Spaces:
Running
Running
import argparse | |
import math | |
import os | |
import random | |
import sys | |
import time | |
from copy import deepcopy | |
from datetime import datetime | |
from pathlib import Path | |
import numpy as np | |
import torch | |
import torch.distributed as dist | |
import torch.nn as nn | |
import yaml | |
from torch.optim import lr_scheduler | |
from tqdm import tqdm | |
FILE = Path(__file__).resolve() | |
ROOT = FILE.parents[0] # root directory | |
if str(ROOT) not in sys.path: | |
sys.path.append(str(ROOT)) # add ROOT to PATH | |
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative | |
import val as validate # for end-of-epoch mAP | |
from models.experimental import attempt_load | |
from models.yolo import Model | |
from utils.autoanchor import check_anchors | |
from utils.autobatch import check_train_batch_size | |
from utils.callbacks import Callbacks | |
from utils.dataloaders import create_dataloader | |
from utils.downloads import attempt_download, is_url | |
from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_img_size, | |
check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, | |
intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, | |
one_cycle, one_flat_cycle, print_args, print_mutation, strip_optimizer, yaml_save) | |
from utils.loggers import Loggers | |
from utils.loggers.comet.comet_utils import check_comet_resume | |
from utils.loss_tal import ComputeLoss | |
from utils.metrics import fitness | |
from utils.plots import plot_evolve | |
from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, | |
smart_optimizer, smart_resume, torch_distributed_zero_first) | |
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html | |
RANK = int(os.getenv('RANK', -1)) | |
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) | |
GIT_INFO = None | |
def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary | |
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ | |
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ | |
opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze | |
callbacks.run('on_pretrain_routine_start') | |
# Directories | |
w = save_dir / 'weights' # weights dir | |
(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir | |
last, best = w / 'last.pt', w / 'best.pt' | |
last_striped, best_striped = w / 'last_striped.pt', w / 'best_striped.pt' | |
# Hyperparameters | |
if isinstance(hyp, str): | |
with open(hyp, errors='ignore') as f: | |
hyp = yaml.safe_load(f) # load hyps dict | |
LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) | |
hyp['anchor_t'] = 5.0 | |
opt.hyp = hyp.copy() # for saving hyps to checkpoints | |
# Save run settings | |
if not evolve: | |
yaml_save(save_dir / 'hyp.yaml', hyp) | |
yaml_save(save_dir / 'opt.yaml', vars(opt)) | |
# Loggers | |
data_dict = None | |
if RANK in {-1, 0}: | |
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance | |
# Register actions | |
for k in methods(loggers): | |
callbacks.register_action(k, callback=getattr(loggers, k)) | |
# Process custom dataset artifact link | |
data_dict = loggers.remote_dataset | |
if resume: # If resuming runs from remote artifact | |
weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size | |
# Config | |
plots = not evolve and not opt.noplots # create plots | |
cuda = device.type != 'cpu' | |
init_seeds(opt.seed + 1 + RANK, deterministic=True) | |
with torch_distributed_zero_first(LOCAL_RANK): | |
data_dict = data_dict or check_dataset(data) # check if None | |
train_path, val_path = data_dict['train'], data_dict['val'] | |
nc = 1 if single_cls else int(data_dict['nc']) # number of classes | |
names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names | |
#is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset | |
is_coco = isinstance(val_path, str) and val_path.endswith('val2017.txt') # COCO dataset | |
# Model | |
check_suffix(weights, '.pt') # check weights | |
pretrained = weights.endswith('.pt') | |
if pretrained: | |
with torch_distributed_zero_first(LOCAL_RANK): | |
weights = attempt_download(weights) # download if not found locally | |
ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak | |
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create | |
exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys | |
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 | |
csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect | |
model.load_state_dict(csd, strict=False) # load | |
LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report | |
else: | |
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create | |
amp = check_amp(model) # check AMP | |
# Freeze | |
freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze | |
for k, v in model.named_parameters(): | |
# v.requires_grad = True # train all layers TODO: uncomment this line as in master | |
# v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) | |
if any(x in k for x in freeze): | |
