train.prototxt

name: "ZF_ILSVRC"
layer {
  name: 'input-data'
  type: 'Python'
  top: 'data'
  top: 'im_info'
  top: 'gt_boxes'
  python_param {
    module: 'roi_data_layer.layer'
    layer: 'RoIDataLayer'
    param_str: "'num_classes': 201"
  }
}

#========= conv1-conv5 ============

layer {
	name: "conv1"
	type: "Convolution"
	bottom: "data"
	top: "conv1"
	param { lr_mult: 1.0 }
	param { lr_mult: 2.0 }
	convolution_param {
		num_output: 96
		kernel_size: 7
		pad: 3
		stride: 2
	}
}
layer {
	name: "relu1"
	type: "ReLU"
	bottom: "conv1"
	top: "conv1"
}
layer {
	name: "norm1"
	type: "LRN"
	bottom: "conv1"
	top: "norm1"
	lrn_param {
		local_size: 3
		alpha: 0.00005
		beta: 0.75
		norm_region: WITHIN_CHANNEL
	}
}
layer {
	name: "pool1"
	type: "Pooling"
	bottom: "norm1"
	top: "pool1"
	pooling_param {
		kernel_size: 3
		stride: 2
		pad: 1
		pool: MAX
	}
}
layer {
	name: "conv2"
	type: "Convolution"
	bottom: "pool1"
	top: "conv2"
	param { lr_mult: 1.0 }
	param { lr_mult: 2.0 }
	convolution_param {
		num_output: 256
		kernel_size: 5
		pad: 2
		stride: 2
	}
}
layer {
	name: "relu2"
	type: "ReLU"
	bottom: "conv2"
	top: "conv2"
}
layer {
	name: "norm2"
	type: "LRN"
	bottom: "conv2"
	top: "norm2"
	lrn_param {
		local_size: 3
		alpha: 0.00005
		beta: 0.75
		norm_region: WITHIN_CHANNEL
	}
}
layer {
	name: "pool2"
	type: "Pooling"
	bottom: "norm2"
	top: "pool2"
	pooling_param {
		kernel_size: 3
		stride: 2
		pad: 1
		pool: MAX
	}
}
layer {
	name: "conv3"
	type: "Convolution"
	bottom: "pool2"
	top: "conv3"
	param { lr_mult: 1.0 }
	param { lr_mult: 2.0 }
	convolution_param {
		num_output: 384
		kernel_size: 3
		pad: 1
		stride: 1
	}
}
layer {
	name: "relu3"
	type: "ReLU"
	bottom: "conv3"
	top: "conv3"
}
layer {
	name: "conv4"
	type: "Convolution"
	bottom: "conv3"
	top: "conv4"
	param { lr_mult: 1.0 }
	param { lr_mult: 2.0 }
	convolution_param {
		num_output: 384
		kernel_size: 3
		pad: 1
		stride: 1
	}
}
layer {
	name: "relu4"
	type: "ReLU"
	bottom: "conv4"
	top: "conv4"
}
layer {
	name: "conv5"
	type: "Convolution"
	bottom: "conv4"
	top: "conv5"
	param { lr_mult: 1.0 }
	param { lr_mult: 2.0 }
	convolution_param {
		num_output: 256
		kernel_size: 3
		pad: 1
		stride: 1
	}
}
layer {
	name: "relu5"
	type: "ReLU"
	bottom: "conv5"
	top: "conv5"
}

#========= RPN ============

layer {
  name: "rpn_conv/3x3"
  type: "Convolution"
  bottom: "conv5"
  top: "rpn/output"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  convolution_param {
    num_output: 256
    kernel_size: 3 pad: 1 stride: 1
    weight_filler { type: "gaussian" std: 0.01 }
    bias_filler { type: "constant" value: 0 }
  }
}
layer {
  name: "rpn_relu/3x3"
  type: "ReLU"
  bottom: "rpn/output"
  top: "rpn/output"
}

#layer {
#  name: "rpn_conv/3x3"
#  type: "Convolution"
#  bottom: "conv5"
#  top: "rpn_conv/3x3"
#  param { lr_mult: 1.0 }
#  param { lr_mult: 2.0 }
#  convolution_param {
#    num_output: 192
#    kernel_size: 3 pad: 1 stride: 1
#    weight_filler { type: "gaussian" std: 0.01 }
#    bias_filler { type: "constant" value: 0 }
#  }
#}
#layer {
#  name: "rpn_conv/5x5"
#  type: "Convolution"
#  bottom: "conv5"
#  top: "rpn_conv/5x5"
#  param { lr_mult: 1.0 }
#  param { lr_mult: 2.0 }
#  convolution_param {
#    num_output: 64
#    kernel_size: 5 pad: 2 stride: 1
#    weight_filler { type: "gaussian" std: 0.0036 }
#    bias_filler { type: "constant" value: 0 }
#  }
#}
#layer {
#  name: "rpn/output"
#  type: "Concat"
#  bottom: "rpn_conv/3x3"
#  bottom: "rpn_conv/5x5"
#  top: "rpn/output"
#}
#layer {
#  name: "rpn_relu/output"
#  type: "ReLU"
#  bottom: "rpn/output"
#  top: "rpn/output"
#}

