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