775 lines
35 KiB
Python
775 lines
35 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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"""
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Train a model on a dataset.
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Usage:
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$ yolo mode=train model=yolov8n.pt data=coco8.yaml imgsz=640 epochs=100 batch=16
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"""
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import gc
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import math
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import os
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import subprocess
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import time
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import warnings
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from copy import deepcopy
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from datetime import datetime, timedelta
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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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from torch import distributed as dist
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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 (
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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 (
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EarlyStopping,
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ModelEMA,
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convert_optimizer_state_dict_to_fp16,
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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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torch_distributed_zero_first,
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)
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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 (SimpleNamespace): Configuration for the trainer.
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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 the last checkpoint.
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best (Path): Path to the best checkpoint.
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save_period (int): Save checkpoint every x epochs (disabled if < 1).
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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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resume (bool): Resume training from a checkpoint.
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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, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""
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Initializes the BaseTrainer class.
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Args:
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cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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self.args = get_cfg(cfg, overrides)
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self.check_resume(overrides)
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self.device = select_device(self.args.device, self.args.batch)
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self.validator = None
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self.metrics = None
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self.plots = {}
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init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
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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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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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self.save_period = self.args.save_period
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self.batch_size = self.args.batch
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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(vars(self.args))
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# Device
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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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with torch_distributed_zero_first(RANK): # avoid auto-downloading dataset multiple times
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self.trainset, self.testset = self.get_dataset()
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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 = ["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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# HUB
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self.hub_session = None
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# Callbacks
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self.callbacks = _callbacks or callbacks.get_default_callbacks()
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if RANK in {-1, 0}:
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callbacks.add_integration_callbacks(self)
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def add_callback(self, event: str, callback):
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"""Appends the given callback."""
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self.callbacks[event].append(callback)
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def set_callback(self, event: str, callback):
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"""Overrides the existing callbacks with the given callback."""
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self.callbacks[event] = [callback]
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def run_callbacks(self, event: str):
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"""Run all existing callbacks associated with a particular event."""
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for callback in self.callbacks.get(event, []):
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callback(self)
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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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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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world_size = 1 # default to device 0
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else: # i.e. device='cpu' or 'mps'
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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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# 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 < 1.0:
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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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cmd, file = generate_ddp_command(world_size, self)
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try:
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LOGGER.info(f'{colorstr("DDP:")} debug command {" ".join(cmd)}')
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subprocess.run(cmd, check=True)
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except Exception as e:
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raise e
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finally:
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ddp_cleanup(self, str(file))
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else:
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self._do_train(world_size)
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def _setup_scheduler(self):
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"""Initialize training learning rate 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: max(1 - x / self.epochs, 0) * (1.0 - self.args.lrf) + self.args.lrf # linear
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self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=self.lf)
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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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# LOGGER.info(f'DDP info: RANK {RANK}, WORLD_SIZE {world_size}, DEVICE {self.device}')
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os.environ["TORCH_NCCL_BLOCKING_WAIT"] = "1" # set to enforce timeout
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dist.init_process_group(
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backend="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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)
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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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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 = (
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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 and v.dtype.is_floating_point: # only floating point Tensor can require gradients
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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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self.amp = torch.tensor(self.args.amp).to(self.device) # True or False
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if self.amp and RANK in {-1, 0}: # Single-GPU and DDP
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callbacks_backup = callbacks.default_callbacks.copy() # backup callbacks as check_amp() resets them
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self.amp = torch.tensor(check_amp(self.model), device=self.device)
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callbacks.default_callbacks = callbacks_backup # restore callbacks
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if RANK > -1 and world_size > 1: # DDP
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dist.broadcast(self.amp, src=0) # broadcast the tensor from rank 0 to all other ranks (returns None)
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self.amp = bool(self.amp) # as boolean
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self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp)
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if world_size > 1:
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self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[RANK], find_unused_parameters=True)
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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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self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
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self.stride = gs # for multiscale training
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# Batch size
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if self.batch_size < 1 and RANK == -1: # single-GPU only, estimate best batch size
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self.args.batch = self.batch_size = check_train_batch_size(
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model=self.model,
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imgsz=self.args.imgsz,
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amp=self.amp,
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batch=self.batch_size,
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)
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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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if RANK in {-1, 0}:
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# Note: When training DOTA dataset, double batch size could get OOM on images with >2000 objects.
