Learning to generate chairs proto

128x128_train.prototxt

name: "CaffeNet"
layers {
  name: "data"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "@YOUR_PATH_TO_DATA@/chairs_128x128_reduced/data-lmdb"
    batch_size: 64
    scale: 0.00390625
    backend: LMDB
  }
}
layers {
  name: "data_aug"
  type: DATA_AUGMENTATION
  bottom: "data"
  top: "data_aug"
  top: "aug_params"
  coeff_schedule_param {
    initial_coeff: 0.1
    final_coeff: 1.
    gamma: 0.00001
  }
  augmentation_param {
    crop_size: 128
    max_multiplier: 1.
    recompute_mean: 0
    zoom: {
      rand_type: "uniform_bernoulli"
      mean: 0.2
      spread: 0.2
      exp: true
      prob: 1.
    }
    squeeze: {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.1
      exp: true
      prob: 1.
    }
    translate {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.1
      prob: 1.
    }
    rotate {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.2
      prob: 1.
    }
    lmult_mult: {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.7
      prob: 1.
      exp: true
    }
    sat_mult: {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.7
      prob: 1.
      exp: true
    }
    col_rotate: {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 1.5
      prob: 1.
    }
  }   
}
layers {
  name: "segm"
  type: DATA
  top: "segm"
  top: "label1"
  data_param {
    source: "@YOUR_PATH_TO_DATA@/chairs_segm_128x128_reduced/data-lmdb"
    batch_size: 64
    scale: 0.00390625
    backend: LMDB
  }
}
layers {
  name: "segm_aug"
  type: DATA_AUGMENTATION
  bottom: "segm"
  top: "segm_aug"
  bottom: "aug_params"
  augmentation_param {
    crop_size: 128
    max_multiplier: 1.
    recompute_mean: 0
    lmult_mult: {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.001
      prob: 0.
      exp: true
    }
    sat_mult: {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.001
      prob: 0.
      exp: true
    }
    col_rotate: {
      rand_type: "uniform_bernoulli"
      mean: 0.
      spread: 0.001
      prob: 0.
    }
  }   
}
layers {
  name: "angles"
  type: HDF5_DATA
  top: "angles"
  top: "labels"
  hdf5_data_param {
    source: "@YOUR_PATH_TO_DATA@/angles_reduced_shuffle.txt"
    batch_size: 64
  }
}
layers{
  name: "label_to_onehot"
  type: LABEL_TO_ONEHOT
  bottom: "label"
  top: "onehot"
  inner_product_param {
    num_output: 843
  }
}
layers {
  name: "fc1_label"
  type: INNER_PRODUCT
  bottom: "onehot"
  top: "fc1_label"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian"
      mean: 0.
      std: 1.
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu1_label"
  type: RELU
  bottom: "fc1_label"
  top: "fc1_label"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "fc2_label"
  type: INNER_PRODUCT
  bottom: "fc1_label"
  top: "fc2_label"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu2_label"
  type: RELU
  bottom: "fc2_label"
  top: "fc2_label"
}
layers {
  name: "fc1_angles"
  type: INNER_PRODUCT
  bottom: "angles"
  top: "fc1_angles"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu1_angles"
  type: RELU
  bottom: "fc1_angles"
  top: "fc1_angles"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "fc2_angles"
  type: INNER_PRODUCT
  bottom: "fc1_angles"
  top: "fc2_angles"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu2_angles"
  type: RELU
  bottom: "fc2_angles"
  top: "fc2_angles"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "fc1_aug_params"
  type: INNER_PRODUCT
  bottom: "aug_params"
  top: "fc1_aug_params"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian"
      mean: 0.
      std: 1.
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu1_aug_params"
  type: RELU
  bottom: "fc1_aug_params"
  top: "fc1_aug_params"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "fc2_aug_params"
  type: INNER_PRODUCT
  bottom: "fc1_aug_params"
  top: "fc2_aug_params"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu2_aug_params"
  type: RELU
  bottom: "fc2_aug_params"
  top: "fc2_aug_params"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "concat"
  type: CONCAT
  bottom: "fc2_label"
  bottom: "fc2_angles"
  bottom: "fc2_aug_params"
  top: "fc2"
  concat_param {
    concat_dim: 1
  }
}
layers {
  name: "fc3"
  type: INNER_PRODUCT
  bottom: "fc2"
  top: "fc3"
  blobs_lr: 1.
