Question

我想禁用caffe中某些卷积层的反向计算，我该怎么做？我使用了propagate_down设置，但是发现它适用于fc层但不适用于卷积层。

请帮助〜

首次更新：我在 test / pool_proj 图层中设置 propagate_down：false 。我不希望它向后（但其他层向后）。但是从日志文件中可以看出该层仍然需要向后。

第二次更新：让我们表示深度学习模型，从输入层到输出层有两条路径，p1：A-> B-> C-> D，p2：A - ＆gt; B-＆gt; C1-＆gt; D，A是输入层，D是fc层，其他是转换层。当从D向后渐变到前一层时，p1与正常的梯度向后过程没有区别，但对于p2，它在C1处停止（但C1层的权重仍然更新，它只是不会将其错误向后移动到前一层）。

prototxt

layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 224
    mean_value: 104
    mean_value: 117
    mean_value: 123
  }
  data_param {
    source: "/media/eric/main/data/ImageNet/ilsvrc12_train_lmdb"
    batch_size: 32
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 224
    mean_value: 104
    mean_value: 117
    mean_value: 123
  }
  data_param {
    source: "/media/eric/main/data/ImageNet/ilsvrc12_val_lmdb"
    batch_size: 50
    backend: LMDB
  }
}
layer {
  name: "conv1/7x7_s2"
  type: "Convolution"
  bottom: "data"
  top: "conv1/7x7_s2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv1/relu_7x7"
  type: "ReLU"
  bottom: "conv1/7x7_s2"
  top: "conv1/7x7_s2"
}
layer {
  name: "pool1/3x3_s2"
  type: "Pooling"
  bottom: "conv1/7x7_s2"
  top: "pool1/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "pool1/norm1"
  type: "LRN"
  bottom: "pool1/3x3_s2"
  top: "pool1/norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2/3x3_reduce"
  type: "Convolution"
  bottom: "pool1/norm1"
  top: "conv2/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv2/relu_3x3_reduce"
  type: "ReLU"
  bottom: "conv2/3x3_reduce"
  top: "conv2/3x3_reduce"
}
layer {
  name: "conv2/3x3"
  type: "Convolution"
  bottom: "conv2/3x3_reduce"
  top: "conv2/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv2/relu_3x3"
  type: "ReLU"
  bottom: "conv2/3x3"
  top: "conv2/3x3"
}
layer {
  name: "conv2/norm2"
  type: "LRN"
  bottom: "conv2/3x3"
  top: "conv2/norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2/3x3_s2"
  type: "Pooling"
  bottom: "conv2/norm2"
  top: "pool2/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}


layer {
  name: "test/5x5_reduce"
  type: "Convolution"
  bottom: "pool2/3x3_s2"
  top: "test/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "test/relu_5x5_reduce"
  type: "ReLU"
  bottom: "test/5x5_reduce"
  top: "test/5x5_reduce"
}
layer {
  name: "test/5x5"
  type: "Convolution"
  bottom: "test/5x5_reduce"
  top: "test/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "test/relu_5x5"
  type: "ReLU"
  bottom: "test/5x5"
  top: "test/5x5"
}
layer {
  name: "test/pool"
  type: "Pooling"
  bottom: "pool2/3x3_s2"
  top: "test/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "test/pool_proj"
  type: "Convolution"
  bottom: "test/pool"
  top: "test/pool_proj"
  propagate_down:false
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "test/relu_pool_proj"
  type: "ReLU"
  bottom: "test/pool_proj"
  top: "test/pool_proj"
}
layer {
  name: "test/output"
  type: "Concat"
  bottom: "test/5x5"
  bottom: "test/pool_proj"
  top: "test/output"
}

layer{
  name: "test_output/pool"
  type: "Pooling"
  bottom: "test/output"
  top: "test/output"
  pooling_param{
     pool: MAX
     kernel_size: 28
  }
}

layer {
  name: "classifier"
  type: "InnerProduct"
  bottom: "test/output"
  top: "classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "loss3"
  type: "SoftmaxWithLoss"
  bottom: "classifier"
  bottom: "label"
  top: "loss3"
  loss_weight: 1
}
layer {
  name: "top-1"
  type: "Accuracy"
  bottom: "classifier"
  bottom: "label"
  top: "top-1"
  include {
    phase: TEST
  }
}
layer {
  name: "top-5"
  type: "Accuracy"
  bottom: "classifier"
  bottom: "label"
  top: "top-5"
  include {
    phase: TEST
  }
  accuracy_param {
    top_k: 5
  }
}

