data2/text/range/10001-15000/886369.txt

Ticket Name: TDA2SG: jacinto-ai-devkit

Query Text:
Part Number: TDA2SG Other Parts Discussed in Thread: TDA2 I Read caffe-jacinto-model CNN train；as below Stage-1: Initial stage with L2 regularization training Stage-2: L1 regularization training Stage-3: Sparsification training The main training script is located ../scripts/train_image_object_detection.sh. My question Is： 1，Is stage-2 train need depend Stage-1，Stage-3 train need depend Stage-2. or Stage-1，Stage-2，Stage-3 Is independent，I can train this stage 1 2 3 at the same time。, 2，I see Stage 1,2,3 mAp is almost equal，so if I can train one of them，not need to train every Stage。 3，CAN TDA4 and TDA2S ues the same weight file（caffemodel） to run the TIDL on the usecase。

Responses:
Hi, If your purpose is to simply run on TDA2, the Stage1 is sufficient. The additional stages after this are required to generate a sparse model, which will run faster on TDA2. They have to be run sequentially, not in parallel. Yes, the same model is expected to run on TDA4 as well. The only difference is that for TDA4 Stage1 itself offer very high speed (other stages are not required as TDA4 is already much faster than TDA2 interms of CNN performance and sparse model doesn't offer additional speedup) Best regards,

thanks。 1,when use tidl_model_import.out.exe, how can I get layersGroupId and conv2dKernelType value,when I change the model (link ssd 512*512), 2 ,how I test the result (stats_tool_out.bin) in the pc.

