 1 , 126 , 4 , 4 , 1 , 1 , 1 , 2016 ,
39, TIDL_ConvolutionLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 43 , 1 , 256 , 2 , 2 , 1 , 16 , 2 , 2 ,
40, TIDL_FlattenLayer , 1, 1 , 1 , 43 , x , x , x , x , x , x , x , 44 , 1 , 16 , 2 , 2 , 1 , 1 , 1 , 64 ,
41, TIDL_ConvolutionLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 45 , 1 , 256 , 2 , 2 , 1 , 84 , 2 , 2 ,
42, TIDL_FlattenLayer , 1, 1 , 1 , 45 , x , x , x , x , x , x , x , 46 , 1 , 84 , 2 , 2 , 1 , 1 , 1 , 336 ,
43, TIDL_ConvolutionLayer , 1, 1 , 1 , 22 , x , x , x , x , x , x , x , 48 , 1 , 256 , 1 , 1 , 1 , 16 , 1 , 1 ,
44, TIDL_FlattenLayer , 1, 1 , 1 , 48 , x , x , x , x , x , x , x , 49 , 1 , 16 , 1 , 1 , 1 , 1 , 1 , 16 ,
45, TIDL_ConvolutionLayer , 1, 1 , 1 , 22 , x , x , x , x , x , x , x , 50 , 1 , 256 , 1 , 1 , 1 , 84 , 1 , 1 ,
46, TIDL_FlattenLayer , 1, 1 , 1 , 50 , x , x , x , x , x , x , x , 51 , 1 , 84 , 1 , 1 , 1 , 1 , 1 , 84 ,
47, TIDL_ConcatLayer , 1, 6 , 1 , 24 , 29 , 34 , 39 , 44 , 49 , x , x , 53 , 1 , 1 , 1 ,65536 , 1 , 1 , 1 ,73680 ,
48, TIDL_ConcatLayer , 1, 6 , 1 , 26 , 31 , 36 , 41 , 46 , 51 , x , x , 54 , 1 , 1 , 1 ,344064 , 1 , 1 , 1 ,386820 ,
49, TIDL_DetectionOutputLayer , 1, 2 , 1 , 53 , 54 , x , x , x , x , x , x , 56 , 1 , 1 , 1 ,73680 , 1 , 1 , 1 , 5600 ,
50, TIDL_DataLayer , 0, 1 , -1 , 56 , x , x , x , x , x , x , x , 0 , 1 , 1 , 1 , 5600 , 0 , 0 , 0 , 0 ,
Layer ID ,inBlkWidth ,inBlkHeight ,inBlkPitch ,outBlkWidth ,outBlkHeight,outBlkPitch ,numInChs ,numOutChs ,numProcInChs,numLclInChs ,numLclOutChs,numProcItrs ,numAccItrs ,numHorBlock ,numVerBlock ,inBlkChPitch,outBlkChPitc,alignOrNot
2 72 72 72 32 32 32 3 32 3 1 8 1 3 8 8 5184 1024 1
3 40 34 40 32 32 32 8 8 8 4 8 1 2 8 8 1360 1024 1
4 40 34 40 32 32 32 32 64 32 6 8 1 6 4 4 1360 1024 1
5 40 34 40 32 32 32 16 16 16 6 8 1 3 4 4 1360 1024 1
6 40 34 40 32 32 32 64 128 64 6 8 1 11 2 2 1360 1024 1
7 40 34 40 32 32 32 32 32 32 6 8 1 6 2 2 1360 1024 1
9 34 10 34 32 8 32 128 256 128 32 8 1 4 1 4 340 256 1
10 34 10 34 32 8 32 64 64 64 32 8 1 2 1 4 340 256 1
11 18 10 18 16 8 16 256 512 256 16 32 1 16 1 2 180 128 1
12 18 10 18 16 8 16 128 128 128 16 32 1 8 1 2 180 128 1
17 32 32 32 32 32 32 128 256 128 7 8 1 19 2 2 1024 1024 1
18 16 8 16 16 8 16 512 256 512 32 32 1 16 1 2 128 128 1
19 8 8 8 8 8 8 512 256 512 32 32 1 16 1 1 64 64 1
20 4 4 4 4 4 4 512 256 512 32 32 1 16 1 1 16 16 1
21 2 2 2 2 2 2 512 256 512 32 32 1 16 1 1 4 4 1
22 1 1 1 1 1 1 512 256 512 32 32 1 16 1 1 1 1 1
23 40 18 40 32 16 32 256 16 256 8 8 1 32 2 4 720 512 1
25 40 18 40 32 16 32 256 88 256 8 8 1 32 2 4 720 512 1
27 18 10 18 16 8 16 256 24 256 16 24 1 16 1 2 180 128 1
29 18 10 18 16 8 16 256 128 256 16 32 1 16 1 2 180 128 1
