improving evolve (#11348)
* improving evole in train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix gen_ranges value in mutation part. Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * fix invalid syntax in line 532 remove on tab from "else" Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * fix range index Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py fix population size add crossover min and max rate Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update comments Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * save population for last generation The latest version incorporates a significant update whereby all hyper parameters are now stored in the population section of "evolve_population.yaml," located in "yolov5\data\hyps," following the transition to the new generation. This development allows for the continuation of a previously abandoned evolution process by utilizing the former population. Additionally, a new argument, "--evolve_population," has been introduced to enable the relocation of the manual "evolve_population.yaml" to any project directory to load for the aforementioned purpose. This enhancement offers greater flexibility and convenience to the users, making it easier for them to resume their evolutionary process. Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove try - except Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Add resume resume_evolve arg for **resume evolve from last generation**. Population will load from data/hyp by default and load all yaml file form them. Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update README.zh-CN.md Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py update pop_size Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> --------- Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Shayan Mousaviniaandpre-commit-ci[bot] authored and GitHub committed
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@@ -468,6 +468,11 @@ def parse_opt(known=False):
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parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
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parser.add_argument('--noplots', action='store_true', help='save no plot files')
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parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
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parser.add_argument('--evolve_population',
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type=str,
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default=ROOT / 'data/hyps',
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help='location for loading population')
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parser.add_argument('--resume_evolve', type=str, default=None, help='resume evolve from last generation')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
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parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
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@@ -555,37 +560,48 @@ def main(opt, callbacks=Callbacks()):
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# Evolve hyperparameters (optional)
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else:
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# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
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# Hyperparameter evolution metadata (including this hyperparameter True-False, lower_limit, upper_limit)
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meta = {
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'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
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'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
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'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
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'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
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'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
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'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
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'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
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'box': (1, 0.02, 0.2), # box loss gain
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'cls': (1, 0.2, 4.0), # cls loss gain
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'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
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'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
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'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
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'iou_t': (0, 0.1, 0.7), # IoU training threshold
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'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
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'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
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'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
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'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
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'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
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'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
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'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
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'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
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'scale': (1, 0.0, 0.9), # image scale (+/- gain)
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'shear': (1, 0.0, 10.0), # image shear (+/- deg)
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'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
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'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
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'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
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'mosaic': (1, 0.0, 1.0), # image mixup (probability)
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'mixup': (1, 0.0, 1.0), # image mixup (probability)
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'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
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'lr0': (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
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'lrf': (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
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'momentum': (False, 0.6, 0.98), # SGD momentum/Adam beta1
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'weight_decay': (False, 0.0, 0.001), # optimizer weight decay
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'warmup_epochs': (False, 0.0, 5.0), # warmup epochs (fractions ok)
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'warmup_momentum': (False, 0.0, 0.95), # warmup initial momentum
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'warmup_bias_lr': (False, 0.0, 0.2), # warmup initial bias lr
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'box': (False, 0.02, 0.2), # box loss gain
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'cls': (False, 0.2, 4.0), # cls loss gain
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'cls_pw': (False, 0.5, 2.0), # cls BCELoss positive_weight
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'obj': (False, 0.2, 4.0), # obj loss gain (scale with pixels)
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'obj_pw': (False, 0.5, 2.0), # obj BCELoss positive_weight
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'iou_t': (False, 0.1, 0.7), # IoU training threshold
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'anchor_t': (False, 2.0, 8.0), # anchor-multiple threshold
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'anchors': (False, 2.0, 10.0), # anchors per output grid (0 to ignore)
