Difference between Finetune prototxt and train prototxt about pva-faster-rcnn HOT 5 CLOSED

Another difference

In finetune/train.prototxt

layer {
  name: "conv2_2"
  type: "Eltwise"
  bottom: "conv2_2/3"
  bottom: "conv2_2/input"
  top: "conv2_2"
  eltwise_param {
    operation: SUM
    coeff: 1
    coeff: 1
  }
}

In train/train.prototxt

layer {
  name: "conv2_2"
  type: "Eltwise"
  bottom: "conv2_2/3"
  bottom: "conv2_1"
  top: "conv2_2"
  eltwise_param {
    operation: SUM
    coeff: 1
    coeff: 1
  }
}

bottom: "conv2_2/input" vs bottom: "conv2_1"

what's the difference? Thanks @sanghoon

from pva-faster-rcnn.

@sanghoon Dear sanghoon, I have the same confuse about this

from pva-faster-rcnn.

hi @SeaOfOcean you can see #5

from pva-faster-rcnn.

@SeaOfOcean, @xiaoxiongli
The first difference comes after merging Conv, BN and shift layers.
Thank you @zimenglan-sysu-512.

The second difference is merely the matter of representation.
"conv2_2/input" is a dummy layer which I added for the ease of prototxt editing,
and it's exactly the same with "conv2_1" (since it's the input for conv2_2)

from pva-faster-rcnn.

got it, thanks :)

from pva-faster-rcnn.