`ultralytics 8.3.81` Fix Metrics `on_plot` circular references (#19318)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: remipiche <rpiche@flyscan.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
 
-__version__ = "8.3.80"
+__version__ = "8.3.81"
 
 import os
 

ultralytics/models/fastsam/val.py

@@ -37,4 +37,4 @@ class FastSAMValidator(SegmentationValidator):
         super().__init__(dataloader, save_dir, pbar, args, _callbacks)
         self.args.task = "segment"
         self.args.plots = False  # disable ConfusionMatrix and other plots to avoid errors
-        self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
+        self.metrics = SegmentMetrics(save_dir=self.save_dir)

ultralytics/models/yolo/detect/val.py

@@ -37,7 +37,7 @@ class DetectionValidator(BaseValidator):
         self.is_lvis = False
         self.class_map = None
         self.args.task = "detect"
-        self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
+        self.metrics = DetMetrics(save_dir=self.save_dir)
         self.iouv = torch.linspace(0.5, 0.95, 10)  # IoU vector for mAP@0.5:0.95
         self.niou = self.iouv.numel()
         self.lb = []  # for autolabelling
@@ -187,7 +187,7 @@ class DetectionValidator(BaseValidator):
         self.nt_per_image = np.bincount(stats["target_img"].astype(int), minlength=self.nc)
         stats.pop("target_img", None)
         if len(stats):
-            self.metrics.process(**stats)
+            self.metrics.process(**stats, on_plot=self.on_plot)
         return self.metrics.results_dict
 
     def print_results(self):

ultralytics/models/yolo/obb/val.py

@@ -28,7 +28,7 @@ class OBBValidator(DetectionValidator):
         """Initialize OBBValidator and set task to 'obb', metrics to OBBMetrics."""
         super().__init__(dataloader, save_dir, pbar, args, _callbacks)
         self.args.task = "obb"
-        self.metrics = OBBMetrics(save_dir=self.save_dir, plot=True, on_plot=self.on_plot)
+        self.metrics = OBBMetrics(save_dir=self.save_dir, plot=True)
 
     def init_metrics(self, model):
         """Initialize evaluation metrics for YOLO."""

ultralytics/models/yolo/pose/val.py

@@ -32,7 +32,7 @@ class PoseValidator(DetectionValidator):
         self.sigma = None
         self.kpt_shape = None
         self.args.task = "pose"
-        self.metrics = PoseMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
+        self.metrics = PoseMetrics(save_dir=self.save_dir)
         if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
             LOGGER.warning(
                 "WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "

ultralytics/models/yolo/segment/val.py

@@ -34,7 +34,7 @@ class SegmentationValidator(DetectionValidator):
         self.plot_masks = None
         self.process = None
         self.args.task = "segment"
-        self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
+        self.metrics = SegmentMetrics(save_dir=self.save_dir)
 
     def preprocess(self, batch):
         """Preprocesses batch by converting masks to float and sending to device."""

ultralytics/utils/metrics.py

@@ -803,13 +803,11 @@ class DetMetrics(SimpleClass):
     Args:
         save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory.
         plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False.
-        on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
         names (dict of str): A dict of strings that represents the names of the classes. Defaults to an empty tuple.
 
     Attributes:
         save_dir (Path): A path to the directory where the output plots will be saved.
         plot (bool): A flag that indicates whether to plot the precision-recall curves for each class.
-        on_plot (func): An optional callback to pass plots path and data when they are rendered.
         names (dict of str): A dict of strings that represents the names of the classes.
         box (Metric): An instance of the Metric class for storing the results of the detection metrics.
         speed (dict): A dictionary for storing the execution time of different parts of the detection process.
@@ -827,17 +825,16 @@ class DetMetrics(SimpleClass):
         curves_results: TODO
     """
 
-    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names={}) -> None:
+    def __init__(self, save_dir=Path("."), plot=False, names={}) -> None:
         """Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names."""
         self.save_dir = save_dir
         self.plot = plot
-        self.on_plot = on_plot
         self.names = names
         self.box = Metric()
         self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
         self.task = "detect"
 
-    def process(self, tp, conf, pred_cls, target_cls):
+    def process(self, tp, conf, pred_cls, target_cls, on_plot=None):
         """Process predicted results for object detection and update metrics."""
         results = ap_per_class(
             tp,
@@ -847,7 +844,7 @@ class DetMetrics(SimpleClass):
             plot=self.plot,
             save_dir=self.save_dir,
             names=self.names,
-            on_plot=self.on_plot,
+            on_plot=on_plot,
         )[2:]
         self.box.nc = len(self.names)
         self.box.update(results)
@@ -903,13 +900,11 @@ class SegmentMetrics(SimpleClass):
     Args:
         save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory.
         plot (bool): Whether to save the detection and segmentation plots. Default is False.
-        on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
         names (list): List of class names. Default is an empty list.
 
