AutoAnchor implementation
Glenn Jocher
parent
95c46f7245
commit
57a0ae3350
|
|
@ -53,18 +53,23 @@ def check_img_size(img_size, s=32):
|
|||
|
||||
|
||||
def check_anchors(dataset, model, thr=4.0, imgsz=640):
|
||||
# Check best possible recall of dataset with current anchors
|
||||
# Check anchor fit to data, recompute if necessary
|
||||
print('\nAnalyzing anchors... ', end='')
|
||||
anchors = model.module.model[-1].anchor_grid if hasattr(model, 'module') else model.model[-1].anchor_grid
|
||||
shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
|
||||
wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])).float() # wh
|
||||
ratio = wh[:, None] / anchors.view(-1, 2).cpu()[None] # ratio
|
||||
m = torch.max(ratio, 1. / ratio).max(2)[0] # max ratio
|
||||
bpr = (m.min(1)[0] < thr).float().mean() # best possible recall
|
||||
mr = (m < thr).float().mean() # match ratio
|
||||
print(('AutoAnchor labels:' + '%10s' * 6) % ('n', 'mean', 'min', 'max', 'matching', 'recall'))
|
||||
print((' ' + '%10.4g' * 6) % (wh.shape[0], wh.mean(), wh.min(), wh.max(), mr, bpr))
|
||||
assert bpr > 0.9, 'Best possible recall %.3g (BPR) below 0.9 threshold. Training cancelled. ' \
|
||||
'Compute new anchors with utils.utils.kmeans_anchors() and update model before training.' % bpr
|
||||
# mr = (m < thr).float().mean() # match ratio
|
||||
|
||||
print('Best Possible Recall (BPR) = %.3f' % bpr, end='')
|
||||
if bpr < 0.99: # threshold to recompute
|
||||
print('. Generating new anchors for improved recall, please wait...' % bpr)
|
||||
new_anchors = kmean_anchors(dataset, n=9, img_size=640, thr=4.0, gen=1000, verbose=False)
|
||||
anchors[:] = torch.tensor(new_anchors).view_as(anchors).type_as(anchors)
|
||||
print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
|
||||
print('') # newline
|
||||
|
||||
|
||||
def check_file(file):
|
||||
|
|
@ -689,14 +694,14 @@ def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
|
|||
shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
|
||||
|
||||
|
||||
def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=(640, 640), thr=0.20, gen=1000):
|
||||
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
|
||||
path: path to dataset *.yaml, or a loaded dataset
|
||||
n: number of anchors
|
||||
img_size: (min, max) image size used for multi-scale training (can be same values)
|
||||
thr: IoU threshold hyperparameter used for training (0.0 - 1.0)
|
||||
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
|
||||
|
||||
Return:
|
||||
|
|
@ -705,52 +710,41 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=(640, 640), thr=0.20
|
|||
Usage:
|
||||
from utils.utils import *; _ = kmean_anchors()
|
||||
"""
|
||||
thr = 1. / thr
|
||||
|
||||
from utils.datasets import LoadImagesAndLabels
|
||||
def metric(k): # 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 fitness(k): # mutation fitness
|
||||
_, best = metric(k)
|
||||
return (best * (best > thr).float()).mean() # fitness
|
||||
|
||||
def print_results(k):
|
||||
k = k[np.argsort(k.prod(1))] # sort small to large
|
||||
iou = wh_iou(wh, torch.Tensor(k))
|
||||
max_iou = iou.max(1)[0]
|
||||
bpr, aat = (max_iou > thr).float().mean(), (iou > thr).float().mean() * n # best possible recall, anch > thr
|
||||
|
||||
# thr = 5.0
|
||||
# r = wh[:, None] / k[None]
|
||||
# ar = torch.max(r, 1. / r).max(2)[0]
|
||||
# max_ar = ar.min(1)[0]
|
||||
# bpr, aat = (max_ar < thr).float().mean(), (ar < thr).float().mean() * n # best possible recall, anch > thr
|
||||
|
||||
print('%.2f iou_thr: %.3f best possible recall, %.2f anchors > thr' % (thr, bpr, aat))
|
||||
print('n=%g, img_size=%s, IoU_all=%.3f/%.3f-mean/best, IoU>thr=%.3f-mean: ' %
|
||||
(n, img_size, iou.mean(), max_iou.mean(), iou[iou > thr].mean()), end='')
|
||||
x, best = metric(k)
|
||||
bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
|
||||
print('thr=%.2f: %.3f 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
|
||||
|
||||
def fitness(k): # mutation fitness
|
||||
iou = wh_iou(wh, torch.Tensor(k)) # iou
|
||||
max_iou = iou.max(1)[0]
|
||||
return (max_iou * (max_iou > thr).float()).mean() # product
|
||||
|
||||
# def fitness_ratio(k): # mutation fitness
|
||||
# # wh(5316,2), k(9,2)
|
||||
# r = wh[:, None] / k[None]
|
||||
# x = torch.max(r, 1. / r).max(2)[0]
|
||||
# m = x.min(1)[0]
|
||||
# return 1. / (m * (m < 5).float()).mean() # product
|
||||
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
|
||||
wh = []
|
||||
with open(path) as f:
|
||||
data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
|
||||
dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
|
||||
nr = 1 if img_size[0] == img_size[1] else 3 # number augmentation repetitions
|
||||
for s, l in zip(dataset.shapes, dataset.labels):
|
||||
# wh.append(l[:, 3:5] * (s / s.max())) # image normalized to letterbox normalized wh
|
||||
wh.append(l[:, 3:5] * s) # image normalized to pixels
|
||||
wh = np.concatenate(wh, 0).repeat(nr, axis=0) # augment 3x
|
||||
# wh *= np.random.uniform(img_size[0], img_size[1], size=(wh.shape[0], 1)) # normalized to pixels (multi-scale)
|
||||
wh = wh[(wh > 2.0).all(1)] # remove below threshold boxes (< 2 pixels wh)
|
||||
shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
|
||||
wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])).float() # wh
|
||||
wh = wh[(wh > 2.0).all(1)].numpy() # filter > 2 pixels
|
||||
|
||||
# Kmeans calculation
|
||||
from scipy.cluster.vq import kmeans
|
||||
|
|
@ -758,10 +752,10 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=(640, 640), thr=0.20
|
|||
s = wh.std(0) # sigmas for whitening
|
||||
k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
|
||||
k *= s
|
||||
wh = torch.Tensor(wh)
|
||||
wh = torch.tensor(wh)
|
||||
k = print_results(k)
|
||||
|
||||
# # Plot
|
||||
# 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
|
||||
|
|
@ -777,7 +771,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=(640, 640), thr=0.20
|
|||
# Evolve
|
||||
npr = np.random
|
||||
f, sh, mp, s = fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
|
||||
for _ in tqdm(range(gen), desc='Evolving anchors'):
|
||||
for _ in tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm:'):
|
||||
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)
|
||||
|
|
@ -785,7 +779,8 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=(640, 640), thr=0.20
|
|||
fg = fitness(kg)
|
||||
if fg > f:
|
||||
f, k = fg, kg.copy()
|
||||
print_results(k)
|
||||
if verbose:
|
||||
print_results(k)
|
||||
k = print_results(k)
|
||||
return k
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue