File size: 15,838 Bytes
5aefcf4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 |
Ticket Name: TDA2: error when setting conv2dKernelType in import file Query Text: Part Number: TDA2 Hi: I train a SSD model changed from "github.com/tidsp/caffe-jacinto-models/tree/caffe-0.16/trained/object_detection/voc0712",only 1 class detection.I only train the first step "initial" model,not do sparsenowthe,and train is OK.And the test result on caffe-jacinto is OK too. Then I do tidl model import,when it run the quantStatsTool,there jump a exception and temminate the process. But when I delete the "conv2dKernelType=......" line int the import file,the model import process run success,but I use the file "stats_tool_out.bin" to checek result ,the result is wrong. Then I find setting the "conv2dKernelType" value to all 0,ther is no exception, when I setting "conv2dKernelType=......" to not all 0,the exception is appear again.the import log is below: C:\PROCESSOR_SDK_VISION_03_03_00_00\ti_components\algorithms\REL.TIDL.01.01.00.00\modules\ti_dl\utils\tidlModelImport>tidl_model_import.out.exe ..\..\test\testvecs\config\import\tidl_import_JDetNet_480x200.txt Caffe Network File : ..\..\test\testvecs\config\caffe_modesl\ssd480x200\initial\deploy.prototxt Caffe Model File : ..\..\test\testvecs\config\caffe_modesl\ssd480x200\initial\ssd480x200_initial_iter_66000.caffemodel TIDL Network File : ..\..\test\testvecs\config\tidl_models\tidl_net_jdetNet_480x200_ssd.bin TIDL Model File : ..\..\test\testvecs\config\tidl_models\tidl_param_jdetNet_480x200_ssd.bin Name of the Network : ssdJacintoNetV2_test Num Inputs : 1 Could not find detection_out Params Num of Layer Detected : 49 0, TIDL_DataLayer , data 0, -1 , 1 , x , x , x , x , x , x , x , x , 0 , 0 , 0 , 0 , 0 , 1 , 3 , 200 , 480 , 0 , 1, TIDL_BatchNormLayer , data/bias 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 200 , 480 , 1 , 3 , 200 , 480 , 288000 , 2, TIDL_ConvolutionLayer , conv1a 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 200 , 480 , 1 , 32 , 100 , 240 , 57600000 , 3, TIDL_ConvolutionLayer , conv1b 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 100 , 240 , 1 , 32 , 50 , 120 , 55296000 , 4, TIDL_ConvolutionLayer , res2a_branch2a 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 50 , 120 , 1 , 64 , 50 , 120 , 110592000 , 5, TIDL_ConvolutionLayer , res2a_branch2b 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 50 , 120 , 1 , 64 , 25 , 60 , 55296000 , 6, TIDL_ConvolutionLayer , res3a_branch2a 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 25 , 60 , 1 , 128 , 25 , 60 , 110592000 , 7, TIDL_ConvolutionLayer , res3a_branch2b 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 25 , 60 , 1 , 128 , 25 , 60 , 55296000 , 8, TIDL_PoolingLayer , pool3 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 25 , 60 , 1 , 128 , 13 , 30 , 199680 , 9, TIDL_ConvolutionLayer , res4a_branch2a 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 13 , 30 , 1 , 256 , 13 , 30 , 115015680 , 10, TIDL_ConvolutionLayer , res4a_branch2b 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 13 , 30 , 1 , 256 , 6 , 15 , 57507840 , 11, TIDL_ConvolutionLayer , res5a_branch2a 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 6 , 15 , 1 , 512 , 6 , 15 , 106168320 , 12, TIDL_ConvolutionLayer , res5a_branch2b 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 6 , 15 , 1 , 512 , 6 , 15 , 53084160 , 13, TIDL_PoolingLayer , pool6 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 6 , 15 , 1 , 512 , 3 , 8 , 49152 , 14, TIDL_PoolingLayer , pool7 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 3 , 8 , 1 , 512 , 2 , 4 , 16384 , 15, TIDL_PoolingLayer , pool8 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 2 , 4 , 1 , 512 , 1 , 2 , 4096 , 