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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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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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self.plot_training_labels()
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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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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(
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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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def _do_train(self, world_size=1):
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"""Train completed, evaluate and plot if specified by arguments."""
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if world_size > 1:
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self._setup_ddp(world_size)
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self._setup_train(world_size)
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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) if self.args.warmup_epochs > 0 else -1 # warmup iterations
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last_opt_step = -1
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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(
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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 ' + (f"{self.args.time} hours..." if self.args.time 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.start_epoch
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self.optimizer.zero_grad() # zero any resumed gradients to ensure stability on train start
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while True:
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self.epoch = epoch
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self.run_callbacks("on_train_epoch_start")
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with warnings.catch_warnings():
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warnings.simplefilter("ignore") # suppress 'Detected lr_scheduler.step() before optimizer.step()'
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self.scheduler.step()
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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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# Update dataloader attributes (optional)
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if epoch == (self.epochs - self.args.close_mosaic):
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self._close_dataloader_mosaic()
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self.train_loader.reset()
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if RANK in {-1, 0}:
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LOGGER.info(self.progress_string())
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pbar = TQDM(enumerate(self.train_loader), total=nb)
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self.tloss = None
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for i, batch in pbar:
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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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xi = [0, nw] # x interp
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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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)
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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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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 = (
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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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# 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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# Timed stopping
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if self.args.time:
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self.stop = (time.time() - self.train_time_start) > (self.args.time * 3600)
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if RANK != -1: # if DDP training
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broadcast_list = [self.stop if RANK == 0 else None]
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dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
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self.stop = broadcast_list[0]
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if self.stop: # training time exceeded
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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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loss_len = self.tloss.shape[0] if len(self.tloss.shape) 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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)
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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.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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# Validation
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|
if self.args.val or final_epoch or self.stopper.possible_stop or self.stop:
|
|
self.metrics, self.fitness = self.validate()
|
|
self.save_metrics(metrics={**self.label_loss_items(self.tloss), **self.metrics, **self.lr})
|
|
self.stop |= self.stopper(epoch + 1, self.fitness) or final_epoch
|
|
if self.args.time:
|
|
self.stop |= (time.time() - self.train_time_start) > (self.args.time * 3600)
|
|
|
|
# Save model
|
|
if self.args.save or final_epoch:
|
|
self.save_model()
|
|
self.run_callbacks("on_model_save")
|
|
|
|
# Scheduler
|
|
t = time.time()
|
|
self.epoch_time = t - self.epoch_time_start
|
|
self.epoch_time_start = t
|
|
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)
|
|
self._setup_scheduler()
|
|
self.scheduler.last_epoch = self.epoch # do not move
|
|
self.stop |= epoch >= self.epochs # stop if exceeded epochs
|
|
self.run_callbacks("on_fit_epoch_end")
|
|
gc.collect()
|
|
torch.cuda.empty_cache() # clear GPU memory at end of epoch, may help reduce CUDA out of memory errors
|
|
|
|
# Early Stopping
|
|
if RANK != -1: # if DDP training
|
|
broadcast_list = [self.stop if RANK == 0 else None]
|
|
dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
|
|
self.stop = broadcast_list[0]
|
|
if self.stop:
|
|
break # must break all DDP ranks
|
|
epoch += 1
|
|
|
|
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."
|
|
)
|
|
self.final_eval()
|
|
if self.args.plots:
|
|
self.plot_metrics()
|
|
self.run_callbacks("on_train_end")
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
self.run_callbacks("teardown")
|
|
|
|
def save_model(self):
|
|
"""Save model training checkpoints with additional metadata."""
