I0208 11:35:30.288038  9036 caffe.cpp:217] Using GPUs 0
I0208 11:35:30.426815  9036 caffe.cpp:222] GPU 0: NVIDIA Tegra X1
I0208 11:35:33.318043  9036 solver.cpp:63] Initializing solver from parameters: 
train_net: "finetune_train.prototxt"
test_net: "finetune_val.prototxt"
test_iter: 30
test_interval: 1000
base_lr: 0.0001
display: 20
max_iter: 100000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0001
stepsize: 20000
snapshot: 10000
snapshot_prefix: "finetune_train"
device_id: 0
train_state {
  level: 0
  stage: ""
}
I0208 11:35:33.318629  9036 solver.cpp:96] Creating training net from train_net file: finetune_train.prototxt
I0208 11:35:33.319537  9036 upgrade_proto.cpp:44] Attempting to upgrade input file specified using deprecated transformation parameters: finetune_train.prototxt
I0208 11:35:33.319586  9036 upgrade_proto.cpp:47] Successfully upgraded file specified using deprecated data transformation parameters.
W0208 11:35:33.319610  9036 upgrade_proto.cpp:49] Note that future Caffe releases will only support transform_param messages for transformation fields.
I0208 11:35:33.319631  9036 upgrade_proto.cpp:53] Attempting to upgrade input file specified using deprecated V1LayerParameter: finetune_train.prototxt
I0208 11:35:33.320008  9036 upgrade_proto.cpp:61] Successfully upgraded file specified using deprecated V1LayerParameter
I0208 11:35:33.320314  9036 net.cpp:58] Initializing net from parameters: 
name: "CaffeNet"
state {
  phase: TRAIN
  level: 0
  stage: ""
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "imagenet_mean.binaryproto"
  }
  data_param {
    source: "train_leveldb"
    batch_size: 128
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 0.1
    decay_mult: 1
  }
  param {
    lr_mult: 0.2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 0.1
    decay_mult: 1
  }
  param {
    lr_mult: 0.2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 0.1
    decay_mult: 1
  }
  param {
    lr_mult: 0.2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 0.1
    decay_mult: 1
  }
  param {
    lr_mult: 0.2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 0.1
    decay_mult: 1
  }
  param {
    lr_mult: 0.2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 0.1
    decay_mult: 1
  }
  param {
    lr_mult: 0.2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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: 0.1
    decay_mult: 1
  }
  param {
    lr_mult: 0.2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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: "fc8_pascal"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_pascal"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8_pascal"
  bottom: "label"
}
I0208 11:35:33.321244  9036 layer_factory.hpp:77] Creating layer data
I0208 11:35:33.322032  9036 net.cpp:100] Creating Layer data
I0208 11:35:33.322089  9036 net.cpp:408] data -> data
I0208 11:35:33.322216  9036 net.cpp:408] data -> label
I0208 11:35:33.322319  9036 data_transformer.cpp:27] Loading mean file from: imagenet_mean.binaryproto
I0208 11:35:33.335590  9042 db_lmdb.cpp:35] Opened lmdb train_leveldb
I0208 11:35:33.413972  9036 data_layer.cpp:41] output data size: 128,3,227,227
I0208 11:35:33.947464  9036 net.cpp:150] Setting up data
I0208 11:35:33.947540  9036 net.cpp:157] Top shape: 128 3 227 227 (19787136)
I0208 11:35:33.947608  9036 net.cpp:157] Top shape: 128 (128)
I0208 11:35:33.947634  9036 net.cpp:165] Memory required for data: 79149056
I0208 11:35:33.947669  9036 layer_factory.hpp:77] Creating layer conv1
I0208 11:35:33.947768  9036 net.cpp:100] Creating Layer conv1
I0208 11:35:33.947842  9036 net.cpp:434] conv1 <- data
I0208 11:35:33.947899  9036 net.cpp:408] conv1 -> conv1
