bayesian_segnet_basic_inference.prototxt

name: "segnet"
layer {
  name: "data"
  type: "DenseImageData"
  top: "data"
  top: "label"
  dense_image_data_param {
    source: "/SegNet/CamVid/test.txt"	# Change this to the absolute path to your data file
    batch_size: 8			# Change this to be the number of Monte Carlo Dropout samples you wish to make
  }
}
layer {
  name: "norm"
  type: "LRN"
  bottom: "data"
  top: "norm"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "norm"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv1"
  top: "conv1"
  name: "conv1_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  top: "pool1_mask"
  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: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv2"
  top: "conv2"
  name: "conv2_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  top: "pool2_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
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
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv3"
  top: "conv3"
  name: "conv3_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  top: "pool3_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop3"
  type: "Dropout"
  bottom: "pool3"
  top: "pool3"
  dropout_param {
    dropout_ratio: 0.5
    sample_weights_test: true
  }
}
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: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv4"
  top: "conv4"
  name: "conv4_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4"
  top: "pool4"
  top: "pool4_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop4"
  type: "Dropout"
  bottom: "pool4"
  top: "pool4"
  dropout_param {
    dropout_ratio: 0.5
    sample_weights_test: true
  }
}
layer {
  name: "upsample4"
  type: "Upsample"
  bottom: "pool4"
  bottom: "pool4_mask"
  top: "upsample4"
  upsample_param {
    scale: 2
    pad_out_h: true
  }
}
layer {
  name: "conv_decode4"
  type: "Convolution"
  bottom: "upsample4"
  top: "conv_decode4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode4"
  top: "conv_decode4"
  name: "conv_decode4_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "decdrop4"
  type: "Dropout"
  bottom: "conv_decode4"
  top: "conv_decode4"
  dropout_param {
    dropout_ratio: 0.5
    sample_weights_test: true
  }
}
layer {
  name: "upsample3"
  type: "Upsample"
  bottom: "conv_decode4"
  bottom: "pool3_mask"
  top: "upsample3"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv_decode3"
  type: "Convolution"
  bottom: "upsample3"
  top: "conv_decode3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode3"
  top: "conv_decode3"
  name: "conv_decode3_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "decdrop3"
  type: "Dropout"
  bottom: "conv_decode3"
  top: "conv_decode3"
  dropout_param {
    dropout_ratio: 0.5
    sample_weights_test: true
  }
}
layer {
  name: "upsample2"
  type: "Upsample"
  bottom: "conv_decode3"
  bottom: "pool2_mask"
  top: "upsample2"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv_decode2"
  type: "Convolution"
  bottom: "upsample2"
  top: "conv_decode2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode2"
  top: "conv_decode2"
  name: "conv_decode2_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "upsample1"
  type: "Upsample"
  bottom: "conv_decode2"
  bottom: "pool1_mask"
  top: "upsample1"
  upsample_param {
    scale: 2
  }
}
layer {
  name: "conv_decode1"
  type: "Convolution"
  bottom: "upsample1"
  top: "conv_decode1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  bottom: "conv_decode1"
  top: "conv_decode1"
  name: "conv_decode1_bn"
  type: "BN"
  bn_param {
	bn_mode: INFERENCE
	scale_filler {
	  type: "constant"
	  value: 1
	}
	shift_filler {
	  type: "constant"
	  value: 0.001
	}
 }
}
layer {
  name: "dense_softmax_inner_prod"
  type: "Convolution"
  bottom: "conv_decode1"
  top: "dense_softmax_inner_prod"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 11
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "dense_softmax_inner_prod"
  top: "prob"
  softmax_param {engine: CAFFE}
}