deeplabv2_vgg16_test

deeplabv2_vgg16_test

# VGG 16-layer network convolutional finetuning
# Network modified to have smaller receptive field (128 pixels)
# and smaller stride (8 pixels) when run in convolutional mode.
#
# In this model we also change max pooling size in the first 4 layers
# from 2 to 3 while retaining stride = 2
# which makes it easier to exactly align responses at different layers.
#

name: "deeplabv2_vgg16_test"

layer {
  name: "data"
  type: "ImageSegData"
  top: "data"
  top: "label"
  top: "data_dim"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 513
    mean_value: 104.008
    mean_value: 116.669
    mean_value: 122.675
  }
  image_data_param {
    root_folder: ""
    source: "camvid/list/val.txt"
    batch_size: 1
    label_type: NONE
  }
}

###################### DeepLab ##########################
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  bottom: "conv4_3"
  top: "pool4"
  name: "pool4"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 3
    pad: 1
    stride: 1
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  convolution_param {
    num_output: 512
    pad: 2
    kernel_size: 3
    dilation: 2
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  convolution_param {
    num_output: 512
    pad: 2
    kernel_size: 3
    dilation: 2
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  convolution_param {
    num_output: 512
    pad: 2
    kernel_size: 3
    dilation: 2
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  bottom: "conv5_3"
  top: "pool5"
  name: "pool5"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}

### hole = 6
layer {
  name: "fc6_1"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6_1"
  convolution_param {
    num_output: 1024
    pad: 6
    kernel_size: 3
    dilation: 6
  }
}
layer {
  name: "relu6_1"
  type: "ReLU"
  bottom: "fc6_1"
  top: "fc6_1"
}
layer {
  name: "drop6_1"
  type: "Dropout"
  bottom: "fc6_1"
  top: "fc6_1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7_1"
  type: "Convolution"
  bottom: "fc6_1"
  top: "fc7_1"
  convolution_param {
    num_output: 1024
    kernel_size: 1
  }
}
layer {
  name: "relu7_1"
  type: "ReLU"
  bottom: "fc7_1"
  top: "fc7_1"
}
layer {
  name: "drop7_1"
  type: "Dropout"
  bottom: "fc7_1"
  top: "fc7_1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_camvid_1"
  type: "Convolution"
  bottom: "fc7_1"
  top: "fc8_camvid_1"
  convolution_param {
    num_output: 12
    kernel_size: 1
  }
}

### hole = 12
layer {
  name: "fc6_2"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6_2"
  convolution_param {
    num_output: 1024
    pad: 12
    kernel_size: 3
    dilation: 12
  }
}
layer {
  name: "relu6_2"
  type: "ReLU"
  bottom: "fc6_2"
  top: "fc6_2"
}
layer {
  name: "drop6_2"
  type: "Dropout"
  bottom: "fc6_2"
  top: "fc6_2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7_2"
  type: "Convolution"
  bottom: "fc6_2"
  top: "fc7_2"
  convolution_param {
    num_output: 1024
    kernel_size: 1
  }
}
layer {
  name: "relu7_2"
  type: "ReLU"
  bottom: "fc7_2"
  top: "fc7_2"
}
layer {
  name: "drop7_2"
  type: "Dropout"
  bottom: "fc7_2"
  top: "fc7_2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_camvid_2"
  type: "Convolution"
  bottom: "fc7_2"
  top: "fc8_camvid_2"
  convolution_param {
    num_output: 12
    kernel_size: 1
  }
}

### hole = 18
layer {
  name: "fc6_3"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6_3"
  convolution_param {
    num_output: 1024
    pad: 18
    kernel_size: 3
    dilation: 18
  }
}
layer {
  name: "relu6_3"
  type: "ReLU"
  bottom: "fc6_3"
  top: "fc6_3"
}
layer {
  name: "drop6_3"
  type: "Dropout"
  bottom: "fc6_3"
  top: "fc6_3"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7_3"
  type: "Convolution"
  bottom: "fc6_3"
  top: "fc7_3"
  convolution_param {
    num_output: 1024
    kernel_size: 1
  }
}
layer {
  name: "relu7_3"
  type: "ReLU"
  bottom: "fc7_3"
  top: "fc7_3"
}
layer {
  name: "drop7_3"
  type: "Dropout"
  bottom: "fc7_3"
  top: "fc7_3"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_camvid_3"
  type: "Convolution"
  bottom: "fc7_3"
  top: "fc8_camvid_3"
  convolution_param {
    num_output: 12
    kernel_size: 1
  }
}

### hole = 24
layer {
  name: "fc6_4"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6_4"
  convolution_param {
    num_output: 1024
    pad: 24
    kernel_size: 3
    dilation: 24
  }
}
layer {
  name: "relu6_4"
  type: "ReLU"
  bottom: "fc6_4"
  top: "fc6_4"
}
layer {
  name: "drop6_4"
  type: "Dropout"
  bottom: "fc6_4"
  top: "fc6_4"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7_4"
  type: "Convolution"
  bottom: "fc6_4"
  top: "fc7_4"
  convolution_param {
    num_output: 1024
    kernel_size: 1
  }
}
layer {
  name: "relu7_4"
  type: "ReLU"
  bottom: "fc7_4"
  top: "fc7_4"
}
layer {
  name: "drop7_4"
  type: "Dropout"
  bottom: "fc7_4"
  top: "fc7_4"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_camvid_4"
  type: "Convolution"
  bottom: "fc7_4"
  top: "fc8_camvid_4"
  convolution_param {
    num_output: 12
    kernel_size: 1
  }
}

### SUM the four branches
layer {
  bottom: "fc8_camvid_1"
  bottom: "fc8_camvid_2"
  bottom: "fc8_camvid_3"
  bottom: "fc8_camvid_4"
  top: "fc8_camvid"
  name: "fc8_camvid"
  type: "Eltwise"
  eltwise_param {
    operation: SUM
  }
}
## original resolution
layer {
  name: "fc8_interp"
  type: "Interp"
  bottom: "fc8_camvid"
  top: "fc8_interp"
  interp_param {
    zoom_factor: 8
  }
}
# layer {
#   bottom: "fc8_interp"
#   top: "fc8_interp_argmax"
#   name: "fc8_interp_argmax"
#   type: "ArgMax"
#   argmax_param {
#     axis: 1
#   }
# }
layer {
  name: "fc8_mat"
  type: "MatWrite"
#  bottom: "fc8_interp_argmax"
  bottom: "fc8_interp"
  include {
    phase: TEST
  }
  mat_write_param {
    prefix: "camvid/features/deeplabv2_vgg16/val/fc8/"
    source: "camvid/list/val_id.txt"
    strip: 0
    period: 1
  }
}
layer {
  name: "silence"
  type: "Silence"
  bottom: "label"
  bottom: "data_dim"
}