New TryExcept decorator (#9154)

utils/__init__.py

@@ -3,6 +3,33 @@
 utils/initialization
 """
 
+import contextlib
+import threading
+
+
+class TryExcept(contextlib.ContextDecorator):
+    # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
+    def __init__(self, msg='default message here'):
+        self.msg = msg
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, exc_type, value, traceback):
+        if value:
+            print(f'{self.msg}: {value}')
+        return True
+
+
+def threaded(func):
+    # Multi-threads a target function and returns thread. Usage: @threaded decorator
+    def wrapper(*args, **kwargs):
+        thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
+        thread.start()
+        return thread
+
+    return wrapper
+
 
 def notebook_init(verbose=True):
     # Check system software and hardware

utils/general.py

@@ -15,7 +15,6 @@ import re
 import shutil
 import signal
 import sys
-import threading
 import time
 import urllib
 from datetime import datetime
@@ -34,6 +33,7 @@ import torch
 import torchvision
 import yaml
 
+from utils import TryExcept
 from utils.downloads import gsutil_getsize
 from utils.metrics import box_iou, fitness
 
@@ -195,27 +195,6 @@ class WorkingDirectory(contextlib.ContextDecorator):
         os.chdir(self.cwd)
 
 
-def try_except(func):
-    # try-except function. Usage: @try_except decorator
-    def handler(*args, **kwargs):
-        try:
-            func(*args, **kwargs)
-        except Exception as e:
-            print(e)
-
-    return handler
-
-
-def threaded(func):
-    # Multi-threads a target function and returns thread. Usage: @threaded decorator
-    def wrapper(*args, **kwargs):
-        thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
-        thread.start()
-        return thread
-
-    return wrapper
-
-
 def methods(instance):
     # Get class/instance methods
     return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
@@ -319,7 +298,7 @@ def git_describe(path=ROOT):  # path must be a directory
         return ''
 
 
-@try_except
+@TryExcept()
 @WorkingDirectory(ROOT)
 def check_git_status(repo='ultralytics/yolov5'):
     # YOLOv5 status check, recommend 'git pull' if code is out of date
@@ -364,7 +343,7 @@ def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=Fals
     return result
 
 
-@try_except
+@TryExcept()
 def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=()):
     # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages)
     prefix = colorstr('red', 'bold', 'requirements:')

utils/metrics.py

@@ -11,6 +11,8 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 
+from utils import TryExcept, threaded
+
 
 def fitness(x):
     # Model fitness as a weighted combination of metrics
@@ -184,36 +186,35 @@ class ConfusionMatrix:
         # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
         return tp[:-1], fp[:-1]  # remove background class
 
+    @TryExcept('WARNING: ConfusionMatrix plot failure')
     def plot(self, normalize=True, save_dir='', names=()):
-        try:
-            import seaborn as sn
-
-            array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1)  # normalize columns
-            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
-
-            fig = plt.figure(figsize=(12, 9), tight_layout=True)
-            nc, nn = self.nc, len(names)  # number of classes, names
-            sn.set(font_scale=1.0 if nc < 50 else 0.8)  # for label size
-            labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
-            with warnings.catch_warnings():
-                warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
-                sn.heatmap(array,
-                           annot=nc < 30,
-                           annot_kws={
-                               "size": 8},
-                           cmap='Blues',
-                           fmt='.2f',
-                           square=True,
-                           vmin=0.0,
-                           xticklabels=names + ['background FP'] if labels else "auto",
-                           yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
-            fig.axes[0].set_xlabel('True')
-            fig.axes[0].set_ylabel('Predicted')
-            plt.title('Confusion Matrix')
-            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
-            plt.close()
-        except Exception as e:
-            print(f'WARNING: ConfusionMatrix plot failure: {e}')
+        import seaborn as sn
+
+        array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1)  # normalize columns
+        array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
+
+        fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
+        nc, nn = self.nc, len(names)  # number of classes, names
+        sn.set(font_scale=1.0 if nc < 50 else 0.8)  # for label size
+        labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
+            sn.heatmap(array,
+                       ax=ax,
+                       annot=nc < 30,
+                       annot_kws={
+                           "size": 8},
+                       cmap='Blues',
+                       fmt='.2f',
+                       square=True,
+                       vmin=0.0,
+                       xticklabels=names + ['background FP'] if labels else "auto",
+                       yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
+        ax.set_ylabel('True')
+        ax.set_ylabel('Predicted')
+        ax.set_title('Confusion Matrix')
+        fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
+        plt.close(fig)
 
     def print(self):
         for i in range(self.nc + 1):
@@ -320,6 +321,7 @@ def wh_iou(wh1, wh2, eps=1e-7):
 # Plots ----------------------------------------------------------------------------------------------------------------
 
 
+@threaded
 def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
     # Precision-recall curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
@@ -336,12 +338,13 @@ def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
     ax.set_ylabel('Precision')
     ax.set_xlim(0, 1)
     ax.set_ylim(0, 1)
-    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    plt.title('Precision-Recall Curve')
+    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    ax.set_title('Precision-Recall Curve')
     fig.savefig(save_dir, dpi=250)
-    plt.close()
+    plt.close(fig)
 
 
+@threaded
 def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
     # Metric-confidence curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
@@ -358,7 +361,7 @@ def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confi
     ax.set_ylabel(ylabel)
     ax.set_xlim(0, 1)
     ax.set_ylim(0, 1)
-    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    plt.title(f'{ylabel}-Confidence Curve')
+    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    ax.set_title(f'{ylabel}-Confidence Curve')
     fig.savefig(save_dir, dpi=250)
-    plt.close()
+    plt.close(fig)

utils/plots.py

@@ -19,8 +19,9 @@ import seaborn as sn
 import torch
 from PIL import Image, ImageDraw, ImageFont
 
+from utils import TryExcept, threaded
 from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_coords, increment_path,
-                           is_ascii, threaded, try_except, xywh2xyxy, xyxy2xywh)
+                           is_ascii, xywh2xyxy, xyxy2xywh)
 from utils.metrics import fitness
 
 # Settings
@@ -339,7 +340,7 @@ def plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_
     plt.savefig(f, dpi=300)
 
 
-@try_except  # known issue https://github.com/ultralytics/yolov5/issues/5395
+@TryExcept()  # known issue https://github.com/ultralytics/yolov5/issues/5395
 def plot_labels(labels, names=(), save_dir=Path('')):
     # plot dataset labels
     LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")