16, TIDL_ConvolutionLayer , ctx_output1 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 16 , 1 , 128 , 25 , 60 , 1 , 256 , 25 , 60 , 49152000 , 17, TIDL_ConvolutionLayer , ctx_output2 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 17 , 1 , 512 , 6 , 15 , 1 , 256 , 6 , 15 , 11796480 , 18, TIDL_ConvolutionLayer , ctx_output3 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 18 , 1 , 512 , 3 , 8 , 1 , 256 , 3 , 8 , 3145728 , 19, TIDL_ConvolutionLayer , ctx_output4 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 19 , 1 , 512 , 2 , 4 , 1 , 256 , 2 , 4 , 1048576 , 20, TIDL_ConvolutionLayer , ctx_output5 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 20 , 1 , 512 , 1 , 2 , 1 , 256 , 1 , 2 , 262144 , 21, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_loc 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 21 , 1 , 256 , 25 , 60 , 1 , 16 , 25 , 60 , 6144000 , 22, TIDL_FlattenLayer , ctx_output1/relu_mbox_loc_perm 2, 1 , 1 , 21 , x , x , x , x , x , x , x , 22 , 1 , 16 , 25 , 60 , 1 , 1 , 1 , 24000 , 1 , 23, TIDL_ConvolutionLayer , ctx_output1/relu_mbox_conf 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 23 , 1 , 256 , 25 , 60 , 1 , 8 , 25 , 60 , 3072000 , 24, TIDL_FlattenLayer , ctx_output1/relu_mbox_conf_perm 2, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 8 , 25 , 60 , 1 , 1 , 1 , 12000 , 1 , 26, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_loc 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 26 , 1 , 256 , 6 , 15 , 1 , 24 , 6 , 15 , 552960 , 27, TIDL_FlattenLayer , ctx_output2/relu_mbox_loc_perm 2, 1 , 1 , 26 , x , x , x , x , x , x , x , 27 , 1 , 24 , 6 , 15 , 1 , 1 , 1 , 2160 , 1 , 28, TIDL_ConvolutionLayer , ctx_output2/relu_mbox_conf 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 28 , 1 , 256 , 6 , 15 , 1 , 12 , 6 , 15 , 276480 , 29, TIDL_FlattenLayer , ctx_output2/relu_mbox_conf_perm 2, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 12 , 6 , 15 , 1 , 1 , 1 , 1080 , 1 , 31, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_loc 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 31 , 1 , 256 , 3 , 8 , 1 , 24 , 3 , 8 , 147456 , 32, TIDL_FlattenLayer , ctx_output3/relu_mbox_loc_perm 2, 1 , 1 , 31 , x , x , x , x , x , x , x , 32 , 1 , 24 , 3 , 8 , 1 , 1 , 1 , 576 , 1 , 33, TIDL_ConvolutionLayer , ctx_output3/relu_mbox_conf 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 33 , 1 , 256 , 3 , 8 , 1 , 12 , 3 , 8 , 73728 , 34, TIDL_FlattenLayer , ctx_output3/relu_mbox_conf_perm 2, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 12 , 3 , 8 , 1 , 1 , 1 , 288 , 1 , 36, TIDL_ConvolutionLayer , ctx_output4/relu_mbox_loc 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 36 , 1 , 256 , 2 , 4 , 1 , 24 , 2 , 4 , 49152 , 37, TIDL_FlattenLayer , ctx_output4/relu_mbox_loc_perm 2, 1 , 1 , 36 , x , x , x , x , x , x , x , 37 , 1 , 24 , 2 , 4 , 1 , 1 , 1 , 192 , 1 , 38, TIDL_ConvolutionLayer , ctx_output4/relu_mbox_conf 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 38 , 1 , 256 , 2 , 4 , 1 , 12 , 2 , 4 , 24576 , 39, TIDL_FlattenLayer , ctx_output4/relu_mbox_conf_perm 2, 1 , 1 , 38 , x , x , x , x , x , x , x , 39 , 1 , 12 , 2 , 4 , 1 , 1 , 1 , 96 , 1 , 41, TIDL_ConvolutionLayer , ctx_output5/relu_mbox_loc 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 41 , 1 , 256 , 1 , 2 , 1 , 16 , 1 , 2 , 8192 , 42, TIDL_FlattenLayer , ctx_output5/relu_mbox_loc_perm 2, 1 , 1 , 41 , x , x , x , x , x , x , x , 42 , 1 , 16 , 1 , 2 , 1 , 1 , 1 , 32 , 1 , 43, TIDL_ConvolutionLayer , ctx_output5/relu_mbox_conf 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 43 , 1 , 256 , 1 , 2 , 1 , 8 , 1 , 2 , 4096 , 44, TIDL_FlattenLayer , ctx_output5/relu_mbox_conf_perm 2, 1 , 1 , 43 , x , x , x , x , x , x , x , 44 , 1 , 8 , 1 , 2 , 1 , 1 , 1 , 16 , 1 , 