ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -23,14 +23,31 @@ from torch import nn, optim
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from ultralytics.cfg import get_cfg, get_save_dir
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from ultralytics.data.utils import check_cls_dataset, check_det_dataset
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from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
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from ultralytics.utils import (DEFAULT_CFG, LOGGER, RANK, TQDM, __version__, callbacks, clean_url, colorstr, emojis,
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yaml_save)
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from ultralytics.utils import (
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DEFAULT_CFG,
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LOGGER,
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RANK,
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TQDM,
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__version__,
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callbacks,
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clean_url,
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colorstr,
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emojis,
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yaml_save,
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)
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from ultralytics.utils.autobatch import check_train_batch_size
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from ultralytics.utils.checks import check_amp, check_file, check_imgsz, check_model_file_from_stem, print_args
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from ultralytics.utils.dist import ddp_cleanup, generate_ddp_command
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from ultralytics.utils.files import get_latest_run
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from ultralytics.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, init_seeds, one_cycle, select_device,
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strip_optimizer)
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from ultralytics.utils.torch_utils import (
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EarlyStopping,
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ModelEMA,
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de_parallel,
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init_seeds,
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one_cycle,
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select_device,
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strip_optimizer,
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)
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class BaseTrainer:
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@ -89,12 +106,12 @@ class BaseTrainer:
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# Dirs
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self.save_dir = get_save_dir(self.args)
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self.args.name = self.save_dir.name # update name for loggers
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self.wdir = self.save_dir / 'weights' # weights dir
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self.wdir = self.save_dir / "weights" # weights dir
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if RANK in (-1, 0):
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self.wdir.mkdir(parents=True, exist_ok=True) # make dir
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self.args.save_dir = str(self.save_dir)
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yaml_save(self.save_dir / 'args.yaml', vars(self.args)) # save run args
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self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt' # checkpoint paths
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yaml_save(self.save_dir / "args.yaml", vars(self.args)) # save run args
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self.last, self.best = self.wdir / "last.pt", self.wdir / "best.pt" # checkpoint paths
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self.save_period = self.args.save_period
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self.batch_size = self.args.batch
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@ -104,18 +121,18 @@ class BaseTrainer:
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print_args(vars(self.args))
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# Device
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if self.device.type in ('cpu', 'mps'):
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if self.device.type in ("cpu", "mps"):
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self.args.workers = 0 # faster CPU training as time dominated by inference, not dataloading
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# Model and Dataset
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self.model = check_model_file_from_stem(self.args.model) # add suffix, i.e. yolov8n -> yolov8n.pt
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try:
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if self.args.task == 'classify':
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if self.args.task == "classify":
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self.data = check_cls_dataset(self.args.data)
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elif self.args.data.split('.')[-1] in ('yaml', 'yml') or self.args.task in ('detect', 'segment', 'pose'):
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elif self.args.data.split(".")[-1] in ("yaml", "yml") or self.args.task in ("detect", "segment", "pose"):
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self.data = check_det_dataset(self.args.data)
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if 'yaml_file' in self.data:
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self.args.data = self.data['yaml_file'] # for validating 'yolo train data=url.zip' usage
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if "yaml_file" in self.data:
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self.args.data = self.data["yaml_file"] # for validating 'yolo train data=url.zip' usage
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except Exception as e:
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raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
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@ -131,8 +148,8 @@ class BaseTrainer:
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self.fitness = None
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self.loss = None
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self.tloss = None
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self.loss_names = ['Loss']
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self.csv = self.save_dir / 'results.csv'
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self.loss_names = ["Loss"]
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self.csv = self.save_dir / "results.csv"
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self.plot_idx = [0, 1, 2]
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# Callbacks
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@ -156,7 +173,7 @@ class BaseTrainer:
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def train(self):
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"""Allow device='', device=None on Multi-GPU systems to default to device=0."""
