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""" |
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Train a YOLOv5 model on a custom dataset |
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Usage: |
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$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 |
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""" |
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import argparse |
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import logging |
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import math |
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import os |
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import random |
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import sys |
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import time |
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from copy import deepcopy |
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from pathlib import Path |
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import numpy as np |
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import torch |
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import torch.distributed as dist |
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import torch.nn as nn |
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import yaml |
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from torch.cuda import amp |
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from torch.nn.parallel import DistributedDataParallel as DDP |
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from torch.optim import Adam, SGD, lr_scheduler |
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from tqdm import tqdm |
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FILE = Path(__file__).absolute() |
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sys.path.append(FILE.parents[0].as_posix()) |
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import val |
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from models.experimental import attempt_load |
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from models.yolo import Model |
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from utils.autoanchor import check_anchors |
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from utils.datasets import create_dataloader |
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from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ |
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strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ |
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check_requirements, print_mutation, set_logging, one_cycle, colorstr, methods |
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from utils.downloads import attempt_download |
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from utils.loss import ComputeLoss |
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from utils.plots import plot_labels, plot_evolve |
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from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, de_parallel |
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from utils.loggers.wandb.wandb_utils import check_wandb_resume |
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from utils.metrics import fitness |
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from utils.loggers import Loggers |
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from utils.callbacks import Callbacks |
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LOGGER = logging.getLogger(__name__) |
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) |
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RANK = int(os.getenv('RANK', -1)) |
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WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) |
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def train(hyp, |
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opt, |
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device, |
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callbacks=Callbacks() |
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): |
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save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, = \ |
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Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ |
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opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze |
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w = save_dir / 'weights' |
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w.mkdir(parents=True, exist_ok=True) |
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last, best = w / 'last.pt', w / 'best.pt' |
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if isinstance(hyp, str): |
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with open(hyp) as f: |
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hyp = yaml.safe_load(f) |
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LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) |
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with open(save_dir / 'hyp.yaml', 'w') as f: |
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yaml.safe_dump(hyp, f, sort_keys=False) |
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with open(save_dir / 'opt.yaml', 'w') as f: |
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yaml.safe_dump(vars(opt), f, sort_keys=False) |
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data_dict = None |
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if RANK in [-1, 0]: |
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loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) |
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if loggers.wandb: |
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data_dict = loggers.wandb.data_dict |
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if resume: |
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weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp |
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for k in methods(loggers): |
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callbacks.register_action(k, callback=getattr(loggers, k)) |
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plots = not evolve |
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cuda = device.type != 'cpu' |
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init_seeds(1 + RANK) |
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with torch_distributed_zero_first(RANK): |
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data_dict = data_dict or check_dataset(data) |
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train_path, val_path = data_dict['train'], data_dict['val'] |
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nc = 1 if single_cls else int(data_dict['nc']) |
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names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] |
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assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' |
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is_coco = data.endswith('coco.yaml') and nc == 80 |
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pretrained = weights.endswith('.pt') |
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if pretrained: |
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with torch_distributed_zero_first(RANK): |
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weights = attempt_download(weights) |
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ckpt = torch.load(weights, map_location=device) |
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model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) |
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exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] |
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csd = ckpt['model'].float().state_dict() |
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csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) |
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model.load_state_dict(csd, strict=False) |
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LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') |
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else: |
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model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) |
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freeze = [f'model.{x}.' for x in range(freeze)] |
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for k, v in model.named_parameters(): |
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v.requires_grad = True |
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if any(x in k for x in freeze): |
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print(f'freezing {k}') |
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v.requires_grad = False |
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nbs = 64 |
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accumulate = max(round(nbs / batch_size), 1) |
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hyp['weight_decay'] *= batch_size * accumulate / nbs |
