Update labels_to_image_weights() (#1545)

utils/general.py

@@ -2,7 +2,6 @@
 
 import glob
 import logging
-import math
 import os
 import platform
 import random
@@ -12,7 +11,7 @@ import time
 from pathlib import Path
 
 import cv2
-import matplotlib
+import math
 import numpy as np
 import torch
 import torchvision
@@ -22,13 +21,10 @@ from utils.google_utils import gsutil_getsize
 from utils.metrics import fitness
 from utils.torch_utils import init_torch_seeds
 
-# Set printoptions
+# Settings
 torch.set_printoptions(linewidth=320, precision=5, profile='long')
 np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
-matplotlib.rc('font', **{'size': 11})
-
-# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
-cv2.setNumThreads(0)
+cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
 
 
 def set_logging(rank=-1):
@@ -121,9 +117,8 @@ def labels_to_class_weights(labels, nc=80):
 
 
 def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
-    # Produces image weights based on class mAPs
-    n = len(labels)
-    class_counts = np.array([np.bincount(labels[i][:, 0].astype(np.int), minlength=nc) for i in range(n)])
+    # Produces image weights based on class_weights and image contents
+    class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
     image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
     # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
     return image_weights