LOGGER.info(f'freezing {k}') | |
v.requires_grad = False | |
# Image size | |
gs = max(int(model.stride.max()), 32) # grid size (max stride) | |
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple | |
# Batch size | |
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size | |
batch_size = check_train_batch_size(model, imgsz, amp) | |
loggers.on_params_update({"batch_size": batch_size}) | |
# Optimizer | |
nbs = 64 # nominal batch size | |
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing | |
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay | |
optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) | |
# Scheduler | |
if opt.cos_lr: | |
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] | |
elif opt.flat_cos_lr: | |
lf = one_flat_cycle(1, hyp['lrf'], epochs) # flat cosine 1->hyp['lrf'] | |
elif opt.fixed_lr: | |
lf = lambda x: 1.0 | |
else: | |
lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear | |
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) | |
# from utils.plots import plot_lr_scheduler; plot_lr_scheduler(optimizer, scheduler, epochs) | |
# EMA | |
ema = ModelEMA(model) if RANK in {-1, 0} else None | |
# Resume | |
best_fitness, start_epoch = 0.0, 0 | |
if pretrained: | |
if resume: | |
best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) | |
del ckpt, csd | |
# DP mode | |
if cuda and RANK == -1 and torch.cuda.device_count() > 1: | |
LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.') | |
model = torch.nn.DataParallel(model) | |
# SyncBatchNorm | |
if opt.sync_bn and cuda and RANK != -1: | |
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) | |
LOGGER.info('Using SyncBatchNorm()') | |
# Trainloader | |
train_loader, dataset = create_dataloader(train_path, | |
imgsz, | |
batch_size // WORLD_SIZE, | |
gs, | |
single_cls, | |
hyp=hyp, | |
augment=True, | |
cache=None if opt.cache == 'val' else opt.cache, | |
rect=opt.rect, | |
rank=LOCAL_RANK, | |
workers=workers, | |
image_weights=opt.image_weights, | |
close_mosaic=opt.close_mosaic != 0, | |
quad=opt.quad, | |
prefix=colorstr('train: '), | |
shuffle=True, | |
min_items=opt.min_items) | |
labels = np.concatenate(dataset.labels, 0) | |
mlc = int(labels[:, 0].max()) # max label class | |
assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' | |
# Process 0 | |
if RANK in {-1, 0}: | |
val_loader = create_dataloader(val_path, | |
imgsz, | |
batch_size // WORLD_SIZE * 2, | |
gs, | |
single_cls, | |
hyp=hyp, | |
cache=None if noval else opt.cache, | |
rect=True, | |
rank=-1, | |
workers=workers * 2, | |
pad=0.5, | |
prefix=colorstr('val: '))[0] | |
if not resume: | |
# if not opt.noautoanchor: | |
# check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor | |
model.half().float() # pre-reduce anchor precision | |
callbacks.run('on_pretrain_routine_end', labels, names) | |
# DDP mode | |
if cuda and RANK != -1: | |
model = smart_DDP(model) | |
# Model attributes | |
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) | |
#hyp['box'] *= 3 / nl # scale to layers | |
#hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers | |
#hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers | |
hyp['label_smoothing'] = opt.label_smoothing | |
model.nc = nc # attach number of classes to model | |
model.hyp = hyp # attach hyperparameters to model | |
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights | |
model.names = names | |
# Start training | |
t0 = time.time() | |
nb = len(train_loader) # number of batches | |
nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) | |
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training | |
last_opt_step = -1 | |
maps = np.zeros(nc) # mAP per class | |
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) | |
scheduler.last_epoch = start_epoch - 1 # do not move | |
scaler = torch.cuda.amp.GradScaler(enabled=amp) | |
stopper, stop = EarlyStopping(patience=opt.patience), False | |
compute_loss = ComputeLoss(model) # init loss class | |
callbacks.run('on_train_start') | |
LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' | |
f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' | |
f"Logging results to {colorstr('bold', save_dir)}\n" | |
f'Starting training for {epochs} epochs...') | |
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ | |
callbacks.run('on_train_epoch_start') | |
model.train() | |
# Update image weights (optional, single-GPU only) | |
if opt.image_weights: | |
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights | |
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights | |
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx | |
if epoch == (epochs - opt.close_mosaic): | |
LOGGER.info("Closing dataloader mosaic") | |
dataset.mosaic = False | |
# Update mosaic border (optional) | |
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) | |
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders | |
mloss = torch.zeros(3, device=device) # mean losses | |
if RANK != -1: | |
train_loader.sampler.set_epoch(epoch) | |
pbar = enumerate(train_loader) | |
LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'cls_loss', 'dfl_loss', 'Instances', 'Size')) | |
if RANK in {-1, 0}: | |
pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar | |
optimizer.zero_grad() | |
for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- | |
callbacks.run('on_train_batch_start') | |
ni = i + nb * epoch # number integrated batches (since train start) | |
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 | |
# Warmup | |
if ni <= nw: | |
xi = [0, nw] # x interp | |
# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) | |
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) | |
for j, x in enumerate(optimizer.param_groups): | |
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 | |
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) | |
if 'momentum' in x: | |
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) | |
# Multi-scale | |
if opt.multi_scale: | |
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size | |
sf = sz / max(imgs.shape[2:]) # scale factor | |
if sf != 1: | |
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) | |
imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) | |
# Forward | |
with torch.cuda.amp.autocast(amp): | |
pred = model(imgs) # forward | |
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size | |
if RANK != -1: | |
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode | |
if opt.quad: | |
loss *= 4. | |
# Backward | |
scaler.scale(loss).backward() | |
# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html | |
if ni - last_opt_step >= accumulate: | |
scaler.unscale_(optimizer) # unscale gradients | |
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients | |
scaler.step(optimizer) # optimizer.step | |
scaler.update() | |
optimizer.zero_grad() | |
if ema: | |
ema.update(model) | |
last_opt_step = ni | |
# Log | |
if RANK in {-1, 0}: | |
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses | |
mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) | |
pbar.set_description(('%11s' * 2 + '%11.4g' * 5) % | |
(f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) | |
callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss)) | |
if callbacks.stop_training: | |
return | |
# end batch ------------------------------------------------------------------------------------------------ | |
# Scheduler | |
lr = [x['lr'] for x in optimizer.param_groups] # for loggers | |
scheduler.step() | |
if RANK in {-1, 0}: | |
# mAP | |
callbacks.run('on_train_epoch_end', epoch=epoch) | |
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) | |
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop | |
if not noval or final_epoch: # Calculate mAP | |
results, maps, _ = validate.run(data_dict, | |
batch_size=batch_size // WORLD_SIZE * 2, | |
imgsz=imgsz, | |
half=amp, | |
model=ema.ema, | |
single_cls=single_cls, | |
dataloader=val_loader, | |
save_dir=save_dir, | |
plots=False, | |
callbacks=callbacks, | |
compute_loss=compute_loss) | |
# Update best mAP | |
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] | |
stop = stopper(epoch=epoch, fitness=fi) # early stop check | |
if fi > best_fitness: | |
best_fitness = fi | |
log_vals = list(mloss) + list(results) + lr | |
callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) | |
# Save model | |
if (not nosave) or (final_epoch and not evolve): # if save | |
ckpt = { | |
'epoch': epoch, | |
'best_fitness': best_fitness, | |
'model': deepcopy(de_parallel(model)).half(), | |
'ema': deepcopy(ema.ema).half(), | |
'updates': ema.updates, | |
'optimizer': optimizer.state_dict(), | |
'opt': vars(opt), | |
'git': GIT_INFO, # {remote, branch, commit} if a git repo | |
'date': datetime.now().isoformat()} | |
# Save last, best and delete | |
torch.save(ckpt, last) | |
if best_fitness == fi: | |
torch.save(ckpt, best) | |
if opt.save_period > 0 and epoch % opt.save_period == 0: | |
torch.save(ckpt, w / f'epoch{epoch}.pt') | |
del ckpt | |
callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) | |
# EarlyStopping | |
if RANK != -1: # if DDP training | |
broadcast_list = [stop if RANK == 0 else None] | |
dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks | |
if RANK != 0: | |
stop = broadcast_list[0] | |
if stop: | |
break # must break all DDP ranks | |
# end epoch ---------------------------------------------------------------------------------------------------- | |
# end training ----------------------------------------------------------------------------------------------------- | |
if RANK in {-1, 0}: | |
LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') | |
for f in last, best: | |
if f.exists(): | |
if f is last: | |
strip_optimizer(f, last_striped) # strip optimizers | |
else: | |
strip_optimizer(f, best_striped) # strip optimizers | |
if f is best: | |
LOGGER.info(f'\nValidating {f}...') | |