|
|
import io
|
|
|
|
import pandas as pd # scope for faster 'import ultralytics'
|
|
|
|
# Serialize ckpt to a byte buffer once (faster than repeated torch.save() calls)
|
|
buffer = io.BytesIO()
|
|
torch.save(
|
|
{
|
|
"epoch": self.epoch,
|
|
"best_fitness": self.best_fitness,
|
|
"model": None, # resume and final checkpoints derive from EMA
|
|
"ema": deepcopy(self.ema.ema).half(),
|
|
"updates": self.ema.updates,
|
|
"optimizer": convert_optimizer_state_dict_to_fp16(deepcopy(self.optimizer.state_dict())),
|
|
"train_args": vars(self.args), # save as dict
|
|
"train_metrics": {**self.metrics, **{"fitness": self.fitness}},
|
|
"train_results": {k.strip(): v for k, v in pd.read_csv(self.csv).to_dict(orient="list").items()},
|
|
"date": datetime.now().isoformat(),
|
|
"version": __version__,
|
|
"license": "AGPL-3.0 (https://ultralytics.com/license)",
|
|
"docs": "https://docs.ultralytics.com",
|
|
},
|
|
buffer,
|
|
)
|
|
serialized_ckpt = buffer.getvalue() # get the serialized content to save
|
|
|
|
# Save checkpoints
|
|
self.last.write_bytes(serialized_ckpt) # save last.pt
|
|
if self.best_fitness == self.fitness:
|
|
self.best.write_bytes(serialized_ckpt) # save best.pt
|
|
if (self.save_period > 0) and (self.epoch > 0) and (self.epoch % self.save_period == 0):
|
|
(self.wdir / f"epoch{self.epoch}.pt").write_bytes(serialized_ckpt) # save epoch, i.e. 'epoch3.pt'
|
|
|
|
def get_dataset(self):
|
|
"""
|
|
Get train, val path from data dict if it exists.
|
|
|
|
Returns None if data format is not recognized.
|
|
"""
|
|
try:
|
|
if self.args.task == "classify":
|
|
data = check_cls_dataset(self.args.data)
|
|
elif self.args.data.split(".")[-1] in {"yaml", "yml"} or self.args.task in {
|
|
"detect",
|
|
"segment",
|
|
"pose",
|
|
"obb",
|
|
}:
|
|
data = check_det_dataset(self.args.data)
|
|
if "yaml_file" in data:
|
|
self.args.data = data["yaml_file"] # for validating 'yolo train data=url.zip' usage
|
|
except Exception as e:
|
|
raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
|
|
self.data = data
|
|
return data["train"], data.get("val") or data.get("test")
|
|
|
|
def setup_model(self):
|
|
"""Load/create/download model for any task."""
|
|
if isinstance(self.model, torch.nn.Module): # if model is loaded beforehand. No setup needed
|
|
return
|
|
|
|
cfg, weights = self.model, None
|
|
ckpt = None
|
|
if str(self.model).endswith(".pt"):
|
|
weights, ckpt = attempt_load_one_weight(self.model)
|
|
cfg = weights.yaml
|
|
elif isinstance(self.args.pretrained, (str, Path)):
|
|
weights, _ = attempt_load_one_weight(self.args.pretrained)
|
|
self.model = self.get_model(cfg=cfg, weights=weights, verbose=RANK == -1) # calls Model(cfg, weights)
|
|
return ckpt
|
|
|
|
def optimizer_step(self):
|
|
"""Perform a single step of the training optimizer with gradient clipping and EMA update."""
|
|
self.scaler.unscale_(self.optimizer) # unscale gradients
|
|
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=10.0) # clip gradients
|
|
self.scaler.step(self.optimizer)
|
|
self.scaler.update()
|
|
self.optimizer.zero_grad()
|
|
if self.ema:
|
|
self.ema.update(self.model)
|
|
|
|
def preprocess_batch(self, batch):
|
|
"""Allows custom preprocessing model inputs and ground truths depending on task type."""
|
|
return batch
|
|
|
|
def validate(self):
|
|
"""
|
|
Runs validation on test set using self.validator.
|
|
|
|
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
|
|
if not self.best_fitness or self.best_fitness < fitness:
|
|
self.best_fitness = fitness
|
|
return metrics, fitness
|
|
|
|
def get_model(self, cfg=None, weights=None, verbose=True):
|
|
"""Get model and raise NotImplementedError for loading cfg files."""
|
|
raise NotImplementedError("This task trainer doesn't support loading cfg files")
|
|
|
|
def get_validator(self):
|
|
"""Returns a NotImplementedError when the get_validator function is called."""
|
|
raise NotImplementedError("get_validator function not implemented in trainer")
|
|
|
|
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")
|
|
|
|
def build_dataset(self, img_path, mode="train", batch=None):
|
|
"""Build dataset."""
|
|
raise NotImplementedError("build_dataset function not implemented in trainer")
|
|
|
|
def label_loss_items(self, loss_items=None, prefix="train"):
|
|
"""
|
|
Returns a loss dict with labelled training loss items tensor.
|
|
|
|
Note:
|
|
This is not needed for classification but necessary for segmentation & detection
|
|
"""
|
|
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"]
|
|
|
|
def build_targets(self, preds, targets):
|
|
"""Builds target tensors for training YOLO model."""
|
|
pass
|
|
|
|
def progress_string(self):
|
|
"""Returns a string describing training progress."""
|
|
return ""
|
|
|
|
# TODO: may need to put these following functions into callback
|
|
def plot_training_samples(self, batch, ni):
|
|
"""Plots training samples during YOLO training."""
|
|
pass
|
|
|
|
def plot_training_labels(self):
|
|
"""Plots training labels for YOLO model."""
|
|
pass
|
|
|
|
def save_metrics(self, metrics):
|
|
"""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")
|
|
|
|
def plot_metrics(self):
|
|
"""Plot and display metrics visually."""
|
|
pass
|
|
|
|
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()}
|
|
|
|
def final_eval(self):
|
|
"""Performs final evaluation and validation for object detection YOLO model."""
|
|
for f in self.last, self.best:
|
|
if f.exists():
|
|
strip_optimizer(f) # strip optimizers
|
|
if f is self.best:
|
|
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")
|
|
|
|
def check_resume(self, overrides):
|
|
"""Check if resume checkpoint exists and update arguments accordingly."""
|
|
resume = self.args.resume
|
|
if resume:
|
|
try:
|
|
exists = isinstance(resume, (str, Path)) and Path(resume).exists()
|
|
last = Path(check_file(resume) if exists else get_latest_run())
|
|
|
|
# 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
|
|
|
|
resume = True
|
|
self.args = get_cfg(ckpt_args)
|
|
self.args.model = self.args.resume = str(last) # reinstate model
|
|
for k in "imgsz", "batch", "device": # allow arg updates to reduce memory or update device on resume
|
|
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
|
|
self.resume = resume
|
|
|
|
def resume_training(self, ckpt):
|
|
"""Resume YOLO training from given epoch and best fitness."""
|
|
if ckpt is None or not self.resume:
|
|
return
|
|
best_fitness = 0.0
|
|
start_epoch = ckpt.get("epoch", -1) + 1
|
|
if ckpt.get("optimizer", None) 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"]
|
|
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 {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
|
|
self.best_fitness = best_fitness
|
|
self.start_epoch = start_epoch
|
|
if start_epoch > (self.epochs - self.args.close_mosaic):
|
|
self._close_dataloader_mosaic()
|
|
|
|
def _close_dataloader_mosaic(self):
|
|
"""Update dataloaders to stop using mosaic augmentation."""
|
|
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")
|
|
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):
|
|
"""
|
|
Constructs an optimizer for the given model, based on the specified optimizer name, learning rate, momentum,
|
|
weight decay, and number of iterations.
|
|
|
|
Args:
|
|
model (torch.nn.Module): The model for which to build an optimizer.
|
|
name (str, optional): The name of the optimizer to use. If 'auto', the optimizer is selected
|
|
based on the number of iterations. Default: 'auto'.
|
|
lr (float, optional): The learning rate for the optimizer. Default: 0.001.
|
|
momentum (float, optional): The momentum factor for the optimizer. Default: 0.9.
|
|
decay (float, optional): The weight decay for the optimizer. Default: 1e-5.
|
|
iterations (float, optional): The number of iterations, which determines the optimizer if
|
|
name is 'auto'. Default: 1e5.
|
|
|
|
Returns:
|
|
(torch.optim.Optimizer): The constructed optimizer.
|
|
"""
|
|
|
|
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
|
|
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)
|
|
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)
|
|
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"}:
|
|
optimizer = getattr(optim, name, optim.Adam)(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
|
|
elif name == "RMSProp":
|
|
optimizer = optim.RMSprop(g[2], lr=lr, momentum=momentum)
|
|
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."
|
|
)
|
|
|
|
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)'
|
|
)
|
|
return optimizer
|