46, TIDL_ConcatLayer , mbox_loc_5headers 2, 5 , 1 , 22 , 27 , 32 , 37 , 42 , x , x , x , 46 , 1 , 1 , 1 , 24000 , 1 , 1 , 1 , 26960 , 1 , 47, TIDL_ConcatLayer , mbox_conf_5headers 2, 5 , 1 , 24 , 29 , 34 , 39 , 44 , x , x , x , 47 , 1 , 1 , 1 , 12000 , 1 , 1 , 1 , 13480 , 1 , 48, TIDL_DetectionOutputLayer , detection_out 2, 2 , 1 , 46 , 47 , x , x , x , x , x , x , 48 , 1 , 1 , 1 , 26960 , 1 , 1 , 1 , 560 , 1 , Total Giga Macs : 0.8528 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 , 200 , 480 , 1, TIDL_BatchNormLayer , 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 200 , 480 , 1 , 3 , 200 , 480 , 2, TIDL_ConvolutionLayer , 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 200 , 480 , 1 , 32 , 100 , 240 , 3, TIDL_ConvolutionLayer , 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 100 , 240 , 1 , 32 , 50 , 120 , 4, TIDL_ConvolutionLayer , 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 50 , 120 , 1 , 64 , 50 , 120 , 5, TIDL_ConvolutionLayer , 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 64 , 50 , 120 , 1 , 64 , 25 , 60 , 6, TIDL_ConvolutionLayer , 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 25 , 60 , 1 , 128 , 25 , 60 , 7, TIDL_ConvolutionLayer , 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 128 , 25 , 60 , 1 , 128 , 25 , 60 , 8, TIDL_PoolingLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 25 , 60 , 1 , 128 , 13 , 30 , 9, TIDL_ConvolutionLayer , 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 13 , 30 , 1 , 256 , 13 , 30 , 10, TIDL_ConvolutionLayer , 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 256 , 13 , 30 , 1 , 256 , 6 , 15 , 11, TIDL_ConvolutionLayer , 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 6 , 15 , 1 , 512 , 6 , 15 , 12, TIDL_ConvolutionLayer , 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 512 , 6 , 15 , 1 , 512 , 6 , 15 , 13, TIDL_PoolingLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 6 , 15 , 1 , 512 , 3 , 8 , 14, TIDL_PoolingLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 3 , 8 , 1 , 512 , 2 , 4 , 15, TIDL_PoolingLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 512 , 2 , 4 , 1 , 512 , 1 , 2 , 16, TIDL_ConvolutionLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 16 , 1 , 128 , 25 , 60 , 1 , 256 , 25 , 60 , 17, TIDL_ConvolutionLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 17 , 1 , 512 , 6 , 15 , 1 , 256 , 6 , 15 , 18, TIDL_ConvolutionLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 18 , 1 , 512 , 3 , 8 , 1 , 256 , 3 , 8 , 19, TIDL_ConvolutionLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 19 , 1 , 512 , 2 , 4 , 1 , 256 , 2 , 4 , 20, TIDL_ConvolutionLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 20 , 1 , 512 , 1 , 2 , 1 , 256 , 1 , 2 , 21, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 21 , 1 , 256 , 25 , 60 , 1 , 16 , 25 , 60 , 22, TIDL_FlattenLayer , 1, 1 , 1 , 21 , x , x , x , x , x , x , x , 22 , 1 , 16 , 25 , 60 , 1 , 1 , 1 ,24000 , 23, TIDL_ConvolutionLayer , 1, 1 , 1 , 16 , x , x , x , x , x , x , x , 23 , 1 , 256 , 25 , 60 , 1 , 8 , 25 , 60 , 24, TIDL_FlattenLayer , 1, 1 , 1 , 23 , x , x , x , x , x , x , x , 24 , 1 , 8 , 25 , 60 , 1 , 1 , 1 ,12000 , 25, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 26 , 1 , 256 , 6 , 15 , 1 , 24 , 6 , 15 , 26, TIDL_FlattenLayer , 1, 1 , 1 , 26 , x , x , x , x , x , x , x , 27 , 1 , 24 , 6 , 15 , 1 , 1 , 1 , 2160 , 27, TIDL_ConvolutionLayer , 1, 1 , 1 , 17 , x , x , x , x , x , x , x , 28 , 1 , 256 , 6 , 15 , 1 , 12 , 6 , 15 , 28, TIDL_FlattenLayer , 1, 1 , 1 , 28 , x , x , x , x , x , x , x , 29 , 1 , 12 , 6 , 15 , 1 , 1 , 1 , 1080 , 29, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 31 , 1 , 256 , 3 , 8 , 1 , 24 , 3 , 8 , 30, TIDL_FlattenLayer , 1, 1 , 1 , 31 , x , x , x , x , x , x , x , 32 , 1 , 24 , 3 , 8 , 1 , 1 , 1 , 576 , 31, TIDL_ConvolutionLayer , 1, 1 , 1 , 18 , x , x , x , x , x , x , x , 33 , 1 , 256 , 3 , 8 , 1 , 12 , 3 , 8 , 32, TIDL_FlattenLayer , 1, 1 , 1 , 33 , x , x , x , x , x , x , x , 34 , 1 , 12 , 3 , 8 , 1 , 1 , 1 , 288 , 33, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 36 , 1 , 256 , 2 , 4 , 1 , 24 , 2 , 4 , 34, TIDL_FlattenLayer , 1, 1 , 1 , 36 , x , x , x , x , x , x , x , 37 , 1 , 24 , 2 , 4 , 1 , 1 , 1 , 192 , 35, TIDL_ConvolutionLayer , 1, 1 , 1 , 19 , x , x , x , x , x , x , x , 38 , 1 , 256 , 2 , 4 , 1 , 12 , 2 , 4 , 36, TIDL_FlattenLayer , 1, 1 , 1 , 38 , x , x , x , x , x , x , x , 39 , 1 , 12 , 2 , 4 , 1 , 1 , 1 , 96 , 37, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 41 , 1 , 256 , 1 , 2 , 1 , 16 , 1 , 2 , 38, TIDL_FlattenLayer , 1, 1 , 1 , 41 , x , x , x , x , x , x , x , 42 , 1 , 16 , 1 , 2 , 1 , 1 , 1 , 32 , 39, TIDL_ConvolutionLayer , 1, 1 , 1 , 20 , x , x , x , x , x , x , x , 43 , 1 , 256 , 1 , 2 , 1 , 8 , 1 , 2 , 40, TIDL_FlattenLayer , 1, 1 , 1 , 43 , x , x , x , x , x , x , x , 44 , 1 , 8 , 1 , 2 , 1 , 1 , 1 , 16 , 41, TIDL_ConcatLayer , 1, 5 , 1 , 22 , 27 , 32 , 37 , 42 , x , x , x , 46 , 1 , 1 , 1 ,24000 , 1 , 1 , 1 ,26960 , 42, TIDL_ConcatLayer , 1, 5 , 1 , 24 , 29 , 34 , 39 , 44 , x , x , x , 47 , 1 , 1 , 1 ,12000 , 1 , 1 , 1 ,13480 , 43, TIDL_DetectionOutputLayer , 1, 2 , 1 , 46 , 47 , x , x , x , x , x , x , 48 , 1 , 1 , 1 ,26960 , 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 , And my 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_modesl\ssd480x200\initial\deploy.prototxt inputParamsFile = "..\..\test\testvecs\config\caffe_modesl\ssd480x200\initial\ssd480x200_initial_iter_66000.caffemodel" outputNetFile = "..\..\test\testvecs\config\tidl_models\tidl_net_jdetNet_480x200_ssd.bin" outputParamsFile = "..\..\test\testvecs\config\tidl_models\tidl_param_jdetNet_480x200_ssd.bin" rawSampleInData = 0 preProcType = 4 sampleInData = "..\..\test\testvecs\input\cqh0.jpg" 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 #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 Responses: Share the deply prototext and caffe model to re-produce the issue Also Post the exception tha you are observing for quick investigation Hi: the exception only has a windows error messagebox,as below: and deploy file and caffe model is follow: model$deploy.7z Hi: I'm sorry,the deploy in .7z pack has some error: the layer name: "mbox_loc_5headers" "shoule change to "mbox_loc"; the layer name: "mbox_conf_5headers" shoule change to "mbox_conf"; the layer name: "mbox_priorbox_5headers" shoule change to "mbox_priorbox"; I trained this model with names without "_5headers",but confuse with some earier deploy file. But after correct those,the exception is still stay same. Regards Hi, We could reproduce the issue. We will debug and provide you the fix. Thanks, Praveen Hi, we found one issue for this specific resolution of 480x200 test case and fixed issue in one source file. Do you have access to TIDL source release so that I can share the fix (two lines of modified code)? Thanks, Praveen Hi: Thanks! I have access to TIDL source release. Regards Okay, then can you please try below fix in tidl_conv2d_base.c file.. at line # 2711, replace below code, numVerBlock = ((imHeight + (tidlConv2dProcHeight-1U)) / tidlConv2dProcHeight); ibufSize = TIDL_conv2dFindIbufSize(blockParams, params->strideW,outElementSize); with this code, numVerBlock = ((imHeight + (tidlConv2dProcHeight-1U)) / tidlConv2dProcHeight); imHeight = tidlConv2dProcHeight * numVerBlock; ibufSize = TIDL_conv2dFindIbufSize(blockParams, params->strideW,outElementSize); and try now? Thanks, Praveen Hi: Thanks!I will try this fix later ,and report the result. Regards Okay thank you.. Regards, Praveen |