Ignore Seaborn plot warnings (#3576)

utils/metrics.py

 # Model validation metrics
 
+import warnings
 from pathlib import Path
 
 import matplotlib.pyplot as plt
@@ -167,6 +168,8 @@ class ConfusionMatrix:
             fig = plt.figure(figsize=(12, 9), tight_layout=True)
             sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size
             labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels
+            with warnings.catch_warnings():
+                warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
                 sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
                            xticklabels=names + ['background FP'] if labels else "auto",
                            yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))

utils/plots.py

@@ -11,7 +11,7 @@ import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import seaborn as sns
+import seaborn as sn
 import torch
 import yaml
 from PIL import Image, ImageDraw, ImageFont
@@ -291,7 +291,7 @@ def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
     x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
 
     # seaborn correlogram
-    sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
+    sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
     plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
     plt.close()
 
@@ -306,8 +306,8 @@ def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
         ax[0].set_xticklabels(names, rotation=90, fontsize=10)
     else:
         ax[0].set_xlabel('classes')
-    sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
-    sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
+    sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
+    sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
 
     # rectangles
     labels[:, 1:3] = 0.5  # center