problem is solved with adapt on GT

experiments/track_on_20.sh

@@ -1,4 +1,4 @@
-#PBS -N track_17_on_20_ada_12
+#PBS -N track_17_on_20_ada_4
 #PBS -m abe
 #PBS -M [email protected]
 #PBS -l nodes=1:ppn=1:gpus=1
@@ -15,4 +15,4 @@ cd /home/abdollahpour.ce.sharif/ByteTrack
 
 
 
-python tools/track.py -t metamot -f exps/example/metamot/yolox_x_mot17_on_mot20.py -d 1 -b 1 -c /home/abdollahpour.ce.sharif/ByteTrack/meta_experiments/train_17_on_20_resume2/best_ckpt.pth.tar --local_rank 0 -expn track_17_on_20_ada_12 --mot20 --adaptation_period 12
+python tools/track.py -t metamot -f exps/example/metamot/yolox_x_mot17_on_mot20.py -d 1 -b 1 -c /home/abdollahpour.ce.sharif/ByteTrack/meta_experiments/train_17_on_20_resume2/best_ckpt.pth.tar --local_rank 0 -expn track_17_on_20_ada_4 --mot20 --adaptation_period 4 --fp16

experiments/track_on_20_with_gt.sh

@@ -13,4 +13,4 @@ cd /home/abdollahpour.ce.sharif/ByteTrack
 
 
 
-python tools/track.py -t metamot -f exps/example/metamot/yolox_x_mot17_on_mot20.py -d 1 -b 1 -c /home/abdollahpour.ce.sharif/ByteTrack/meta_experiments/train_17_on_20_resume2/best_ckpt.pth.tar --local_rank 0 -expn track_17_on_20_ada_4_with_GT --mot20 --adaptation_period 4 --fp16 --use_existing_files
+python tools/track.py -t metamot -f exps/example/metamot/yolox_x_mot17_on_mot20.py -d 1 -b 1 -c /home/abdollahpour.ce.sharif/ByteTrack/meta_experiments/train_17_on_20_resume2/best_ckpt.pth.tar --local_rank 0 -expn track_17_on_20_ada_4_with_GT --mot20 --adaptation_period 4 --fp16

yolox/core/meta_trainer.py

@@ -138,11 +138,11 @@ class MetaTrainer:
 
         self.optimizer.zero_grad()
 
-        logger.info("loss Norm: {} , scale {}".format(torch.norm(loss), self.scaler.get_scale()))
-        loss = self.scaler.scale(loss)
-        logger.info("loss Norm: {} , scale {}".format(torch.norm(loss), self.scaler.get_scale()))
-        # self.scaler.scale(loss).backward()
-        loss.backward()
+        # logger.info("loss Norm: {} , scale {}".format(torch.norm(loss), self.scaler.get_scale()))
+        # loss = self.scaler.scale(loss)
+        # logger.info("loss Norm: {} , scale {}".format(torch.norm(loss), self.scaler.get_scale()))
+        self.scaler.scale(loss).backward()
+        # loss.backward()
         self.scaler.step(self.optimizer)
         self.scaler.update()
 

yolox/evaluators/mot_evaluator.py

@@ -112,7 +112,7 @@ class MOTEvaluator:
         else:
             learner = model
         # TODO half to amp_test
-        self.scaler = torch.cuda.amp.GradScaler(enabled=half,init_scale=8192)
+        self.scaler = torch.cuda.amp.GradScaler(enabled=half, init_scale=2730)
 
         learner = learner.eval()
         self.amp_training = False

yolox/exp/meta_yolox_base.py

@@ -63,7 +63,7 @@ class MetaExp(BaseMetaExp):
         # -----------------  Meta-learning ------------------ #
         self.first_order = True
         self.inner_lr = 1e-6
-        # self.inner_lr = 1e-10
+        # self.inner_lr = 1e-8
 
     def get_model(self):
         from yolox.models import YOLOPAFPN, YOLOX, YOLOXHead