faster rcnn源码解读（五）之layer（网络里的input-data）

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faster rcnn用python版本的 https://github.com/rbgirshick/py-faster-rcnn

layer源码地址： https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/roi_data_layer/layer.py

源码

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# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""The data layer used during training to train a Fast R-CNN network.

RoIDataLayer implements a Caffe Python layer.
"""

import caffe
from fast_rcnn.config import cfg
from roi_data_layer.minibatch import get_minibatch
import numpy as np
import yaml
from multiprocessing import Process, Queue

class RoIDataLayer(caffe.Layer):
    """Fast R-CNN data layer used for training."""

    def _shuffle_roidb_inds(self):
        """Randomly permute the training roidb."""
        if cfg.TRAIN.ASPECT_GROUPING:
            widths = np.array([r['width'] for r in self._roidb])
            heights = np.array([r['height'] for r in self._roidb])
            horz = (widths >= heights)
            vert = np.logical_not(horz)
            horz_inds = np.where(horz)[0]
            vert_inds = np.where(vert)[0]
            inds = np.hstack((
                np.random.permutation(horz_inds),
                np.random.permutation(vert_inds)))
            inds = np.reshape(inds, (-1, 2))
            row_perm = np.random.permutation(np.arange(inds.shape[0]))
            inds = np.reshape(inds[row_perm, :], (-1,))
            self._perm = inds#把roidb的索引打乱，造成的shuffle，打乱的索引存储的地方
        else:
            self._perm = np.random.permutation(np.arange(len(self._roidb)))
        self._cur = 0

    def _get_next_minibatch_inds(self):
        """Return the roidb indices for the next minibatch."""
        if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb):
            self._shuffle_roidb_inds()

        db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH]
        self._cur += cfg.TRAIN.IMS_PER_BATCH#相当于一个指向_perm的指针，每次取走图片后，他会跟着变化#cfg.TRAIN.IMS_PER_BATCH： （猜测，每次取图片的数量）
        return db_inds#本次取得图片的索引

    def _get_next_minibatch(self):#取得本次图片的索引，即db_inds
        """Return the blobs to be used for the next minibatch.

        If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a
        separate process and made available through self._blob_queue.
        """
        if cfg.TRAIN.USE_PREFETCH:
            return self._blob_queue.get()
        else:
            db_inds = self._get_next_minibatch_inds()
            minibatch_db = [self._roidb[i] for i in db_inds]#本次的roidb
            return get_minibatch(minibatch_db, self._num_classes)

    def set_roidb(self, roidb):
        """Set the roidb to be used by this layer during training."""
        self._roidb = roidb
        self._shuffle_roidb_inds()
        if cfg.TRAIN.USE_PREFETCH:
            self._blob_queue = Queue(10)
            self._prefetch_process = BlobFetcher(self._blob_queue,
                                                 self._roidb,
                                                 self._num_classes)
            self._prefetch_process.start()
            # Terminate the child process when the parent exists
            def cleanup():
                print 'Terminating BlobFetcher'
                self._prefetch_process.terminate()
                self._prefetch_process.join()
            import atexit
            atexit.register(cleanup)

    def setup(self, bottom, top):
        """Setup the RoIDataLayer."""

        # parse the layer parameter string, which must be valid YAML
        layer_params = yaml.load(self.param_str_)

        self._num_classes = layer_params['num_classes']#网络里的类别数值21

        self._name_to_top_map = {}#{'gt_boxes': 2, 'data': 0, 'im_info': 1}字典的value值是top的对应索引

        # data blob: holds a batch of N images, each with 3 channels
        idx = 0
        top[idx].reshape(cfg.TRAIN.IMS_PER_BATCH, 3,
            max(cfg.TRAIN.SCALES), cfg.TRAIN.MAX_SIZE)
        self._name_to_top_map['data'] = idx
        idx += 1

        if cfg.TRAIN.HAS_RPN:
            top[idx].reshape(1, 3)
            self._name_to_top_map['im_info'] = idx
            idx += 1

            top[idx].reshape(1, 4)
            self._name_to_top_map['gt_boxes'] = idx
            idx += 1
        else: # not using RPN
            # rois blob: holds R regions of interest, each is a 5-tuple
            # (n, x1, y1, x2, y2) specifying an image batch index n and a
            # rectangle (x1, y1, x2, y2)
            top[idx].reshape(1, 5)
            self._name_to_top_map['rois'] = idx
            idx += 1

