error in making skip-layer connection network based on VGG16 ?

[helxsz]

I am currently reading the paper on 'CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection', it is using the skip-connection to fuse conv3-3, conv4-3 and conv5-3 together, the steps are shown below

• Extract the feature maps of the face region (at multiple scales conv3-3, conv4-3, conv5-3) and apply RoI-Pooling to it (i.e. convert to a fixed height and width).
• L2-normalize each feature map.
• Concatenate the (RoI-pooled and normalized) feature maps of the face (at multiple scales) with each other (creates one tensor).
• Apply a 1x1 convolution to the face tensor.
• Apply two fully connected layers to the face tensor, creating a vector.

I used the caffe and made a prototxt based on faster-RCNN VGG16 ,  the following parts are added into the original prototxt 

# roi pooling the conv3-3 layer and L2 normalize it 

layer {

  name: "roi_pool3"

  type: "ROIPooling"

  bottom: "conv3_3"

  bottom: "rois"

  top: "pool3_roi"

  roi_pooling_param {

    pooled_w: 7

    pooled_h: 7

   spatial_scale: 0.25 # 1/4

  }

}




layer {

  name:"roi_pool3_l2norm"

  type:"L2Norm"

  bottom: "pool3_roi"

  top:"pool3_roi"

}




-------------




# roi pooling the conv4-3 layer and L2 normalize it 




layer {

  name: "roi_pool4"

  type: "ROIPooling"

  bottom: "conv4_3"

  bottom: "rois"

  top: "pool4_roi"

  roi_pooling_param {

    pooled_w: 7

    pooled_h: 7

    spatial_scale: 0.125 # 1/8

  }

}




layer {

  name:"roi_pool4_l2norm"

  type:"L2Norm"

  bottom: "pool4_roi"

 top:"pool4_roi"

}

 --------------------------

# roi pooling the conv5-3 layer and L2 normalize it 

layer {

  name: "roi_pool5"

  type: "ROIPooling"

  bottom: "conv5_3"

  bottom: "rois"

  top: "pool5"

  roi_pooling_param {

    pooled_w: 7

    pooled_h: 7

    spatial_scale: 0.0625 # 1/16

  }

}







layer {

  name:"roi_pool5_l2norm"

  type:"L2Norm"

  bottom: "pool5"

  top:"pool5"

}







# concat roi_pool3, roi_pool4, roi_pool5 and apply 1*1 conv




layer {

  name:"roi_concat"

  type: "Concat"

  concat_param {

    axis: 1

  }

  bottom: "pool5"

  bottom: "pool4_roi"

  bottom: "pool3_roi"      

  top:"roi_concat"

}




layer {

  name:"roi_concat_1*1_conv"

  type:"Convolution"

  top:"roi_concat_1*1_conv"

  bottom:"roi_concat"

  param {

   lr_mult: 1

  }

  param {

    lr_mult: 2

  }

  convolution_param {

    num_output: 128

    pad: 1

    kernel_size: 1

    weight_filler{

                type:"xavier"

    }

        bias_filler{

                type:"constant"        

        }

  }

}




layer {

  name: "fc6"

  type: "InnerProduct"

  bottom: "roi_concat_1*1_conv"

  top: "fc6"

  param {

    lr_mult: 1

  }

  param {

    lr_mult: 2

  }

  inner_product_param {

    num_output: 4096

  }

}







during the training, I met such a issue




F0616 16:43:02.899025  3712 net.cpp:757] Cannot copy param 0 weights from layer 'fc6'; shape mismatch.  Source param shape is 1 1 4096 25088 (102760448); target param shape is 4096 10368 (42467328). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer.

  

I could find out what goes wrong, I need some help from you if you can spot some problem or explanation.

Really appreciated!!