659 lines
34 KiB
Python
659 lines
34 KiB
Python
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Train a YOLOv5 segment model on a segment dataset
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Models and datasets download automatically from the latest YOLOv5 release.
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Usage - Single-GPU training:
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$ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended)
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$ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch
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Usage - Multi-GPU DDP training:
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$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
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Models: https://github.com/ultralytics/yolov5/tree/master/models
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Datasets: https://github.com/ultralytics/yolov5/tree/master/data
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Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
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"""
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import argparse
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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 datetime import datetime
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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.optim import lr_scheduler
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from tqdm import tqdm
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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import segment.val as validate # for end-of-epoch mAP
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from models.experimental import attempt_load
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from models.yolo import SegmentationModel
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from utils.autoanchor import check_anchors
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from utils.autobatch import check_train_batch_size
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from utils.callbacks import Callbacks
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from utils.downloads import attempt_download, is_url
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from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info,
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check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr,
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get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
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labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
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from utils.loggers import GenericLogger
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from utils.plots import plot_evolve, plot_labels
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from utils.segment.dataloaders import create_dataloader
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from utils.segment.loss import ComputeLoss
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from utils.segment.metrics import KEYS, fitness
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from utils.segment.plots import plot_images_and_masks, plot_results_with_masks
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from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
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smart_resume, torch_distributed_zero_first)
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
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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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GIT_INFO = check_git_info()
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def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary
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save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, mask_ratio = \
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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, opt.mask_ratio
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# callbacks.run('on_pretrain_routine_start')
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# Directories
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w = save_dir / 'weights' # weights dir
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(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
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last, best = w / 'last.pt', w / 'best.pt'
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# Hyperparameters
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if isinstance(hyp, str):
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with open(hyp, errors='ignore') as f:
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hyp = yaml.safe_load(f) # load hyps dict
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LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
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opt.hyp = hyp.copy() # for saving hyps to checkpoints
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# Save run settings
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if not evolve:
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yaml_save(save_dir / 'hyp.yaml', hyp)
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yaml_save(save_dir / 'opt.yaml', vars(opt))
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# Loggers
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data_dict = None
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if RANK in {-1, 0}:
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logger = GenericLogger(opt=opt, console_logger=LOGGER)
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# Config
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plots = not evolve and not opt.noplots # create plots
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overlap = not opt.no_overlap
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cuda = device.type != 'cpu'
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init_seeds(opt.seed + 1 + RANK, deterministic=True)
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with torch_distributed_zero_first(LOCAL_RANK):
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data_dict = data_dict or check_dataset(data) # check if None
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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']) # number of classes
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names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
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is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset
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# Model
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check_suffix(weights, '.pt') # check weights
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pretrained = weights.endswith('.pt')
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if pretrained:
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with torch_distributed_zero_first(LOCAL_RANK):
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weights = attempt_download(weights) # download if not found locally
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ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
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model = SegmentationModel(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 [] # exclude keys
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csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
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model.load_state_dict(csd, strict=False) # load
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LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report
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else:
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model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
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amp = check_amp(model) # check AMP
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# Freeze
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freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
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for k, v in model.named_parameters():
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v.requires_grad = True # train all layers
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# v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
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if any(x in k for x in freeze):
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LOGGER.info(f'freezing {k}')
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v.requires_grad = False
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# Image size
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gs = max(int(model.stride.max()), 32) # grid size (max stride)
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imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
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# Batch size
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if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
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batch_size = check_train_batch_size(model, imgsz, amp)
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logger.update_params({"batch_size": batch_size})
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# loggers.on_params_update({"batch_size": batch_size})
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# Optimizer
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nbs = 64 # nominal batch size
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accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
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hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
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optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])
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# Scheduler
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if opt.cos_lr:
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lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
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else:
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lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
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# EMA
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ema = ModelEMA(model) if RANK in {-1, 0} else None
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# Resume
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best_fitness, start_epoch = 0.0, 0
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if pretrained:
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if resume:
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best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
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del ckpt, csd
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# DP mode
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if cuda and RANK == -1 and torch.cuda.device_count() > 1:
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LOGGER.warning('WARNING ⚠️ DP not recommended, 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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# SyncBatchNorm
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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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# Trainloader
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train_loader, dataset = create_dataloader(
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train_path,
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imgsz,
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batch_size // WORLD_SIZE,
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gs,
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single_cls,
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hyp=hyp,
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augment=True,
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cache=None if opt.cache == 'val' else opt.cache,
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rect=opt.rect,
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rank=LOCAL_RANK,
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workers=workers,
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image_weights=opt.image_weights,
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quad=opt.quad,
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prefix=colorstr('train: '),
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shuffle=True,
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mask_downsample_ratio=mask_ratio,
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overlap_mask=overlap,
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)
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labels = np.concatenate(dataset.labels, 0)
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mlc = int(labels[:, 0].max()) # max label class
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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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# Process 0
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if RANK in {-1, 0}:
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val_loader = create_dataloader(val_path,
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imgsz,
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batch_size // WORLD_SIZE * 2,
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gs,
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single_cls,
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hyp=hyp,
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cache=None if noval else opt.cache,
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rect=True,
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rank=-1,
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workers=workers * 2,
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pad=0.5,
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mask_downsample_ratio=mask_ratio,
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overlap_mask=overlap,
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prefix=colorstr('val: '))[0]
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if not resume:
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if not opt.noautoanchor:
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check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor
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model.half().float() # pre-reduce anchor precision
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if plots:
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plot_labels(labels, names, save_dir)
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# callbacks.run('on_pretrain_routine_end', labels, names)
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# DDP mode
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if cuda and RANK != -1:
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model = smart_DDP(model)
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# Model attributes
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nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
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hyp['box'] *= 3 / nl # scale to layers
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hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers
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hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
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hyp['label_smoothing'] = opt.label_smoothing
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model.nc = nc # attach number of classes to model
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model.hyp = hyp # attach hyperparameters to model
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
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model.names = names
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# Start training
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t0 = time.time()
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nb = len(train_loader) # number of batches
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nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
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# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
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last_opt_step = -1
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maps = np.zeros(nc) # mAP per class
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results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
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scheduler.last_epoch = start_epoch - 1 # do not move
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scaler = torch.cuda.amp.GradScaler(enabled=amp)
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stopper, stop = EarlyStopping(patience=opt.patience), False
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compute_loss = ComputeLoss(model, overlap=overlap) # init loss class
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# callbacks.run('on_train_start')
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LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
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f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
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f"Logging results to {colorstr('bold', save_dir)}\n"
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f'Starting training for {epochs} epochs...')
