Question

问题

我正在尝试加载预训练的网络，但出现以下错误

F1101 23：03：41.857909 73 net.cpp：757]无法复制参数0的权重来自图层“ fc4”；形状不匹配。源参数形状为5124096 （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，所以看来在保存和重新加载网络权重时，层以某种方式变平了。

如何解决此错误？

要复制

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

似乎您正在使用预训练的网络，其中"fc4"层是完全连接的层（也称为type: "InnerProduct"层），并且已“重塑”为卷积层。
由于内积层和卷积层都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）。
正如您已经正确确定的那样，原始的经过预训练的完全连接层的权重已“平整”保存，而不会像caffe所期望的那样是卷积层。

我认为可以使用share_mode: PERMISSIVE解决此问题：

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
  }
}

错误表示加载预训练的网络时尺寸展平

问题

要复制

1 个答案: