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| Ticket Name: TDA4VM: Segment output result is not correct for TDA4 board | |
| Query Text: | |
| Part Number: TDA4VM Other Parts Discussed in Thread: TDA2 Dear: I met a problem that is :I convert a caffer segment model use import tool, it can works well on pc test, but can't output correct result on tda4 board. Here is the convert files and output result for pc test: modelType = 0 inputNetFile = "../longhorn/segment/512x512/forward_small512_512_iter_30000.prototxt" inputParamsFile = "../longhorn/segment/512x512/forward_small512_512_iter_30000.caffemodel" outputNetFile = "../longhorn/segment/512x512/out/tidl_net_jSegNet21v2.bin" outputParamsFile = "../longhorn/segment/512x512/out/tidl_io_jSegNet21v2_" inDataFormat = 0 perfSimConfig = ../../test/testvecs/config/import/device_config.cfg inData = "../../test/testvecs/config/test_pic_512x512.txt" postProcType = 3 But when run on TDA4 board, Program can run well, only the output result is not correct. Also we use the same caffer model convertd with tda2 tools, it can get correct output on tda2 board. Here attached the deploy file, can you help me analysis where the problem is? Also can you give me a test deploy file to train a new model to test? forward_small512_512_iter_30000 .txt name: "jsegnet21v2_deploy" | |
| input: "data" | |
| input_shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 512 | |
| dim: 512 | |
| } | |
| layer { | |
| name: "data/bias" | |
| type: "Bias" | |
| bottom: "data" | |
| top: "data/bias" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| bias_param { | |
| filler { | |
| type: "constant" | |
| value: -128.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1a" | |
| type: "Convolution" | |
| bottom: "data/bias" | |
| top: "conv1a" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv1a/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1a" | |
| top: "conv1a" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv1b/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1b" | |
| top: "conv1b" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 1 | |
| } | |
| } | |
| layer { | |
| name: "res2a_branch2a/bn" | |
| type: "BatchNorm" | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| scale_bias: true | |
| } | |
| } | |
| layer { | |
| name: "res2a_branch2a/relu" | |
| type: "ReLU" | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "res2a_branch2a" | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 1 | |
| } | |
| } | |
| layer { | |
| name: "res3a_branch2a/bn" | |
| type: "BatchNorm" | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| scale_bias: true | |
| } | |
| } | |
| layer { | |
| name: "res3a_branch2a/relu" | |
| type: "ReLU" | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "res3a_branch2a" | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 1 | |
| } | |
| } | |
| layer { | |
| name: "res4a_branch2a/bn" | |
| type: "BatchNorm" | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| scale_bias: true | |
| } | |
| } | |
| layer { | |
| name: "res4a_branch2a/relu" | |
| type: "ReLU" | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "res4a_branch2a" | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "res5a_branch2a/bn" | |
| type: "BatchNorm" | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| scale_bias: true | |
| } | |
| } | |
| layer { | |
| name: "res5a_branch2a/relu" | |
| type: "ReLU" | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| } | |
| layer { | |
| name: "out5a" | |
| type: "Convolution" | |
| bottom: "res5a_branch2a" | |
| top: "out5a" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "out5a/bn" | |
| type: "BatchNorm" | |
| bottom: "out5a" | |
| top: "out5a" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| 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_branch2a" | |
| top: "out3a" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 1 | |
| } | |
| } | |
| layer { | |
| name: "out3a/bn" | |
| type: "BatchNorm" | |
| bottom: "out3a" | |
| top: "out3a" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 1 | |
| } | |
| } | |
| layer { | |
| name: "ctx_conv1/bn" | |
| type: "BatchNorm" | |
| bottom: "ctx_conv1" | |
| top: "ctx_conv1" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "ctx_conv2/bn" | |
| type: "BatchNorm" | |
| bottom: "ctx_conv2" | |
| top: "ctx_conv2" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "ctx_conv3/bn" | |
| type: "BatchNorm" | |
| bottom: "ctx_conv3" | |
| top: "ctx_conv3" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| 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: 0.0 | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "ctx_conv4/bn" | |
| type: "BatchNorm" | |
| bottom: "ctx_conv4" | |
| top: "ctx_conv4" | |
| batch_norm_param { | |
| moving_average_fraction: 0.990000009537 | |
| eps: 9.99999974738e-05 | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 5 | |
| bias_term: true | |
| pad: 1 | |
| kernel_size: 3 | |
| kernel_size: 3 | |
| group: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| 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: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 5 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 4 | |
| group: 5 | |
| 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: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 5 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 4 | |
| group: 5 | |
| 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: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 5 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 4 | |
| group: 5 | |
| stride: 2 | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "argMaxOut" | |
| type: "ArgMax" | |
| bottom: "out_deconv_final_up8" | |
| top: "argMaxOut" | |
| argmax_param { | |
| axis: 1 | |
| } | |
| } | |
| Responses: | |
| Hi, Can you tell which release of TIDL you are using? Also can you generate the layer level traces on PC emulation mode and on EVM and compare to figure out which layer is mismatching. Regards, Anshu | |
| Thanks for your replying, the tidl version is tidl_j7_01_03_00_11. After comparing the generate layer output result, the most different layer is the last therr Deconvolution layer. After the last three layer, the output image is totally blank. Here is the last three deconvolution layer: layer { name: "out_deconv_final_up2" type: "Deconvolution" bottom: "ctx_final" top: "out_deconv_final_up2" param { lr_mult: 0.0 decay_mult: 0.0 } convolution_param { num_output: 5 bias_term: false pad: 1 kernel_size: 4 group: 5 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: 0.0 decay_mult: 0.0 } convolution_param { num_output: 5 bias_term: false pad: 1 kernel_size: 4 group: 5 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: 0.0 decay_mult: 0.0 } convolution_param { num_output: 5 bias_term: false pad: 1 kernel_size: 4 group: 5 stride: 2 weight_filler { type: "bilinear" } } } | |
| Hi, Can you try this with the latest TIDL release ( 1.4.0.8) which is part of SDK 7.2 and let us know if you are still seeing the issue? Regards, Anshu | |