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550 lines
23 KiB
550 lines
23 KiB
"""
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Simple training loop; Boilerplate that could apply to any arbitrary neural network,
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"""
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import os
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import subprocess
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import time
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from collections import defaultdict
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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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from omegaconf import OmegaConf # noqa
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from torch.cuda import amp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim import lr_scheduler
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from tqdm import tqdm
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import ultralytics.yolo.utils as utils
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import ultralytics.yolo.utils.callbacks as callbacks
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from ultralytics import __version__
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
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from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, colorstr, yaml_save
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from ultralytics.yolo.utils.checks import check_file, print_args
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from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
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from ultralytics.yolo.utils.files import get_latest_run, increment_path
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from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds, one_cycle, strip_optimizer
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class BaseTrainer:
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"""
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BaseTrainer
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A base class for creating trainers.
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Attributes:
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args (OmegaConf): Configuration for the trainer.
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check_resume (method): Method to check if training should be resumed from a saved checkpoint.
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console (logging.Logger): Logger instance.
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validator (BaseValidator): Validator instance.
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model (nn.Module): Model instance.
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callbacks (defaultdict): Dictionary of callbacks.
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save_dir (Path): Directory to save results.
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wdir (Path): Directory to save weights.
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last (Path): Path to last checkpoint.
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best (Path): Path to best checkpoint.
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batch_size (int): Batch size for training.
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epochs (int): Number of epochs to train for.
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start_epoch (int): Starting epoch for training.
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device (torch.device): Device to use for training.
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amp (bool): Flag to enable AMP (Automatic Mixed Precision).
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scaler (amp.GradScaler): Gradient scaler for AMP.
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data (str): Path to data.
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trainset (torch.utils.data.Dataset): Training dataset.
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testset (torch.utils.data.Dataset): Testing dataset.
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ema (nn.Module): EMA (Exponential Moving Average) of the model.
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lf (nn.Module): Loss function.
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scheduler (torch.optim.lr_scheduler._LRScheduler): Learning rate scheduler.
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best_fitness (float): The best fitness value achieved.
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fitness (float): Current fitness value.
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loss (float): Current loss value.
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tloss (float): Total loss value.
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loss_names (list): List of loss names.
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csv (Path): Path to results CSV file.
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"""
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def __init__(self, config=DEFAULT_CONFIG, overrides=None):
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"""
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Initializes the BaseTrainer class.
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Args:
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config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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if overrides is None:
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overrides = {}
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self.args = get_config(config, overrides)
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self.check_resume()
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init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
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self.console = LOGGER
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self.validator = None
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self.model = None
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self.callbacks = defaultdict(list)
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# dirs
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project = self.args.project or f"runs/{self.args.task}"
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name = self.args.name or f"{self.args.mode}"
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self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True)
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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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yaml_save(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True)) # 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.batch_size = self.args.batch_size
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self.epochs = self.args.epochs
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self.start_epoch = 0
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if RANK == -1:
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print_args(dict(self.args))
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# device
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self.device = utils.torch_utils.select_device(self.args.device, self.batch_size)
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self.amp = self.device.type != 'cpu'
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self.scaler = amp.GradScaler(enabled=self.amp)
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# Model and Dataloaders.