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if isinstance(self.args.device, str) and len(self.args.device): # i.e. device='0' or device='0,1,2,3'
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world_size = len(self.args.device.split(','))
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world_size = len(self.args.device.split(","))
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elif isinstance(self.args.device, (tuple, list)): # i.e. device=[0, 1, 2, 3] (multi-GPU from CLI is list)
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world_size = len(self.args.device)
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elif torch.cuda.is_available(): # i.e. device=None or device='' or device=number
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@ -165,14 +182,16 @@ class BaseTrainer:
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world_size = 0
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# Run subprocess if DDP training, else train normally
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if world_size > 1 and 'LOCAL_RANK' not in os.environ:
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if world_size > 1 and "LOCAL_RANK" not in os.environ:
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# Argument checks
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if self.args.rect:
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LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with Multi-GPU training, setting 'rect=False'")
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self.args.rect = False
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if self.args.batch == -1:
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LOGGER.warning("WARNING ⚠️ 'batch=-1' for AutoBatch is incompatible with Multi-GPU training, setting "
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"default 'batch=16'")
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LOGGER.warning(
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"WARNING ⚠️ 'batch=-1' for AutoBatch is incompatible with Multi-GPU training, setting "
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"default 'batch=16'"
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)
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self.args.batch = 16
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# Command
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@ -199,37 +218,45 @@ class BaseTrainer:
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def _setup_ddp(self, world_size):
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"""Initializes and sets the DistributedDataParallel parameters for training."""
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torch.cuda.set_device(RANK)
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self.device = torch.device('cuda', RANK)
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self.device = torch.device("cuda", RANK)
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# LOGGER.info(f'DDP info: RANK {RANK}, WORLD_SIZE {world_size}, DEVICE {self.device}')
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os.environ['NCCL_BLOCKING_WAIT'] = '1' # set to enforce timeout
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os.environ["NCCL_BLOCKING_WAIT"] = "1" # set to enforce timeout
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dist.init_process_group(
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'nccl' if dist.is_nccl_available() else 'gloo',
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"nccl" if dist.is_nccl_available() else "gloo",
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timeout=timedelta(seconds=10800), # 3 hours
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rank=RANK,
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world_size=world_size)
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world_size=world_size,
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)
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def _setup_train(self, world_size):
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"""Builds dataloaders and optimizer on correct rank process."""
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# Model
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self.run_callbacks('on_pretrain_routine_start')
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self.run_callbacks("on_pretrain_routine_start")
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ckpt = self.setup_model()
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self.model = self.model.to(self.device)
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self.set_model_attributes()
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# Freeze layers
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freeze_list = self.args.freeze if isinstance(
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self.args.freeze, list) else range(self.args.freeze) if isinstance(self.args.freeze, int) else []
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always_freeze_names = ['.dfl'] # always freeze these layers
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freeze_layer_names = [f'model.{x}.' for x in freeze_list] + always_freeze_names
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freeze_list = (
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self.args.freeze
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if isinstance(self.args.freeze, list)
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else range(self.args.freeze)
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if isinstance(self.args.freeze, int)
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else []
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)
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always_freeze_names = [".dfl"] # always freeze these layers
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freeze_layer_names = [f"model.{x}." for x in freeze_list] + always_freeze_names
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for k, v in self.model.named_parameters():
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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_layer_names):
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LOGGER.info(f"Freezing layer '{k}'")
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v.requires_grad = False
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elif not v.requires_grad:
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LOGGER.info(f"WARNING ⚠️ setting 'requires_grad=True' for frozen layer '{k}'. "
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'See ultralytics.engine.trainer for customization of frozen layers.')
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LOGGER.info(
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f"WARNING ⚠️ setting 'requires_grad=True' for frozen layer '{k}'. "
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"See ultralytics.engine.trainer for customization of frozen layers."
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)
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v.requires_grad = True
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# Check AMP
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@ -246,7 +273,7 @@ class BaseTrainer:
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self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[RANK])
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# Check imgsz
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gs = max(int(self.model.stride.max() if hasattr(self.model, 'stride') else 32), 32) # grid size (max stride)
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gs = max(int(self.model.stride.max() if hasattr(self.model, "stride") else 32), 32) # grid size (max stride)
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self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
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self.stride = gs # for multi-scale training
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@ -256,15 +283,14 @@ class BaseTrainer:
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# Dataloaders
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batch_size = self.batch_size // max(world_size, 1)
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self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode='train')
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self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode="train")
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if RANK in (-1, 0):
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# NOTE: When training DOTA dataset, double batch size could get OOM cause some images got more than 2000 objects.
