'best.pt' inherit all-epochs results curves from 'last.pt' (#15791)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/engine/trainer.py

@@ -56,8 +56,6 @@ from ultralytics.utils.torch_utils import (
 
 class BaseTrainer:
     """
-    BaseTrainer.
-
     A base class for creating trainers.
 
     Attributes:
@@ -478,12 +476,16 @@ class BaseTrainer:
         torch.cuda.empty_cache()
         self.run_callbacks("teardown")
 
+    def read_results_csv(self):
+        """Read results.csv into a dict using pandas."""
+        import pandas as pd  # scope for faster 'import ultralytics'
+
+        return {k.strip(): v for k, v in pd.read_csv(self.csv).to_dict(orient="list").items()}
+
     def save_model(self):
         """Save model training checkpoints with additional metadata."""
         import io
 
-        import pandas as pd  # scope for faster 'import ultralytics'
-
         # Serialize ckpt to a byte buffer once (faster than repeated torch.save() calls)
         buffer = io.BytesIO()
         torch.save(
@@ -496,7 +498,7 @@ class BaseTrainer:
                 "optimizer": convert_optimizer_state_dict_to_fp16(deepcopy(self.optimizer.state_dict())),
                 "train_args": vars(self.args),  # save as dict
                 "train_metrics": {**self.metrics, **{"fitness": self.fitness}},
-                "train_results": {k.strip(): v for k, v in pd.read_csv(self.csv).to_dict(orient="list").items()},
+                "train_results": self.read_results_csv(),
                 "date": datetime.now().isoformat(),
                 "version": __version__,
                 "license": "AGPL-3.0 (https://ultralytics.com/license)",
@@ -646,6 +648,9 @@ class BaseTrainer:
             if f.exists():
                 strip_optimizer(f)  # strip optimizers
                 if f is self.best:
+                    if self.last.is_file():  # update best.pt train_metrics from last.pt
+                        k = "train_results"
+                        torch.save({**torch.load(self.best), **{k: torch.load(self.last)[k]}}, self.best)
                     LOGGER.info(f"\nValidating {f}...")
                     self.validator.args.plots = self.args.plots
                     self.metrics = self.validator(model=f)

ultralytics/hub/session.py

@@ -276,7 +276,7 @@ class HUBTrainingSession:
 
             # if request related to metrics upload and exceed retries
             if response is None and kwargs.get("metrics"):
-                self.metrics_upload_failed_queue.update(kwargs.get("metrics", None))
+                self.metrics_upload_failed_queue.update(kwargs.get("metrics"))
 
             return response
 

ultralytics/utils/__init__.py

@@ -716,7 +716,7 @@ def colorstr(*input):
     In the second form, 'blue' and 'bold' will be applied by default.
 
     Args:
-        *input (str): A sequence of strings where the first n-1 strings are color and style arguments,
+        *input (str | Path): A sequence of strings where the first n-1 strings are color and style arguments,
                       and the last string is the one to be colored.
 
     Supported Colors and Styles:

ultralytics/utils/checks.py

@@ -23,6 +23,7 @@ from ultralytics.utils import (
     ASSETS,
     AUTOINSTALL,
     IS_COLAB,
+    IS_GIT_DIR,
     IS_JUPYTER,
     IS_KAGGLE,
     IS_PIP_PACKAGE,
@@ -582,10 +583,9 @@ def check_yolo(verbose=True, device=""):
 
 def collect_system_info():
     """Collect and print relevant system information including OS, Python, RAM, CPU, and CUDA."""
-
     import psutil
 
-    from ultralytics.utils import ENVIRONMENT, IS_GIT_DIR
+    from ultralytics.utils import ENVIRONMENT  # scope to avoid circular import
     from ultralytics.utils.torch_utils import get_cpu_info
 
     ram_info = psutil.virtual_memory().total / (1024**3)  # Convert bytes to GB