31 10 10 10 8 8 8 256 24 256 16 24 1 16 1 1 100 64 1
33 10 10 10 8 8 8 256 128 256 16 32 1 16 1 1 100 64 1
35 6 6 6 4 4 4 256 24 256 16 24 1 16 1 1 36 16 1
37 6 6 6 4 4 4 256 128 256 16 32 1 16 1 1 36 16 1
39 4 4 4 2 2 2 256 16 256 16 16 1 16 1 1 16 4 1
41 4 4 4 2 2 2 256 96 256 16 32 1 16 1 1 16 4 1
43 3 3 3 1 1 1 256 16 256 16 16 1 16 1 1 9 1 1
45 3 3 3 1 1 1 256 96 256 16 32 1 16 1 1 9 1 1
Processing Frame Number : 0
Layer 1 : Out Q : 254 , TIDL_BatchNormLayer , PASSED #MMACs = 0.79, 0.79, Sparsity : 0.00
Layer 2 : Out Q : 4549 , TIDL_ConvolutionLayer, PASSED #MMACs = 157.29, 98.83, Sparsity : 37.17
Layer 3 : Out Q : 4847 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 62.39, Sparsity : 58.68
Layer 4 : Out Q : 9980 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 92.47, Sparsity : 69.38
Layer 5 : Out Q : 10715 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 68.88, Sparsity : 54.38
Layer 6 : Out Q : 13324 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 95.81, Sparsity : 68.27
Layer 7 : Out Q : 13881 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 55.38, Sparsity : 63.32
Layer 8 :TIDL_PoolingLayer, PASSED #MMACs = 0.13, 0.13, Sparsity : 0.00
Layer 9 : Out Q : 18874 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 301.99, Sparsity : 0.00
Layer 10 : Out Q : 17236 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 150.99, Sparsity : 0.00
Layer 11 : Out Q : 22001 , TIDL_ConvolutionLayer, PASSED #MMACs = 301.99, 301.99, Sparsity : 0.00
Layer 12 : Out Q : 40120 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 150.99, Sparsity : 0.00
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 :TIDL_PoolingLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 17 : Out Q : 24360 , TIDL_ConvolutionLayer, PASSED #MMACs = 134.22, 151.63, Sparsity : -12.98
Layer 18 : Out Q : 28099 , TIDL_ConvolutionLayer, PASSED #MMACs = 33.55, 33.55, Sparsity : 0.00
Layer 19 : Out Q : 15785 , TIDL_ConvolutionLayer, PASSED #MMACs = 8.39, 8.39, Sparsity : 0.00
Layer 20 : Out Q : 24262 , TIDL_ConvolutionLayer, PASSED #MMACs = 2.10, 2.10, Sparsity : 0.00
Layer 21 : Out Q : 25753 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.52, 0.52, Sparsity : 0.00
Layer 22 : Out Q : 36535 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.13, 0.13, Sparsity : 0.00
Layer 23 : Out Q : 3367 , TIDL_ConvolutionLayer, PASSED #MMACs = 150.99, 91.31, Sparsity : 39.53
Layer 24 :TIDL_FlattenLayer, PASSED #MMACs = 0.07, 0.07, Sparsity : 0.00
Layer 25 : Out Q : 4179 , TIDL_ConvolutionLayer, PASSED #MMACs = 830.47, 213.45, Sparsity : 74.30
Layer 26 :TIDL_FlattenLayer, PASSED #MMACs = 0.34, 0.34, Sparsity : 0.00
Layer 27 : Out Q : 10075 , TIDL_ConvolutionLayer, PASSED #MMACs = 14.16, 14.16, Sparsity : 0.00