            # labels blob: R categorical labels in [0, ..., K] for K foreground
            # classes plus background
            top[idx].reshape(1)
            self._name_to_top_map['labels'] = idx
            idx += 1

            if cfg.TRAIN.BBOX_REG:
                # bbox_targets blob: R bounding-box regression targets with 4
                # targets per class
                top[idx].reshape(1, self._num_classes * 4)
                self._name_to_top_map['bbox_targets'] = idx
                idx += 1

                # bbox_inside_weights blob: At most 4 targets per roi are active;
                # thisbinary vector sepcifies the subset of active targets
                top[idx].reshape(1, self._num_classes * 4)
                self._name_to_top_map['bbox_inside_weights'] = idx
                idx += 1

                top[idx].reshape(1, self._num_classes * 4)
                self._name_to_top_map['bbox_outside_weights'] = idx
                idx += 1

        print 'RoiDataLayer: name_to_top:', self._name_to_top_map
        assert len(top) == len(self._name_to_top_map)

    def forward(self, bottom, top):
        """Get blobs and copy them into this layer's top blob vector."""
        blobs = self._get_next_minibatch()

        for blob_name, blob in blobs.iteritems():
            top_ind = self._name_to_top_map[blob_name]
            # Reshape net's input blobs
            top[top_ind].reshape(*(blob.shape))
            # Copy data into net's input blobs
            top[top_ind].data[...] = blob.astype(np.float32, copy=False)

    def backward(self, top, propagate_down, bottom):
        """This layer does not propagate gradients."""
        pass

    def reshape(self, bottom, top):
        """Reshaping happens during the call to forward."""
        pass

class BlobFetcher(Process):
    """Experimental class for prefetching blobs in a separate process."""
    def __init__(self, queue, roidb, num_classes):
        super(BlobFetcher, self).__init__()
        self._queue = queue
        self._roidb = roidb
        self._num_classes = num_classes
        self._perm = None
        self._cur = 0
        self._shuffle_roidb_inds()
        # fix the random seed for reproducibility
        np.random.seed(cfg.RNG_SEED)

    def _shuffle_roidb_inds(self):
        """Randomly permute the training roidb."""
        # TODO(rbg): remove duplicated code
        self._perm = np.random.permutation(np.arange(len(self._roidb)))
        self._cur = 0

    def _get_next_minibatch_inds(self):
        """Return the roidb indices for the next minibatch."""
        # TODO(rbg): remove duplicated code
        if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb):
            self._shuffle_roidb_inds()

        db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH]
        self._cur += cfg.TRAIN.IMS_PER_BATCH
        return db_inds

    def run(self):
        print 'BlobFetcher started'
        while True:
            db_inds = self._get_next_minibatch_inds()
            minibatch_db = [self._roidb[i] for i in db_inds]
            blobs = get_minibatch(minibatch_db, self._num_classes)
            self._queue.put(blobs)

代码讲解

下面的roidb都只是一次batch的

3.1 setup 在caffe.SGDSolver时调用；setup的top（list猜测是c++的vector）的每个项是caffe._caffe.Blob
（猜测，输出的Top shape就是上面的top,在setup中被shape；top[0],1 3 [600] 1000;top[1],1 3;top[2], 1 4)（疑问，在forward中blob的数据shape被重置，有时大小甚至会不定）

3.2 name_to_top: {‘gt_boxes’: 2, ‘data’: 0, ‘im_info’: 1} 字典的value值是top的对应索引

3.3 solver.step(1) 会调用layer的reshape、forward

3.4 self._perm： 把roidb的索引打乱，造成图片的shuffle，打乱的索引存储的地方

3.5 cfg.TRAIN.IMS_PER_BATCH：（猜测，每次取图片的数量）

3.6 self._cur： 相当于一个指向_perm的指针，每次取走图片后，他会跟着变化

3.7 db_inds： 本次取得图片的索引

3.8 def _get_next_minibatch_inds(self)： 取得本次图片的索引，即db_inds

3.9 minibatch_db： 本次的 roidb

3.10 _num_classes： 网络里的类别数值 21

3.11 forward（）： 得到blob并处理放进top

solver.step(1)->reshape->forward -> _get_next_minbatch->_get_next_minbatch_inds-> (前面在layers里,现在进入minibatch组建真正的blob)get_minibatch

文章作者:Lily

原始链接:/2018/04/08/faster%20rcnn%E6%BA%90%E7%A0%81%E8%A7%A3%E8%AF%BB%EF%BC%88%E4%BA%94%EF%BC%89%E4%B9%8Blayer%EF%BC%88%E7%BD%91%E7%BB%9C%E9%87%8C%E7%9A%84input-data%EF%BC%89/

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