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EarlyStopper updates #4679

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Sep 5, 2021
Merged

EarlyStopper updates #4679

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6 changes: 3 additions & 3 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -344,7 +344,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# mAP
callbacks.on_train_epoch_end(epoch=epoch)
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
final_epoch = epoch + 1 == epochs
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
if not noval or final_epoch: # Calculate mAP
results, maps, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
Expand Down Expand Up @@ -384,7 +384,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
callbacks.on_model_save(last, epoch, final_epoch, best_fitness, fi)

# Stop Single-GPU
if stopper(epoch=epoch, fitness=fi):
if RANK == -1 and stopper(epoch=epoch, fitness=fi):
break

# Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
Expand Down Expand Up @@ -462,7 +462,7 @@ 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)')
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
opt = parser.parse_known_args()[0] if known else parser.parse_args()
return opt

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7 changes: 5 additions & 2 deletions utils/torch_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -298,13 +298,16 @@ class EarlyStopping:
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
self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop
self.possible_stop = False # possible stop may occur next epoch

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
delta = epoch - self.best_epoch # epochs without improvement
self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch
stop = delta >= self.patience # stop training if patience exceeded
if stop:
LOGGER.info(f'EarlyStopping patience {self.patience} exceeded, stopping training.')
return stop
Expand Down