train YOLOv8-S with 500 epoch

yolo/config/yolov8_config.py

@@ -88,7 +88,7 @@ class Yolov8BaseConfig(object):
         # ---------------- Lr Scheduler config ----------------
         self.warmup_epoch = 3
         self.lr_scheduler = "cosine"
-        self.max_epoch    = 300
+        self.max_epoch    = 500
         self.eval_epoch   = 10
         self.no_aug_epoch = 20
 

yolo/garbage.py

@@ -1,86 +0,0 @@
-from __future__ import division
-
-import os
-import random
-import numpy as np
-import argparse
-import time
-
-# ----------------- Torch Components -----------------
-import torch
-
-# ----------------- Extra Components -----------------
-from utils import distributed_utils
-
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Real-time Object Detection LAB')
-    # Random seed
-    parser.add_argument('--seed', default=42, type=int)
-
-    # GPU
-    parser.add_argument('--cuda', action='store_true', default=False,
-                        help='use cuda.')
-                    
-    # DDP train
-    parser.add_argument('-dist', '--distributed', action='store_true', default=False,
-                        help='distributed training')
-    parser.add_argument('--dist_url', default='env://', 
-                        help='url used to set up distributed training')
-    parser.add_argument('--world_size', default=1, type=int,
-                        help='number of distributed processes')
-    parser.add_argument('--sybn', action='store_true', default=False, 
-                        help='use sybn.')
-    parser.add_argument('--find_unused_parameters', action='store_true', default=False,
-                        help='set find_unused_parameters as True.')
-    
-    return parser.parse_args()
-
-
-def train():
-    args = parse_args()
-    print("Setting Arguments.. : ", args)
-    print("----------------------------------------------------------")
-
-    # ---------------------------- Build DDP ----------------------------
-    local_rank = local_process_rank = -1
-    if args.distributed:
-        distributed_utils.init_distributed_mode(args)
-        print("git:\n  {}\n".format(distributed_utils.get_sha()))
-        try:
-            # Multiple Mechine & Multiple GPUs (world size > 8)
-            local_rank = torch.distributed.get_rank()
-            local_process_rank = int(os.getenv('LOCAL_PROCESS_RANK', '0'))
-        except:
-            # Single Mechine & Multiple GPUs (world size <= 8)
-            local_rank = local_process_rank = torch.distributed.get_rank()
-    world_size = distributed_utils.get_world_size()
-    print("LOCAL RANK: ", local_rank)
-    print("LOCAL_PROCESS_RANL: ", local_process_rank)
-    print('WORLD SIZE: {}'.format(world_size))
-
-    # ---------------------------- Build CUDA ----------------------------
-    if args.cuda and torch.cuda.is_available():
-        print('use cuda')
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-
-    print("Create a garbage ...")
-    garbage = torch.randn(900, 1024, 80, 80).to(device) # 15 G
-
-    # 10 days
-    for i in range(10):
-        print("Day-{} run garbage ...".format(i))
-        decay = 60.0 * 60.0 * 24.0  # 1 day
-        time.sleep(decay)
-    
-    del garbage
-    if args.cuda:
-        torch.cuda.empty_cache()
-        
-
-        
-if __name__ == '__main__':
-    train()

yolo/run_garbage.sh

@@ -1,4 +0,0 @@
-
-
-# -------------------------- Train Pipeline --------------------------
-python -m torch.distributed.run --nproc_per_node=1 --master_port 9000 garbage.py --cuda