ultralytics 8.0.20 CLI yolo simplifications, DDP and ONNX fixes (#608)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sid Prabhakaran <s2siddhu@gmail.com>

ultralytics/yolo/v8/classify/predict.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
+import sys
 
 import torch
 
@@ -63,12 +64,18 @@ class ClassificationPredictor(BasePredictor):
         return log_string
 
 
-def predict(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
-    cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
+def predict(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
+    source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
         else "https://ultralytics.com/images/bus.jpg"
-    predictor = ClassificationPredictor(cfg)
-    predictor.predict_cli()
+
+    args = dict(model=model, source=source, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model)(**args)
+    else:
+        predictor = ClassificationPredictor(args)
+        predictor.predict_cli()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/classify/train.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
+import sys
 
 import torch
 import torchvision
@@ -135,22 +136,18 @@ class ClassificationTrainer(BaseTrainer):
                 #     self.run_callbacks('on_fit_epoch_end')
 
 
-def train(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
-    cfg.data = cfg.data or "mnist160"  # or yolo.ClassificationDataset("mnist")
+def train(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
+    data = cfg.data or "mnist160"  # or yolo.ClassificationDataset("mnist")
+    device = cfg.device if cfg.device is not None else ''
 
-    # Reproduce ImageNet results
-    # cfg.lr0 = 0.1
-    # cfg.weight_decay = 5e-5
-    # cfg.label_smoothing = 0.1
-    # cfg.warmup_epochs = 0.0
-
-    cfg.device = cfg.device if cfg.device is not None else ''
-    # trainer = ClassificationTrainer(cfg)
-    # trainer.train()
-    from ultralytics import YOLO
-    model = YOLO(cfg.model)
-    model.train(**vars(cfg))
+    args = dict(model=model, data=data, device=device, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model).train(**args)
+    else:
+        trainer = ClassificationTrainer(args)
+        trainer.train()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/classify/val.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
+import sys
 
 from ultralytics.yolo.data import build_classification_dataloader
 from ultralytics.yolo.engine.validator import BaseValidator
@@ -45,11 +46,17 @@ class ClassificationValidator(BaseValidator):
         self.logger.info(pf % ("all", self.metrics.top1, self.metrics.top5))
 
 
-def val(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
-    cfg.data = cfg.data or "mnist160"
-    validator = ClassificationValidator(args=cfg)
-    validator(model=cfg.model)
+def val(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
+    data = cfg.data or "mnist160"
+
+    args = dict(model=model, data=data, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model).val(**args)
+    else:
+        validator = ClassificationValidator(args=args)
+        validator(model=args['model'])
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/detect/predict.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
+import sys
 
 import torch
 
@@ -81,12 +82,18 @@ class DetectionPredictor(BasePredictor):
         return log_string
 
 
-def predict(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n.pt"
-    cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
+def predict(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n.pt"
+    source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
         else "https://ultralytics.com/images/bus.jpg"
-    predictor = DetectionPredictor(cfg)
-    predictor.predict_cli()
+
+    args = dict(model=model, source=source, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model)(**args)
+    else:
+        predictor = DetectionPredictor(args)
+        predictor.predict_cli()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/detect/train.py

@@ -1,5 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-
+import sys
 from copy import copy
 
 import torch
@@ -194,15 +194,18 @@ class Loss:
         return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
 
 
-def train(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n.pt"
-    cfg.data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
-    cfg.device = cfg.device if cfg.device is not None else ''
-    # trainer = DetectionTrainer(cfg)
-    # trainer.train()
-    from ultralytics import YOLO
-    model = YOLO(cfg.model)
-    model.train(**vars(cfg))
+def train(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n.pt"
+    data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
+    device = cfg.device if cfg.device is not None else ''
+
+    args = dict(model=model, data=data, device=device, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model).train(**args)
+    else:
+        trainer = DetectionTrainer(args)
+        trainer.train()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/detect/val.py

@@ -1,6 +1,7 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 import os
+import sys
 from pathlib import Path
 
 import numpy as np
@@ -232,11 +233,17 @@ class DetectionValidator(BaseValidator):
         return stats
 
 
-def val(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n.pt"
-    cfg.data = cfg.data or "coco128.yaml"
-    validator = DetectionValidator(args=cfg)
-    validator(model=cfg.model)
+def val(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n.pt"
+    data = cfg.data or "coco128.yaml"
+
+    args = dict(model=model, data=data, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model).val(**args)
+    else:
+        validator = DetectionValidator(args=args)
+        validator(model=args['model'])
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/segment/predict.py

@@ -1,5 +1,7 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
+import sys
+
 import torch
 
 from ultralytics.yolo.engine.results import Results
@@ -98,12 +100,18 @@ class SegmentationPredictor(DetectionPredictor):
         return log_string
 
 
-def predict(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n-seg.pt"
-    cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
+def predict(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n-seg.pt"
+    source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
         else "https://ultralytics.com/images/bus.jpg"
-    predictor = SegmentationPredictor(cfg)
-    predictor.predict_cli()
+
+    args = dict(model=model, source=source, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model)(**args)
+    else:
+        predictor = SegmentationPredictor(args)
+        predictor.predict_cli()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/segment/train.py

@@ -1,5 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-
+import sys
 from copy import copy
 
 import torch
@@ -140,15 +140,18 @@ class SegLoss(Loss):
         return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
 
 
-def train(cfg=DEFAULT_CFG):
-    cfg.model = cfg.model or "yolov8n-seg.pt"
-    cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
-    cfg.device = cfg.device if cfg.device is not None else ''
-    # trainer = SegmentationTrainer(cfg)
-    # trainer.train()
-    from ultralytics import YOLO
-    model = YOLO(cfg.model)
-    model.train(**vars(cfg))
+def train(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n-seg.pt"
+    data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
+    device = cfg.device if cfg.device is not None else ''
+
+    args = dict(model=model, data=data, device=device, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model).train(**args)
+    else:
+        trainer = SegmentationTrainer(args)
+        trainer.train()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/segment/val.py

@@ -1,6 +1,7 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 import os
+import sys
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 
@@ -242,10 +243,17 @@ class SegmentationValidator(DetectionValidator):
         return stats
 
 
-def val(cfg=DEFAULT_CFG):
-    cfg.data = cfg.data or "coco128-seg.yaml"
-    validator = SegmentationValidator(args=cfg)
-    validator(model=cfg.model)
+def val(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or "yolov8n-seg.pt"
+    data = cfg.data or "coco128-seg.yaml"
+
+    args = dict(model=model, data=data, verbose=True)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model).val(**args)
+    else:
+        validator = SegmentationValidator(args=args)
+        validator(model=args['model'])
 
 
 if __name__ == "__main__":