Increase plot_labels() speed (#1736)

train.py

@@ -205,7 +205,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
             # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
             # model._initialize_biases(cf.to(device))
             if plots:
-                Thread(target=plot_labels, args=(labels, save_dir, loggers), daemon=True).start()
+                plot_labels(labels, save_dir, loggers)
                 if tb_writer:
                     tb_writer.add_histogram('classes', c, 0)
 

utils/plots.py

@@ -11,6 +11,8 @@
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
+import seaborn as sns
 import torch
 import yaml
 from PIL import Image, ImageDraw
@@ -253,34 +255,24 @@ def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_tx
 
 def plot_labels(labels, save_dir=Path(''), loggers=None):
     # plot dataset labels
+    print('Plotting labels... ')
     c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
     nc = int(c.max() + 1)  # number of classes
     colors = color_list()
+    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
 
     # seaborn correlogram
-    try:
-        import seaborn as sns
-        import pandas as pd
-        x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
-        sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
-                     plot_kws=dict(s=3, edgecolor=None, linewidth=1, alpha=0.02),
-                     diag_kws=dict(bins=50))
-        plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
-        plt.close()
-    except Exception as e:
-        pass
+    sns.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()
 
     # matplotlib labels
     matplotlib.use('svg')  # faster
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
     ax[0].set_xlabel('classes')
-    ax[2].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
-    ax[2].set_xlabel('x')
-    ax[2].set_ylabel('y')
-    ax[3].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
-    ax[3].set_xlabel('width')
-    ax[3].set_ylabel('height')
+    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)
 
     # rectangles
     labels[:, 1:3] = 0.5  # center