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@@ -815,7 +815,6 @@ class RTCTrainer(object):
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else:
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self.model_ema = None
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-
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def train(self, model):
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for epoch in range(self.start_epoch, self.args.max_epoch):
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if self.args.distributed:
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@@ -862,7 +861,6 @@ class RTCTrainer(object):
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if (epoch % self.args.eval_epoch) == 0 or (epoch == self.args.max_epoch - 1):
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self.eval(model_eval)
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-
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def eval(self, model):
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# chech model
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model_eval = model if self.model_ema is None else self.model_ema.ema
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@@ -914,7 +912,6 @@ class RTCTrainer(object):
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# wait for all processes to synchronize
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dist.barrier()
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-
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def train_one_epoch(self, model):
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# basic parameters
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epoch_size = len(self.train_loader)
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@@ -1007,7 +1004,6 @@ class RTCTrainer(object):
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# LR Schedule
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if not self.second_stage:
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self.lr_scheduler.step()
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-
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def refine_targets(self, targets, min_box_size):
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# rescale targets
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@@ -1024,7 +1020,6 @@ class RTCTrainer(object):
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return targets
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-
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def rescale_image_targets(self, images, targets, stride, min_box_size, multi_scale_range=[0.5, 1.5]):
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"""
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Deployed for Multi scale trick.
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@@ -1063,7 +1058,6 @@ class RTCTrainer(object):
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return images, targets, new_img_size
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-
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def check_second_stage(self):
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# set second stage
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print('============== Second stage of Training ==============')
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@@ -1098,7 +1092,6 @@ class RTCTrainer(object):
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args=self.args, trans_config=self.trans_cfg, max_stride=self.model_cfg['max_stride'], is_train=True)
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self.train_loader.dataset.transform = self.train_transform
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-
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def check_third_stage(self):
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# set third stage
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print('============== Third stage of Training ==============')
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@@ -1117,7 +1110,7 @@ class RTCTrainer(object):
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self.train_transform, self.trans_cfg = build_transform(
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args=self.args, trans_config=self.trans_cfg, max_stride=self.model_cfg['max_stride'], is_train=True)
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self.train_loader.dataset.transform = self.train_transform
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-
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+
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# RTRDet Trainer
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class RTRTrainer(object):
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