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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 | |
| from utils.general import colorstr | |
| def check_anchor_order(m): | |
| # Check anchor order against stride order for YOLO 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/coco.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) | |