move evolve into seperate script
Edward Yang
parent
c0a733ce3f
commit
52b6d53b9a
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@ -0,0 +1,167 @@
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import yaml
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from pathlib import Path
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import numpy as np
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import random
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import argparse
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import time
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from utils.callbacks import Callbacks
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from utils.general import LOGGER, check_yaml, check_file, print_args, print_mutation
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from utils.metrics import fitness
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from utils.plots import plot_evolve
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from utils.torch_utils import select_device
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import torch
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import torch.distributed as dist
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from train import train, ROOT, LOCAL_RANK, RANK, WORLD_SIZE
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def parse_opt(known=False):
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default=Path('yolov5s.pt'), help='initial weights path')
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parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
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parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path, used for initial guess')
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parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
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parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
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parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
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parser.add_argument('--rect', action='store_true', help='rectangular training')
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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('--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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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
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parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
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parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
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parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
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parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
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parser.add_argument('--save_dir', default=Path('./runs/train'), help='save to directory')
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parser.add_argument('--quad', action='store_true', help='quad dataloader')
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parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
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parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
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parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
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parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
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parser.add_argument('--seed', type=int, default=0, help='Global training seed')
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parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
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parser.add_argument('--generations', type=int, default=300, help="Number of generations to evolve hyperparameters for")
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return parser.parse_known_args()[0] if known else parser.parse_args()
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def main(opt, callbacks = Callbacks()):
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# Checks
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if RANK in {-1, 0}:
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print_args(vars(opt))
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opt.data, opt.cfg, opt.hyp, opt.weights = \
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check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights) # checks
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assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
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opt.exist_ok = True
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opt.resume = False
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opt.evolve = opt.generations # pass resume to exist_ok and disable resume
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opt.save_period = 0
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# DDP mode
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device = select_device(opt.device, batch_size=opt.batch_size)
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if LOCAL_RANK != -1:
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msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
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assert not opt.image_weights, f'--image-weights {msg}'
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assert not opt.evolve, f'--evolve {msg}'
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assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
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assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
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assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
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torch.cuda.set_device(LOCAL_RANK)
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device = torch.device('cuda', LOCAL_RANK)
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dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
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# Hyperparameter evolution metadata (mutation scale 0-1, 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': (3, 3.0, 3.0), # 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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with open(opt.hyp, errors='ignore') as f:
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hyp = yaml.safe_load(f) # load hyps dict
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if 'anchors' not in hyp: # anchors commented in hyp.yaml
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hyp['anchors'] = 3
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if opt.noautoanchor:
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del hyp['anchors'], meta['anchors']
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opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
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# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
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evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
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for _ in range(opt.generations): # 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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# 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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# 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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# 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)
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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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f"Results saved to {colorstr('bold', save_dir)}\n"
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f'Usage example: $ python train.py --hyp {evolve_yaml}')
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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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105
train.py
105
train.py
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@ -56,7 +56,6 @@ from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, ch
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from utils.loggers import Loggers
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from utils.loss import ComputeLoss
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from utils.metrics import fitness
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from utils.plots import plot_evolve
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from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
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smart_resume, torch_distributed_zero_first)
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@ -74,7 +73,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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# Directories
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w = save_dir / 'weights' # weights dir
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(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
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w.mkdir(parents=True, exist_ok=True) # make dir
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last, best = w / 'last.pt', w / 'best.pt'
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# Hyperparameters
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@ -369,7 +368,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
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# Save model
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if (not nosave) or (final_epoch and not evolve): # if save
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if (not nosave) or final_epoch: # if save
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ckpt = {
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'epoch': epoch,
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'best_fitness': best_fitness,
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@ -446,7 +445,6 @@ def parse_opt(known=False):
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parser.add_argument('--noval', action='store_true', help='only validate final epoch')
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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('--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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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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@ -482,7 +480,7 @@ def main(opt, callbacks=Callbacks()):
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print_args(vars(opt))
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# Resume (from specified or most recent last.pt)
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if opt.resume and not opt.evolve:
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if opt.resume:
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last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
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opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml
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opt_data = opt.data # original dataset
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@ -499,8 +497,6 @@ def main(opt, callbacks=Callbacks()):
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opt.data, opt.cfg, opt.hyp, opt.weights = \
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check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights) # checks
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assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
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if opt.evolve:
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opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
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if opt.name == 'cfg':
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opt.name = Path(opt.cfg).stem # use model.yaml as name
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@ -509,7 +505,6 @@ def main(opt, callbacks=Callbacks()):
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if LOCAL_RANK != -1:
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msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
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assert not opt.image_weights, f'--image-weights {msg}'
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assert not opt.evolve, f'--evolve {msg}'
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assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
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assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
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assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
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@ -518,98 +513,7 @@ def main(opt, callbacks=Callbacks()):
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dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
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# Train
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if not opt.evolve:
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train(opt.hyp, opt, device, 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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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': (3, 3.0, 3.0), # 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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with open(opt.hyp, errors='ignore') as f:
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hyp = yaml.safe_load(f) # load hyps dict
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if 'anchors' not in hyp: # anchors commented in hyp.yaml
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hyp['anchors'] = 3
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if opt.noautoanchor:
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del hyp['anchors'], meta['anchors']
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opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
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# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
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evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / '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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# 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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# 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
|
||||
hyp[k] = round(hyp[k], 5) # significant digits
|
||||
|
||||
# Train mutation
|
||||
results = train(hyp.copy(), opt, device, callbacks)
|
||||
callbacks = Callbacks()
|
||||
# Write mutation results
|
||||
keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss',
|
||||
'val/obj_loss', 'val/cls_loss')
|
||||
print_mutation(keys, results, hyp.copy(), save_dir)
|
||||
|
||||
# Plot results
|
||||
plot_evolve(evolve_csv)
|
||||
LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
|
||||
f"Results saved to {colorstr('bold', save_dir)}\n"
|
||||
f'Usage example: $ python train.py --hyp {evolve_yaml}')
|
||||
train(opt.hyp, opt, device, callbacks)
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
|
|
@ -623,4 +527,5 @@ def run(**kwargs):
|
|||
|
||||
if __name__ == '__main__':
|
||||
opt = parse_opt()
|
||||
opt.evolve = None
|
||||
main(opt)
|
||||
|
|
|
|||
Loading…
Reference in New Issue