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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 time |
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from pathlib import Path |
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from threading import Thread |
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from warnings import warn |
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import numpy as np |
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import torch.distributed as dist |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.optim as optim |
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import torch.optim.lr_scheduler as lr_scheduler |
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import torch.utils.data |
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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.utils.tensorboard import SummaryWriter |
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from tqdm import tqdm |
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import test |
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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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fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ |
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print_mutation, set_logging |
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from utils.google_utils import attempt_download |
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from utils.loss import compute_loss |
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from utils.plots import plot_images, plot_labels, plot_results, plot_evolution |
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from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first |
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logger = logging.getLogger(__name__) |
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try: |
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import wandb |
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except ImportError: |
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wandb = None |
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logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)") |
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def train(hyp, opt, device, tb_writer=None, wandb=None): |
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logger.info(f'Hyperparameters {hyp}') |
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save_dir, epochs, batch_size, total_batch_size, weights, rank = \ |
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Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank |
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wdir = save_dir / 'weights' |
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wdir.mkdir(parents=True, exist_ok=True) |
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last = wdir / 'last.pt' |
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best = wdir / 'best.pt' |
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results_file = save_dir / 'results.txt' |
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with open(save_dir / 'hyp.yaml', 'w') as f: |
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yaml.dump(hyp, f, sort_keys=False) |
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with open(save_dir / 'opt.yaml', 'w') as f: |
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yaml.dump(vars(opt), f, sort_keys=False) |
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plots = not opt.evolve |
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cuda = device.type != 'cpu' |
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init_seeds(2 + rank) |
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with open(opt.data) as f: |
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data_dict = yaml.load(f, Loader=yaml.FullLoader) |
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with torch_distributed_zero_first(rank): |
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check_dataset(data_dict) |
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train_path = data_dict['train'] |
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test_path = data_dict['val'] |
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nc = 1 if opt.single_cls else int(data_dict['nc']) |
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names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] |
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assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) |
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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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attempt_download(weights) |
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ckpt = torch.load(weights, map_location=device) |
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if hyp.get('anchors'): |
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ckpt['model'].yaml['anchors'] = round(hyp['anchors']) |
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model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) |
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exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] |
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state_dict = ckpt['model'].float().state_dict() |
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state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) |
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model.load_state_dict(state_dict, strict=False) |
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logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) |
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else: |
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model = Model(opt.cfg, ch=3, nc=nc).to(device) |
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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('freezing %s' % k) |
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v.requires_grad = False |
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nbs = 64 |
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accumulate = max(round(nbs / total_batch_size), 1) |
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hyp['weight_decay'] *= total_batch_size * accumulate / nbs |
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pg0, pg1, pg2 = [], [], [] |
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for k, v in model.named_modules(): |
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if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): |
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pg2.append(v.bias) |
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if isinstance(v, nn.BatchNorm2d): |
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pg0.append(v.weight) |
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elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): |
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pg1.append(v.weight) |
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if opt.adam: |
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optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) |
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else: |
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optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) |
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optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) |
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optimizer.add_param_group({'params': pg2}) |
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logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) |
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del pg0, pg1, pg2 |
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lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf'] |
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) |
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if wandb and wandb.run is None: |
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opt.hyp = hyp |
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wandb_run = wandb.init(config=opt, resume="allow", |
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project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, |
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name=save_dir.stem, |
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id=ckpt.get('wandb_id') if 'ckpt' in locals() else None) |
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loggers = {'wandb': wandb} |
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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 ckpt.get('training_results') is not None: |
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with open(results_file, 'w') as file: |
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file.write(ckpt['training_results']) |
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start_epoch = ckpt['epoch'] + 1 |
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if opt.resume: |
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assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) |
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if epochs < start_epoch: |
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logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % |
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(weights, ckpt['epoch'], epochs)) |
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epochs += ckpt['epoch'] |
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del ckpt, state_dict |
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gs = int(max(model.stride)) |
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imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] |
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if cuda and rank == -1 and torch.cuda.device_count() > 1: |
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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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ema = ModelEMA(model) if rank in [-1, 0] else None |
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if cuda and rank != -1: |
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model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank) |
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dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, |
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hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, |
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world_size=opt.world_size, workers=opt.workers, |
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image_weights=opt.image_weights) |
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mlc = np.concatenate(dataset.labels, 0)[:, 0].max() |
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nb = len(dataloader) |
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assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) |
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if rank in [-1, 0]: |
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ema.updates = start_epoch * nb // accumulate |
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testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, |
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hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, |
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rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0] |
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if not opt.resume: |
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labels = np.concatenate(dataset.labels, 0) |
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c = torch.tensor(labels[:, 0]) |
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if plots: |
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Thread(target=plot_labels, args=(labels, save_dir, loggers), daemon=True).start() |
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if tb_writer: |
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tb_writer.add_histogram('classes', c, 0) |
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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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hyp['cls'] *= nc / 80. |
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model.nc = nc |
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model.hyp = hyp |
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model.gr = 1.0 |
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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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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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logger.info('Image sizes %g train, %g test\n' |
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'Using %g dataloader workers\nLogging results to %s\n' |
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'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, 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(4, device=device) |
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if rank != -1: |
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dataloader.sampler.set_epoch(epoch) |
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pbar = enumerate(dataloader) |
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logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', '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 / total_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 = F.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), model) |
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if rank != -1: |
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loss *= opt.world_size |
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scaler.scale(loss).backward() |
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if ni % accumulate == 0: |
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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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if rank in [-1, 0]: |