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for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
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# callbacks.run('on_train_epoch_start')
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model.train()
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# Update image weights (optional, single-GPU only)
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if opt.image_weights:
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cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
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iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
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dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
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# Update mosaic border (optional)
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# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
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# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
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mloss = torch.zeros(4, device=device) # mean losses
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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' + '%11s' * 8) %
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('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
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if RANK in {-1, 0}:
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pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
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optimizer.zero_grad()
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for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------
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# callbacks.run('on_train_batch_start')
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ni = i + nb * epoch # number integrated batches (since train start)
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imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
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# Warmup
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if ni <= nw:
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xi = [0, nw] # x interp
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# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
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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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# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
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x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 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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# Multi-scale
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if opt.multi_scale:
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sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
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sf = sz / max(imgs.shape[2:]) # scale factor
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if sf != 1:
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ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
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imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
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# Forward
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with torch.cuda.amp.autocast(amp):
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pred = model(imgs) # forward
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loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float())
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if RANK != -1:
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loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
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if opt.quad:
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loss *= 4.
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# Backward
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scaler.scale(loss).backward()
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# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
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if ni - last_opt_step >= accumulate:
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scaler.unscale_(optimizer) # unscale gradients
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
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scaler.step(optimizer) # optimizer.step
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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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# Log
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if RANK in {-1, 0}:
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
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pbar.set_description(('%11s' * 2 + '%11.4g' * 6) %
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(f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
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# callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths)
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# if callbacks.stop_training:
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# return
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# Mosaic plots
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if plots:
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if ni < 3:
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plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg")
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if ni == 10:
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files = sorted(save_dir.glob('train*.jpg'))
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logger.log_images(files, "Mosaics", epoch)
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# end batch ------------------------------------------------------------------------------------------------
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# Scheduler
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lr = [x['lr'] for x in optimizer.param_groups] # for loggers
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scheduler.step()
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if RANK in {-1, 0}:
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# mAP
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# callbacks.run('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) or stopper.possible_stop
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if not noval or final_epoch: # Calculate mAP
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results, maps, _ = validate.run(data_dict,
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batch_size=batch_size // WORLD_SIZE * 2,
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imgsz=imgsz,
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half=amp,
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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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plots=False,
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callbacks=callbacks,
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compute_loss=compute_loss,
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mask_downsample_ratio=mask_ratio,
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overlap=overlap)
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# Update best mAP
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fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
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stop = stopper(epoch=epoch, fitness=fi) # early stop check
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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.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
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# Log val metrics and media
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metrics_dict = dict(zip(KEYS, log_vals))
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logger.log_metrics(metrics_dict, epoch)
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# Save model
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if (not nosave) or (final_epoch and not evolve): # if save
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ckpt = {
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'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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'opt': vars(opt),
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'git': GIT_INFO, # {remote, branch, commit} if a git repo
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'date': datetime.now().isoformat()}
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|
|
# 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')
|
|
logger.log_model(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():
|
|
strip_optimizer(f) # 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(),
|
|
iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65
|
|
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,
|
|
mask_downsample_ratio=mask_ratio,
|
|
overlap=overlap) # val best model with plots
|
|
if is_coco:
|
|
# callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
|
|
metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr))
|
|
logger.log_metrics(metrics_dict, epoch)
|
|
|
|
# callbacks.run('on_train_end', last, best, epoch, results)
|
|
# on train end callback using genericLogger
|
|
logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs)
|
|
if not opt.evolve:
|
|
logger.log_model(best, epoch)
|
|
if plots:
|
|
plot_results_with_masks(file=save_dir / 'results.csv') # save results.png
|
|
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
|
|
files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter
|
|
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
|
logger.log_images(files, "Results", epoch + 1)
|
|
logger.log_images(sorted(save_dir.glob('val*.jpg')), "Validation", epoch + 1)
|
|
torch.cuda.empty_cache()
|
|
return results
|
|
|
|
|
|
def parse_opt(known=False):
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s-seg.pt', help='initial weights path')
|
|
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
|
|
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.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'], 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-seg', 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('--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')
|
|
|
|
# Instance Segmentation Args
|
|
parser.add_argument('--mask-ratio', type=int, default=4, help='Downsample the truth masks to saving memory')
|
|
parser.add_argument('--no-overlap', action='store_true', help='Overlap masks train faster at slightly less mAP')
|
|
|
|
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))
|
|
check_git_status()
|
|
check_requirements()
|
|
|
|
# Resume
|
|
if opt.resume and not opt.evolve: # resume from specified or most recent last.pt
|
|
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 YOLOv5 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
|
|
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='yolov5m.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)
|