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- # Auto-anchor utils
-
- import numpy as np
- import torch
- import yaml
- from scipy.cluster.vq import kmeans
- from tqdm import tqdm
-
-
- 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
- print('\nAnalyzing 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
-
- bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
- print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
- if bpr < 0.98: # threshold to recompute
- print('. Attempting to improve anchors, please wait...')
- na = m.anchor_grid.numel() // 2 # number of anchors
- new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
- new_bpr = metric(new_anchors.reshape(-1, 2))[0]
- if new_bpr > bpr: # replace anchors
- new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
- m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid) # for inference
- m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss
- check_anchor_order(m)
- print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
- else:
- print('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
-
- 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('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
- print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
- (n, img_size, x.mean(), best.mean(), x[x > thr].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.FullLoader) # 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('WARNING: Extremely small objects found. '
- '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
- wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
-
- # Kmeans calculation
- print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
- s = wh.std(0) # sigmas for whitening
- k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
- 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='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 = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
- if verbose:
- print_results(k)
-
- return print_results(k)
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