# Ultralytics YOLO 🚀, AGPL-3.0 license from copy import copy import numpy as np from ultralytics.nn.tasks import DetectionModel from ultralytics.yolo import v8 from ultralytics.yolo.data import build_dataloader, build_yolo_dataset from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader from ultralytics.yolo.engine.trainer import BaseTrainer from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results from ultralytics.yolo.utils.torch_utils import de_parallel, torch_distributed_zero_first # BaseTrainer python usage class DetectionTrainer(BaseTrainer): def build_dataset(self, img_path, mode='train', batch=None): """Build YOLO Dataset Args: img_path (str): Path to the folder containing images. mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode. batch (int, optional): Size of batches, this is for `rect`. Defaults to None. """ gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32) return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == 'val', stride=gs) def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'): """TODO: manage splits differently.""" # Calculate stride - check if model is initialized if self.args.v5loader: LOGGER.warning("WARNING ⚠️ 'v5loader' feature is deprecated and will be removed soon. You can train using " 'the default YOLOv8 dataloader instead, no argument is needed.') gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32) return create_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size, stride=gs, hyp=vars(self.args), augment=mode == 'train', cache=self.args.cache, pad=0 if mode == 'train' else 0.5, rect=self.args.rect or mode == 'val', rank=rank, workers=self.args.workers, close_mosaic=self.args.close_mosaic != 0, prefix=colorstr(f'{mode}: '), shuffle=mode == 'train', seed=self.args.seed)[0] assert mode in ['train', 'val'] with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = self.build_dataset(dataset_path, mode, batch_size) shuffle = mode == 'train' if getattr(dataset, 'rect', False) and shuffle: LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False") shuffle = False workers = self.args.workers if mode == 'train' else self.args.workers * 2 return build_dataloader(dataset, batch_size, workers, shuffle, rank) # return dataloader def preprocess_batch(self, batch): """Preprocesses a batch of images by scaling and converting to float.""" batch['img'] = batch['img'].to(self.device, non_blocking=True).float() / 255 return batch def set_model_attributes(self): """nl = de_parallel(self.model).model[-1].nl # number of detection layers (to scale hyps).""" # self.args.box *= 3 / nl # scale to layers # self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers # self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl # scale to image size and layers self.model.nc = self.data['nc'] # attach number of classes to model self.model.names = self.data['names'] # attach class names to model self.model.args = self.args # attach hyperparameters to model # TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc def get_model(self, cfg=None, weights=None, verbose=True): """Return a YOLO detection model.""" model = DetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1) if weights: model.load(weights) return model def get_validator(self): """Returns a DetectionValidator for YOLO model validation.""" self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss' return v8.detect.DetectionValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args)) 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 keys = [f'{prefix}/{x}' for x in self.loss_names] if loss_items is not None: loss_items = [round(float(x), 5) for x in loss_items] # convert tensors to 5 decimal place floats return dict(zip(keys, loss_items)) else: return keys def progress_string(self): """Returns a formatted string of training progress with epoch, GPU memory, loss, instances and size.""" return ('\n' + '%11s' * (4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size') def plot_training_samples(self, batch, ni): """Plots training samples with their annotations.""" plot_images(images=batch['img'], batch_idx=batch['batch_idx'], cls=batch['cls'].squeeze(-1), bboxes=batch['bboxes'], paths=batch['im_file'], fname=self.save_dir / f'train_batch{ni}.jpg', on_plot=self.on_plot) def plot_metrics(self): """Plots metrics from a CSV file.""" plot_results(file=self.csv, on_plot=self.on_plot) # save results.png def plot_training_labels(self): """Create a labeled training plot of the YOLO model.""" boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0) cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0) plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot) def train(cfg=DEFAULT_CFG, use_python=False): """Train and optimize YOLO model given training data and device.""" model = cfg.model or 'yolov8n.pt' data = cfg.data or 'coco128.yaml' # or yolo.ClassificationDataset("mnist") device = cfg.device if cfg.device is not None else '' args = dict(model=model, data=data, device=device) if use_python: from ultralytics import YOLO YOLO(model).train(**args) else: trainer = DetectionTrainer(overrides=args) trainer.train() if __name__ == '__main__': train()