| class RoIDataLayer(caffe.Layer): """Fast R-CNN data layer used for training.""" def _shuffle_roidb_inds(self): """Randomly permute the training roidb.""" 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 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. """ 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] 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) #这个函数相当于ｃａｆｆｅ ｃ＋＋接口的ＳｅｔＵｐ def setup(self, bottom, top): """Setup the RoIDataLayer.""" # parse the layer parameter string, which must be valid YAML #下面的这句的作用是解析输入的参数（输入的参数是ｐｙｔｈｏｎ字典的形式的字符串），通过下面的函数就生成ｌａｙｅｒ_params这个字典变量 layer_params = yaml.load(self.param_str_) self._num_classes = layer_params['num_classes'] self._name_to_top_map = { 'data': 0, 'rois': 1, 'labels': 2} # data blob: holds a batch of N images, each with 3 channels # The height and width (100 x 100) are dummy values top[0].reshape(1, 3, 100, 100) # 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) ｒｅｇｉｏｎ of interest 这个表示ｆａｓｔ ｒｃｎｎ使用的预选框（ｓｅｌｅｃｔ ｓｅａｒｃｈ） top[1].reshape(1, 5) # labels blob: R categorical labels in [0, ..., K] for K foreground # classes plus background top[2].reshape(1) #训练的时候需要输出ｇｒｏｕｎｄ ｔｒｕｔｈ和ｂｏｕｎｄｉｎｇ ｂｏｘ的ｌｏｓｓ权重 if cfg.TRAIN.BBOX_REG: self._name_to_top_map['bbox_targets'] = 3 self._name_to_top_map['bbox_loss_weights'] = 4 # bbox_targets blob: R bounding-box regression targets with 4 # targets per class top[3].reshape(1, self._num_classes * 4) # bbox_loss_weights blob: At most 4 targets per roi are active; # thisbinary vector sepcifies the subset of active targets top[4].reshape(1, self._num_classes * 4) def forward(self, bottom, top): """Get blobs and copy them into this layer's top blob vector.""" blobs = self._get_next_minibatch() #字典的遍历方式 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 |
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