  blobs_lr: 2
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu3"
  type: RELU
  bottom: "fc3"
  top: "fc3"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "fc4"
  type: INNER_PRODUCT
  bottom: "fc3"
  top: "fc4"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu4"
  type: RELU
  bottom: "fc4"
  top: "fc4"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "fc5"
  type: INNER_PRODUCT
  bottom: "fc4"
  top: "fc5"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 16384
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu5"
  type: RELU
  bottom: "fc5"
  top: "fc5"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "reshape"
  type: RESHAPE
  bottom: "fc5"
  top: "fc5_reshape"
  reshape_param {
    channels: 256
    height: 8
    width: 8
  }
}
layers {
  name: "deconv6"           
  type: DECONVOLUTION
  bottom: "fc5_reshape"
  top: "deconv6"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 256
    output_height: 16 
    output_width: 16
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 8
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu6"
  type: RELU
  bottom: "deconv6"
  top: "deconv6"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "deconv7"           
  type: DECONVOLUTION
  bottom: "deconv6"
  top: "deconv7"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 92
    output_height: 32 
    output_width: 32
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 8
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu7"
  type: RELU
  bottom: "deconv7"
  top: "deconv7"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "deconv8_new"           
  type: DECONVOLUTION
  bottom: "deconv7"
  top: "deconv8"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 92
    output_height: 64 
    output_width: 64
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 8
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu8"
  type: RELU
  bottom: "deconv8"
  top: "deconv8"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "deconv9_new"           
  type: DECONVOLUTION
  bottom: "deconv8"
  top: "deconv9"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 3
    output_height: 128 
    output_width: 128
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "fc5_segm"
  type: INNER_PRODUCT
  bottom: "fc4"
  top: "fc5_segm"
  blobs_lr: 1.
  blobs_lr: 2.
  weight_decay: 0.
  weight_decay: 0. 
  inner_product_param {
    num_output: 8192
    weight_filler {
      type: "gaussian_xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layers {
  name: "relu5_segm"
  type: RELU
  bottom: "fc5_segm"
  top: "fc5_segm"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "reshape"
  type: RESHAPE
  bottom: "fc5_segm"
  top: "fc5_segm_reshape"
  reshape_param {
    channels: 128
    height: 8
    width: 8
  }
}
layers {
  name: "deconv6_segm"           
  type: DECONVOLUTION
  bottom: "fc5_segm_reshape"
  top: "deconv6_segm"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 92
    output_height: 16 
    output_width: 16
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 8
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu6_segm"
  type: RELU
  bottom: "deconv6_segm"
  top: "deconv6_segm"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "deconv7_segm"           
  type: DECONVOLUTION
  bottom: "deconv6_segm"
  top: "deconv7_segm"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 32
    output_height: 32 
    output_width: 32
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 8
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu7_segm"
  type: RELU
  bottom: "deconv7_segm"
  top: "deconv7_segm"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "deconv8_segm_new"           
  type: DECONVOLUTION
  bottom: "deconv7_segm"
  top: "deconv8_segm"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 32
    output_height: 64 
    output_width: 64
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 8
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "relu8_segm"
  type: RELU
  bottom: "deconv8_segm"
  top: "deconv8_segm"
#   relu_param {
#     negative_slope: 0.1
#   }
}
layers {
  name: "deconv9_segm_new"           
  type: DECONVOLUTION
  bottom: "deconv8_segm"
  top: "deconv9_segm"
  blobs_lr: 1.
  blobs_lr: 0.
  weight_decay: 0.
  weight_decay: 0.
  deconvolution_param {
    output_channels: 3
    output_height: 128 
    output_width: 128
    pad: 2
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian_xavier"
      xavier_coeff: 0.1
    }
    bias_filler {
      type: "constant"
#       value: 0.001
    }
  }
}
layers {
  name: "eltwise"
  type: ELTWISE
  bottom: "data_aug"
  bottom: "segm_aug"
  top: "segm_data"
  eltwise_param {
    operation: PROD
  }  
} 
layers {
  name: "recon_data"
  loss_param {
    coeff: 0.1
  }
  type: EUCLIDEAN_LOSS
  bottom: "deconv9"
  bottom: "segm_data"
}
layers {
  name: "recon_segm"
  loss_param {
    coeff: 0.01
  }
  type: EUCLIDEAN_LOSS
  bottom: "deconv9_segm"
  bottom: "segm_aug"
}