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LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}") |
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g0, g1, g2 = [], [], [] |
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for v in model.modules(): |
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if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): |
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g2.append(v.bias) |
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if isinstance(v, nn.BatchNorm2d): |
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g0.append(v.weight) |
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elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): |
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g1.append(v.weight) |
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if opt.adam: |
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optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) |
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else: |
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optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) |
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optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']}) |
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optimizer.add_param_group({'params': g2}) |
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LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups " |
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f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias") |
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del g0, g1, g2 |
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if opt.linear_lr: |
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lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] |
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else: |
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lf = one_cycle(1, hyp['lrf'], epochs) |
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) |
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ema = ModelEMA(model) if RANK in [-1, 0] else None |
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start_epoch, best_fitness = 0, 0.0 |
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if pretrained: |
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if ckpt['optimizer'] is not None: |
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optimizer.load_state_dict(ckpt['optimizer']) |
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best_fitness = ckpt['best_fitness'] |
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if ema and ckpt.get('ema'): |
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ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) |
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ema.updates = ckpt['updates'] |
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start_epoch = ckpt['epoch'] + 1 |
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if resume: |
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assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.' |
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if epochs < start_epoch: |
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LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") |
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epochs += ckpt['epoch'] |
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del ckpt, csd |
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gs = max(int(model.stride.max()), 32) |
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nl = model.model[-1].nl |
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imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) |
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if cuda and RANK == -1 and torch.cuda.device_count() > 1: |
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logging.warning('DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n' |
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'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') |
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model = torch.nn.DataParallel(model) |
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if opt.sync_bn and cuda and RANK != -1: |
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model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) |
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LOGGER.info('Using SyncBatchNorm()') |
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train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, |
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hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=RANK, |
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workers=workers, image_weights=opt.image_weights, quad=opt.quad, |
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prefix=colorstr('train: ')) |
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mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) |
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nb = len(train_loader) |
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assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' |
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if RANK in [-1, 0]: |
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val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, |
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hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, |
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workers=workers, pad=0.5, |
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prefix=colorstr('val: '))[0] |
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if not resume: |
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labels = np.concatenate(dataset.labels, 0) |
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if plots: |
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plot_labels(labels, names, save_dir) |
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if not opt.noautoanchor: |
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check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) |
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model.half().float() |
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callbacks.on_pretrain_routine_end() |
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if cuda and RANK != -1: |
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model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) |
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hyp['box'] *= 3. / nl |
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hyp['cls'] *= nc / 80. * 3. / nl |
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hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl |
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hyp['label_smoothing'] = opt.label_smoothing |
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model.nc = nc |
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model.hyp = hyp |
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc |
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model.names = names |
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t0 = time.time() |
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nw = max(round(hyp['warmup_epochs'] * nb), 1000) |
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last_opt_step = -1 |
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maps = np.zeros(nc) |
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results = (0, 0, 0, 0, 0, 0, 0) |
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scheduler.last_epoch = start_epoch - 1 |
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scaler = amp.GradScaler(enabled=cuda) |
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compute_loss = ComputeLoss(model) |
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LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' |
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f'Using {train_loader.num_workers} dataloader workers\n' |
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f'Logging results to {save_dir}\n' |
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f'Starting training for {epochs} epochs...') |
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for epoch in range(start_epoch, epochs): |
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model.train() |
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if opt.image_weights: |
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if RANK in [-1, 0]: |
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cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc |
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iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) |
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dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) |
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if RANK != -1: |
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indices = (torch.tensor(dataset.indices) if RANK == 0 else torch.zeros(dataset.n)).int() |
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dist.broadcast(indices, 0) |
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if RANK != 0: |
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dataset.indices = indices.cpu().numpy() |
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mloss = torch.zeros(3, device=device) |
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if RANK != -1: |
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train_loader.sampler.set_epoch(epoch) |
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pbar = enumerate(train_loader) |
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LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size')) |
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if RANK in [-1, 0]: |
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pbar = tqdm(pbar, total=nb) |
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optimizer.zero_grad() |
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for i, (imgs, targets, paths, _) in pbar: |
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ni = i + nb * epoch |
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imgs = imgs.to(device, non_blocking=True).float() / 255.0 |
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if ni <= nw: |
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xi = [0, nw] |
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accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) |
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for j, x in enumerate(optimizer.param_groups): |
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x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) |
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if 'momentum' in x: |
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x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) |
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if opt.multi_scale: |
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sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs |
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sf = sz / max(imgs.shape[2:]) |
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if sf != 1: |
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ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] |
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imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) |
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with amp.autocast(enabled=cuda): |
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pred = model(imgs) |
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loss, loss_items = compute_loss(pred, targets.to(device)) |
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if RANK != -1: |
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loss *= WORLD_SIZE |
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if opt.quad: |
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loss *= 4. |
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scaler.scale(loss).backward() |
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if ni - last_opt_step >= accumulate: |
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scaler.step(optimizer) |
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scaler.update() |
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optimizer.zero_grad() |
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if ema: |
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ema.update(model) |
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last_opt_step = ni |
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if RANK in [-1, 0]: |
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mloss = (mloss * i + loss_items) / (i + 1) |
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mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' |
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pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % ( |
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f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) |
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callbacks.on_train_batch_end(ni, model, imgs, targets, paths, plots) |
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lr = [x['lr'] for x in optimizer.param_groups] |
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scheduler.step() |
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if RANK in [-1, 0]: |
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callbacks.on_train_epoch_end(epoch=epoch) |
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ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) |
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final_epoch = epoch + 1 == epochs |
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if not noval or final_epoch: |
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results, maps, _ = val.run(data_dict, |
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batch_size=batch_size // WORLD_SIZE * 2, |
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imgsz=imgsz, |
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model=ema.ema, |
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single_cls=single_cls, |
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dataloader=val_loader, |
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save_dir=save_dir, |
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save_json=is_coco and final_epoch, |
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verbose=nc < 50 and final_epoch, |
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plots=plots and final_epoch, |
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callbacks=callbacks, |
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compute_loss=compute_loss) |
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fi = fitness(np.array(results).reshape(1, -1)) |
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if fi > best_fitness: |
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best_fitness = fi |
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log_vals = list(mloss) + list(results) + lr |
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callbacks.on_fit_epoch_end(log_vals, epoch, best_fitness, fi) |
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if (not nosave) or (final_epoch and not evolve): |
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ckpt = {'epoch': epoch, |
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'best_fitness': best_fitness, |
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'model': deepcopy(de_parallel(model)).half(), |
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'ema': deepcopy(ema.ema).half(), |
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'updates': ema.updates, |
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'optimizer': optimizer.state_dict(), |
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'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None} |
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torch.save(ckpt, last) |
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if best_fitness == fi: |
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torch.save(ckpt, best) |
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del ckpt |
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callbacks.on_model_save(last, epoch, final_epoch, best_fitness, fi) |
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if RANK in [-1, 0]: |
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LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') |
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if not evolve: |
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if is_coco: |
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for m in [last, best] if best.exists() else [last]: |
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results, _, _ = val.run(data_dict, |
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batch_size=batch_size // WORLD_SIZE * 2, |
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imgsz=imgsz, |
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model=attempt_load(m, device).half(), |
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iou_thres=0.7, |
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single_cls=single_cls, |
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dataloader=val_loader, |
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save_dir=save_dir, |
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save_json=True, |
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plots=False) |
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for f in last, best: |
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if f.exists(): |
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strip_optimizer(f) |
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callbacks.on_train_end(last, best, plots, epoch) |
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") |
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torch.cuda.empty_cache() |
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return results |
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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='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='data/coco128.yaml', help='dataset.yaml path') |
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parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch.yaml', help='hyperparameters path') |
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parser.add_argument('--epochs', type=int, default=300) |
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parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') |
