Zeroes in Hyperparameter Evolution?

[HighMans]

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• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Evolution

Bug

When doing evolutions based off the hyp.scratch-high.yaml, I noticed that the degrees, shear, perspective and flipud parameters always stayed zero, despite having a non-zero mutation scale.

What I think is happening is if the initial starting condition of the evolution and the lower limit defined in the meta dictionary is zero, then the parameter will always be zero.

Then when it comes time to do the mutation, since the mutation is based only on a multiplied scalar, any value that is zero stays zero forever.

yolov5/train.py

Line 598 in 91a81d4

hyp[k] = float(x[i + 7] * v[i]) # mutate

I'm also not sure if it's possible for a zero to be the output of the mutation process, but if it is -- I think it's possible that it could also be stuck at zero forever too.

yolov5/train.py

Lines 575 to 599 in 91a81d4

	if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
	# Select parent(s)
	parent = 'single' # parent selection method: 'single' or 'weighted'
	x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
	n = min(5, len(x)) # number of previous results to consider
	x = x[np.argsort(-fitness(x))][:n] # top n mutations
	w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
	if parent == 'single' or len(x) == 1:
	# x = x[random.randint(0, n - 1)] # random selection
	x = x[random.choices(range(n), weights=w)[0]] # weighted selection
	elif parent == 'weighted':
	x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
	# Mutate
	mp, s = 0.8, 0.2 # mutation probability, sigma
	npr = np.random
	npr.seed(int(time.time()))
	g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
	ng = len(meta)
	v = np.ones(ng)
	while all(v == 1): # mutate until a change occurs (prevent duplicates)
	v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
	for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
	hyp[k] = float(x[i + 7] * v[i]) # mutate

Environment

No response

Minimal Reproducible Example

from pprint import pprint

hyp = {'anchor_t': 0,
       'anchors': 0,
       'box': 0,
       'cls': 0,
       'cls_pw': 0,
       'copy_paste': 0,
       'degrees': 0,
       'fl_gamma': 0,
       'fliplr': 0,
       'flipud': 0,
       'hsv_h': 0,
       'hsv_s': 0,
       'hsv_v': 0,
       'iou_t': 0,
       'lr0': 0,
       'lrf': 0,
       'mixup': 0,
       'momentum': 0,
       'mosaic': 0,
       'obj': 0,
       'obj_pw': 0,
       'perspective': 0.0,
       'scale': 0.0,
       'shear': 0.0,
       'translate': 0.0,
       'warmup_bias_lr': 0.0,
       'warmup_epochs': 0.0,
       'warmup_momentum': 0.0,
       'weight_decay': 0.0}

meta = {
    'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)
    'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)
    'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1
    'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay
    'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
    'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
    'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
    'box': (1, 0.02, 0.2),  # box loss gain
    'cls': (1, 0.2, 4.0),  # cls loss gain
    'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
    'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
    'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
    'iou_t': (0, 0.1, 0.7),  # IoU training threshold
    'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
    'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
    'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
    'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
    'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
    'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
    'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)
    'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)
    'scale': (1, 0.0, 0.9),  # image scale (+/- gain)
    'shear': (1, 0.0, 10.0),  # image shear (+/- deg)
    'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
    'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
    'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
    'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
    'mixup': (1, 0.0, 1.0),  # image mixup (probability)
    'copy_paste': (1, 0.0, 1.0)}  # segment copy-paste (probability)

for k, v in meta.items():
    hyp[k] = max(hyp[k], v[1])  # lower limit
    hyp[k] = min(hyp[k], v[2])  # upper limit
    hyp[k] = round(hyp[k], 5)  # significant digits

pprint(hyp)

Additional

Example code produced from snippit in train.py.

yolov5/train.py

Lines 529 to 605 in 91a81d4

	else:
	# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
	meta = {
	'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
	'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
	'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
	'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
	'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
	'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
	'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
	'box': (1, 0.02, 0.2), # box loss gain
	'cls': (1, 0.2, 4.0), # cls loss gain
	'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
	'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
	'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
	'iou_t': (0, 0.1, 0.7), # IoU training threshold
	'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
	'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
	'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
	'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
	'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
	'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
	'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
	'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
	'scale': (1, 0.0, 0.9), # image scale (+/- gain)
	'shear': (1, 0.0, 10.0), # image shear (+/- deg)
	'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
	'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
	'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
	'mosaic': (1, 0.0, 1.0), # image mixup (probability)
	'mixup': (1, 0.0, 1.0), # image mixup (probability)
	'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
	with open(opt.hyp, errors='ignore') as f:
	hyp = yaml.safe_load(f) # load hyps dict
	if 'anchors' not in hyp: # anchors commented in hyp.yaml
	hyp['anchors'] = 3
	if opt.noautoanchor:
	del hyp['anchors'], meta['anchors']
	opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
	# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
	evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
	if opt.bucket:
	os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists
	for _ in range(opt.evolve): # generations to evolve
	if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
	# Select parent(s)
	parent = 'single' # parent selection method: 'single' or 'weighted'
	x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
	n = min(5, len(x)) # number of previous results to consider
	x = x[np.argsort(-fitness(x))][:n] # top n mutations
	w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
	if parent == 'single' or len(x) == 1:
	# x = x[random.randint(0, n - 1)] # random selection
	x = x[random.choices(range(n), weights=w)[0]] # weighted selection
	elif parent == 'weighted':
	x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
	# Mutate
	mp, s = 0.8, 0.2 # mutation probability, sigma
	npr = np.random
	npr.seed(int(time.time()))
	g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
	ng = len(meta)
	v = np.ones(ng)
	while all(v == 1): # mutate until a change occurs (prevent duplicates)
	v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
	for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
	hyp[k] = float(x[i + 7] * v[i]) # mutate
	# Constrain to limits
	for k, v in meta.items():
	hyp[k] = max(hyp[k], v[1]) # lower limit
	hyp[k] = min(hyp[k], v[2]) # upper limit
	hyp[k] = round(hyp[k], 5) # significant digits

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

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[glenn-jocher]

@HighMans yes that's correct, zero initial values will stay zero. If you want to mutate values initalize these to non-zero values. See 'intiial conditions' section of hyperparameter tutorial:

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