Error compiling network

[idata]

Hi,

I'm trying to convert a Caffe network with AbsVal layer but getting an error.Network git at: https://github.com/guozhongluo/head-pose-estimation-and-face-landmarkCan someone please advice ?Thanks,Tsahi.

[idata]

@tsahim I can't see AbsVal layer in this project as well as weight (.caffemodel file). Please explain how do you manage to convert this network for NCS so we can triage the problem.

[idata]

Wrong link sorry,

this is the library at: https://github.com/jiangwqcooler/face-landmark

and this is the orig proto.txt (before shape mod ):

name: "landmark"

input: "data"input_dim: 1input_dim: 1input_dim: 60input_dim: 60#the actual netlayer 1

layer {

name: "Conv1" type: "Convolution" bottom: "data" top: "Conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 20 pad: 2 kernel_size: 5 stride: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.2 } }}

layer {

name: "ActivationTangH1" bottom: "Conv1" top: "ActivationTangH1" type: "TanH"}

layer {

name: "ActivationAbs1" bottom: "ActivationTangH1" top: "Abs1" type: "AbsVal"}

layer {

name: "Pool1" type: "Pooling" bottom: "Abs1" top: "Pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 }}

layer {

name: "Conv2" type: "Convolution" bottom: "Pool1" top: "Conv2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 48 pad: 2 kernel_size: 5 stride: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.2 } }}

layer {

name: "ActivationTangH2" bottom: "Conv2" top: "ActivationTangH2" type: "TanH"}

layer {

name: "ActivationAbs2" bottom: "ActivationTangH2" top: "Abs2" type: "AbsVal"}

layer {

name: "Pool2" type: "Pooling" bottom: "Abs2" top: "Pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 }}layer 3

layer {

name: "Conv3" type: "Convolution" bottom: "Pool2" top: "Conv3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 0 kernel_size: 3 stride: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.2 } }}

layer {

name: "ActivationTangH3" bottom: "Conv3" top: "ActivationTangH3" type: "TanH"}

layer {

name: "ActivationAbs3" bottom: "ActivationTangH3" top: "Abs3" type: "AbsVal"}

layer {

name: "Pool3" type: "Pooling" bottom: "Abs3" top: "Pool3" pooling_param { pool: MAX kernel_size: 3 stride: 2 }}layer 4

layer {

name: "Conv4" type: "Convolution" bottom: "Pool3" top: "Conv4" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 80 pad: 0 kernel_size: 3 stride: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.2 } }}

layer {

name: "ActivationTangH4" bottom: "Conv4" top: "ActivationTangH4" type: "TanH"}

layer {

name: "ActivationAbs4" bottom: "ActivationTangH4" top: "Abs4" type: "AbsVal"}#

layer {

name: "Dense1" type: "InnerProduct" bottom: "Abs4" top: "Dense1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 512 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } }}

layer {

name: "ActivationTangH5" bottom: "Dense1" top: "ActivationTangH5" type: "TanH"}

layer {

name: "ActivationAbs5" bottom: "ActivationTangH5" top: "Abs5" type: "AbsVal"}

layer {

name: "Dense3" type: "InnerProduct" bottom: "Abs5" top: "Dense3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 136 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } }}

[idata]

@tsahim I believe that we may not yet support the Absolute Value layer. Can you post the error that you are receiving?