日志

I1116 15:44:04.405261 19358 net.cpp:226] loss3 needs backward computation.
I1116 15:44:04.405283 19358 net.cpp:226] classifier needs backward computation.
I1116 15:44:04.405302 19358 net.cpp:226] test_output/pool needs backward computation.
I1116 15:44:04.405320 19358 net.cpp:226] test/output needs backward computation.
I1116 15:44:04.405339 19358 net.cpp:226] test/relu_pool_proj needs backward computation.
I1116 15:44:04.405357 19358 net.cpp:226] test/pool_proj needs backward computation.
I1116 15:44:04.405375 19358 net.cpp:228] test/pool does not need backward computation.
I1116 15:44:04.405395 19358 net.cpp:226] test/relu_5x5 needs backward computation.
I1116 15:44:04.405412 19358 net.cpp:226] test/5x5 needs backward computation.
I1116 15:44:04.405431 19358 net.cpp:226] test/relu_5x5_reduce needs backward computation.
I1116 15:44:04.405448 19358 net.cpp:226] test/5x5_reduce needs backward computation.
I1116 15:44:04.405468 19358 net.cpp:226] pool2/3x3_s2_pool2/3x3_s2_0_split needs backward computation.
I1116 15:44:04.405485 19358 net.cpp:226] pool2/3x3_s2 needs backward computation.
I1116 15:44:04.405505 19358 net.cpp:226] conv2/norm2 needs backward computation.
I1116 15:44:04.405522 19358 net.cpp:226] conv2/relu_3x3 needs backward computation.
I1116 15:44:04.405542 19358 net.cpp:226] conv2/3x3 needs backward computation.
I1116 15:44:04.405560 19358 net.cpp:226] conv2/relu_3x3_reduce needs backward computation.
I1116 15:44:04.405578 19358 net.cpp:226] conv2/3x3_reduce needs backward computation.
I1116 15:44:04.405596 19358 net.cpp:226] pool1/norm1 needs backward computation.
I1116 15:44:04.405616 19358 net.cpp:226] pool1/3x3_s2 needs backward computation.
I1116 15:44:04.405632 19358 net.cpp:226] conv1/relu_7x7 needs backward computation.
I1116 15:44:04.405652 19358 net.cpp:226] conv1/7x7_s2 needs backward computation.
I1116 15:44:04.405670 19358 net.cpp:228] data does not need backward computation.
I1116 15:44:04.405705 19358 net.cpp:270] This network produces output loss3
I1116 15:44:04.405745 19358 net.cpp:283] Network initialization done.

Answer 1

来自Evan Shelhamer（https://groups.google.com/forum/#!topic/caffe-users/54Z-B-CXmLE）：

propagate_down旨在沿某些路径关闭backprop   从损失的角度来看，并没有完全关闭   图形。如果渐变通过另一条路径传播到图层，或者   正常化如重量衰减不被禁用，参数为   这些图层仍将更新。我怀疑腐烂还在继续   这些图层，所以你可以为权重和设置decay_mult：0   偏见。

另一方面，设置lr_mult：0可修复参数并跳过   在没有必要的地方支持。

在某些早期图层中有decay_mult: 1，因此仍会计算渐变。在您不希望更新权重的所有图层中设置lr_mult: 0。

例如，更改以下内容：

layer {
  name: "conv1/7x7_s2"
  type: "Convolution"
  bottom: "data"
  top: "conv1/7x7_s2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

到

layer {
  name: "conv1/7x7_s2"
  type: "Convolution"
  bottom: "data"
  top: "conv1/7x7_s2"
  param {
    lr_mult: 0
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

另作参考：

https://github.com/BVLC/caffe/issues/4984

如何防止caffe中特定层的反向计算

1 个答案: