【玩转yolov5】请看代码之自动anchor计算
早在yolov2时就了解到不同于faster-rcnn中手动设置的anchor，yolov2中的 anchor是通过k-means聚类算法得的，这样更贴合实际的训练数据。这次借学习yolov5的机会把其中关于自动anchor计算的逻辑再梳理一遍，重点就是分析一下utils/autoanchor.py文件的相关函数。除非显示地设置noautoanchor参数为True，否则训练过程中默认会使用自动 anchor 计算，即调用check_anchors函数。
....
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
...参数dataset代表的是训练集，hyp['anchor_t']这个超参数是一个界定anchor与label匹配程度的阈值，imgsz自然就是网络输入尺寸，后面的讲解中按默认的640来推演。
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)
#0.1-1.1
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='')
#只有当由默认设定的anchor计算出来的bpr这一指标少于设定的阈值时才会通过聚类算法重新计算anchor
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
yolov5的网络结构配置文件(如:models/yolov5s.yaml)已经给出了默认的一组anchor，只有当bpr小于0.98时才会重新计算anchor，所以这里首先需要搞清楚bpr是什么，它又是怎么计算的？我们知道在训练过程中任何一个gt框它归根结底是要落到特征图中的某个网格的。在yolov5中默认设置了9种anchor，在具体计算bpr(best possible recall)的时候，会考虑这9类anchor的宽高和gt框的宽高之间的差距。上述代码中变量wh用来存储训练数据中所有gt框的宽高，是一个shape为(N,2)的tensor，这里的2自然就是表示的宽和高，N为gt框的总的个数。metric根据默认anchor和wh来具体计算bpr,aat(anchors above threshold)两个指标。
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, aatmetric这个函数初看一脸懵逼，再看拍案叫绝。输入参数k存储anchors，调用时被reshape后的尺寸为(9,2)。接下来要计算每个gt框的宽高和所有这9个anchor的宽高的比例值，得到的r其shape为(N,9,2)。x=torch.min(r,1./r).min(2)[0]，怎么理解这句代码呢？w_gt/w_anchor或者h_gt/h_anchor这个比例值可能大于1也可能小于1，通过torch.min(r,1./r)的方式统一到<=1的情形，然后再从中选取较小的这个值。得到的x其shape为(n,9)，x.max(1)[0]为每个gt框选择匹配宽高比例值最好的那一个值。这样就可以计算aat和bpr了。计算出来的bpr不小于0.98就会重新聚类，否则就返回默认的anchor设定。
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()
"""
#import pdb
#pdb.set_trace()
thr = 1. / thr
def metric(k, wh): # compute metrics
#计算数据集中的gt框与anchor对应宽和高的比例即:gt_w/k_w,gt_h/k_h
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))] #计算每一行的乘积(w*h),然后排序得到排序后的k
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)]) #数据集中gt框的wh
# Filter,表示宽或者高小于3个像素,目标太小
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)))
#label大于2个像素的框拿来聚类,[...]内的相当于一个筛选器,为True的留下
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('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
#计算宽和高的标准差->[w_std,h_std]
s = wh.std(0) # sigmas for whitening
#开始聚类,仍然是聚成n类,返回聚类后的anchors k
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)
# 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)