kmean_anchors() update

utils/utils.py

 import torch
 import torch.nn as nn
 import torchvision
+import yaml
 from scipy.signal import butter, filtfilt
 from tqdm import tqdm
 
             shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg'))  # copy images
 
 
-def kmean_anchors(path='./data/coco128.txt', n=9, img_size=(640, 640), thr=0.20, gen=1000):
-    # Creates kmeans anchors for use in *.cfg files: from utils.utils import *; _ = kmean_anchors()
-    # n: number of anchors
-    # img_size: (min, max) image size used for multi-scale training (can be same values)
-    # thr: IoU threshold hyperparameter used for training (0.0 - 1.0)
-    # gen: generations to evolve anchors using genetic algorithm
+def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=(640, 640), thr=0.20, gen=1000):
+    """ Creates kmeans-evolved anchors from training dataset
+
+        Arguments:
+            path: path to dataset *.yaml
+            n: number of anchors
+            img_size: (min, max) image size used for multi-scale training (can be same values)
+            thr: IoU threshold hyperparameter used for training (0.0 - 1.0)
+            gen: generations to evolve anchors using genetic algorithm
+
+        Return:
+            k: kmeans evolved anchors
+
+        Usage:
+            from utils.utils import *; _ = kmean_anchors()
+    """
+
     from utils.datasets import LoadImagesAndLabels
 
     def print_results(k):
 
     # Get label wh
     wh = []
-    dataset = LoadImagesAndLabels(path, augment=True, rect=True)
+    with open(path) as f:
+        data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+    dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
     nr = 1 if img_size[0] == img_size[1] else 3  # number augmentation repetitions
     for s, l in zip(dataset.shapes, dataset.labels):
         # wh.append(l[:, 3:5] * (s / s.max()))  # image normalized to letterbox normalized wh
             f, k = fg, kg.copy()
             print_results(k)
     k = print_results(k)
-
     return k