moving to HPC

.gitignore

@@ -139,4 +139,5 @@ docs/api
 events.out.tfevents*
 pretrained
 YOLOX_outputs
+.idea/*
 

exps/example/metamot/yolox_x_mot17_on_mot20.py

@@ -21,6 +21,9 @@ class Exp(MyMetaExp):
         self.train_dir = '/media/external_10TB/10TB/vision/ByteTrackData/MOT17/annotations'
         onlyfiles = [f for f in listdir(self.train_dir) if isfile(join(self.train_dir, f))]
         self.train_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__('FRCNN')]
+        # # TODO: remove
+        # self.train_anns = self.train_anns[:1]
+
         self.val_dir = '/media/external_10TB/10TB/vision/ByteTrackData/MOT20/annotations'
         onlyfiles = [f for f in listdir(self.val_dir) if isfile(join(self.val_dir, f))]
         self.val_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__(

yolox/core/meta_trainer.py

@@ -162,8 +162,10 @@ class MetaTrainer:
         logger.info(
             "Model Summary: {}".format(get_model_info(model, self.exp.test_size))
         )
+        # exit()
         model.to(self.device)
-
+        # from torchsummary import summary
+        # summary(model, input_size=(3, 300, 300), device='cuda')
         # value of epoch will be set in `resume_train`
         model = self.resume_train(model)
 

yolox/exp/meta_yolox_base.py

@@ -23,6 +23,7 @@ class MetaExp(BaseMetaExp):
 
         # ---------------- dataloader config ---------------- #
         # set worker to 4 for shorter dataloader init time
+        # TODO: deal with this multi threading
         self.data_num_workers = 4
         self.input_size = (640, 640)
         self.random_size = (14, 26)