Ankle/yolov5_birdproject
1
0 Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity

Update Actions with Lychee and GitHub Token (#12592)

Browse Source 
* Add Ultralytics Actions

* Auto-format by Ultralytics actions

* Update format.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Paula DerrengerUltralyticsAssistantGlenn Jocher
 authored and GitHub committed 2024-01-08 01:29:14 +01:00
1 parent f56355d321
commit ad97dc6a6d
47 files changed
+4217 -3462
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

utils/autoanchor.py
+37 -35
View File
@@ -1,7 +1,5 @@
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
AutoAnchor utils
"""
"""AutoAnchor utils."""
import random
@@ -13,7 +11,7 @@ from tqdm import tqdm
from utils import TryExcept
from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
PREFIX = colorstr('AutoAnchor: ')
PREFIX = colorstr("AutoAnchor: ")
def check_anchor_order(m):
@@ -22,14 +20,14 @@ def check_anchor_order(m):
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')
LOGGER.info(f"{PREFIX}Reversing anchor order")
m.anchors[:] = m.anchors.flip(0)
@TryExcept(f'{PREFIX}ERROR')
@TryExcept(f"{PREFIX}ERROR")
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()
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
@@ -45,11 +43,11 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
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). '
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 ✅')
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...')
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]
@@ -58,28 +56,29 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
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)'
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)'
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
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
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
Return:
k: kmeans evolved anchors
Usage:
from utils.autoanchor import *; _ = kmean_anchors()
Usage:
from utils.autoanchor import *; _ = kmean_anchors()
"""
from scipy.cluster.vq import kmeans
@@ -100,20 +99,23 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
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: '
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]))
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:
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)
dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True)
# Get label wh
shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
@@ -122,19 +124,19 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
# 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')
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...')
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')
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)
@@ -162,7 +164,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
fg = anchor_fitness(kg)
if fg > f:
f, k = fg, kg.copy()
pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}"
if verbose:
print_results(k, verbose)