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self.model = self.args.model
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self.data = self.args.data
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if self.data.endswith(".yaml"):
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self.data = check_dataset_yaml(self.data)
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else:
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self.data = check_dataset(self.data)
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self.trainset, self.testset = self.get_dataset(self.data)
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self.ema = None
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# Optimization utils init
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self.lf = None
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self.scheduler = None
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# epoch level metrics
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self.best_fitness = None
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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 = None
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self.csv = self.save_dir / 'results.csv'
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for callback, func in callbacks.default_callbacks.items():
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self.add_callback(callback, func)
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if RANK in {0, -1}:
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callbacks.add_integration_callbacks(self)
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def add_callback(self, onevent: str, callback):
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"""
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appends the given callback
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"""
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self.callbacks[onevent].append(callback)
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def set_callback(self, onevent: str, callback):
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"""
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overrides the existing callbacks with the given callback
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"""
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self.callbacks[onevent] = [callback]
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def trigger_callbacks(self, onevent: str):
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for callback in self.callbacks.get(onevent, []):
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callback(self)
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def train(self):
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world_size = torch.cuda.device_count()
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if world_size > 1 and "LOCAL_RANK" not in os.environ:
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command = generate_ddp_command(world_size, self)
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try:
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subprocess.run(command)
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except Exception as e:
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self.console(e)
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finally:
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ddp_cleanup(command, self)
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else:
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self._do_train(int(os.getenv("RANK", -1)), world_size)
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def _setup_ddp(self, rank, world_size):
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# os.environ['MASTER_ADDR'] = 'localhost'
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# os.environ['MASTER_PORT'] = '9020'
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torch.cuda.set_device(rank)
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self.device = torch.device('cuda', rank)
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self.console.info(f"DDP settings: RANK {rank}, WORLD_SIZE {world_size}, DEVICE {self.device}")
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dist.init_process_group("nccl" if dist.is_nccl_available() else "gloo", rank=rank, world_size=world_size)
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def _setup_train(self, rank, world_size):
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"""
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Builds dataloaders and optimizer on correct rank process
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"""
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# model
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self.trigger_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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if world_size > 1:
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self.model = DDP(self.model, device_ids=[rank])
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# Optimizer
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self.accumulate = max(round(self.args.nbs / self.batch_size), 1) # accumulate loss before optimizing
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self.args.weight_decay *= self.batch_size * self.accumulate / self.args.nbs # scale weight_decay
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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=self.args.weight_decay)
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# Scheduler
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if self.args.cos_lr:
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self.lf = one_cycle(1, self.args.lrf, self.epochs) # cosine 1->hyp['lrf']
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else:
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self.lf = lambda x: (1 - x / self.epochs) * (1.0 - self.args.lrf) + self.args.lrf # linear
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self.scheduler = lr_scheduler.LambdaLR(self.optimizer, lr_lambda=self.lf)
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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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# dataloaders
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batch_size = self.batch_size // world_size if world_size > 1 else self.batch_size
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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 {0, -1}:
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self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode="val")
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self.validator = self.get_validator()
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metric_keys = self.validator.metric_keys + self.label_loss_items(prefix="val")
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self.metrics = dict(zip(metric_keys, [0] * len(metric_keys))) # TODO: init metrics for plot_results()?
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self.ema = ModelEMA(self.model)
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self.trigger_callbacks("on_pretrain_routine_end")
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def _do_train(self, rank=-1, world_size=1):
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if world_size > 1:
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self._setup_ddp(rank, world_size)
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self._setup_train(rank, world_size)
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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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nb = len(self.train_loader) # number of batches
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nw = max(round(self.args.warmup_epochs * nb), 100) # number of warmup iterations
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last_opt_step = -1
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self.trigger_callbacks("on_train_start")
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self.log(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 {self.epochs} epochs...")
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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.trigger_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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pbar = enumerate(self.train_loader)
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if rank in {-1, 0}:
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self.console.info(self.progress_string())
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pbar = tqdm(enumerate(self.train_loader), total=len(self.train_loader), bar_format=TQDM_BAR_FORMAT)
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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.trigger_callbacks("on_train_batch_start")
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# update dataloader attributes (optional)
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if epoch == (self.epochs - self.args.close_mosaic) and hasattr(self.train_loader.dataset, 'mosaic'):
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LOGGER.info("Closing dataloader mosaic")
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self.train_loader.dataset.mosaic = False
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# warmup
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ni = i + nb * epoch
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if ni <= nw:
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xi = [0, nw] # x interp
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self.accumulate = max(1, 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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# forward
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with torch.cuda.amp.autocast(self.amp):
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batch = self.preprocess_batch(batch)
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preds = self.model(batch["img"])
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self.loss, self.loss_items = self.criterion(preds, 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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# backward
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self.scaler.scale(self.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 >= self.accumulate:
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self.optimizer_step()
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last_opt_step = ni
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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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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.trigger_callbacks('on_batch_end')
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if self.args.plots and ni < 3:
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self.plot_training_samples(batch, ni)
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self.trigger_callbacks("on_train_batch_end")
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lr = {f"lr{ir}": x['lr'] for ir, x in enumerate(self.optimizer.param_groups)} # for loggers
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self.scheduler.step()
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self.trigger_callbacks("on_train_epoch_end")
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if rank in {-1, 0}:
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# validation
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self.trigger_callbacks('on_val_start')
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self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights'])
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final_epoch = (epoch + 1 == self.epochs)
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if not self.args.noval or final_epoch:
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self.metrics, self.fitness = self.validate()
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self.trigger_callbacks('on_val_end')
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self.save_metrics(metrics={**self.label_loss_items(self.tloss), **self.metrics, **lr})
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# save model
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if (not self.args.nosave) or (epoch + 1 == self.epochs):
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self.save_model()
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self.trigger_callbacks('on_model_save')
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tnow = time.time()
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self.epoch_time = tnow - self.epoch_time_start
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self.epoch_time_start = tnow
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# TODO: termination condition
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if rank in {-1, 0}:
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# do the last evaluation with best.pt
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self.log(f'\n{epoch - self.start_epoch + 1} epochs completed in '
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f'{(time.time() - self.train_time_start) / 3600:.3f} hours.')
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self.final_eval()
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if self.args.plots:
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self.plot_metrics()
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self.log(f"Results saved to {colorstr('bold', self.save_dir)}")
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self.trigger_callbacks('on_train_end')
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torch.cuda.empty_cache()
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self.trigger_callbacks('teardown')
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def save_model(self):
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ckpt = {
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'epoch': self.epoch,
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'best_fitness': self.best_fitness,
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'model': deepcopy(de_parallel(self.model)).half(),
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'ema': deepcopy(self.ema.ema).half(),
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'updates': self.ema.updates,
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'optimizer': self.optimizer.state_dict(),
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'train_args': self.args,
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'date': datetime.now().isoformat(),
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'version': __version__}
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# Save last, best and delete
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torch.save(ckpt, self.last)
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if self.best_fitness == self.fitness:
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torch.save(ckpt, self.best)
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del ckpt
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def get_dataset(self, data):
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"""
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Get train, val path from data dict if it exists. Returns None if data format is not recognized
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"""
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return data["train"], data.get("val") or data.get("test")
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def setup_model(self):
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"""
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load/create/download model for any task
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"""
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if isinstance(self.model, torch.nn.Module): # if loaded model is passed
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return
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# We should improve the code flow here. This function looks hacky
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model = self.model
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pretrained = not (str(model).endswith(".yaml"))
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# config
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if not pretrained:
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model = check_file(model)
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ckpt = self.load_ckpt(model) if pretrained else None
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self.model = self.load_model(model_cfg=None if pretrained else model, weights=ckpt) # model
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return ckpt
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def load_ckpt(self, ckpt):
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return torch.load(ckpt, map_location='cpu')
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def optimizer_step(self):
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self.scaler.unscale_(self.optimizer) # unscale gradients
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torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=10.0) # clip gradients
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self.scaler.step(self.optimizer)
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self.scaler.update()
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self.optimizer.zero_grad()
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if self.ema:
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self.ema.update(self.model)
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def preprocess_batch(self, batch):
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"""
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Allows custom preprocessing model inputs and ground truths depending on task type
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"""
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return batch
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def validate(self):
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"""
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Runs validation on test set using self.validator.
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# TODO: discuss validator class. Enforce that a validator metrics dict should contain
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"fitness" metric.