Layer 28 :TIDL_FlattenLayer, PASSED #MMACs = 0.01, 0.01, Sparsity : 0.00
Layer 29 : Out Q : 3156 , TIDL_ConvolutionLayer, PASSED #MMACs = 75.50, 75.50, Sparsity : 0.00
Layer 30 :TIDL_FlattenLayer, PASSED #MMACs = 0.03, 0.03, Sparsity : 0.00
Layer 31 : Out Q : 11307 , TIDL_ConvolutionLayer, PASSED #MMACs = 3.54, 3.54, Sparsity : 0.00
Layer 32 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 33 : Out Q : 3079 , TIDL_ConvolutionLayer, PASSED #MMACs = 18.87, 18.87, Sparsity : 0.00
Layer 34 :TIDL_FlattenLayer, PASSED #MMACs = 0.01, 0.01, Sparsity : 0.00
Layer 35 : Out Q : 10912 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.88, 0.88, Sparsity : 0.00
Layer 36 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 37 : Out Q : 3436 , TIDL_ConvolutionLayer, PASSED #MMACs = 4.72, 4.72, Sparsity : 0.00
Layer 38 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 39 : Out Q : 18888 , 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 : 4465 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.88, 0.88, Sparsity : 0.00
Layer 42 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 43 : Out Q : 19092 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.04, 0.04, Sparsity : 0.00
Layer 44 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 45 : Out Q : 4767 , TIDL_ConvolutionLayer, PASSED #MMACs = 0.22, 0.22, Sparsity : 0.00
Layer 46 :TIDL_FlattenLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 47 : Out Q : 3380 , TIDL_ConcatLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : nan
Layer 48 : Out Q : 3091 , TIDL_ConcatLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : nan
Layer 49 :
Target: number label value xmin ymin xmax ymax
Target: 0.00 7.00 0.92 0.71 0.37 1.00 0.73
Target: 1.00 7.00 0.11 0.81 0.36 1.00 0.47
Target: 2.00 15.00 0.10 0.24 0.38 0.28 0.45
Target: 3.00 15.00 0.10 0.35 0.64 0.46 0.78
Target: 4.00 15.00 0.09 0.81 0.59 0.84 0.70
Target: 5.00 7.00 0.09 0.13 0.00 0.25 0.11
Target: 6.00 7.00 0.08 0.82 0.39 0.99 0.55
Target: 7.00 7.00 0.08 0.69 0.41 0.90 0.51
Target: 8.00 7.00 0.08 0.69 0.37 0.90 0.46
Target: 9.00 15.00 0.08 0.70 0.40 0.76 0.48
Target: 10.00 15.00 0.08 0.68 0.42 0.72 0.48
Target: 11.00 15.00 0.08 0.39 0.38 0.43 0.46
Target: 12.00 3.00 0.08 0.36 0.66 0.44 0.74
Target: 13.00 4.00 0.07 0.46 0.01 0.60 0.04
Target: 14.00 15.00 0.07 0.71 0.39 0.75 0.46
Target: 15.00 9.00 0.07 0.72 0.51 0.82 0.65
Target: 16.00 15.00 0.07 0.94 0.28 0.99 0.38
Target: 17.00 15.00 0.07 0.38 0.37 0.44 0.43
Target: 18.00 15.00 0.07 0.25 0.39 0.29 0.45
Target: 19.00 4.00 0.07 0.31 0.02 0.46 0.06
Target: 20.00 15.00 0.07 0.68 0.29 0.73 0.39
Target: 21.00 15.00 0.07 0.67 0.39 0.77 0.49
Target: 22.00 15.00 0.07 0.36 0.67 0.45 0.88
