caffe数据层示例逐步

问题描述

我想找到一个caffe python数据层示例来学习.我知道Fast-RCNN有一个python数据层，但是自从我对对象检测不熟悉.
所以我的问题是，是否有一个python数据层示例，在这里我可以学习如何定义自己的数据准备过程?
例如，如何定义python数据层会做更多的数据扩充(例如平移，旋转等)而不是caffe "ImageDataLayer".

I want to find a caffe python data layer example to learn.I know that Fast-RCNN has a python data layer, but it's rather complicated since Iam not familiar with object detection.
So my question is, is there a python data layer example where I can learn how to define my own data preparation procedure?
For example, how to do define a python data layer do much more data augmentation(such as translation, rotation etc.) than caffe "ImageDataLayer".

非常感谢您

推荐答案

您可以使用"Python"层:用python实现的层，用于将数据馈入网络. (请参见在此处添加一个type: "Python"图层的示例.)

You can use a "Python" layer: a layer implemented in python to feed data into your net. (See an example for adding a type: "Python" layer here).

import sys, os
sys.path.insert(0, os.environ['CAFFE_ROOT']+'/python')
import caffe
class myInputLayer(caffe.Layer):
  def setup(self,bottom,top):
    # read parameters from `self.param_str`
    ...
  def reshape(self,bottom,top):
    # no "bottom"s for input layer
    if len(bottom)>0:
      raise Exception('cannot have bottoms for input layer')
    # make sure you have the right number of "top"s
    if len(top)!= ...
       raise ...
    top[0].reshape( ... ) # reshape the outputs to the proper sizes

  def forward(self,bottom,top):
    # do your magic here... feed **one** batch to `top`
    top[0].data[...] = one_batch_of_data


  def backward(self, top, propagate_down, bottom):
    # no back-prop for input layers
    pass

有关param_str的更多信息，请参见此线程.
您可以在此处此处预取找到数据加载层的草图.

For more information on param_str see this thread.
You can find a sketch of a data loading layer with pre-fetch here.

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