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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('--resume', nargs='?', const=True, default=False, help='resume most recent training') |
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') |
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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 check') |
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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('--bucket', type=str, default='', help='gsutil bucket') |
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parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "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('--adam', action='store_true', help='use torch.optim.Adam() 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='maximum number of dataloader workers') |
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parser.add_argument('--project', default='runs/train', help='save to project/name') |
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parser.add_argument('--entity', default=None, help='W&B entity') |
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parser.add_argument('--name', default='exp', help='save to project/name') |
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') |
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parser.add_argument('--quad', action='store_true', help='quad dataloader') |
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parser.add_argument('--linear-lr', action='store_true', help='linear LR') |
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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('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') |
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parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') |
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parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') |
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parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') |
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parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') |
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parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24') |
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opt = parser.parse_known_args()[0] if known else parser.parse_args() |
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return opt |
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|
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def main(opt): |
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|
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set_logging(RANK) |
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if RANK in [-1, 0]: |
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print(colorstr('train: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items())) |
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check_git_status() |
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check_requirements(requirements=FILE.parent / 'requirements.txt', exclude=['thop']) |
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|
|
|
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if opt.resume and not check_wandb_resume(opt) and not opt.evolve: |
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ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() |
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assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' |
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with open(Path(ckpt).parent.parent / 'opt.yaml') as f: |
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opt = argparse.Namespace(**yaml.safe_load(f)) |
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opt.cfg, opt.weights, opt.resume = '', ckpt, True |
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LOGGER.info(f'Resuming training from {ckpt}') |
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else: |
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opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) |
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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.project = 'runs/evolve' |
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opt.exist_ok = opt.resume |
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opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) |
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|
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|
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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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from datetime import timedelta |
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assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' |
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assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count' |
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assert not opt.image_weights, '--image-weights argument is not compatible with DDP training' |
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assert not opt.evolve, '--evolve argument is not compatible with DDP training' |
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assert not opt.sync_bn, '--sync-bn known training issue, see https://github.com/ultralytics/yolov5/issues/3998' |
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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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|
|
|
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if not opt.evolve: |
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train(opt.hyp, opt, device) |
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if WORLD_SIZE > 1 and RANK == 0: |
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_ = [print('Destroying process group... ', end=''), dist.destroy_process_group(), print('Done.')] |
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|
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|
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else: |
|
|
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meta = {'lr0': (1, 1e-5, 1e-1), |
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'lrf': (1, 0.01, 1.0), |
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'momentum': (0.3, 0.6, 0.98), |
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'weight_decay': (1, 0.0, 0.001), |
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'warmup_epochs': (1, 0.0, 5.0), |
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'warmup_momentum': (1, 0.0, 0.95), |
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'warmup_bias_lr': (1, 0.0, 0.2), |
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'box': (1, 0.02, 0.2), |
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'cls': (1, 0.2, 4.0), |
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'cls_pw': (1, 0.5, 2.0), |
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'obj': (1, 0.2, 4.0), |
|
'obj_pw': (1, 0.5, 2.0), |
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'iou_t': (0, 0.1, 0.7), |
|
'anchor_t': (1, 2.0, 8.0), |
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'anchors': (2, 2.0, 10.0), |
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'fl_gamma': (0, 0.0, 2.0), |
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'hsv_h': (1, 0.0, 0.1), |
|
'hsv_s': (1, 0.0, 0.9), |
|
'hsv_v': (1, 0.0, 0.9), |
|
'degrees': (1, 0.0, 45.0), |
|
'translate': (1, 0.0, 0.9), |
|
'scale': (1, 0.0, 0.9), |
|
'shear': (1, 0.0, 10.0), |
|
'perspective': (0, 0.0, 0.001), |
|
'flipud': (1, 0.0, 1.0), |
|
'fliplr': (0, 0.0, 1.0), |
|
'mosaic': (1, 0.0, 1.0), |
|
'mixup': (1, 0.0, 1.0), |
|
'copy_paste': (1, 0.0, 1.0)} |
|
|
|
with open(opt.hyp) as f: |
|
hyp = yaml.safe_load(f) |
|
if 'anchors' not in hyp: |
|
hyp['anchors'] = 3 |
|
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) |
|
|
|
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 {save_dir}') |
|
|
|
for _ in range(opt.evolve): |
|
if evolve_csv.exists(): |
|
|
|
parent = 'single' |
|
x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) |
|
n = min(5, len(x)) |
|
x = x[np.argsort(-fitness(x))][:n] |
|
w = fitness(x) - fitness(x).min() + 1E-6 |
|
if parent == 'single' or len(x) == 1: |
|
|
|
x = x[random.choices(range(n), weights=w)[0]] |
|
elif parent == 'weighted': |
|
x = (x * w.reshape(n, 1)).sum(0) / w.sum() |
|
|
|
|
|
mp, s = 0.8, 0.2 |
|
npr = np.random |
|
npr.seed(int(time.time())) |
|
g = np.array([x[0] for x in meta.values()]) |
|
ng = len(meta) |
|
v = np.ones(ng) |
|
while all(v == 1): |
|
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()): |
|
hyp[k] = float(x[i + 7] * v[i]) |
|
|
|
|
|
for k, v in meta.items(): |
|
hyp[k] = max(hyp[k], v[1]) |
|
hyp[k] = min(hyp[k], v[2]) |
|
hyp[k] = round(hyp[k], 5) |
|
|
|
|
|
results = train(hyp.copy(), opt, device) |
|
|
|
|
|
print_mutation(results, hyp.copy(), save_dir, opt.bucket) |
|
|
|
|
|
plot_evolve(evolve_csv) |
|
print(f'Hyperparameter evolution finished\n' |
|
f"Results saved to {colorstr('bold', save_dir)}\n" |
|
f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}') |
|
|
|
|
|
def run(**kwargs): |
|
|
|
opt = parse_opt(True) |
|
for k, v in kwargs.items(): |
|
setattr(opt, k, v) |
|
main(opt) |
|
|
|
|
|
if __name__ == "__main__": |
|
opt = parse_opt() |
|
main(opt) |
|
|