Start export implementation (#110)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/v8/__init__.py

@@ -6,4 +6,4 @@ ROOT = Path(__file__).parents[0]  # yolov8 ROOT
 
 __all__ = ["classify", "segment", "detect"]
 
-from ultralytics.yolo.utils.configs import hydra_patch  # noqa (patch hydra cli)
+from ultralytics.yolo.configs import hydra_patch  # noqa (patch hydra cli)

ultralytics/yolo/v8/classify/predict.py

@@ -55,7 +55,7 @@ class ClassificationPredictor(BasePredictor):
 def predict(cfg):
     cfg.model = cfg.model or "squeezenet1_0"
     sz = cfg.imgsz
-    if type(sz) != int:  # recieved listConfig
+    if type(sz) != int:  # received listConfig
         cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]]  # expand
     else:
         cfg.imgsz = [sz, sz]

ultralytics/yolo/v8/classify/train.py

@@ -4,7 +4,8 @@ import torch
 from ultralytics.nn.tasks import ClassificationModel, get_model
 from ultralytics.yolo import v8
 from ultralytics.yolo.data import build_classification_dataloader
-from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
+from ultralytics.yolo.engine.trainer import BaseTrainer
+from ultralytics.yolo.utils import DEFAULT_CONFIG
 
 
 class ClassificationTrainer(BaseTrainer):

ultralytics/yolo/v8/detect/predict.py

@@ -85,7 +85,7 @@ class DetectionPredictor(BasePredictor):
 def predict(cfg):
     cfg.model = cfg.model or "n.pt"
     sz = cfg.imgsz
-    if type(sz) != int:  # recieved listConfig
+    if type(sz) != int:  # received listConfig
         cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]]  # expand
     else:
         cfg.imgsz = [sz, sz]

ultralytics/yolo/v8/detect/train.py

@@ -6,8 +6,8 @@ from ultralytics.nn.tasks import DetectionModel
 from ultralytics.yolo import v8
 from ultralytics.yolo.data import build_dataloader
 from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
-from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
-from ultralytics.yolo.utils import colorstr
+from ultralytics.yolo.engine.trainer import BaseTrainer
+from ultralytics.yolo.utils import DEFAULT_CONFIG, colorstr
 from ultralytics.yolo.utils.loss import BboxLoss
 from ultralytics.yolo.utils.ops import xywh2xyxy
 from ultralytics.yolo.utils.plotting import plot_images, plot_results
@@ -185,7 +185,7 @@ class Loss:
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "models/yolov8n.yaml"
+    cfg.model = cfg.model or "yolov8n.yaml"
     cfg.data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
     # cfg.imgsz = 160
     # cfg.epochs = 5

ultralytics/yolo/v8/segment/predict.py

@@ -98,7 +98,7 @@ class SegmentationPredictor(DetectionPredictor):
 def predict(cfg):
     cfg.model = cfg.model or "n.pt"
     sz = cfg.imgsz
-    if type(sz) != int:  # recieved listConfig
+    if type(sz) != int:  # received listConfig
         cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]]  # expand
     else:
         cfg.imgsz = [sz, sz]

ultralytics/yolo/v8/segment/train.py

@@ -12,11 +12,9 @@ from ultralytics.yolo.utils.plotting import plot_images, plot_results
 from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
 from ultralytics.yolo.utils.torch_utils import de_parallel
 
-from ..detect import DetectionTrainer
-
 
 # BaseTrainer python usage
-class SegmentationTrainer(DetectionTrainer):
+class SegmentationTrainer(v8.detect.DetectionTrainer):
 
     def load_model(self, model_cfg=None, weights=None, verbose=True):
         model = SegmentationModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], verbose=verbose)
@@ -174,7 +172,7 @@ class SegLoss:
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "models/yolov8n-seg.yaml"
+    cfg.model = cfg.model or "yolov8n-seg.yaml"
     cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
     trainer = SegmentationTrainer(cfg)
     trainer.train()