Target: 23.00 7.00 0.07 0.62 0.40 0.79 0.52
Target: 24.00 15.00 0.06 0.75 0.18 0.80 0.29
Target: 25.00 15.00 0.06 0.42 0.39 0.47 0.47
Target: 26.00 9.00 0.06 0.72 0.00 0.98 0.25
Target: 27.00 15.00 0.06 0.85 0.38 0.91 0.47
Target: 28.00 16.00 0.06 0.92 0.00 1.01 0.13
Target: 29.00 15.00 0.06 0.69 0.26 0.74 0.33
Target: 30.00 15.00 0.06 0.44 0.20 0.47 0.28
Target: 31.00 4.00 0.06 0.48 -0.01 0.58 0.03
Target: 32.00 7.00 0.06 0.85 0.41 0.98 0.66
Target: 33.00 15.00 0.06 0.93 0.32 1.00 0.57
Target: 34.00 5.00 0.06 0.86 0.47 0.91 0.59
Target: 35.00 4.00 0.06 0.40 0.00 0.53 0.04
Target: 36.00 15.00 0.06 0.40 0.37 0.45 0.46
Target: 37.00 7.00 0.06 0.19 0.44 0.26 0.50
Target: 38.00 15.00 0.06 0.44 0.39 0.49 0.46
Target: 39.00 15.00 0.06 0.45 0.28 0.50 0.41
Target: 40.00 15.00 0.06 0.17 0.32 0.20 0.45
Target: 41.00 15.00 0.06 0.29 0.40 0.33 0.46
Target: 42.00 15.00 0.06 0.28 0.40 0.31 0.45
Target: 43.00 7.00 0.06 0.18 -0.00 0.28 0.05
Target: 44.00 15.00 0.06 0.95 0.05 1.00 0.18
Target: 45.00 15.00 0.06 0.18 0.01 0.24 0.11
Target: 46.00 15.00 0.06 0.67 0.40 0.73 0.47
Target: 47.00 15.00 0.06 0.70 0.23 0.73 0.28
Target: 48.00 7.00 0.06 0.33 0.40 0.40 0.46
Target: 49.00 4.00 0.06 0.37 0.49 0.48 0.53
Target: 50.00 15.00 0.06 0.70 0.26 0.73 0.30
Target: 51.00 7.00 0.06 0.22 0.43 0.34 0.50
Target: 52.00 15.00 0.06 0.10 0.20 0.97 0.93
Target: 53.00 4.00 0.06 -0.00 0.45 0.12 0.54
Target: 54.00 4.00 0.06 0.00 0.47 0.09 0.53
Target: 55.00 7.00 0.06 0.72 0.37 0.83 0.60
Target: 56.00 15.00 0.06 0.01 0.41 0.71 1.01
Target: 57.00 15.00 0.06 0.87 0.37 0.92 0.46
Target: 58.00 15.00 0.06 0.18 0.14 0.22 0.20
Target: 59.00 15.00 0.06 0.17 0.15 0.20 0.19
Target: 60.00 15.00 0.06 0.74 0.18 0.78 0.27
Target: 61.00 15.00 0.06 0.17 0.14 0.20 0.21
Target: 62.00 7.00 0.06 0.69 0.41 0.77 0.63
Target: 63.00 15.00 0.06 0.72 0.16 0.77 0.24
Target: 64.00 15.00 0.05 0.73 0.39 0.78 0.48
Target: 65.00 15.00 0.05 0.13 0.11 0.17 0.15
Target: 66.00 3.00 0.05 0.33 0.56 0.40 0.61
Target: 67.00 7.00 0.05 0.63 0.36 0.80 0.47
Target: 68.00 15.00 0.05 0.70 0.38 0.74 0.46
Target: 69.00 15.00 0.05 0.44 0.17 0.48 0.28
Target: 70.00 15.00 0.05 0.23 0.00 0.28 0.10
Target: 71.00 15.00 0.05 0.34 0.32 0.39 0.40
Target: 72.00 4.00 0.05 0.36 0.37 0.46 0.46
Target: 73.00 15.00 0.05 0.55 0.40 0.57 0.46
Target: 74.00 15.00 0.05 0.37 0.35 0.40 0.40
Target: 75.00 15.00 0.05 0.30 0.40 0.35 0.46
Target: 76.00 15.00 0.05 0.25 0.08 0.29 0.15
Target: 77.00 15.00 0.05 0.58 0.39 0.63 0.48
Target: 78.00 15.00 0.05 0.91 0.39 0.96 0.46
Target: 79.00 15.00 0.05 0.46 0.22 0.50 0.29
Target: 80.00 15.00 0.05 0.37 0.40 0.40 0.46
Target: 81.00 4.00 0.05 0.03 0.42 0.15 0.51
Target: 82.00 15.00 0.05 0.58 0.35 0.67 0.47
