加载预训练网络时，错误表明尺寸变平

Question

问题

我正在尝试加载预训练网络，但出现以下错误

F1101 23:03:41.857909 73 net.cpp:757] 无法复制参数 0 权重 从层'fc4'；形状不匹配。源参数形状为 512 4096 (2097152);目标参数形状为 512 256 4 4 (2097152)。要学习这个 层的参数从头开始，而不是从保存的网络中复制， 重命名图层。

我注意到 512 x 256 x 4 x 4 == 512 x 4096，所以似乎在保存和重新加载网络权重时，图层以某种方式变平了。

如何解决这个错误？

重现

我正在尝试在this GitHub repository 中使用 D-CNN 预训练网络。

我用

加载网络

import caffe
net = caffe.Net('deploy_D-CNN.prototxt', 'D-CNN.caffemodel', caffe.TEST)

prototxt文件是

name: "D-CNN"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 259
input_dim: 259
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 64
    kernel_size: 5
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    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"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "fc4"
  type: "Convolution"
  bottom: "conv3"
  top: "fc4"
  convolution_param {
    num_output: 512
    pad: 0
    kernel_size: 4
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "fc4"
  top: "fc4"
}
layer {
  name: "drop4"
  type: "Dropout"
  bottom: "fc4"
  top: "fc4"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer { 
  name: "pool5_spm3"
  type: "Pooling"
  bottom: "fc4"
  top: "pool5_spm3"
  pooling_param {
    pool: MAX
    kernel_size: 10
    stride: 10
  }
}
layer {
  name: "pool5_spm3_flatten"
  type: "Flatten"
  bottom: "pool5_spm3"
  top: "pool5_spm3_flatten"
}
layer { 
  name: "pool5_spm2"
  type: "Pooling"
  bottom: "fc4"
  top: "pool5_spm2"
  pooling_param {
    pool: MAX
    kernel_size: 14
    stride: 14
  }
}
layer {
  name: "pool5_spm2_flatten"
  type: "Flatten"
  bottom: "pool5_spm2"
  top: "pool5_spm2_flatten"
}
layer { 
  name: "pool5_spm1"
  type: "Pooling"
  bottom: "fc4"
  top: "pool5_spm1"
  pooling_param {
    pool: MAX
    kernel_size: 29
    stride: 29
  }
}
layer {
  name: "pool5_spm1_flatten"
  type: "Flatten"
  bottom: "pool5_spm1"
  top: "pool5_spm1_flatten"
}
layer {
  name: "pool5_spm"
  type: "Concat"
  bottom: "pool5_spm1_flatten"
  bottom: "pool5_spm2_flatten"
  bottom: "pool5_spm3_flatten"
  top: "pool5_spm"
  concat_param {
    concat_dim: 1
  }
}


layer {
  name: "fc4_2"
  type: "InnerProduct"
  bottom: "pool5_spm"
  top: "fc4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "fc4_2"
  top: "fc4_2"
}
layer {
  name: "drop4"
  type: "Dropout"
  bottom: "fc4_2"
  top: "fc4_2"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layer {
  name: "fc5"
  type: "InnerProduct"
  bottom: "fc4_2"
  top: "fc5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 19
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "fc5"
  top: "prob"
}

Answer 1

您似乎正在使用一个预训练网络，其中 "fc4" 层是一个全连接层（又名 type: "InnerProduct" 层），它被“重塑”为一个卷积层。
由于内积层和卷积层对输入执行大致相同的线性运算，因此可以在某些假设下进行此更改（例如，参见here）。
正如您已经正确识别的那样，原始预训练的全连接层的权重被保存为“扁平化”w.r.t caffe 期望卷积层的形状。

我认为可以使用share_mode: PERMISSIVE：

layer {
  name: "fc4"
  type: "Convolution"
  bottom: "conv3"
  top: "fc4"
  convolution_param {
    num_output: 512
    pad: 0
    kernel_size: 4
  }
  param {
    lr_mult: 1
    decay_mult: 1
    share_mode: PERMISSIVE  # should help caffe overcome the shape mismatch
  }
  param {
    lr_mult: 2
    decay_mult: 0
    share_mode: PERMISSIVE
  }
}