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yolo/bgf/detect/__init__.py

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+# BGF-YOLO based on Ultralytics YOLOv8x 8.0.109 object detection model with same license, AGPL-3.0 license
+
+from .predict import DetectionPredictor, predict
+from .train import DetectionTrainer, train
+from .val import DetectionValidator, val
+
+__all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val'

yolo/bgf/detect/predict.py

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+# BGF-YOLO based on Ultralytics YOLOv8x 8.0.109 object detection model with same license, AGPL-3.0 license
+
+import torch
+
+from ...yolo.engine.predictor import BasePredictor
+from ...yolo.engine.results import Results
+from ...yolo.utils import DEFAULT_CFG, ROOT, ops
+
+
+class DetectionPredictor(BasePredictor):
+
+    def postprocess(self, preds, img, orig_imgs):
+        """Postprocesses predictions and returns a list of Results objects."""
+        preds = ops.non_max_suppression(preds,
+                                        self.args.conf,
+                                        self.args.iou,
+                                        agnostic=self.args.agnostic_nms,
+                                        max_det=self.args.max_det,
+                                        classes=self.args.classes)
+
+        results = []
+        for i, pred in enumerate(preds):
+            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
+            if not isinstance(orig_imgs, torch.Tensor):
+                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+            path = self.batch[0]
+            img_path = path[i] if isinstance(path, list) else path
+            results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
+        return results
+
+
+def predict(cfg=DEFAULT_CFG, use_python=False):
+    model = cfg.model or 'yolov8n.pt'
+    source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
+        else 'https://ultralytics.com/images/bus.jpg'
+
+    args = dict(model=model, source=source)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model)(**args)
+    else:
+        predictor = DetectionPredictor(overrides=args)
+        predictor.predict_cli()
+
+
+if __name__ == '__main__':
+    predict()

yolo/bgf/detect/train.py

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+# BGF-YOLO based on Ultralytics YOLOv8x 8.0.109 object detection model with same license, AGPL-3.0 license
+from copy import copy
+
+import numpy as np
+import sys
+sys.path.append("/root/BGF-YOLO")
+from ...nn.tasks import DetectionModel
+from ...yolo import bgf
+from ...yolo.data import build_dataloader, build_yolo_dataset
+from ...yolo.data.dataloaders.v5loader import create_dataloader
+from ...yolo.engine.trainer import BaseTrainer
+from ...yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
+from ...yolo.utils.plotting import plot_images, plot_labels, plot_results
+from ...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 'yolov8x.pt'
+    data = cfg.data or 'coco.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()