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'fl_gamma': (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
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'hsv_h': (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
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'hsv_s': (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
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'hsv_v': (True, 0.0, 0.9), # image HSV-Value augmentation (fraction)
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'degrees': (True, 0.0, 45.0), # image rotation (+/- deg)
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'translate': (True, 0.0, 0.9), # image translation (+/- fraction)
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'scale': (True, 0.0, 0.9), # image scale (+/- gain)
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'shear': (True, 0.0, 10.0), # image shear (+/- deg)
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'perspective': (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
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'flipud': (True, 0.0, 1.0), # image flip up-down (probability)
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'fliplr': (True, 0.0, 1.0), # image flip left-right (probability)
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'mosaic': (True, 0.0, 1.0), # image mixup (probability)
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'mixup': (True, 0.0, 1.0), # image mixup (probability)
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'copy_paste': (True, 0.0, 1.0)} # segment copy-paste (probability)
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# GA configs
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pop_size = 50
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mutation_rate_min = 0.01
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mutation_rate_max = 0.5
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crossover_rate_min = 0.5
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crossover_rate_max = 1
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min_elite_size = 2
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max_elite_size = 5
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tournament_size_min = 2
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tournament_size_max = 10
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with open(opt.hyp, errors='ignore') as f:
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hyp = yaml.safe_load(f) # load hyps dict
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@@ -604,46 +620,128 @@ def main(opt, callbacks=Callbacks()):
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f'gs://{opt.bucket}/evolve.csv',
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str(evolve_csv), ])
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for _ in range(opt.evolve): # generations to evolve
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if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
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# Select parent(s)
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parent = 'single' # parent selection method: 'single' or 'weighted'
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x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
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n = min(5, len(x)) # number of previous results to consider
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x = x[np.argsort(-fitness(x))][:n] # top n mutations
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w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
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if parent == 'single' or len(x) == 1:
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# x = x[random.randint(0, n - 1)] # random selection
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x = x[random.choices(range(n), weights=w)[0]] # weighted selection
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elif parent == 'weighted':
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x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
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# Delete the items in meta dictionary whose first value is False
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del_ = []
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for item in meta.keys():
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if meta[item][0] is False:
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del_.append(item)
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hyp_GA = hyp.copy() # Make a copy of hyp dictionary
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for item in del_:
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del meta[item] # Remove the item from meta dictionary
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del hyp_GA[item] # Remove the item from hyp_GA dictionary
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# Mutate
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mp, s = 0.8, 0.2 # mutation probability, sigma
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npr = np.random
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npr.seed(int(time.time()))
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g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
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ng = len(meta)
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v = np.ones(ng)
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while all(v == 1): # mutate until a change occurs (prevent duplicates)
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v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
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for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
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hyp[k] = float(x[i + 7] * v[i]) # mutate
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# Set lower_limit and upper_limit arrays to hold the search space boundaries
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lower_limit = np.array([meta[k][1] for k in hyp_GA.keys()])
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upper_limit = np.array([meta[k][2] for k in hyp_GA.keys()])
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# Constrain to limits
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for k, v in meta.items():
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hyp[k] = max(hyp[k], v[1]) # lower limit
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hyp[k] = min(hyp[k], v[2]) # upper limit
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hyp[k] = round(hyp[k], 5) # significant digits
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# Create gene_ranges list to hold the range of values for each gene in the population
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gene_ranges = []
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for i in range(len(upper_limit)):
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gene_ranges.append((lower_limit[i], upper_limit[i]))
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# Train mutation
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results = train(hyp.copy(), opt, device, callbacks)
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callbacks = Callbacks()
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# Write mutation results
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keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss',
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'val/obj_loss', 'val/cls_loss')
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print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)
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# Initialize the population with initial_values or random values
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initial_values = []
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# If resuming evolution from a previous checkpoint
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if opt.resume_evolve is not None:
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assert os.path.isfile(ROOT / opt.resume_evolve), 'evolve population path is wrong!'