layer {
  name: "rpn_cls_score"
  type: "Convolution"
  bottom: "rpn/output"
  top: "rpn_cls_score"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  convolution_param {
    num_output: 18   # 2(bg/fg) * 9(anchors)
    kernel_size: 1 pad: 0 stride: 1
    weight_filler { type: "gaussian" std: 0.01 }
    bias_filler { type: "constant" value: 0 }
  }
}
layer {
  name: "rpn_bbox_pred"
  type: "Convolution"
  bottom: "rpn/output"
  top: "rpn_bbox_pred"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  convolution_param {
    num_output: 36   # 4 * 9(anchors)
    kernel_size: 1 pad: 0 stride: 1
    weight_filler { type: "gaussian" std: 0.01 }
    bias_filler { type: "constant" value: 0 }
  }
}
layer {
   bottom: "rpn_cls_score"
   top: "rpn_cls_score_reshape"
   name: "rpn_cls_score_reshape"
   type: "Reshape"
   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
}
layer {
  name: 'rpn-data'
  type: 'Python'
  bottom: 'rpn_cls_score'
  bottom: 'gt_boxes'
  bottom: 'im_info'
  bottom: 'data'
  top: 'rpn_labels'
  top: 'rpn_bbox_targets'
  top: 'rpn_bbox_inside_weights'
  top: 'rpn_bbox_outside_weights'
  python_param {
    module: 'rpn.anchor_target_layer'
    layer: 'AnchorTargetLayer'
    param_str: "'feat_stride': 16"
  }
}
layer {
  name: "rpn_loss_cls"
  type: "SoftmaxWithLoss"
  bottom: "rpn_cls_score_reshape"
  bottom: "rpn_labels"
  propagate_down: 1
  propagate_down: 0
  top: "rpn_cls_loss"
  loss_weight: 1
  loss_param {
    ignore_label: -1
    normalize: true
  }
}
layer {
  name: "rpn_loss_bbox"
  type: "SmoothL1Loss"
  bottom: "rpn_bbox_pred"
  bottom: "rpn_bbox_targets"
  bottom: 'rpn_bbox_inside_weights'
  bottom: 'rpn_bbox_outside_weights'
  top: "rpn_loss_bbox"
  loss_weight: 1
  smooth_l1_loss_param { sigma: 3.0 }
}

#========= RoI Proposal ============

layer {
  name: "rpn_cls_prob"
  type: "Softmax"
  bottom: "rpn_cls_score_reshape"
  top: "rpn_cls_prob"
}
layer {
  name: 'rpn_cls_prob_reshape'
  type: 'Reshape'
  bottom: 'rpn_cls_prob'
  top: 'rpn_cls_prob_reshape'
  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
}
layer {
  name: 'proposal'
  type: 'Python'
  bottom: 'rpn_cls_prob_reshape'
  bottom: 'rpn_bbox_pred'
  bottom: 'im_info'
  top: 'rpn_rois'
#  top: 'rpn_scores'
  python_param {
    module: 'rpn.proposal_layer'
    layer: 'ProposalLayer'
    param_str: "'feat_stride': 16"
  }
}
#layer {
#  name: 'debug-data'
#  type: 'Python'
#  bottom: 'data'
#  bottom: 'rpn_rois'
#  bottom: 'rpn_scores'
#  python_param {
#    module: 'rpn.debug_layer'
#    layer: 'RPNDebugLayer'
#  }
#}
layer {
  name: 'roi-data'
  type: 'Python'
  bottom: 'rpn_rois'
  bottom: 'gt_boxes'
  top: 'rois'
  top: 'labels'
  top: 'bbox_targets'
  top: 'bbox_inside_weights'
  top: 'bbox_outside_weights'
  python_param {
    module: 'rpn.proposal_target_layer'
    layer: 'ProposalTargetLayer'
    param_str: "'num_classes': 201"
  }
}

#========= RCNN ============

layer {
  name: "roi_pool_conv5"
  type: "ROIPooling"
  bottom: "conv5"
  bottom: "rois"
  top: "roi_pool_conv5"
  roi_pooling_param {
    pooled_w: 6
    pooled_h: 6
    spatial_scale: 0.0625 # 1/16
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "roi_pool_conv5"
  top: "fc6"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  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
    scale_train: false
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  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
    scale_train: false
  }
}
layer {
  name: "cls_score"
  type: "InnerProduct"
  bottom: "fc7"
  top: "cls_score"
  param { lr_mult: 1.0 }
  param { lr_mult: 2.0 }
  inner_product_param {
    num_output: 201
    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: 1.0 }
  param { lr_mult: 2.0 }
  inner_product_param {
    num_output: 804
    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: "cls_loss"
  loss_weight: 1
  loss_param {
    ignore_label: -1
    normalize: true
  }
}
layer {
  name: "loss_bbox"
  type: "SmoothL1Loss"
  bottom: "bbox_pred"
  bottom: "bbox_targets"
  bottom: 'bbox_inside_weights'
  bottom: 'bbox_outside_weights'
  top: "bbox_loss"
  loss_weight: 1
}