I0208 11:35:40.263168  9036 net.cpp:150] Setting up conv1
I0208 11:35:40.263258  9036 net.cpp:157] Top shape: 128 96 55 55 (37171200)
I0208 11:35:40.263294  9036 net.cpp:165] Memory required for data: 227833856
I0208 11:35:40.263368  9036 layer_factory.hpp:77] Creating layer relu1
I0208 11:35:40.263406  9036 net.cpp:100] Creating Layer relu1
I0208 11:35:40.263437  9036 net.cpp:434] relu1 <- conv1
I0208 11:35:40.263464  9036 net.cpp:395] relu1 -> conv1 (in-place)
I0208 11:35:40.267197  9036 net.cpp:150] Setting up relu1
I0208 11:35:40.267256  9036 net.cpp:157] Top shape: 128 96 55 55 (37171200)
I0208 11:35:40.267287  9036 net.cpp:165] Memory required for data: 376518656
I0208 11:35:40.267308  9036 layer_factory.hpp:77] Creating layer pool1
I0208 11:35:40.267347  9036 net.cpp:100] Creating Layer pool1
I0208 11:35:40.267371  9036 net.cpp:434] pool1 <- conv1
I0208 11:35:40.267421  9036 net.cpp:408] pool1 -> pool1
I0208 11:35:40.267596  9036 net.cpp:150] Setting up pool1
I0208 11:35:40.267623  9036 net.cpp:157] Top shape: 128 96 27 27 (8957952)
I0208 11:35:40.267647  9036 net.cpp:165] Memory required for data: 412350464
I0208 11:35:40.267664  9036 layer_factory.hpp:77] Creating layer norm1
I0208 11:35:40.267704  9036 net.cpp:100] Creating Layer norm1
I0208 11:35:40.267724  9036 net.cpp:434] norm1 <- pool1
I0208 11:35:40.267746  9036 net.cpp:408] norm1 -> norm1
I0208 11:35:40.272389  9036 net.cpp:150] Setting up norm1
I0208 11:35:40.272447  9036 net.cpp:157] Top shape: 128 96 27 27 (8957952)
I0208 11:35:40.272476  9036 net.cpp:165] Memory required for data: 448182272
I0208 11:35:40.272497  9036 layer_factory.hpp:77] Creating layer conv2
I0208 11:35:40.272557  9036 net.cpp:100] Creating Layer conv2
I0208 11:35:40.272579  9036 net.cpp:434] conv2 <- norm1
I0208 11:35:40.272611  9036 net.cpp:408] conv2 -> conv2
I0208 11:35:44.283610  9036 net.cpp:150] Setting up conv2
I0208 11:35:44.304832  9036 net.cpp:157] Top shape: 128 256 27 27 (23887872)
I0208 11:35:44.305240  9036 net.cpp:165] Memory required for data: 543733760
I0208 11:35:44.309028  9036 layer_factory.hpp:77] Creating layer relu2
I0208 11:35:44.325594  9036 net.cpp:100] Creating Layer relu2
I0208 11:35:44.325816  9036 net.cpp:434] relu2 <- conv2
I0208 11:35:44.326498  9036 net.cpp:395] relu2 -> conv2 (in-place)
I0208 11:35:44.507019  9036 net.cpp:150] Setting up relu2
I0208 11:35:44.507427  9036 net.cpp:157] Top shape: 128 256 27 27 (23887872)
I0208 11:35:44.507485  9036 net.cpp:165] Memory required for data: 639285248
I0208 11:35:44.507526  9036 layer_factory.hpp:77] Creating layer pool2
I0208 11:35:44.507771  9036 net.cpp:100] Creating Layer pool2
I0208 11:35:44.507833  9036 net.cpp:434] pool2 <- conv2
I0208 11:35:44.507918  9036 net.cpp:408] pool2 -> pool2
I0208 11:35:44.508889  9036 net.cpp:150] Setting up pool2
I0208 11:35:44.508961  9036 net.cpp:157] Top shape: 128 256 13 13 (5537792)
I0208 11:35:44.509027  9036 net.cpp:165] Memory required for data: 661436416
I0208 11:35:44.509080  9036 layer_factory.hpp:77] Creating layer norm2
I0208 11:35:44.509166  9036 net.cpp:100] Creating Layer norm2
I0208 11:35:44.509222  9036 net.cpp:434] norm2 <- pool2
I0208 11:35:44.509287  9036 net.cpp:408] norm2 -> norm2
I0208 11:35:44.514129  9036 net.cpp:150] Setting up norm2
I0208 11:35:44.514218  9036 net.cpp:157] Top shape: 128 256 13 13 (5537792)
I0208 11:35:44.514272  9036 net.cpp:165] Memory required for data: 683587584
I0208 11:35:44.514314  9036 layer_factory.hpp:77] Creating layer conv3
I0208 11:35:44.514560  9036 net.cpp:100] Creating Layer conv3
I0208 11:35:44.514611  9036 net.cpp:434] conv3 <- norm2
I0208 11:35:44.514669  9036 net.cpp:408] conv3 -> conv3
Killed