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self.test_loader = self.get_dataloader(self.testset,
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batch_size=batch_size if self.args.task == 'obb' else batch_size * 2,
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rank=-1,
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mode='val')
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self.test_loader = self.get_dataloader(
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self.testset, batch_size=batch_size if self.args.task == "obb" else batch_size * 2, rank=-1, mode="val"
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)
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self.validator = self.get_validator()
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metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
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metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix="val")
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self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))
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self.ema = ModelEMA(self.model)
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if self.args.plots:
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@ -274,18 +300,20 @@ class BaseTrainer:
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self.accumulate = max(round(self.args.nbs / self.batch_size), 1) # accumulate loss before optimizing
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weight_decay = self.args.weight_decay * self.batch_size * self.accumulate / self.args.nbs # scale weight_decay
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iterations = math.ceil(len(self.train_loader.dataset) / max(self.batch_size, self.args.nbs)) * self.epochs
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self.optimizer = self.build_optimizer(model=self.model,
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name=self.args.optimizer,
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lr=self.args.lr0,
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momentum=self.args.momentum,
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decay=weight_decay,
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iterations=iterations)
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self.optimizer = self.build_optimizer(
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model=self.model,
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name=self.args.optimizer,
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lr=self.args.lr0,
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momentum=self.args.momentum,
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decay=weight_decay,
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iterations=iterations,
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)
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# Scheduler
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self._setup_scheduler()
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self.stopper, self.stop = EarlyStopping(patience=self.args.patience), False
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self.resume_training(ckpt)
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self.scheduler.last_epoch = self.start_epoch - 1 # do not move
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self.run_callbacks('on_pretrain_routine_end')
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self.run_callbacks("on_pretrain_routine_end")
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def _do_train(self, world_size=1):
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"""Train completed, evaluate and plot if specified by arguments."""
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@ -299,19 +327,23 @@ class BaseTrainer:
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self.epoch_time = None
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self.epoch_time_start = time.time()
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self.train_time_start = time.time()
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self.run_callbacks('on_train_start')
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LOGGER.info(f'Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n'
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f'Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n'
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f"Logging results to {colorstr('bold', self.save_dir)}\n"
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f'Starting training for '
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f'{self.args.time} hours...' if self.args.time else f'{self.epochs} epochs...')
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self.run_callbacks("on_train_start")
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LOGGER.info(
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f'Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n'
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f'Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n'
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f"Logging results to {colorstr('bold', self.save_dir)}\n"
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f'Starting training for '
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f'{self.args.time} hours...'
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if self.args.time
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else f"{self.epochs} epochs..."
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)
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if self.args.close_mosaic:
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base_idx = (self.epochs - self.args.close_mosaic) * nb
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self.plot_idx.extend([base_idx, base_idx + 1, base_idx + 2])
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epoch = self.epochs # predefine for resume fully trained model edge cases
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for epoch in range(self.start_epoch, self.epochs):
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self.epoch = epoch
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self.run_callbacks('on_train_epoch_start')
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self.run_callbacks("on_train_epoch_start")
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self.model.train()
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if RANK != -1:
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self.train_loader.sampler.set_epoch(epoch)
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@ -327,7 +359,7 @@ class BaseTrainer:
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self.tloss = None
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self.optimizer.zero_grad()
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for i, batch in pbar:
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self.run_callbacks('on_train_batch_start')
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self.run_callbacks("on_train_batch_start")
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# Warmup
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ni = i + nb * epoch
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if ni <= nw:
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@ -335,10 +367,11 @@ class BaseTrainer:
