caffe 数据层示例一步一步 [英] caffe data layer example step by step

问题描述

想找一个caffe python数据层的例子来学习.我知道 Fast-RCNN 有一个 python 数据层，但它相当复杂，因为我不熟悉物体检测.
所以我的问题是，是否有一个 python 数据层示例，我可以在其中学习如何定义我自己的数据准备程序?
例如，如何定义一个python数据层做更多的数据增强(如平移、旋转等)比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 I am 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

推荐答案

您可以使用 "Python" 层:在 Python 中实现的层，用于将数据输入您的网络.(在此处查看添加type:Python"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，请参阅此主题.
您可以在此处找到带有预取的数据加载层的草图.

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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