Target: 83.00 15.00 0.05 0.37 0.33 0.47 0.45
Target: 84.00 15.00 0.05 0.68 0.26 0.72 0.32
Target: 85.00 4.00 0.05 0.33 0.40 0.45 0.49
Target: 86.00 15.00 0.05 0.82 0.39 0.92 0.49
Target: 87.00 15.00 0.05 0.68 0.43 0.72 0.52
Target: 88.00 15.00 0.05 0.86 0.06 0.91 0.20
Target: 89.00 9.00 0.05 -0.00 0.37 0.11 0.49
Target: 90.00 15.00 0.05 0.45 0.31 0.54 0.43
Target: 91.00 15.00 0.05 0.30 0.40 0.32 0.44
Target: 92.00 15.00 0.05 0.44 0.32 0.49 0.42
Target: 93.00 15.00 0.05 0.43 0.22 0.45 0.27
Target: 94.00 15.00 0.05 0.43 0.22 0.47 0.34
Target: 95.00 7.00 0.05 0.94 0.39 1.00 0.48
Target: 96.00 7.00 0.05 0.18 0.00 0.29 0.12
Target: 97.00 15.00 0.05 0.31 0.30 0.42 0.42
Target: 98.00 7.00 0.05 0.51 0.36 0.93 0.73
Target: 99.00 16.00 0.05 0.77 0.20 0.92 0.35
Target: 100.00 3.00 0.05 0.32 0.58 0.39 0.63
Target: 101.00 15.00 0.05 0.18 0.15 0.21 0.19
Target: 102.00 15.00 0.05 0.87 0.02 0.92 0.14
Target: 103.00 16.00 0.05 0.94 0.03 0.99 0.11
Target: 104.00 15.00 0.05 0.56 0.39 0.59 0.45
Target: 105.00 15.00 0.05 0.67 0.41 0.70 0.48
Target: 106.00 15.00 0.05 0.89 0.36 0.94 0.46
Target: 107.00 15.00 0.05 0.53 0.40 0.56 0.46
Target: 108.00 4.00 0.05 0.38 0.02 0.51 0.05
Target: 109.00 15.00 0.05 0.91 0.28 1.01 0.40
Target: 110.00 15.00 0.05 0.47 0.31 0.51 0.40
Target: 111.00 15.00 0.05 0.70 0.21 0.73 0.27
Target: 112.00 15.00 0.05 0.56 0.21 0.59 0.25
Target: 113.00 15.00 0.05 0.47 0.22 0.51 0.30
Target: 114.00 15.00 0.05 0.72 0.38 0.81 0.49
Target: 115.00 15.00 0.05 0.85 0.28 0.91 0.38
Target: 116.00 15.00 0.05 0.20 0.37 0.25 0.46
Target: 117.00 15.00 0.05 0.67 0.02 0.72 0.09
Target: 118.00 5.00 0.05 0.83 0.46 0.92 0.59
Target: 119.00 15.00 0.05 0.93 0.39 0.97 0.46
Target: 120.00 15.00 0.05 0.49 0.19 0.53 0.25
Target: 121.00 15.00 0.05 0.70 0.40 0.79 0.52
Target: 122.00 15.00 0.05 0.82 0.60 0.87 0.70
Target: 123.00 15.00 0.05 0.83 0.37 0.97 0.47
Target: 124.00 15.00 0.05 0.43 0.20 0.45 0.25
Target: 125.00 15.00 0.05 0.47 0.40 0.51 0.45
Target: 126.00 7.00 0.05 0.20 0.39 0.29 0.45
Target: 127.00 15.00 0.05 0.31 0.40 0.34 0.44
Target: 128.00 15.00 0.05 0.42 0.19 0.51 0.43
Target: 129.00 7.00 0.05 0.96 0.20 1.01 0.27
Target: 130.00 15.00 0.05 0.63 0.11 0.67 0.18
Target: 131.00 15.00 0.05 0.68 0.27 0.73 0.35
Target: 132.00 15.00 0.05 0.26 0.05 0.31 0.12
Target: 133.00 7.00 0.05 0.74 0.50 0.95 0.68
Target: 134.00 15.00 0.05 0.42 0.68 0.52 0.91
Target: 135.00 19.00 0.05 0.02 0.28 0.38 0.57
Target: 136.00 7.00 0.05 0.10 -0.01 0.23 0.07
Target: 137.00 15.00 0.05 0.77 0.18 0.81 0.27
Target: 138.00 15.00 0.05 0.73 0.13 0.78 0.23
Target: 139.00 15.00 0.05 0.81 0.00 0.86 0.12
Target: 140.00 3.00 0.05 0.30 0.59 0.39 0.64
Target: 141.00 5.00 0.05 0.84 0.48 0.89 0.58