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self.accumulate = max(1, int(np.interp(ni, xi, [1, self.args.nbs / self.batch_size]).round()))
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for j, x in enumerate(self.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(
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ni, xi, [self.args.warmup_bias_lr if j == 0 else 0.0, x['initial_lr'] * self.lf(epoch)])
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if 'momentum' in x:
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x['momentum'] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])
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x["lr"] = np.interp(
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ni, xi, [self.args.warmup_bias_lr if j == 0 else 0.0, x["initial_lr"] * self.lf(epoch)]
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)
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if "momentum" in x:
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x["momentum"] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])
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# Forward
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with torch.cuda.amp.autocast(self.amp):
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@ -346,8 +379,9 @@ class BaseTrainer:
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self.loss, self.loss_items = self.model(batch)
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if RANK != -1:
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self.loss *= world_size
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self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
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else self.loss_items
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self.tloss = (
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(self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None else self.loss_items
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)
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# Backward
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self.scaler.scale(self.loss).backward()
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@ -368,24 +402,25 @@ class BaseTrainer:
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break
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# Log
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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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mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
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loss_len = self.tloss.shape[0] if len(self.tloss.size()) else 1
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losses = self.tloss if loss_len > 1 else torch.unsqueeze(self.tloss, 0)
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if RANK in (-1, 0):
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pbar.set_description(
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('%11s' * 2 + '%11.4g' * (2 + loss_len)) %
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(f'{epoch + 1}/{self.epochs}', mem, *losses, batch['cls'].shape[0], batch['img'].shape[-1]))
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self.run_callbacks('on_batch_end')
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("%11s" * 2 + "%11.4g" * (2 + loss_len))
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% (f"{epoch + 1}/{self.epochs}", mem, *losses, batch["cls"].shape[0], batch["img"].shape[-1])
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)
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self.run_callbacks("on_batch_end")
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if self.args.plots and ni in self.plot_idx:
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self.plot_training_samples(batch, ni)
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self.run_callbacks('on_train_batch_end')
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self.run_callbacks("on_train_batch_end")
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self.lr = {f'lr/pg{ir}': x['lr'] for ir, x in enumerate(self.optimizer.param_groups)} # for loggers
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self.run_callbacks('on_train_epoch_end')
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self.lr = {f"lr/pg{ir}": x["lr"] for ir, x in enumerate(self.optimizer.param_groups)} # for loggers
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self.run_callbacks("on_train_epoch_end")
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if RANK in (-1, 0):
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final_epoch = epoch + 1 == self.epochs
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self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights'])
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self.ema.update_attr(self.model, include=["yaml", "nc", "args", "names", "stride", "class_weights"])
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# Validation
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if self.args.val or final_epoch or self.stopper.possible_stop or self.stop:
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@ -398,14 +433,14 @@ class BaseTrainer:
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# Save model
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if self.args.save or final_epoch:
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self.save_model()
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self.run_callbacks('on_model_save')
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self.run_callbacks("on_model_save")
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# Scheduler
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t = time.time()
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self.epoch_time = t - self.epoch_time_start
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self.epoch_time_start = t
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with warnings.catch_warnings():
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warnings.simplefilter('ignore') # suppress 'Detected lr_scheduler.step() before optimizer.step()'
|
||||
warnings.simplefilter("ignore") # suppress 'Detected lr_scheduler.step() before optimizer.step()'
|
||||
if self.args.time:
|
||||
mean_epoch_time = (t - self.train_time_start) / (epoch - self.start_epoch + 1)
|
||||
self.epochs = self.args.epochs = math.ceil(self.args.time * 3600 / mean_epoch_time)
|
||||
|
|
@ -413,7 +448,7 @@ class BaseTrainer:
|
|||
self.scheduler.last_epoch = self.epoch # do not move
|
||||
self.stop |= epoch >= self.epochs # stop if exceeded epochs
|
||||
self.scheduler.step()
|
||||
self.run_callbacks('on_fit_epoch_end')
|
||||
self.run_callbacks("on_fit_epoch_end")
|
||||
torch.cuda.empty_cache() # clear GPU memory at end of epoch, may help reduce CUDA out of memory errors
|
||||
|
||||
# Early Stopping
|
||||
|
|
@ -426,39 +461,43 @@ class BaseTrainer:
|
|||
|
||||
if RANK in (-1, 0):
|
||||
# Do final val with best.pt
|
||||
LOGGER.info(f'\n{epoch - self.start_epoch + 1} epochs completed in '
|
||||
f'{(time.time() - self.train_time_start) / 3600:.3f} hours.')
|
||||
LOGGER.info(
|
||||
f"\n{epoch - self.start_epoch + 1} epochs completed in "
|
||||
f"{(time.time() - self.train_time_start) / 3600:.3f} hours."
