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| # 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() | |