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| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
| """ | |
| AutoAnchor utils | |
| """ | |
| import random | |
| import numpy as np | |
| import torch | |
| import yaml | |
| from tqdm import tqdm | |
| from utils import TryExcept | |
| from utils.general import LOGGER, colorstr | |
| PREFIX = colorstr('AutoAnchor: ') | |
| def check_anchor_order(m): | |
| # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary | |
| a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer | |
| da = a[-1] - a[0] # delta a | |
| ds = m.stride[-1] - m.stride[0] # delta s | |
| if da and (da.sign() != ds.sign()): # same order | |
| LOGGER.info(f'{PREFIX}Reversing anchor order') | |
| m.anchors[:] = m.anchors.flip(0) | |
| def check_anchors(dataset, model, thr=4.0, imgsz=640): | |
| # Check anchor fit to data, recompute if necessary | |
| 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 | |
| stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides | |
| anchors = m.anchors.clone() * stride # current anchors | |
| bpr, aat = metric(anchors.cpu().view(-1, 2)) | |
| s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). ' | |
| if bpr > 0.98: # threshold to recompute | |
| LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅') | |
| else: | |
| LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...') | |
| na = m.anchors.numel() // 2 # number of anchors | |
| anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) | |
| new_bpr = metric(anchors)[0] | |
| if new_bpr > bpr: # replace anchors | |
| anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) | |
| m.anchors[:] = anchors.clone().view_as(m.anchors) | |
| check_anchor_order(m) # must be in pixel-space (not grid-space) | |
| m.anchors /= stride | |
| s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)' | |
| else: | |
| s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)' | |
| LOGGER.info(s) | |
| def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): | |
| """ Creates kmeans-evolved anchors from training dataset | |
| Arguments: | |
| dataset: path to data.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() | |
| """ | |
| from scipy.cluster.vq import kmeans | |
| npr = np.random | |
| 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, verbose=True): | |
| 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 | |
| s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \ | |
| 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: ' | |
| for x in k: | |
| s += '%i,%i, ' % (round(x[0]), round(x[1])) | |
| if verbose: | |
| LOGGER.info(s[:-2]) | |
| return k | |
| if isinstance(dataset, str): # *.yaml file | |
| with open(dataset, errors='ignore') as f: | |
| data_dict = yaml.safe_load(f) # model dict | |
| from utils.dataloaders import LoadImagesAndLabels | |
| dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) | |
| # 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: | |
| LOGGER.info(f'{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size') | |
| wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels | |
| # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 | |
| # Kmeans init | |
| try: | |
| LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...') | |
| assert n <= len(wh) # apply overdetermined constraint | |
| s = wh.std(0) # sigmas for whitening | |
| k = kmeans(wh / s, n, iter=30)[0] * s # points | |
| assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar | |
| except Exception: | |
| LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init') | |
| k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init | |
| wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) | |
| k = print_results(k, verbose=False) | |
| # 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 | |
| f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma | |
| pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # 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) * random.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, verbose) | |
| return print_results(k).astype(np.float32) | |