results, _, _ = validate.run( | |
data_dict, | |
batch_size=batch_size // WORLD_SIZE * 2, | |
imgsz=imgsz, | |
model=attempt_load(f, device).half(), | |
single_cls=single_cls, | |
dataloader=val_loader, | |
save_dir=save_dir, | |
save_json=is_coco, | |
verbose=True, | |
plots=plots, | |
callbacks=callbacks, | |
compute_loss=compute_loss) # val best model with plots | |
if is_coco: | |
callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) | |
callbacks.run('on_train_end', last, best, epoch, results) | |
torch.cuda.empty_cache() | |
return results | |
def parse_opt(known=False): | |
parser = argparse.ArgumentParser() | |
# parser.add_argument('--weights', type=str, default=ROOT / 'yolo.pt', help='initial weights path') | |
# parser.add_argument('--cfg', type=str, default='', help='model.yaml path') | |
parser.add_argument('--weights', type=str, default='', help='initial weights path') | |
parser.add_argument('--cfg', type=str, default='yolo.yaml', help='model.yaml path') | |
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') | |
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') | |
parser.add_argument('--epochs', type=int, default=100, help='total training epochs') | |
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') | |
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') | |
parser.add_argument('--rect', action='store_true', help='rectangular training') | |
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') | |
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') | |
parser.add_argument('--noval', action='store_true', help='only validate final epoch') | |
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') | |
parser.add_argument('--noplots', action='store_true', help='save no plot files') | |
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') | |
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') | |
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') | |
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') | |
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') | |
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') | |
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') | |
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW', 'LION'], default='SGD', help='optimizer') | |
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') | |
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') | |
parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') | |
parser.add_argument('--name', default='exp', help='save to project/name') | |
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') | |
parser.add_argument('--quad', action='store_true', help='quad dataloader') | |
parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') | |
parser.add_argument('--flat-cos-lr', action='store_true', help='flat cosine LR scheduler') | |
parser.add_argument('--fixed-lr', action='store_true', help='fixed LR scheduler') | |
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') | |
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') | |
parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') | |
parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') | |
parser.add_argument('--seed', type=int, default=0, help='Global training seed') | |
parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') | |
parser.add_argument('--min-items', type=int, default=0, help='Experimental') | |
parser.add_argument('--close-mosaic', type=int, default=0, help='Experimental') | |
# Logger arguments | |
parser.add_argument('--entity', default=None, help='Entity') | |
parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') | |
parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') | |
parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') | |
return parser.parse_known_args()[0] if known else parser.parse_args() | |
def main(opt, callbacks=Callbacks()): | |
# Checks | |
if RANK in {-1, 0}: | |
print_args(vars(opt)) | |
# Resume (from specified or most recent last.pt) | |
if opt.resume and not check_comet_resume(opt) and not opt.evolve: | |
last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) | |
opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml | |
opt_data = opt.data # original dataset | |
if opt_yaml.is_file(): | |
with open(opt_yaml, errors='ignore') as f: | |
d = yaml.safe_load(f) | |
else: | |
d = torch.load(last, map_location='cpu')['opt'] | |
opt = argparse.Namespace(**d) # replace | |
opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate | |
if is_url(opt_data): | |
opt.data = check_file(opt_data) # avoid HUB resume auth timeout | |
else: | |
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ | |
check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks | |
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' | |
if opt.evolve: | |
if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve | |
opt.project = str(ROOT / 'runs/evolve') | |
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume | |
if opt.name == 'cfg': | |
opt.name = Path(opt.cfg).stem # use model.yaml as name | |
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) | |
# DDP mode | |
device = select_device(opt.device, batch_size=opt.batch_size) | |
if LOCAL_RANK != -1: | |
msg = 'is not compatible with YOLO Multi-GPU DDP training' | |
assert not opt.image_weights, f'--image-weights {msg}' | |
assert not opt.evolve, f'--evolve {msg}' | |
assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' | |
assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' | |
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' | |
torch.cuda.set_device(LOCAL_RANK) | |
device = torch.device('cuda', LOCAL_RANK) | |
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") | |
# Train | |
if not opt.evolve: | |
train(opt.hyp, opt, device, callbacks) | |
# Evolve hyperparameters (optional) | |
else: | |
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) | |
meta = { | |
'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) | |
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) | |
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 | |
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay | |
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) | |
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum | |
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr | |
'box': (1, 0.02, 0.2), # box loss gain | |
'cls': (1, 0.2, 4.0), # cls loss gain | |
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight | |
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) | |
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight | |
'iou_t': (0, 0.1, 0.7), # IoU training threshold | |
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold | |
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) | |
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) | |
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) | |
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) | |
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) | |
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) | |
'translate': (1, 0.0, 0.9), # image translation (+/- fraction) | |
'scale': (1, 0.0, 0.9), # image scale (+/- gain) | |
'shear': (1, 0.0, 10.0), # image shear (+/- deg) | |
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 | |
'flipud': (1, 0.0, 1.0), # image flip up-down (probability) | |
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) | |
'mosaic': (1, 0.0, 1.0), # image mixup (probability) | |
'mixup': (1, 0.0, 1.0), # image mixup (probability) | |
'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) | |
with open(opt.hyp, errors='ignore') as f: | |
hyp = yaml.safe_load(f) # load hyps dict | |
if 'anchors' not in hyp: # anchors commented in hyp.yaml | |
hyp['anchors'] = 3 | |
if opt.noautoanchor: | |
del hyp['anchors'], meta['anchors'] | |
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch | |
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices | |
evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' | |
if opt.bucket: | |
os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists | |
for _ in range(opt.evolve): # generations to evolve | |
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate | |
# Select parent(s) | |
parent = 'single' # parent selection method: 'single' or 'weighted' | |
x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) | |
n = min(5, len(x)) # number of previous results to consider | |
x = x[np.argsort(-fitness(x))][:n] # top n mutations | |
w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) | |
if parent == 'single' or len(x) == 1: | |
# x = x[random.randint(0, n - 1)] # random selection | |
x = x[random.choices(range(n), weights=w)[0]] # weighted selection | |
elif parent == 'weighted': | |
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination | |
# Mutate | |
mp, s = 0.8, 0.2 # mutation probability, sigma | |
npr = np.random | |
npr.seed(int(time.time())) | |
g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 | |
ng = len(meta) | |
v = np.ones(ng) | |
while all(v == 1): # mutate until a change occurs (prevent duplicates) | |
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) | |
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) | |
hyp[k] = float(x[i + 7] * v[i]) # mutate | |
# Constrain to limits | |
for k, v in meta.items(): | |
hyp[k] = max(hyp[k], v[1]) # lower limit | |
hyp[k] = min(hyp[k], v[2]) # upper limit | |
hyp[k] = round(hyp[k], 5) # significant digits | |
# 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, opt.bucket) | |
# 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}') | |
def run(**kwargs): | |
# Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolo.pt') | |
opt = parse_opt(True) | |
for k, v in kwargs.items(): | |
setattr(opt, k, v) | |
main(opt) | |
return opt | |
if __name__ == "__main__": | |
opt = parse_opt() | |
main(opt) | |