def get_minibatch_blob_names(is_training=True): """Return blob names in the order in which they are read by the data loader. """ # data blob: holds a batch of N images, each with 3 channels blob_names = ['data'] blob_names += ['normalizer'] # focal loss at fast_rcnn_heads # blob_names += ['normalizer_fcn'] # focal loss at mask_res_top # blob_names += ['pose_pred'] blob_names += ['pose_pred_4'] blob_names += ['pose_pred_8'] blob_names += ['pose_pred_16'] blob_names += ['pose_pred_32'] blob_names += ['pose_line_8'] blob_names += ['pose_line_16'] # seg_gt_label, add segementation on top of fpn2-5 blob_names += ['seg_gt_label'] if cfg.RPN.RPN_ON: # RPN-only or end-to-end Faster R-CNN blob_names += rpn_roi_data.get_rpn_blob_names(is_training=is_training) elif cfg.RETINANET.RETINANET_ON: blob_names += retinanet_roi_data.get_retinanet_blob_names( is_training=is_training ) else: # Fast R-CNN like models trained on precomputed proposals blob_names += fast_rcnn_roi_data.get_fast_rcnn_blob_names( is_training=is_training ) return blob_names
def get_minibatch_blob_names(is_training=True): """Return blob names in the order in which they are read by the data loader. """ # data blob: holds a batch of N images, each with 3 channels blob_names = ['data'] if cfg.REID.APM: blob_names += reid_apm_roi_data.get_reid_blob_names( is_training=is_training) else: blob_names += reid_roi_data.get_reid_blob_names( is_training=is_training) return blob_names if cfg.RPN.RPN_ON: # RPN-only or end-to-end Faster R-CNN blob_names += rpn_roi_data.get_rpn_blob_names(is_training=is_training) elif cfg.RETINANET.RETINANET_ON: blob_names += retinanet_roi_data.get_retinanet_blob_names( is_training=is_training) else: # Fast R-CNN like models trained on precomputed proposals blob_names += fast_rcnn_roi_data.get_fast_rcnn_blob_names( is_training=is_training) return blob_names
def get_minibatch_blob_names(is_training=True): #按照数据加载器(data loader)读取的顺序返回数据blob的name blob_names = ['data'] if cfg.RPN.RPN_ON: # RPN-only or end-to-end Faster R-CNN blob_names blob_names += rpn_roi_data.get_rpn_blob_names(is_training=is_training) elif cfg.RETINANET.RETINANET_ON: blob_names += retinanet_roi_data.get_retinanet_blob_names( is_training=is_training) else: # Fast R-CNN like models trained on precomputed proposals blob_names += fast_rcnn_roi_data.get_fast_rcnn_blob_names( is_training=is_training) return blob_names
def get_minibatch_blob_names(is_training=True): """Return blob names in the order in which they are read by the data loader. """ # data blob: holds a batch of N images, each with 3 channels blob_names = ['data'] if cfg.RPN.RPN_ON: # RPN-only or end-to-end Faster R-CNN blob_names += rpn_roi_data.get_rpn_blob_names(is_training=is_training) elif cfg.RETINANET.RETINANET_ON: blob_names += retinanet_roi_data.get_retinanet_blob_names( is_training=is_training ) else: # Fast R-CNN like models trained on precomputed proposals blob_names += fast_rcnn_roi_data.get_fast_rcnn_blob_names( is_training=is_training ) return blob_names
def get_minibatch_blob_names(is_training=True): """Return blob names in the order in which they are read by the data loader. """ # data blob: holds a batch of N images, each with 3 channels blob_names = ['data'] if cfg.RPN.RPN_ON: # RPN-only or end-to-end Faster R-CNN blob_names += rpn_roi_data.get_rpn_blob_names(is_training=is_training) elif cfg.RETINANET.RETINANET_ON: blob_names += retinanet_roi_data.get_retinanet_blob_names( is_training=is_training) else: # Fast R-CNN like models trained on precomputed proposals blob_names += fast_rcnn_roi_data.get_fast_rcnn_blob_names( is_training=is_training) # Include pre-calculated blobs blob_names += list(cfg.DATA_LOADER.EXTRA_BLOBS) if 'track_n_rois' in cfg.DATA_LOADER.EXTRA_BLOBS: blob_names += ['track_n_rois_one', 'track_n_rois_two'] return blob_names