bnm_scheduler = BNMomentumScheduler(detector, bn_lambda=bn_lbmd, last_epoch=-1) def get_current_lr(epoch): lr = BASE_LEARNING_RATE for i, lr_decay_epoch in enumerate(LR_DECAY_STEPS): if epoch >= lr_decay_epoch: lr *= LR_DECAY_RATES[i] return lr def adjust_learning_rate(optimizer, epoch): lr = get_current_lr(epoch) for param_group in optimizer.param_groups: param_group['lr'] = lr # TFBoard Visualizers TRAIN_VISUALIZER = TfVisualizer(LOG_DIR, 'train') TEST_VISUALIZER = TfVisualizer(LOG_DIR, 'test') # Used for Pseudo box generation and AP calculation CONFIG_DICT = {'dataset_config': DATASET_CONFIG, 'remove_empty_box': False, 'use_3d_nms': True, 'nms_iou': 0.25, 'use_old_type_nms': False, 'cls_nms': True, 'per_class_proposal': True, 'conf_thresh': 0.05} print('************************** GLOBAL CONFIG END **************************') # ------------------------------------------------------------------------- GLOBAL CONFIG END def update_ema_variables(model, ema_model, alpha, global_step): # Use the true average until the exponential average is more correct alpha = min(1 - 1 / (global_step + 1), alpha) for ema_param, param in zip(ema_model.parameters(), model.parameters()):
def get_current_lr(epoch): lr = BASE_LEARNING_RATE for i, lr_decay_epoch in enumerate(LR_DECAY_STEPS): if epoch >= lr_decay_epoch: lr *= LR_DECAY_RATES[i] return lr def adjust_learning_rate(optimizer, epoch): lr = get_current_lr(epoch) for param_group in optimizer.param_groups: param_group['lr'] = lr # TFBoard Visualizers TRAIN_VISUALIZER = TfVisualizer(FLAGS, 'train') TEST_VISUALIZER = TfVisualizer(FLAGS, 'test') # Used for AP calculation CONFIG_DICT = { 'remove_empty_box': False, 'use_3d_nms': True, 'nms_iou': 0.25, 'use_old_type_nms': False, 'cls_nms': True, 'per_class_proposal': True, 'conf_thresh': 0.05, 'dataset_config': DATASET_CONFIG } # ------------------------------------------------------------------------- GLOBAL CONFIG END
def get_current_lr(epoch): lr = BASE_LEARNING_RATE for i, lr_decay_epoch in enumerate(LR_DECAY_STEPS): if epoch >= lr_decay_epoch: lr *= LR_DECAY_RATES[i] return lr def adjust_learning_rate(optimizer, epoch): lr = get_current_lr(epoch) for param_group in optimizer.param_groups: param_group["lr"] = lr # TFBoard Visualizers TRAIN_VISUALIZER = TfVisualizer(FLAGS, "train") TEST_VISUALIZER = TfVisualizer(FLAGS, "test") # Used for AP calculation CONFIG_DICT = { "remove_empty_box": False, "use_3d_nms": True, "nms_iou": 0.25, "use_old_type_nms": False, "cls_nms": True, "per_class_proposal": True, "conf_thresh": 0.05, "dataset_config": DATASET_CONFIG, } # ------------------------------------------------------------------------- GLOBAL CONFIG END