Check failed: K_ == new_K (6656 vs. 4096) Input size incompatible with inner product parameters












0















There is another similar question here. But that is not duplicate to my question as they are more on input dimension mismatch from network's requirement.



For me is I use customized network but input dimension is still the same.
I am not using any per-trained network as it is my own network.



I have problem with training on customized network with py-faster-rcnn method.
The original VGG16 NET has the following RCNN network.



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: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "cls_score"
type: "InnerProduct"
bottom: "fc7"
top: "cls_score"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bbox_pred"
type: "InnerProduct"
bottom: "fc7"
top: "bbox_pred"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 16
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss_cls"
type: "SoftmaxWithLoss"
bottom: "cls_score"
bottom: "labels"
propagate_down: 1
propagate_down: 0
top: "loss_cls"
loss_weight: 1
}
layer {
name: "loss_bbox"
type: "SmoothL1Loss"
bottom: "bbox_pred"
bottom: "bbox_targets"
bottom: "bbox_inside_weights"
bottom: "bbox_outside_weights"
top: "loss_bbox"
loss_weight: 1
}


Which has roi_pool5 fed into inner product layer with output channels 4096.
roi_pool5 has input conv5_3 with 512 channels.



For me, my RCNN network has Inception layers with 64 channels. Then fed to innerproduct layer with output 512 channels.



According to discussion here, innerproduct layer is designed to accept fixed size of input. What does it mean to fixed size of input?



Where should I check to meet innerproduct layer's fixed size requirement?
My RCNN network is shown here.










share|improve this question





























    0















    There is another similar question here. But that is not duplicate to my question as they are more on input dimension mismatch from network's requirement.



    For me is I use customized network but input dimension is still the same.
    I am not using any per-trained network as it is my own network.



    I have problem with training on customized network with py-faster-rcnn method.
    The original VGG16 NET has the following RCNN network.



    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: "fc6"
    type: "InnerProduct"
    bottom: "pool5"
    top: "fc6"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    inner_product_param {
    num_output: 4096
    }
    }
    layer {
    name: "relu6"
    type: "ReLU"
    bottom: "fc6"
    top: "fc6"
    }
    layer {
    name: "drop6"
    type: "Dropout"
    bottom: "fc6"
    top: "fc6"
    dropout_param {
    dropout_ratio: 0.5
    }
    }
    layer {
    name: "fc7"
    type: "InnerProduct"
    bottom: "fc6"
    top: "fc7"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    inner_product_param {
    num_output: 4096
    }
    }
    layer {
    name: "relu7"
    type: "ReLU"
    bottom: "fc7"
    top: "fc7"
    }
    layer {
    name: "drop7"
    type: "Dropout"
    bottom: "fc7"
    top: "fc7"
    dropout_param {
    dropout_ratio: 0.5
    }
    }
    layer {
    name: "cls_score"
    type: "InnerProduct"
    bottom: "fc7"
    top: "cls_score"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    inner_product_param {
    num_output: 4
    weight_filler {
    type: "gaussian"
    std: 0.01
    }
    bias_filler {
    type: "constant"
    value: 0
    }
    }
    }
    layer {
    name: "bbox_pred"
    type: "InnerProduct"
    bottom: "fc7"
    top: "bbox_pred"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    inner_product_param {
    num_output: 16
    weight_filler {
    type: "gaussian"
    std: 0.001
    }
    bias_filler {
    type: "constant"
    value: 0
    }
    }
    }
    layer {
    name: "loss_cls"
    type: "SoftmaxWithLoss"
    bottom: "cls_score"
    bottom: "labels"
    propagate_down: 1
    propagate_down: 0
    top: "loss_cls"
    loss_weight: 1
    }
    layer {
    name: "loss_bbox"
    type: "SmoothL1Loss"
    bottom: "bbox_pred"
    bottom: "bbox_targets"
    bottom: "bbox_inside_weights"
    bottom: "bbox_outside_weights"
    top: "loss_bbox"
    loss_weight: 1
    }


    Which has roi_pool5 fed into inner product layer with output channels 4096.
    roi_pool5 has input conv5_3 with 512 channels.



    For me, my RCNN network has Inception layers with 64 channels. Then fed to innerproduct layer with output 512 channels.



    According to discussion here, innerproduct layer is designed to accept fixed size of input. What does it mean to fixed size of input?



    Where should I check to meet innerproduct layer's fixed size requirement?
    My RCNN network is shown here.










    share|improve this question



























      0












      0








      0








      There is another similar question here. But that is not duplicate to my question as they are more on input dimension mismatch from network's requirement.



      For me is I use customized network but input dimension is still the same.
      I am not using any per-trained network as it is my own network.



      I have problem with training on customized network with py-faster-rcnn method.
      The original VGG16 NET has the following RCNN network.



