name: "WSDDN"
layer {
  name: 'data'
  type: 'Python'
  top: 'data'
  top: 'rois'
  top: 'binarylabel'
  top: 'boxscores'
  python_param {
    module: 'roi_data_layer.layer'
    layer: 'RoIWeakDataLayer'
    param_str: "'num_classes': 21"
  }
}
##################################
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  # learning rate and decay multipliers for the filters
  param { lr_mult: 0 decay_mult: 0 }
  # learning rate and decay multipliers for the biases
  param { lr_mult: 0 decay_mult: 0 }
  convolution_param {
    num_output: 96
    kernel_size: 7
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    # number of channels to sum over??
    local_size: 5
    # scaling parameter
    alpha: 0.0005
    beta: 0.75
    k: 2
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 5
    stride: 2
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0005
    beta: 0.75
    k: 2
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu5"
type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "roi_pool5"
  type: "ROIPooling"
  bottom: "conv5"
  bottom: "rois"
  top: "pool5"
  roi_pooling_param {
    pooled_w: 6
    pooled_h: 6
    spatial_scale: 0.0625 # 1/16
  }
}
layer {
  name: "box_sc"
  type: "Scale"
  bottom: "conv5"
  bottom: "rois"
  top: "pool5"
  roi_pooling_param {
    pooled_w: 6
    pooled_h: 6
    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: 1024
  }
}
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: "fc8c"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8c"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 21
  }
}
layer {
  name: "fc8d"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8d"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 21
  }
}
layer {
  name: "classification_prob"
  type: "Softmax"
  bottom: "fc8c"
  top: "classification_prob"
  softmax_param {
    axis: 2
  }
}
layer {
  name: "detection_prob"
  type: "Softmax"
  bottom: "fc8d"
  top: "detection_prob"
  softmax_param {
    axis: 1
  }
}
##################################
layer {
  name: "eltwise-prod"
  type: "Eltwise"
  bottom: "classification_prob"
  bottom: "detection_prob"
  top: "cls_prob"
  eltwise_param { operation: PROD }
}
layer {
  name: 'reshape'
  type: 'Python'
  bottom: 'cls_prob'
  top: 'cls_prob_reshaped'
  python_param {
    module: 'roi_data_layer.layer'
    layer: 'SecretAssignmentLayer'
  }
}
layer {
  name: "WeakPred"
  type: "Reduction"
  bottom: "cls_prob_reshaped"
  top: "weakpred"
  reduction_param {
	operation: SUM
	axis: 2
  }
}

#weakpred is of dimensions 1 x Nclasses = 1x20
########## Loss section ##########