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mloss = (mloss * i + loss_items) / (i + 1) |
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mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) |
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s = ('%10s' * 2 + '%10.4g' * 6) % ( |
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'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) |
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pbar.set_description(s) |
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if plots and ni < 3: |
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f = save_dir / f'train_batch{ni}.jpg' |
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Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() |
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elif plots and ni == 3 and wandb: |
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wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]}) |
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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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if ema: |
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ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) |
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final_epoch = epoch + 1 == epochs |
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if not opt.notest or final_epoch: |
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results, maps, times = test.test(opt.data, |
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batch_size=total_batch_size, |
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imgsz=imgsz_test, |
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model=ema.ema, |
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single_cls=opt.single_cls, |
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dataloader=testloader, |
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save_dir=save_dir, |
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plots=plots and final_epoch, |
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log_imgs=opt.log_imgs if wandb else 0) |
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with open(results_file, 'a') as f: |
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f.write(s + '%10.4g' * 7 % results + '\n') |
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if len(opt.name) and opt.bucket: |
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os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) |
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tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', |
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'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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'x/lr0', 'x/lr1', 'x/lr2'] |
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for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): |
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if tb_writer: |
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tb_writer.add_scalar(tag, x, epoch) |
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if wandb: |
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wandb.log({tag: x}) |
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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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save = (not opt.nosave) or (final_epoch and not opt.evolve) |
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if save: |
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with open(results_file, 'r') as f: |
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ckpt = {'epoch': epoch, |
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'best_fitness': best_fitness, |
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'training_results': f.read(), |
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'model': ema.ema, |
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'optimizer': None if final_epoch else optimizer.state_dict(), |
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'wandb_id': wandb_run.id if 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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if rank in [-1, 0]: |
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final = best if best.exists() else last |
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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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if opt.bucket: |
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os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') |
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if plots: |
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plot_results(save_dir=save_dir) |
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if wandb: |
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files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png'] |
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wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files |
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if (save_dir / f).exists()]}) |
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if opt.log_artifacts: |
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wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem) |
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logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) |
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if opt.data.endswith('coco.yaml') and nc == 80: |
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for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]): |
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results, _, _ = test.test(opt.data, |
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batch_size=total_batch_size, |
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imgsz=imgsz_test, |
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conf_thres=conf, |
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iou_thres=iou, |
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model=attempt_load(final, device).half(), |
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single_cls=opt.single_cls, |
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dataloader=testloader, |
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save_dir=save_dir, |
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save_json=save_json, |
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plots=False) |
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else: |
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dist.destroy_process_group() |
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wandb.run.finish() if wandb and wandb.run else None |
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torch.cuda.empty_cache() |
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return results |
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if __name__ == '__main__': |
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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='data.yaml path') |
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parser.add_argument('--hyp', type=str, default='data/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('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') |
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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('--notest', action='store_true', help='only test 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', action='store_true', help='evolve hyperparameters') |
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') |
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parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') |
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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('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') |
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parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100') |
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parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model') |
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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('--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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opt = parser.parse_args() |
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opt.total_batch_size = opt.batch_size |
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opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 |
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opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 |
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set_logging(opt.global_rank) |
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if opt.global_rank in [-1, 0]: |
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check_git_status() |
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if opt.resume: |
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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.load(f, Loader=yaml.FullLoader)) |
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opt.cfg, opt.weights, opt.resume = '', ckpt, True |
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logger.info('Resuming training from %s' % ckpt) |
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else: |
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|
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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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opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) |
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opt.name = 'evolve' if opt.evolve else opt.name |
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opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) |
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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 opt.local_rank != -1: |
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assert torch.cuda.device_count() > opt.local_rank |
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torch.cuda.set_device(opt.local_rank) |
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device = torch.device('cuda', opt.local_rank) |
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dist.init_process_group(backend='nccl', init_method='env://') |
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assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' |
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opt.batch_size = opt.total_batch_size // opt.world_size |
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|
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|
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with open(opt.hyp) as f: |
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hyp = yaml.load(f, Loader=yaml.FullLoader) |
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if 'box' not in hyp: |
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warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' % |
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(opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120')) |
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hyp['box'] = hyp.pop('giou') |
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|
|
|
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logger.info(opt) |
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if not opt.evolve: |
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tb_writer = None |
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if opt.global_rank in [-1, 0]: |
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logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/') |
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tb_writer = SummaryWriter(opt.save_dir) |
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train(hyp, opt, device, tb_writer, wandb) |
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|
|
|
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else: |
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|
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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), |
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'obj_pw': (1, 0.5, 2.0), |
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'iou_t': (0, 0.1, 0.7), |
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'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), |
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'hsv_s': (1, 0.0, 0.9), |
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'hsv_v': (1, 0.0, 0.9), |
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'degrees': (1, 0.0, 45.0), |
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'translate': (1, 0.0, 0.9), |
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'scale': (1, 0.0, 0.9), |
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'shear': (1, 0.0, 10.0), |
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'perspective': (0, 0.0, 0.001), |
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'flipud': (1, 0.0, 1.0), |
|
'fliplr': (0, 0.0, 1.0), |
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'mosaic': (1, 0.0, 1.0), |
|
'mixup': (1, 0.0, 1.0)} |
|
|
|
assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' |
|
opt.notest, opt.nosave = True, True |
|
|
|
yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' |
|
if opt.bucket: |
|
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) |
|
|
|
for _ in range(300): |
|
if Path('evolve.txt').exists(): |
|
|
|
parent = 'single' |
|
x = np.loadtxt('evolve.txt', ndmin=2) |
|
n = min(5, len(x)) |
|
x = x[np.argsort(-fitness(x))][:n] |
|
w = fitness(x) - fitness(x).min() |
|
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, wandb=wandb) |
|
|
|
|
|
print_mutation(hyp.copy(), results, yaml_file, opt.bucket) |
|
|
|
|
|
plot_evolution(yaml_file) |
|
print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n' |
|
f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}') |
|
|