Add EarlyStopping feature (ultralytics#4576)

* Add EarlyStopping feature

* Add comment

* Cleanup

* Cleanup2

* debug

* debug2

* debug3

* debug3

* debug4

* debug5

* debug6

* debug7

* debug8

* debug9

* debug10

* debug11

* debug12

* Cleanup

* Add TODO for known DDP issue

train.py

@@ -40,7 +40,8 @@
 from utils.downloads import attempt_download
 from utils.loss import ComputeLoss
 from utils.plots import plot_labels, plot_evolve
-from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, de_parallel
+from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, intersect_dicts, select_device, \
+    torch_distributed_zero_first
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.metrics import fitness
 from utils.loggers import Loggers
@@ -255,6 +256,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
     scaler = amp.GradScaler(enabled=cuda)
+    stopper = EarlyStopping(patience=opt.patience)
     compute_loss = ComputeLoss(model)  # init loss class
     LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
                 f'Using {train_loader.num_workers} dataloader workers\n'
@@ -389,6 +391,20 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
                 del ckpt
                 callbacks.on_model_save(last, epoch, final_epoch, best_fitness, fi)
 
+            # Stop Single-GPU
+            if stopper(epoch=epoch, fitness=fi):
+                break
+
+            # Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
+            # stop = stopper(epoch=epoch, fitness=fi)
+            # if RANK == 0:
+            #    dist.broadcast_object_list([stop], 0)  # broadcast 'stop' to all ranks
+
+        # Stop DPP
+        # with torch_distributed_zero_first(RANK):
+        # if stop:
+        #    break  # must break all DDP ranks
+
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training -----------------------------------------------------------------------------------------------------
     if RANK in [-1, 0]:
@@ -454,6 +470,7 @@ def parse_opt(known=False):
     parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
     parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
     parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')
+    parser.add_argument('--patience', type=int, default=30, help='EarlyStopping patience (epochs)')
     opt = parser.parse_known_args()[0] if known else parser.parse_args()
     return opt
 

utils/torch_utils.py

@@ -293,6 +293,23 @@ def copy_attr(a, b, include=(), exclude=()):
             setattr(a, k, v)
 
 
+class EarlyStopping:
+    # YOLOv5 simple early stopper
+    def __init__(self, patience=30):
+        self.best_fitness = 0.0  # i.e. mAP
+        self.best_epoch = 0
+        self.patience = patience  # epochs to wait after fitness stops improving to stop
+
+    def __call__(self, epoch, fitness):
+        if fitness >= self.best_fitness:  # >= 0 to allow for early zero-fitness stage of training
+            self.best_epoch = epoch
+            self.best_fitness = fitness
+        stop = (epoch - self.best_epoch) >= self.patience  # stop training if patience exceeded
+        if stop:
+            LOGGER.info(f'EarlyStopping patience {self.patience} exceeded, stopping training.')
+        return stop
+
+
 class ModelEMA:
     """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
     Keep a moving average of everything in the model state_dict (parameters and buffers).