# Ultralytics YOLO 🚀, GPL-3.0 license from copy import copy import numpy as np import torch import torch.nn as nn from ultralytics.nn.tasks import DetectionModel from ultralytics.yolo import v8 from ultralytics.yolo.data import build_dataloader from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader from ultralytics.yolo.engine.trainer import BaseTrainer from ultralytics.yolo.utils import DEFAULT_CFG, RANK, colorstr from ultralytics.yolo.utils.loss import BboxLoss from ultralytics.yolo.utils.ops import xywh2xyxy from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors from ultralytics.yolo.utils.torch_utils import de_parallel # BaseTrainer python usage class DetectionTrainer(BaseTrainer): def get_dataloader(self, dataset_path, batch_size, mode='train', rank=0): # TODO: manage splits differently # calculate stride - check if model is initialized 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] if self.args.v5loader else \ build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode, rect=mode == 'val', names=self.data['names'])[0] def preprocess_batch(self, batch): 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): model = DetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1) if weights: model.load(weights) return model def get_validator(self): 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 criterion(self, preds, batch): if not hasattr(self, 'compute_loss'): self.compute_loss = Loss(de_parallel(self.model)) return self.compute_loss(preds, batch) 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): return ('\n' + '%11s' * (4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size') def plot_training_samples(self, batch, ni): 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') def plot_metrics(self): plot_results(file=self.csv) # save results.png def plot_training_labels(self): 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) # Criterion class for computing training losses class Loss: def __init__(self, model): # model must be de-paralleled device = next(model.parameters()).device # get model device h = model.args # hyperparameters m = model.model[-1] # Detect() module self.bce = nn.BCEWithLogitsLoss(reduction='none') self.hyp = h self.stride = m.stride # model strides self.nc = m.nc # number of classes self.no = m.no self.reg_max = m.reg_max self.device = device self.use_dfl = m.reg_max > 1 self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0) self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device) self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device) def preprocess(self, targets, batch_size, scale_tensor): if targets.shape[0] == 0: out = torch.zeros(batch_size, 0, 5, device=self.device) else: i = targets[:, 0] # image index _, counts = i.unique(return_counts=True) out = torch.zeros(batch_size, counts.max(), 5, device=self.device) for j in range(batch_size): matches = i == j n = matches.sum() if n: out[j, :n] = targets[matches, 1:] out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor)) return out def bbox_decode(self, anchor_points, pred_dist): if self.use_dfl: b, a, c = pred_dist.shape # batch, anchors, channels pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype)) # pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype)) # pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2) return dist2bbox(pred_dist, anchor_points, xywh=False) def __call__(self, preds, batch): loss = torch.zeros(3, device=self.device) # box, cls, dfl feats = preds[1] if isinstance(preds, tuple) else preds pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split( (self.reg_max * 4, self.nc), 1) pred_scores = pred_scores.permute(0, 2, 1).contiguous() pred_distri = pred_distri.permute(0, 2, 1).contiguous() dtype = pred_scores.dtype batch_size = pred_scores.shape[0] imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w) anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5) # targets targets = torch.cat((batch['batch_idx'].view(-1, 1), batch['cls'].view(-1, 1), batch['bboxes']), 1) targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]]) gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0) # pboxes pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4) _, target_bboxes, target_scores, fg_mask, _ = self.assigner( pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype), anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt) target_bboxes /= stride_tensor target_scores_sum = max(target_scores.sum(), 1) # cls loss # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE # bbox loss if fg_mask.sum(): loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask) loss[0] *= self.hyp.box # box gain loss[1] *= self.hyp.cls # cls gain loss[2] *= self.hyp.dfl # dfl gain return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) def train(cfg=DEFAULT_CFG, use_python=False): 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()