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with open(ROOT / opt.resume_evolve, errors='ignore') as f:
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evolve_population = yaml.safe_load(f)
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for value in evolve_population.values():
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value = np.array([value[k] for k in hyp_GA.keys()])
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initial_values.append(list(value))
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# If not resuming from a previous checkpoint, generate initial values from .yaml files in opt.evolve_population
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else:
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yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith('.yaml')]
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for file_name in yaml_files:
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with open(os.path.join(opt.evolve_population, file_name)) as yaml_file:
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value = yaml.safe_load(yaml_file)
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value = np.array([value[k] for k in hyp_GA.keys()])
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initial_values.append(list(value))
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# Generate random values within the search space for the rest of the population
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if (initial_values is None):
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population = [generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size)]
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else:
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if (pop_size > 1):
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population = [
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generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size - len(initial_values))]
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for initial_value in initial_values:
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population = [initial_value] + population
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# Run the genetic algorithm for a fixed number of generations
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list_keys = list(hyp_GA.keys())
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for generation in range(opt.evolve):
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if (generation >= 1):
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save_dict = {}
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for i in range(len(population)):
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little_dict = {}
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for j in range(len(population[i])):
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little_dict[list_keys[j]] = float(population[i][j])
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save_dict['gen' + str(generation) + 'number' + str(i)] = little_dict
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with open(save_dir / 'evolve_population.yaml', 'w') as outfile:
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yaml.dump(save_dict, outfile, default_flow_style=False)
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# Adaptive elite size
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elite_size = min_elite_size + int((max_elite_size - min_elite_size) * (generation / opt.evolve))
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# Evaluate the fitness of each individual in the population
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fitness_scores = []
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for individual in population:
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for key, value in zip(hyp_GA.keys(), individual):
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hyp_GA[key] = value
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hyp.update(hyp_GA)
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results = train(hyp.copy(), opt, device, callbacks)
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callbacks = Callbacks()
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# Write mutation results
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keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
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'val/box_loss', 'val/obj_loss', 'val/cls_loss')
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print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)
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fitness_scores.append(results[2])
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# Select the fittest individuals for reproduction using adaptive tournament selection
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selected_indices = []
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for i in range(pop_size - elite_size):
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# Adaptive tournament size
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tournament_size = max(max(2, tournament_size_min),
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int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10))))
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# Perform tournament selection to choose the best individual
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tournament_indices = random.sample(range(pop_size), tournament_size)
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tournament_fitness = [fitness_scores[j] for j in tournament_indices]
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winner_index = tournament_indices[tournament_fitness.index(max(tournament_fitness))]
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selected_indices.append(winner_index)
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# Add the elite individuals to the selected indices
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elite_indices = [i for i in range(pop_size) if fitness_scores[i] in sorted(fitness_scores)[-elite_size:]]
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selected_indices.extend(elite_indices)
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# Create the next generation through crossover and mutation
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next_generation = []
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for i in range(pop_size):
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parent1_index = selected_indices[random.randint(0, pop_size - 1)]
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parent2_index = selected_indices[random.randint(0, pop_size - 1)]
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# Adaptive crossover rate
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crossover_rate = max(crossover_rate_min,
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min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve)))
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if random.uniform(0, 1) < crossover_rate:
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crossover_point = random.randint(1, len(hyp_GA) - 1)
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child = population[parent1_index][:crossover_point] + population[parent2_index][crossover_point:]
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else:
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child = population[parent1_index]
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# Adaptive mutation rate
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mutation_rate = max(mutation_rate_min,
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min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve)))
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for j in range(len(hyp_GA)):
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if random.uniform(0, 1) < mutation_rate:
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child[j] += random.uniform(-0.1, 0.1)
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child[j] = min(max(child[j], gene_ranges[j][0]), gene_ranges[j][1])
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next_generation.append(child)
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# Replace the old population with the new generation
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population = next_generation
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# Print the best solution found
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best_index = fitness_scores.index(max(fitness_scores))
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best_individual = population[best_index]
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print('Best solution found:', best_individual)
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# Plot results
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plot_evolve(evolve_csv)
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LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
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@@ -651,6 +749,14 @@ def main(opt, callbacks=Callbacks()):
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f'Usage example: $ python train.py --hyp {evolve_yaml}')
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def generate_individual(input_ranges, individual_length):
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individual = []
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for i in range(individual_length):
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lower_bound, upper_bound = input_ranges[i]
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individual.append(random.uniform(lower_bound, upper_bound))
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return individual
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def run(**kwargs):
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# Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
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opt = parse_opt(True)
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