# 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: "vgg128_noup_pool3_cocomix"

layers {
  name: "data"
  type: IMAGE_SEG_DATA
  top: "data"
  top: "label"
  top: "data_dim"
  image_data_param {
    root_folder: "/rmt/data/pascal/VOCdevkit/VOC2012"
    source: "voc12/list/test.txt"
    batch_size: 1
    has_label: false
  }
  transform_param {
    mean_value: 104.008
    mean_value: 116.669
    mean_value: 122.675
    crop_size: 513
    mirror: false
  }
  include: { phase: TEST }
}

### NETWORK ###

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

layers {
  bottom: "pool5"
  top: "fc6"
  name: "fc6"
  type: CONVOLUTION
  strict_dim: false
  convolution_param {
    num_output: 4096
    pad: 6
    hole: 4
    kernel_size: 4
  }
}
layers {
  bottom: "fc6"
  top: "fc6"
  name: "relu6"
  type: RELU
}
layers {
  bottom: "fc6"
  top: "fc6"
  name: "drop6"
  type: DROPOUT
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  bottom: "fc6"
  top: "fc7"
  name: "fc7"
  type: CONVOLUTION
  strict_dim: false
  convolution_param {
    num_output: 4096
    kernel_size: 1
  }
}
layers {
  bottom: "fc7"
  top: "fc7"
  name: "relu7"
  type: RELU
}
layers {
  bottom: "fc7"
  top: "fc7"
  name: "drop7"
  type: DROPOUT
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  bottom: "fc7"
  top: "fc8_voc12"
  name: "fc8_voc12"
  type: CONVOLUTION
  strict_dim: false
  convolution_param {
    num_output: 21
    kernel_size: 1
  }
}
layers {
  bottom: "fc8_voc12"
  top: "fc8_interp"
  name: "fc8_interp"
  type: INTERP
  interp_param {
    zoom_factor: 8
  }
}

layers {
  name: "fc8_mat"
  type: MAT_WRITE
  bottom: "fc8_interp"
  mat_write_param {
    prefix: "voc12/features2/vgg128_noup_pool3_cocomix/test/fc8/"
    source: "voc12/list/test_id.txt"
    strip: 0
    period: 1
  }
  include: { phase: TEST }
}

layers {
  bottom: "fc8_interp"
  bottom: "data_dim"
  bottom: "data"
  top: "fc8_crf"
  name: "fc8_crf"
  type: DENSE_CRF
  dense_crf_param { 
    max_iter: 10
    pos_w: 3
    pos_xy_std: 3
    bi_w: 5
    bi_xy_std: 67
    bi_rgb_std: 3
  }
  include: { phase: TEST }
}

layers {
  name: "crf_mat"
  type: MAT_WRITE
  bottom: "fc8_crf"
  mat_write_param {
    prefix: "voc12/features2/vgg128_noup_pool3_cocomix/test/crf/"
    source: "voc12/list/test_id.txt"
    strip: 0
    period: 1
  }
  include: { phase: TEST }
}

layers {
  bottom: "label"
  name: "silence"
  type: SILENCE
  include: { phase: TEST }
}