def __init__(self, faster_rcnn, optimizer):
super(FasterRCNNTrainer, self).__init__()
self.faster_rcnn = faster_rcnn
self.rpn_sigma = 1
self.roi_sigma = 1
self.anchor_target_creator = AnchorTargetCreator()
self.proposal_target_creator = ProposalTargetCreator()
self.loc_normalize_mean = [0, 0, 0, 0]
self.loc_normalize_std = [0.1, 0.1, 0.2, 0.2]
self.optimizer = optimizer
def __init__(self):
super(trainer, self).__init__()
self.total_loss = 0.
self.rpn_reg_loss = 0.
self.rpn_cls_loss = 0.
self.reg_loss = 0.
self.cls_loss = 0.
self.model=FRCNN('train')
self.model.get_data_loader(shuffule=False)
self.model.get_network()
self.n_sample = [256, 128] # number of samples for two stage targets
self.at = AnchorTargetCreator(self.n_sample[0]) # generate labels for rpn
self.pt = ProposalTargetCreator(self.n_sample[1]) # generate labels for classifier
self.post_thre = n_train_post_nms # number of rois kept for each image
def __init__(self, faster_rcnn,optimizer):
super(FasterRCNNTrainer, self).__init__()
self.faster_rcnn = faster_rcnn
self.rpn_sigma = 3
self.roi_sigma = 1
# target creator create gt_bbox gt_label etc as training targets.
self.anchor_target_creator = AnchorTargetCreator()
self.proposal_target_creator = ProposalTargetCreator()
self.loc_normalize_mean = faster_rcnn.loc_normalize_mean
self.loc_normalize_std = faster_rcnn.loc_normalize_std
self.optimizer = optimizer