hi,what`s the value of layersGroupId and conv2dKernelType in the follow deploy? name: "jsegnet21v2_deploy" input: "data" input_shape { dim: 1 dim: 3 dim: 512 dim: 1024 } layer { name: "data/bias" type: "Bias" bottom: "data" top: "data/bias" param { lr_mult: 0 decay_mult: 0 } bias_param { filler { type: "constant" value: -128 } } } layer { name: "conv1a" type: "Convolution" bottom: "data/bias" top: "conv1a" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 32 bias_term: true pad: 2 kernel_size: 5 group: 1 stride: 2 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "conv1a/bn" type: "BatchNorm" bottom: "conv1a" top: "conv1a" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "conv1a/relu" type: "ReLU" bottom: "conv1a" top: "conv1a" } layer { name: "conv1b" type: "Convolution" bottom: "conv1a" top: "conv1b" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 32 bias_term: true pad: 1 kernel_size: 3 group: 4 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "conv1b/bn" type: "BatchNorm" bottom: "conv1b" top: "conv1b" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "conv1b/relu" type: "ReLU" bottom: "conv1b" top: "conv1b" } layer { name: "pool1" type: "Pooling" bottom: "conv1b" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "res2a_branch2a" type: "Convolution" bottom: "pool1" top: "res2a_branch2a" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 1 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "res2a_branch2a/bn" type: "BatchNorm" bottom: "res2a_branch2a" top: "res2a_branch2a" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res2a_branch2a/relu" type: "ReLU" bottom: "res2a_branch2a" top: "res2a_branch2a" } layer { name: "res2a_branch2b" type: "Convolution" bottom: "res2a_branch2a" top: "res2a_branch2b" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 1 kernel_size: 3 group: 4 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "res2a_branch2b/bn" type: "BatchNorm" bottom: "res2a_branch2b" top: "res2a_branch2b" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res2a_branch2b/relu" type: "ReLU" bottom: "res2a_branch2b" top: "res2a_branch2b" } layer { name: "pool2" type: "Pooling" bottom: "res2a_branch2b" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "res3a_branch2a" type: "Convolution" bottom: "pool2" top: "res3a_branch2a" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 bias_term: true pad: 1 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "res3a_branch2a/bn" type: "BatchNorm" bottom: "res3a_branch2a" top: "res3a_branch2a" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res3a_branch2a/relu" type: "ReLU" bottom: "res3a_branch2a" top: "res3a_branch2a" } layer { name: "res3a_branch2b" type: "Convolution" bottom: "res3a_branch2a" top: "res3a_branch2b" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 bias_term: true pad: 1 kernel_size: 3 group: 4 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "res3a_branch2b/bn" type: "BatchNorm" bottom: "res3a_branch2b" top: "res3a_branch2b" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res3a_branch2b/relu" type: "ReLU" bottom: "res3a_branch2b" top: "res3a_branch2b" } layer { name: "pool3" type: "Pooling" bottom: "res3a_branch2b" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "res4a_branch2a" type: "Convolution" bottom: "pool3" top: "res4a_branch2a" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 bias_term: true pad: 1 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "res4a_branch2a/bn" type: "BatchNorm" bottom: "res4a_branch2a" top: "res4a_branch2a" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res4a_branch2a/relu" type: "ReLU" bottom: "res4a_branch2a" top: "res4a_branch2a" } layer { name: "res4a_branch2b" type: "Convolution" bottom: "res4a_branch2a" top: "res4a_branch2b" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 bias_term: true pad: 1 kernel_size: 3 group: 4 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "res4a_branch2b/bn" type: "BatchNorm" bottom: "res4a_branch2b" top: "res4a_branch2b" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res4a_branch2b/relu" type: "ReLU" bottom: "res4a_branch2b" top: "res4a_branch2b" } layer { name: "pool4" type: "Pooling" bottom: "res4a_branch2b" top: "pool4" pooling_param { pool: MAX kernel_size: 1 stride: 1 } } layer { name: "res5a_branch2a" type: "Convolution" bottom: "pool4" top: "res5a_branch2a" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 bias_term: true pad: 2 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 2 } } layer { name: "res5a_branch2a/bn" type: "BatchNorm" bottom: "res5a_branch2a" top: "res5a_branch2a" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res5a_branch2a/relu" type: "ReLU" bottom: "res5a_branch2a" top: "res5a_branch2a" } layer { name: "res5a_branch2b" type: "Convolution" bottom: "res5a_branch2a" top: "res5a_branch2b" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 bias_term: true pad: 2 kernel_size: 3 group: 4 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 2 } } layer { name: "res5a_branch2b/bn" type: "BatchNorm" bottom: "res5a_branch2b" top: "res5a_branch2b" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "res5a_branch2b/relu" type: "ReLU" bottom: "res5a_branch2b" top: "res5a_branch2b" } layer { name: "out5a" type: "Convolution" bottom: "res5a_branch2b" top: "out5a" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 4 kernel_size: 3 group: 2 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 4 } } layer { name: "out5a/bn" type: "BatchNorm" bottom: "out5a" top: "out5a" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "out5a/relu" type: "ReLU" bottom: "out5a" top: "out5a" } layer { name: "out5a_up2" type: "Deconvolution" bottom: "out5a" top: "out5a_up2" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 64 bias_term: false pad: 1 kernel_size: 4 group: 64 stride: 2 weight_filler { type: "bilinear" } } } layer { name: "out3a" type: "Convolution" bottom: "res3a_branch2b" top: "out3a" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 1 kernel_size: 3 group: 2 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "out3a/bn" type: "BatchNorm" bottom: "out3a" top: "out3a" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "out3a/relu" type: "ReLU" bottom: "out3a" top: "out3a" } layer { name: "out3_out5_combined" type: "Eltwise" bottom: "out5a_up2" bottom: "out3a" top: "out3_out5_combined" } layer { name: "ctx_conv1" type: "Convolution" bottom: "out3_out5_combined" top: "ctx_conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 1 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "ctx_conv1/bn" type: "BatchNorm" bottom: "ctx_conv1" top: "ctx_conv1" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "ctx_conv1/relu" type: "ReLU" bottom: "ctx_conv1" top: "ctx_conv1" } layer { name: "ctx_conv2" type: "Convolution" bottom: "ctx_conv1" top: "ctx_conv2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 4 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 4 } } layer { name: "ctx_conv2/bn" type: "BatchNorm" bottom: "ctx_conv2" top: "ctx_conv2" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "ctx_conv2/relu" type: "ReLU" bottom: "ctx_conv2" top: "ctx_conv2" } layer { name: "ctx_conv3" type: "Convolution" bottom: "ctx_conv2" top: "ctx_conv3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 4 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 4 } } layer { name: "ctx_conv3/bn" type: "BatchNorm" bottom: "ctx_conv3" top: "ctx_conv3" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "ctx_conv3/relu" type: "ReLU" bottom: "ctx_conv3" top: "ctx_conv3" } layer { name: "ctx_conv4" type: "Convolution" bottom: "ctx_conv3" top: "ctx_conv4" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 bias_term: true pad: 4 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 4 } } layer { name: "ctx_conv4/bn" type: "BatchNorm" bottom: "ctx_conv4" top: "ctx_conv4" batch_norm_param { moving_average_fraction: 0.99 eps: 0.0001 scale_bias: true } } layer { name: "ctx_conv4/relu" type: "ReLU" bottom: "ctx_conv4" top: "ctx_conv4" } layer { name: "ctx_final" type: "Convolution" bottom: "ctx_conv4" top: "ctx_final" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 8 bias_term: true pad: 1 kernel_size: 3 kernel_size: 3 group: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "ctx_final/relu" type: "ReLU" bottom: "ctx_final" top: "ctx_final" } layer { name: "out_deconv_final_up2" type: "Deconvolution" bottom: "ctx_final" top: "out_deconv_final_up2" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 8 bias_term: false pad: 1 kernel_size: 4 group: 8 stride: 2 weight_filler { type: "bilinear" } } } layer { name: "out_deconv_final_up4" type: "Deconvolution" bottom: "out_deconv_final_up2" top: "out_deconv_final_up4" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 8 bias_term: false pad: 1 kernel_size: 4 group: 8 stride: 2 weight_filler { type: "bilinear" } } } layer { name: "out_deconv_final_up8" type: "Deconvolution" bottom: "out_deconv_final_up4" top: "out_deconv_final_up8" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 8 bias_term: false pad: 1 kernel_size: 4 group: 8 stride: 2 weight_filler { type: "bilinear" } } } layer { name: "argMaxOut" type: "ArgMax" bottom: "out_deconv_final_up8" top: "argMaxOut" argmax_param { axis: 1 } }

The parameters that you mentioned are not part of Caffe / Caffe-jacinto - but I am guessing it may be for TIDL. You would have to consult the TIDL user guide or ask questions to TIDL experts (use TIDL as tag).