|
||||
)
|
||||
self.final_eval()
|
||||
if self.args.plots:
|
||||
self.plot_metrics()
|
||||
self.run_callbacks('on_train_end')
|
||||
self.run_callbacks("on_train_end")
|
||||
torch.cuda.empty_cache()
|
||||
self.run_callbacks('teardown')
|
||||
self.run_callbacks("teardown")
|
||||
|
||||
def save_model(self):
|
||||
"""Save model training checkpoints with additional metadata."""
|
||||
import pandas as pd # scope for faster startup
|
||||
metrics = {**self.metrics, **{'fitness': self.fitness}}
|
||||
results = {k.strip(): v for k, v in pd.read_csv(self.csv).to_dict(orient='list').items()}
|
||||
|
||||
metrics = {**self.metrics, **{"fitness": self.fitness}}
|
||||
results = {k.strip(): v for k, v in pd.read_csv(self.csv).to_dict(orient="list").items()}
|
||||
ckpt = {
|
||||
'epoch': self.epoch,
|
||||
'best_fitness': self.best_fitness,
|
||||
'model': deepcopy(de_parallel(self.model)).half(),
|
||||
'ema': deepcopy(self.ema.ema).half(),
|
||||
'updates': self.ema.updates,
|
||||
'optimizer': self.optimizer.state_dict(),
|
||||
'train_args': vars(self.args), # save as dict
|
||||
'train_metrics': metrics,
|
||||
'train_results': results,
|
||||
'date': datetime.now().isoformat(),
|
||||
'version': __version__}
|
||||
"epoch": self.epoch,
|
||||
"best_fitness": self.best_fitness,
|
||||
"model": deepcopy(de_parallel(self.model)).half(),
|
||||
"ema": deepcopy(self.ema.ema).half(),
|
||||
"updates": self.ema.updates,
|
||||
"optimizer": self.optimizer.state_dict(),
|
||||
"train_args": vars(self.args), # save as dict
|
||||
"train_metrics": metrics,
|
||||
"train_results": results,
|
||||
"date": datetime.now().isoformat(),
|
||||
"version": __version__,
|
||||
}
|
||||
|
||||
# Save last and best
|
||||
torch.save(ckpt, self.last)
|
||||
if self.best_fitness == self.fitness:
|
||||
torch.save(ckpt, self.best)
|
||||
if (self.save_period > 0) and (self.epoch > 0) and (self.epoch % self.save_period == 0):
|
||||
torch.save(ckpt, self.wdir / f'epoch{self.epoch}.pt')
|
||||
torch.save(ckpt, self.wdir / f"epoch{self.epoch}.pt")
|
||||
|
||||
@staticmethod
|
||||
def get_dataset(data):
|
||||
|
|
@ -467,7 +506,7 @@ class BaseTrainer:
|
|||
|
||||
Returns None if data format is not recognized.
|
||||
"""
|
||||
return data['train'], data.get('val') or data.get('test')
|
||||
return data["train"], data.get("val") or data.get("test")
|
||||
|
||||
def setup_model(self):
|
||||
"""Load/create/download model for any task."""
|
||||
|
|
@ -476,9 +515,9 @@ class BaseTrainer:
|
|||
|
||||
model, weights = self.model, None
|
||||
ckpt = None
|
||||
if str(model).endswith('.pt'):
|
||||
if str(model).endswith(".pt"):
|
||||
weights, ckpt = attempt_load_one_weight(model)
|
||||
cfg = ckpt['model'].yaml
|
||||
cfg = ckpt["model"].yaml
|
||||
else:
|
||||
cfg = model
|
||||
self.model = self.get_model(cfg=cfg, weights=weights, verbose=RANK == -1) # calls Model(cfg, weights)
|
||||
|
|
@ -505,7 +544,7 @@ class BaseTrainer:
|
|||
The returned dict is expected to contain "fitness" key.
|
||||
"""
|
||||
metrics = self.validator(self)
|
||||
fitness = metrics.pop('fitness', -self.loss.detach().cpu().numpy()) # use loss as fitness measure if not found
|
||||
fitness = metrics.pop("fitness", -self.loss.detach().cpu().numpy()) # use loss as fitness measure if not found
|
||||
if not self.best_fitness or self.best_fitness < fitness:
|
||||
self.best_fitness = fitness
|
||||
return metrics, fitness
|
||||
|
|
@ -516,24 +555,24 @@ class BaseTrainer:
|
|||
|
||||
def get_validator(self):
|
||||
"""Returns a NotImplementedError when the get_validator function is called."""
