def __init__(self):
model = get_model()
criterion = get_loss
optimizer = get_optimizer(model)
super().__init__(model, criterion, optimizer)
self.metric_meter['loss'] = meter.AverageValueMeter()
self.metric_meter['acc'] = meter.AverageValueMeter()
def __init__(self):
model = get_model(opt.num_classes)
criterion = get_loss
optimizer = get_optimizer(model)
super().__init__(model=model, criterion=criterion, optimizer=optimizer)
self.config += ('Crop size: ' + str(opt.crop_size) + '\n')
self.best_metric = 0
for m in all_metrcis:
self.metric_meter[m] = meter.AverageValueMeter()
def __init__(self, convert):
self.convert = convert
model = get_model(convert)
criterion = get_loss
optimizer = get_optimizer(model)
super().__init__(model, criterion, optimizer)
self.config += ('text: ' + opt.txt + '\n' + 'train text length: ' +
str(opt.len) + '\n')
self.config += ('predict text length: ' + str(opt.predict_len) + '\n')
self.metric_meter['loss'] = meter.AverageValueMeter()
def __init__(self):
faster_rcnn = get_model()
optimizer = get_optimizer(faster_rcnn)
super().__init__(faster_rcnn, optimizer=optimizer)
self.rpn_sigma = opt.rpn_sigma
self.roi_sigma = opt.roi_sigma
# 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 = self.model.loc_normalize_mean
self.loc_normalize_std = self.model.loc_normalize_std
self.faster_rcnn_loc_loss = _faster_rcnn_loc_loss
self.class_loss = nn.CrossEntropyLoss()
# Indicators for training status.
# self.rpn_cm = meter.ConfusionMeter(2)
# self.roi_cm = meter.ConfusionMeter(2)
for k in LossTuple._fields:
self.metric_meter[k] = meter.AverageValueMeter()