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# YOLOv5 π by Ultralytics, GPL-3.0 license | |
""" | |
AutoAnchor utils | |
""" | |
import random | |
import numpy as np | |
import torch | |
import yaml | |
from tqdm import tqdm | |
from utils.general import LOGGER, colorstr, emojis | |
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(emojis(f'{s}Current anchors are a good fit to dataset β ')) | |
else: | |
LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset β οΈ, attempting to improve...')) | |
na = m.anchors.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: | |
LOGGER.info(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.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 β (optional: update model *.yaml to use these anchors in the future)' | |
else: | |
s = f'{PREFIX}Done β οΈ (original anchors better than new anchors, proceeding with original anchors)' | |
LOGGER.info(emojis(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 i, x in enumerate(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.datasets 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)] # 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) | |