|
||||
raise NotImplementedError('get_validator function not implemented in trainer')
|
||||
raise NotImplementedError("get_validator function not implemented in trainer")
|
||||
|
||||
def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
|
||||
def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):
|
||||
"""Returns dataloader derived from torch.data.Dataloader."""
|
||||
raise NotImplementedError('get_dataloader function not implemented in trainer')
|
||||
raise NotImplementedError("get_dataloader function not implemented in trainer")
|
||||
|
||||
def build_dataset(self, img_path, mode='train', batch=None):
|
||||
def build_dataset(self, img_path, mode="train", batch=None):
|
||||
"""Build dataset."""
|
||||
raise NotImplementedError('build_dataset function not implemented in trainer')
|
||||
raise NotImplementedError("build_dataset function not implemented in trainer")
|
||||
|
||||
def label_loss_items(self, loss_items=None, prefix='train'):
|
||||
def label_loss_items(self, loss_items=None, prefix="train"):
|
||||
"""Returns a loss dict with labelled training loss items tensor."""
|
||||
# Not needed for classification but necessary for segmentation & detection
|
||||
return {'loss': loss_items} if loss_items is not None else ['loss']
|
||||
return {"loss": loss_items} if loss_items is not None else ["loss"]
|
||||
|
||||
def set_model_attributes(self):
|
||||
"""To set or update model parameters before training."""
|
||||
self.model.names = self.data['names']
|
||||
self.model.names = self.data["names"]
|
||||
|
||||
def build_targets(self, preds, targets):
|
||||
"""Builds target tensors for training YOLO model."""
|
||||
|
|
@ -541,7 +580,7 @@ class BaseTrainer:
|
|||
|
||||
def progress_string(self):
|
||||
"""Returns a string describing training progress."""
|
||||
return ''
|
||||
return ""
|
||||
|
||||
# TODO: may need to put these following functions into callback
|
||||
def plot_training_samples(self, batch, ni):
|
||||
|
|
@ -556,9 +595,9 @@ class BaseTrainer:
|
|||
"""Saves training metrics to a CSV file."""
|
||||
keys, vals = list(metrics.keys()), list(metrics.values())
|
||||
n = len(metrics) + 1 # number of cols
|
||||
s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header
|
||||
with open(self.csv, 'a') as f:
|
||||
f.write(s + ('%23.5g,' * n % tuple([self.epoch + 1] + vals)).rstrip(',') + '\n')
|
||||
s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n") # header
|
||||
with open(self.csv, "a") as f:
|
||||
f.write(s + ("%23.5g," * n % tuple([self.epoch + 1] + vals)).rstrip(",") + "\n")
|
||||
|
||||
def plot_metrics(self):
|
||||
"""Plot and display metrics visually."""
|
||||
|
|
@ -567,7 +606,7 @@ class BaseTrainer:
|
|||
def on_plot(self, name, data=None):
|
||||
"""Registers plots (e.g. to be consumed in callbacks)"""
|
||||
path = Path(name)
|
||||
self.plots[path] = {'data': data, 'timestamp': time.time()}
|
||||
self.plots[path] = {"data": data, "timestamp": time.time()}
|
||||
|
||||
def final_eval(self):
|
||||
"""Performs final evaluation and validation for object detection YOLO model."""
|
||||
|
|
@ -575,11 +614,11 @@ class BaseTrainer:
|
|||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if f is self.best:
|
||||
LOGGER.info(f'\nValidating {f}...')
|
||||
LOGGER.info(f"\nValidating {f}...")
|
||||
self.validator.args.plots = self.args.plots
|
||||
self.metrics = self.validator(model=f)
|
||||
self.metrics.pop('fitness', None)
|
||||
self.run_callbacks('on_fit_epoch_end')
|
||||
self.metrics.pop("fitness", None)
|
||||
self.run_callbacks("on_fit_epoch_end")
|
||||
|
||||
def check_resume(self, overrides):
|
||||
"""Check if resume checkpoint exists and update arguments accordingly."""
