text: # Auto-anchor utils
  
  import numpy as np
  import torch
  import yaml
  from scipy.cluster.vq import kmeans
  from tqdm import tqdm
  
  from utils.general import colorstr
  
  
  def check_anchor_order(m):
      # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
      a = m.anchor_grid.prod(-1).view(-1)  # anchor area
      da = a[-1] - a[0]  # delta a
      ds = m.stride[-1] - m.stride[0]  # delta s
      if da.sign() != ds.sign():  # same order
          print('Reversing anchor order')
          m.anchors[:] = m.anchors.flip(0)
          m.anchor_grid[:] = m.anchor_grid.flip(0)
  
  
  def check_anchors(dataset, model, thr=4.0, imgsz=640):
      # Check anchor fit to data, recompute if necessary
      prefix = colorstr('autoanchor: ')
      print(f'\n{prefix}Analyzing anchors... ', end='')
      m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
      shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
      scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
      wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
  
      def metric(k):  # compute metric
          r = wh[:, None] / k[None]
          x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
          best = x.max(1)[0]  # best_x
          aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
          bpr = (best > 1. / thr).float().mean()  # best possible recall
          return bpr, aat
  
      anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
      bpr, aat = metric(anchors)
      print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
      if bpr < 0.98:  # threshold to recompute
          print('. Attempting to improve anchors, please wait...')
          na = m.anchor_grid.numel() // 2  # number of anchors
          try:
              anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
          except Exception as e:
              print(f'{prefix}ERROR: {e}')
          new_bpr = metric(anchors)[0]
          if new_bpr > bpr:  # replace anchors
              anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
              m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
              m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
              check_anchor_order(m)
              print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
          else:
              print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
      print('')  # newline
  
  
  def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
      """ Creates kmeans-evolved anchors from training dataset
  
          Arguments:
              path: path to dataset *.yaml, or a loaded dataset
              n: number of anchors
              img_size: image size used for training
              thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
              gen: generations to evolve anchors using genetic algorithm
              verbose: print all results
  
          Return:
              k: kmeans evolved anchors
  
          Usage:
              from utils.autoanchor import *; _ = kmean_anchors()
      """
      thr = 1. / thr
      prefix = colorstr('autoanchor: ')
  
      def metric(k, wh):  # compute metrics
          r = wh[:, None] / k[None]
          x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
          # x = wh_iou(wh, torch.tensor(k))  # iou metric
          return x, x.max(1)[0]  # x, best_x
  
      def anchor_fitness(k):  # mutation fitness
          _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
          return (best * (best > thr).float()).mean()  # fitness
  
      def print_results(k):
          k = k[np.argsort(k.prod(1))]  # sort small to large
          x, best = metric(k, wh0)
          bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
          print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
          print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
                f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
          for i, x in enumerate(k):
              print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
          return k
  
      if isinstance(path, str):  # *.yaml file
          with open(path) as f:
              data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
          from utils.datasets import LoadImagesAndLabels
          dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
      else:
          dataset = path  # dataset
  
      # Get label wh
      shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
      wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
  
      # Filter
      i = (wh0 < 3.0).any(1).sum()
      if i:
          print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
      wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
      # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
  
      # Kmeans calculation
      print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
      s = wh.std(0)  # sigmas for whitening
      k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
      assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
      k *= s
      wh = torch.tensor(wh, dtype=torch.float32)  # filtered
      wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
      k = print_results(k)
  
      # Plot
      # k, d = [None] * 20, [None] * 20
      # for i in tqdm(range(1, 21)):
      #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
      # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
      # ax = ax.ravel()
      # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
      # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
      # ax[0].hist(wh[wh[:, 0]<100, 0],400)
      # ax[1].hist(wh[wh[:, 1]<100, 1],400)
      # fig.savefig('wh.png', dpi=200)
  
      # Evolve
      npr = np.random
      f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
      pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
      for _ in pbar:
          v = np.ones(sh)
          while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
              v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
          kg = (k.copy() * v).clip(min=2.0)
          fg = anchor_fitness(kg)
          if fg > f:
              f, k = fg, kg.copy()
              pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
              if verbose:
                  print_results(k)
  
      return print_results(k)
  