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ultralytics 8.0.235 YOLOv8 OBB train, val, predict and export (#4499)

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Glenn Jocher 2024-01-05 03:00:26 +01:00 • committed by GitHub
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7
ultralytics/engine/trainer.py
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@ -249,6 +249,7 @@ class BaseTrainer:
# Check imgsz
gs = max(int(self.model.stride.max() if hasattr(self.model, 'stride') else 32), 32) # grid size (max stride)
self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
self.stride = gs # for multi-scale training
# Batch size
if self.batch_size == -1 and RANK == -1: # single-GPU only, estimate best batch size
@ -258,7 +259,11 @@ class BaseTrainer:
batch_size = self.batch_size // max(world_size, 1)
self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode='train')
if RANK in (-1, 0):
self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode='val')
# NOTE: When training DOTA dataset, double batch size could get OOM cause some images got more than 2000 objects.
self.test_loader = self.get_dataloader(self.testset,
batch_size=batch_size if self.args.task == 'obb' else batch_size * 2,
rank=-1,
mode='val')
self.validator = self.get_validator()
metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))