      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: "fc6"
      type: "InnerProduct"
      bottom: "pool5"
      top: "fc6"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 4096
      }
      }
      layer {
      name: "relu6"
      type: "ReLU"
      bottom: "fc6"
      top: "fc6"
      }
      layer {
      name: "drop6"
      type: "Dropout"
      bottom: "fc6"
      top: "fc6"
      dropout_param {
      dropout_ratio: 0.5
      }
      }
      layer {
      name: "fc7"
      type: "InnerProduct"
      bottom: "fc6"
      top: "fc7"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 4096
      }
      }
      layer {
      name: "relu7"
      type: "ReLU"
      bottom: "fc7"
      top: "fc7"
      }
      layer {
      name: "drop7"
      type: "Dropout"
      bottom: "fc7"
      top: "fc7"
      dropout_param {
      dropout_ratio: 0.5
      }
      }
      layer {
      name: "cls_score"
      type: "InnerProduct"
      bottom: "fc7"
      top: "cls_score"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 4
      weight_filler {
      type: "gaussian"
      std: 0.01
      }
      bias_filler {
      type: "constant"
      value: 0
      }
      }
      }
      layer {
      name: "bbox_pred"
      type: "InnerProduct"
      bottom: "fc7"
      top: "bbox_pred"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 16
      weight_filler {
      type: "gaussian"
      std: 0.001
      }
      bias_filler {
      type: "constant"
      value: 0
      }
      }
      }
      layer {
      name: "loss_cls"
      type: "SoftmaxWithLoss"
      bottom: "cls_score"
      bottom: "labels"
      propagate_down: 1
      propagate_down: 0
      top: "loss_cls"
      loss_weight: 1
      }
      layer {
      name: "loss_bbox"
      type: "SmoothL1Loss"
      bottom: "bbox_pred"
      bottom: "bbox_targets"
      bottom: "bbox_inside_weights"
      bottom: "bbox_outside_weights"
      top: "loss_bbox"
      loss_weight: 1
      }


      Which has roi_pool5 fed into inner product layer with output channels 4096.
      roi_pool5 has input conv5_3 with 512 channels.



      For me, my RCNN network has Inception layers with 64 channels. Then fed to innerproduct layer with output 512 channels.



      According to discussion here, innerproduct layer is designed to accept fixed size of input. What does it mean to fixed size of input?



      Where should I check to meet innerproduct layer's fixed size requirement?
      My RCNN network is shown here.










      share|improve this question
















      There is another similar question here. But that is not duplicate to my question as they are more on input dimension mismatch from network's requirement.



      For me is I use customized network but input dimension is still the same.
      I am not using any per-trained network as it is my own network.



      I have problem with training on customized network with py-faster-rcnn method.
      The original VGG16 NET has the following RCNN network.



      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: "fc6"
      type: "InnerProduct"
      bottom: "pool5"
      top: "fc6"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 4096
      }
      }
      layer {
      name: "relu6"
      type: "ReLU"
      bottom: "fc6"
      top: "fc6"
      }
      layer {
      name: "drop6"
      type: "Dropout"
      bottom: "fc6"
      top: "fc6"
      dropout_param {
      dropout_ratio: 0.5
      }
      }
      layer {
      name: "fc7"
      type: "InnerProduct"
      bottom: "fc6"
      top: "fc7"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 4096
      }
      }
      layer {
      name: "relu7"
      type: "ReLU"
      bottom: "fc7"
      top: "fc7"
      }
      layer {
      name: "drop7"
      type: "Dropout"
      bottom: "fc7"
      top: "fc7"
      dropout_param {
      dropout_ratio: 0.5
      }
      }
      layer {
      name: "cls_score"
      type: "InnerProduct"
      bottom: "fc7"
      top: "cls_score"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 4
      weight_filler {
      type: "gaussian"
      std: 0.01
      }
      bias_filler {
      type: "constant"
      value: 0
      }
      }
      }
      layer {
      name: "bbox_pred"
      type: "InnerProduct"
      bottom: "fc7"
      top: "bbox_pred"
      param {
      lr_mult: 1
      }
      param {
      lr_mult: 2
      }
      inner_product_param {
      num_output: 16
      weight_filler {
      type: "gaussian"
      std: 0.001
      }
      bias_filler {
      type: "constant"
      value: 0
      }
      }
      }
      layer {
      name: "loss_cls"
      type: "SoftmaxWithLoss"
      bottom: "cls_score"
      bottom: "labels"
      propagate_down: 1
      propagate_down: 0
      top: "loss_cls"
      loss_weight: 1
      }
      layer {
      name: "loss_bbox"
      type: "SmoothL1Loss"
      bottom: "bbox_pred"
      bottom: "bbox_targets"
      bottom: "bbox_inside_weights"
      bottom: "bbox_outside_weights"
      top: "loss_bbox"
      loss_weight: 1
      }


      Which has roi_pool5 fed into inner product layer with output channels 4096.
      roi_pool5 has input conv5_3 with 512 channels.



      For me, my RCNN network has Inception layers with 64 channels. Then fed to innerproduct layer with output 512 channels.



      According to discussion here, innerproduct layer is designed to accept fixed size of input. What does it mean to fixed size of input?



      Where should I check to meet innerproduct layer's fixed size requirement?
      My RCNN network is shown here.







      tensorflow deep-learning caffe






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 22 '18 at 6:10







      batuman

















      asked Nov 22 '18 at 6:02









      batumanbatuman

      2,585744111




      2,585744111
























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