     Attributes:
         save_dir (Path): Path to the directory where the output plots should be saved.
         plot (bool): Whether to save the detection and segmentation plots.
-        on_plot (func): An optional callback to pass plots path and data when they are rendered.
         names (list): List of class names.
         box (Metric): An instance of the Metric class to calculate box detection metrics.
         seg (Metric): An instance of the Metric class to calculate mask segmentation metrics.
@@ -925,18 +920,17 @@ class SegmentMetrics(SimpleClass):
         results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
     """
 
-    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
+    def __init__(self, save_dir=Path("."), plot=False, names=()) -> None:
         """Initialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names."""
         self.save_dir = save_dir
         self.plot = plot
-        self.on_plot = on_plot
         self.names = names
         self.box = Metric()
         self.seg = Metric()
         self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
         self.task = "segment"
 
-    def process(self, tp, tp_m, conf, pred_cls, target_cls):
+    def process(self, tp, tp_m, conf, pred_cls, target_cls, on_plot=None):
         """
         Processes the detection and segmentation metrics over the given set of predictions.
 
@@ -946,6 +940,7 @@ class SegmentMetrics(SimpleClass):
             conf (list): List of confidence scores.
             pred_cls (list): List of predicted classes.
             target_cls (list): List of target classes.
+            on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
         """
         results_mask = ap_per_class(
             tp_m,
@@ -953,7 +948,7 @@ class SegmentMetrics(SimpleClass):
             pred_cls,
             target_cls,
             plot=self.plot,
-            on_plot=self.on_plot,
+            on_plot=on_plot,
             save_dir=self.save_dir,
             names=self.names,
             prefix="Mask",
@@ -966,7 +961,7 @@ class SegmentMetrics(SimpleClass):
             pred_cls,
             target_cls,
             plot=self.plot,
-            on_plot=self.on_plot,
+            on_plot=on_plot,
             save_dir=self.save_dir,
             names=self.names,
             prefix="Box",
@@ -1043,13 +1038,11 @@ class PoseMetrics(SegmentMetrics):
     Args:
         save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory.
         plot (bool): Whether to save the detection and segmentation plots. Default is False.
-        on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
         names (list): List of class names. Default is an empty list.
 
     Attributes:
         save_dir (Path): Path to the directory where the output plots should be saved.
         plot (bool): Whether to save the detection and segmentation plots.
-        on_plot (func): An optional callback to pass plots path and data when they are rendered.
         names (list): List of class names.
         box (Metric): An instance of the Metric class to calculate box detection metrics.
         pose (Metric): An instance of the Metric class to calculate mask segmentation metrics.
@@ -1065,19 +1058,18 @@ class PoseMetrics(SegmentMetrics):
         results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
     """
 
-    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
+    def __init__(self, save_dir=Path("."), plot=False, names=()) -> None:
         """Initialize the PoseMetrics class with directory path, class names, and plotting options."""
         super().__init__(save_dir, plot, names)
         self.save_dir = save_dir
         self.plot = plot
-        self.on_plot = on_plot
         self.names = names
         self.box = Metric()
         self.pose = Metric()
         self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
         self.task = "pose"
 
-    def process(self, tp, tp_p, conf, pred_cls, target_cls):
+    def process(self, tp, tp_p, conf, pred_cls, target_cls, on_plot=None):
         """
         Processes the detection and pose metrics over the given set of predictions.
 
@@ -1087,6 +1079,7 @@ class PoseMetrics(SegmentMetrics):
             conf (list): List of confidence scores.
             pred_cls (list): List of predicted classes.
             target_cls (list): List of target classes.
+            on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
         """
         results_pose = ap_per_class(
             tp_p,
@@ -1094,7 +1087,7 @@ class PoseMetrics(SegmentMetrics):
             pred_cls,
             target_cls,
             plot=self.plot,
-            on_plot=self.on_plot,
+            on_plot=on_plot,
             save_dir=self.save_dir,
             names=self.names,
             prefix="Pose",
@@ -1107,7 +1100,7 @@ class PoseMetrics(SegmentMetrics):
             pred_cls,
             target_cls,
             plot=self.plot,
-            on_plot=self.on_plot,
+            on_plot=on_plot,
             save_dir=self.save_dir,
             names=self.names,
             prefix="Box",
@@ -1226,16 +1219,15 @@ class ClassifyMetrics(SimpleClass):
 class OBBMetrics(SimpleClass):
     """Metrics for evaluating oriented bounding box (OBB) detection, see https://arxiv.org/pdf/2106.06072.pdf."""
 
-    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
+    def __init__(self, save_dir=Path("."), plot=False, names=()) -> None:
         """Initialize an OBBMetrics instance with directory, plotting, callback, and class names."""
         self.save_dir = save_dir
         self.plot = plot
-        self.on_plot = on_plot
         self.names = names
         self.box = Metric()
         self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
 
-    def process(self, tp, conf, pred_cls, target_cls):
+    def process(self, tp, conf, pred_cls, target_cls, on_plot=None):
         """Process predicted results for object detection and update metrics."""
         results = ap_per_class(
             tp,
@@ -1245,7 +1237,7 @@ class OBBMetrics(SimpleClass):
             plot=self.plot,
             save_dir=self.save_dir,
             names=self.names,
-            on_plot=self.on_plot,
+            on_plot=on_plot,
         )[2:]
         self.box.nc = len(self.names)
         self.box.update(results)