Faster-RCNN_TF代码解读14：roi_data_layer/layer.py

# --------------------------------------------------------
# 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.
"""

from fast_rcnn.config import cfg
from roi_data_layer.minibatch import get_minibatch
import numpy as np

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

    def __init__(self, roidb, num_classes):
        """Set the roidb to be used by this layer during training."""
        self._roidb = roidb
        self._num_classes = num_classes
        self._shuffle_roidb_inds()

    def _shuffle_roidb_inds(self):
        """Randomly permute the training roidb."""
        #将np.arange(len(self._roidb))随机排序，返回该随机排序的array
        self._perm = np.random.permutation(np.arange(len(self._roidb)))
        self._cur = 

    #依次取batch大小个roi的index
    def _get_next_minibatch_inds(self):
        """Return the roidb indices for the next minibatch."""

        #False
        if cfg.TRAIN.HAS_RPN:
            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
        else:
            # sample images
            #cfg.TRAIN.IMS_PER_BATCH为2
            #self._perm为该随机排序的array，self._cur为目前取到了多少个roi
            db_inds = np.zeros((cfg.TRAIN.IMS_PER_BATCH), dtype=np.int32)
            i = 
            while (i < cfg.TRAIN.IMS_PER_BATCH):
                ind = self._perm[self._cur]
                #当前图片中有多少个物体
                num_objs = self._roidb[ind]['boxes'].shape[]
                if num_objs != :
                    db_inds[i] = ind
                    i += 

                self._cur += 
                if self._cur >= len(self._roidb):
                    self._shuffle_roidb_inds()

        return db_inds

    def _get_next_minibatch(self):
        """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.
        """
        #取到下一个batch的引索
        db_inds = self._get_next_minibatch_inds()
        #把对应引索的图像信息（dict）取出，放去一个列表
        minibatch_db = [self._roidb[i] for i in db_inds]
        return get_minibatch(minibatch_db, self._num_classes)

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