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New YOLOv8 Results() class for prediction outputs (#314)

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Ayush Chaurasia 2023-01-17 19:02:34 +05:30 • committed by GitHub
parent 0cb87f7dd3
commit c6985da9de
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32 changed files with 813 additions and 259 deletions
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6
ultralytics/yolo/engine/trainer.py
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@ -30,7 +30,7 @@ from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import (DEFAULT_CONFIG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
yaml_save)
from ultralytics.yolo.utils.autobatch import check_train_batch_size
from ultralytics.yolo.utils.checks import check_file, print_args
from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
from ultralytics.yolo.utils.files import get_latest_run, increment_path
from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds, one_cycle, strip_optimizer
@ -203,7 +203,9 @@ class BaseTrainer:
self.set_model_attributes()
if world_size > 1:
self.model = DDP(self.model, device_ids=[rank])
# 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 * 2)
# Batch size
if self.batch_size == -1:
if RANK == -1: # single-GPU only, estimate best batch size