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"""
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metrics = self.validator(self)
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fitness = metrics.pop("fitness", -self.loss.detach().cpu().numpy()) # use loss as fitness measure if not found
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if not self.best_fitness or self.best_fitness < fitness:
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self.best_fitness = fitness
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return metrics, fitness
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def log(self, text, rank=-1):
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"""
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Logs the given text to given ranks process if provided, otherwise logs to all ranks
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:param text: text to log
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:param rank: List[Int]
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"""
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if rank in {-1, 0}:
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self.console.info(text)
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def load_model(self, model_cfg=None, weights=None, verbose=True):
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raise NotImplementedError("This task trainer doesn't support loading cfg files")
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def get_validator(self):
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raise NotImplementedError("get_validator function not implemented in trainer")
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def get_dataloader(self, dataset_path, batch_size=16, rank=0):
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"""
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Returns dataloader derived from torch.data.Dataloader
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"""
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raise NotImplementedError("get_dataloader function not implemented in trainer")
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def criterion(self, preds, batch):
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"""
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Returns loss and individual loss items as Tensor
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"""
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raise NotImplementedError("criterion function not implemented in trainer")
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def label_loss_items(self, loss_items=None, prefix="train"):
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"""
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Returns a loss dict with labelled training loss items tensor
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"""
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# Not needed for classification but necessary for segmentation & detection
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return {"loss": loss_items} if loss_items is not None else ["loss"]
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def set_model_attributes(self):
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"""
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To set or update model parameters before training.
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"""
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self.model.names = self.data["names"]
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|
def build_targets(self, preds, targets):
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|
pass
|
|
|
|
def progress_string(self):
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|
return ""
|
|
|
|
# TODO: may need to put these following functions into callback
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|
def plot_training_samples(self, batch, ni):
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|
pass
|
|
|
|
def save_metrics(self, metrics):
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|
keys, vals = list(metrics.keys()), list(metrics.values())
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|
n = len(metrics) + 1 # number of cols
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|
s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header
|
|
with open(self.csv, 'a') as f:
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|
f.write(s + ('%23.5g,' * n % tuple([self.epoch] + vals)).rstrip(',') + '\n')
|
|
|
|
def plot_metrics(self):
|
|
pass
|
|
|
|
def final_eval(self):
|
|
for f in self.last, self.best:
|
|
if f.exists():
|
|
strip_optimizer(f) # strip optimizers
|
|
if f is self.best:
|
|
self.console.info(f'\nValidating {f}...')
|
|
self.validator.args.save_json = True
|
|
self.metrics = self.validator(model=f)
|
|
self.metrics.pop('fitness', None)
|
|
self.trigger_callbacks('on_val_end')
|
|
|
|
def check_resume(self):
|
|
resume = self.args.resume
|
|
if resume:
|
|
last = Path(check_file(resume) if isinstance(resume, str) else get_latest_run())
|
|
args_yaml = last.parent.parent / 'args.yaml' # train options yaml
|
|
if args_yaml.is_file():
|
|
args = get_config(args_yaml) # replace
|
|
args.model, args.resume, args.exist_ok = str(last), True, True # reinstate
|
|
self.args = args
|
|
|
|
def resume_training(self, ckpt):
|
|
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']
|
|
if self.args.resume:
|
|
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 --resume, i.e. 'yolo task=... mode=train model={self.args.model}'"
|
|
LOGGER.info(
|
|
f'Resuming training from {self.args.model} from epoch {start_epoch} 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
|
|
self.best_fitness = best_fitness
|
|
self.start_epoch = start_epoch
|
|
|
|
@staticmethod
|
|
def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
|
|
"""
|
|
Builds an optimizer with the specified parameters and parameter groups.
|
|
|
|
Args:
|
|
model (nn.Module): model to optimize
|
|
name (str): name of the optimizer to use
|
|
lr (float): learning rate
|
|
momentum (float): momentum
|
|
decay (float): weight decay
|
|
|
|
Returns:
|
|
optimizer (torch.optim.Optimizer): the built optimizer
|
|
"""
|
|
g = [], [], [] # optimizer parameter groups
|
|
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
|
|
for v in model.modules():
|
|
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias (no decay)
|
|
g[2].append(v.bias)
|
|
if isinstance(v, bn): # weight (no decay)
|
|
g[1].append(v.weight)
|
|
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
|
|
g[0].append(v.weight)
|
|
|
|
if name == 'Adam':
|
|
optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum
|
|
elif name == 'AdamW':
|
|
optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
|
|
elif name == 'RMSProp':
|
|
optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
|
|
elif name == 'SGD':
|
|
optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
|
|
else:
|
|
raise NotImplementedError(f'Optimizer {name} not implemented.')
|
|
|
|
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}) with parameter groups "
|
|
f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
|
|
return optimizer
|