Target: 142.00 15.00 0.05 0.19 0.12 0.24 0.19
Target: 143.00 15.00 0.05 0.11 0.10 0.16 0.14
Target: 144.00 7.00 0.05 0.74 0.51 1.05 0.81
Target: 145.00 15.00 0.05 0.70 0.45 0.75 0.55
Target: 146.00 4.00 0.05 0.30 0.01 0.44 0.04
Target: 147.00 15.00 0.05 0.17 0.14 0.20 0.17
Target: 148.00 3.00 0.05 0.30 0.50 0.37 0.55
Target: 149.00 3.00 0.05 0.30 0.56 0.41 0.65
Target: 150.00 15.00 0.05 0.54 0.05 0.57 0.09
Target: 151.00 15.00 0.05 0.66 0.25 0.71 0.32
Target: 152.00 9.00 0.05 0.60 0.01 0.97 0.39
Target: 153.00 15.00 0.05 0.68 0.47 0.72 0.54
Target: 154.00 15.00 0.05 0.60 0.38 0.66 0.48
Target: 155.00 15.00 0.05 0.13 0.34 0.17 0.46
Target: 156.00 9.00 0.05 0.85 0.01 0.97 0.24
Target: 157.00 15.00 0.05 0.26 0.09 0.32 0.17
Target: 158.00 15.00 0.05 0.56 0.04 0.59 0.09
Target: 159.00 15.00 0.05 0.68 0.23 0.72 0.28
Target: 160.00 15.00 0.05 0.55 0.37 0.65 0.48
Target: 161.00 15.00 0.05 0.96 0.04 1.01 0.13
Target: 162.00 15.00 0.05 0.39 0.36 0.42 0.41
Target: 163.00 15.00 0.05 0.69 0.26 0.71 0.30
Target: 164.00 15.00 0.05 0.18 0.13 0.21 0.17
Target: 165.00 15.00 0.05 0.28 0.04 0.33 0.10
Target: 166.00 7.00 0.05 0.54 0.66 0.74 0.78
Target: 167.00 15.00 0.05 0.45 0.17 0.50 0.26
Target: 168.00 15.00 0.05 0.47 0.19 0.51 0.26
Target: 169.00 4.00 0.05 0.35 0.50 0.45 0.54
Target: 170.00 15.00 0.05 0.37 0.38 0.40 0.42
Target: 171.00 15.00 0.05 0.65 0.40 0.68 0.47
Target: 172.00 15.00 0.05 0.82 0.26 0.92 0.39
Target: 173.00 15.00 0.05 0.69 0.44 0.76 0.50
Target: 174.00 15.00 0.05 0.82 0.38 0.89 0.46
Target: 175.00 4.00 0.05 -0.01 0.50 0.08 0.54
Target: 176.00 7.00 0.05 0.30 0.40 0.37 0.45
Target: 177.00 15.00 0.05 0.33 0.36 0.38 0.41
Target: 178.00 15.00 0.05 0.44 0.21 0.49 0.32
Target: 179.00 15.00 0.05 0.24 0.05 0.28 0.14
Target: 180.00 15.00 0.05 0.08 0.42 0.12 0.49
Target: 181.00 15.00 0.05 0.61 0.13 0.66 0.19
Target: 182.00 15.00 0.05 0.66 0.28 0.71 0.35
Target: 183.00 15.00 0.05 0.42 0.33 0.47 0.42
Target: 184.00 15.00 0.05 0.63 0.10 0.67 0.15
Target: 185.00 15.00 0.05 0.14 0.34 0.24 0.46
Target: 186.00 15.00 0.05 0.66 0.04 0.71 0.09
Target: 187.00 7.00 0.04 0.61 0.36 1.03 0.56
Target: 188.00 7.00 0.04 0.65 0.44 0.75 0.48
Target: 189.00 15.00 0.04 0.30 0.69 0.54 0.97
Target: 190.00 4.00 0.04 0.52 -0.01 0.63 0.03
Target: 191.00 9.00 0.04 0.78 0.69 1.00 0.99
Target: 192.00 15.00 0.04 0.18 0.22 0.20 0.28
Target: 193.00 9.00 0.04 0.80 -0.00 0.91 0.24
Target: 194.00 7.00 0.04 0.90 0.47 1.01 0.68
Target: 195.00 3.00 0.04 0.71 0.80 0.77 0.87
Target: 196.00 3.00 0.04 0.30 0.61 0.38 0.68
Target: 197.00 4.00 0.04 0.53 0.01 0.65 0.04
Target: 198.00 7.00 0.04 0.83 -0.00 0.92 0.06
Target: 199.00 3.00 0.04 0.03 0.25 0.08 0.31
#MMACs = 0.01, 0.01, Sparsity : 0.00
End of config list found !
