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I want to disable the backward computations in certain convolution layers in caffe, how do I do this?
I have used propagate_down setting，however find out it works for fc layer but not convolution layer.

Please help~

first update: I set propagate_down:false in test/pool_proj layer. I don't want it to backward(but other layer backward). But from the log file, it says that the layer still needs backward.

second update: Let's denote a deep learning model, there are two path from input layer to output layer, p1: A->B->C->D, p2: A->B->C1->D, A is the input layer and D is fc layer, others are conv layer. When gradient backward from D to previous layers, p1 has no different from the normal gradient-backward procedure, but for p2, it stop at C1(but the weight of C1 layer still update, it just doesn't backward its error to previous layers).

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
  }
}

log

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.
Lehyu
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  • `propagate_down` should be the way to prevent the gradients from propagating now. (1) what do you mean by "not working"? (2) can you post the prototxt part for the relevant conv layer? (3) can you post relevant [`debug_info` log](http://stackoverflow.com/q/40510706/1714410)? – Shai Nov 15 '16 at 16:05
  • Thank you for your advice. I have updated the question. – Lehyu Nov 16 '16 at 07:55

1 Answers1

2

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

propagate_down is intended to switch off backprop along certain paths from the loss while not entirely turning off layers earlier in the graph. If gradients propagate to a layer by another path, or regularization such as weight decay is not disabled, the parameters of these layers will still be updated. I suspect decay is still on for these layers, so you could set decay_mult: 0 for the weights and biases.

Setting lr_mult: 0 on the other hand fixes parameters and skips backprop where it is unnecessary.

You have decay_mult: 1 in some of the early layers, so the gradients are still calculated. Set lr_mult: 0 in all of the layers that you don't want the weights updated.

For example, change the following:

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
    }
  }
}

to

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
    }
  }
}

Also for reference:

Jonathan
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  • Thank you for your answer. Let's denote a deep learning model, there are two path from input layer to output layer, p1: A->B->C->D, p2: A->B->C1->D, A is the input layer and D is fc layer, others are conv layer. When gradient backward from D to previous layers, p1 has no different from the normal gradient-backward procedure, but for p2, it stop at C1(but the weight of C1 layer still update, it just doesn't backward its error to previous layers). – Lehyu Nov 17 '16 at 10:59