|
||||
|
|
@ -591,19 +630,21 @@ class BaseTrainer:
|
|||
|
||||
# Check that resume data YAML exists, otherwise strip to force re-download of dataset
|
||||
ckpt_args = attempt_load_weights(last).args
|
||||
if not Path(ckpt_args['data']).exists():
|
||||
ckpt_args['data'] = self.args.data
|
||||
if not Path(ckpt_args["data"]).exists():
|
||||
ckpt_args["data"] = self.args.data
|
||||
|
||||
resume = True
|
||||
self.args = get_cfg(ckpt_args)
|
||||
self.args.model = str(last) # reinstate model
|
||||
for k in 'imgsz', 'batch': # allow arg updates to reduce memory on resume if crashed due to CUDA OOM
|
||||
for k in "imgsz", "batch": # allow arg updates to reduce memory on resume if crashed due to CUDA OOM
|
||||
if k in overrides:
|
||||
setattr(self.args, k, overrides[k])
|
||||
|
||||
except Exception as e:
|
||||
raise FileNotFoundError('Resume checkpoint not found. Please pass a valid checkpoint to resume from, '
|
||||
"i.e. 'yolo train resume model=path/to/last.pt'") from e
|
||||
raise FileNotFoundError(
|
||||
"Resume checkpoint not found. Please pass a valid checkpoint to resume from, "
|
||||
"i.e. 'yolo train resume model=path/to/last.pt'"
|
||||
) from e
|
||||
self.resume = resume
|
||||
|
||||
def resume_training(self, ckpt):
|
||||
|
|
@ -611,23 +652,26 @@ class BaseTrainer:
|
|||
if ckpt is None:
|
||||
return
|
||||
best_fitness = 0.0
|
||||
start_epoch = ckpt['epoch'] + 1
|
||||
if ckpt['optimizer'] is not None:
|
||||
self.optimizer.load_state_dict(ckpt['optimizer']) # optimizer
|
||||
best_fitness = ckpt['best_fitness']
|
||||
if self.ema and ckpt.get('ema'):
|
||||
self.ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA
|
||||
self.ema.updates = ckpt['updates']
|
||||
start_epoch = ckpt["epoch"] + 1
|
||||
if ckpt["optimizer"] is not None:
|
||||
self.optimizer.load_state_dict(ckpt["optimizer"]) # optimizer
|
||||
best_fitness = ckpt["best_fitness"]
|
||||
if self.ema and ckpt.get("ema"):
|
||||
self.ema.ema.load_state_dict(ckpt["ema"].float().state_dict()) # EMA
|
||||
self.ema.updates = ckpt["updates"]
|
||||
if self.resume:
|
||||
assert start_epoch > 0, \
|
||||
f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
|
||||
assert start_epoch > 0, (
|
||||
f"{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n"
|
||||
f"Start a new training without resuming, i.e. 'yolo train model={self.args.model}'"
|
||||
)
|
||||
LOGGER.info(
|
||||
f'Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs')
|
||||
f"Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs"
|
||||
)
|
||||
if self.epochs < start_epoch:
|
||||
LOGGER.info(
|
||||
f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs.")
|
||||
self.epochs += ckpt['epoch'] # finetune additional epochs
|
||||
f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs."
|
||||
)
|
||||
self.epochs += ckpt["epoch"] # finetune additional epochs
|
||||
self.best_fitness = best_fitness
|
||||
self.start_epoch = start_epoch
|
||||
if start_epoch > (self.epochs - self.args.close_mosaic):
|
||||
|
|
@ -635,13 +679,13 @@ class BaseTrainer:
|
|||
|
||||
def _close_dataloader_mosaic(self):
|
||||
"""Update dataloaders to stop using mosaic augmentation."""
|
||||
if hasattr(self.train_loader.dataset, 'mosaic'):
|
||||
if hasattr(self.train_loader.dataset, "mosaic"):
|
||||
self.train_loader.dataset.mosaic = False
|
||||
if hasattr(self.train_loader.dataset, 'close_mosaic'):
|
||||
LOGGER.info('Closing dataloader mosaic')
|
||||
if hasattr(self.train_loader.dataset, "close_mosaic"):
|
||||
LOGGER.info("Closing dataloader mosaic")
|
||||
self.train_loader.dataset.close_mosaic(hyp=self.args)
|
||||
|
||||
def build_optimizer(self, model, name='auto', lr=0.001, momentum=0.9, decay=1e-5, iterations=1e5):
|
||||
def build_optimizer(self, model, name="auto", lr=0.001, momentum=0.9, decay=1e-5, iterations=1e5):
|
||||
"""
|
||||
Constructs an optimizer for the given model, based on the specified optimizer name, learning rate, momentum,
|
||||
weight decay, and number of iterations.
