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