|
||||
|
|
@ -661,41 +705,45 @@ class BaseTrainer:
|
|||
"""
|
||||
|
||||
g = [], [], [] # optimizer parameter groups
|
||||
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
|
||||
if name == 'auto':
|
||||
LOGGER.info(f"{colorstr('optimizer:')} 'optimizer=auto' found, "
|
||||
f"ignoring 'lr0={self.args.lr0}' and 'momentum={self.args.momentum}' and "
|
||||
f"determining best 'optimizer', 'lr0' and 'momentum' automatically... ")
|
||||
nc = getattr(model, 'nc', 10) # number of classes
|
||||
bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d()
|
||||
if name == "auto":
|
||||
LOGGER.info(
|
||||
f"{colorstr('optimizer:')} 'optimizer=auto' found, "
|
||||
f"ignoring 'lr0={self.args.lr0}' and 'momentum={self.args.momentum}' and "
|
||||
f"determining best 'optimizer', 'lr0' and 'momentum' automatically... "
|
||||
)
|
||||
nc = getattr(model, "nc", 10) # number of classes
|
||||
lr_fit = round(0.002 * 5 / (4 + nc), 6) # lr0 fit equation to 6 decimal places
|
||||
name, lr, momentum = ('SGD', 0.01, 0.9) if iterations > 10000 else ('AdamW', lr_fit, 0.9)
|
||||
name, lr, momentum = ("SGD", 0.01, 0.9) if iterations > 10000 else ("AdamW", lr_fit, 0.9)
|
||||
self.args.warmup_bias_lr = 0.0 # no higher than 0.01 for Adam
|
||||
|
||||
for module_name, module in model.named_modules():
|
||||
for param_name, param in module.named_parameters(recurse=False):
|
||||
fullname = f'{module_name}.{param_name}' if module_name else param_name
|
||||
if 'bias' in fullname: # bias (no decay)
|
||||
fullname = f"{module_name}.{param_name}" if module_name else param_name
|
||||
if "bias" in fullname: # bias (no decay)
|
||||
g[2].append(param)
|
||||
elif isinstance(module, bn): # weight (no decay)
|
||||
g[1].append(param)
|
||||
else: # weight (with decay)
|
||||
g[0].append(param)
|
||||
|
||||
if name in ('Adam', 'Adamax', 'AdamW', 'NAdam', 'RAdam'):
|
||||
if name in ("Adam", "Adamax", "AdamW", "NAdam", "RAdam"):
|
||||
optimizer = getattr(optim, name, optim.Adam)(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
|
||||
elif name == 'RMSProp':
|
||||
elif name == "RMSProp":
|
||||
optimizer = optim.RMSprop(g[2], lr=lr, momentum=momentum)
|
||||
elif name == 'SGD':
|
||||
elif name == "SGD":
|
||||
optimizer = optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f"Optimizer '{name}' not found in list of available optimizers "
|
||||
f'[Adam, AdamW, NAdam, RAdam, RMSProp, SGD, auto].'
|
||||
'To request support for addition optimizers please visit https://github.com/ultralytics/ultralytics.')
|
||||
f"[Adam, AdamW, NAdam, RAdam, RMSProp, SGD, auto]."
|
||||
"To request support for addition optimizers please visit https://github.com/ultralytics/ultralytics."
|
||||
)
|
||||
|
||||
optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay
|
||||
optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights)
|
||||
optimizer.add_param_group({"params": g[0], "weight_decay": decay}) # add g0 with weight_decay
|
||||
optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights)
|
||||
LOGGER.info(
|
||||
f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}, momentum={momentum}) with parameter groups "
|
||||
f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias(decay=0.0)')
|
||||
f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias(decay=0.0)'
|
||||
)
|
||||
return optimizer
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue