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| # Ultralytics YOLO 🚀, GPL-3.0 license | |
| from copy import copy | |
| import torch | |
| import torch.nn.functional as F | |
| from ultralytics.nn.tasks import SegmentationModel | |
| from ultralytics.yolo import v8 | |
| from ultralytics.yolo.utils import DEFAULT_CFG, RANK | |
| from ultralytics.yolo.utils.ops import crop_mask, xyxy2xywh | |
| from ultralytics.yolo.utils.plotting import plot_images, plot_results | |
| from ultralytics.yolo.utils.tal import make_anchors | |
| from ultralytics.yolo.utils.torch_utils import de_parallel | |
| from ultralytics.yolo.v8.detect.train import Loss | |
| # BaseTrainer python usage | |
| class SegmentationTrainer(v8.detect.DetectionTrainer): | |
| def __init__(self, cfg=DEFAULT_CFG, overrides=None): | |
| if overrides is None: | |
| overrides = {} | |
| overrides['task'] = 'segment' | |
| super().__init__(cfg, overrides) | |
| def get_model(self, cfg=None, weights=None, verbose=True): | |
| model = SegmentationModel(cfg, ch=3, 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', 'seg_loss', 'cls_loss', 'dfl_loss' | |
| return v8.segment.SegmentationValidator(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 = SegLoss(de_parallel(self.model), overlap=self.args.overlap_mask) | |
| return self.compute_loss(preds, batch) | |
| def plot_training_samples(self, batch, ni): | |
| images = batch['img'] | |
| masks = batch['masks'] | |
| cls = batch['cls'].squeeze(-1) | |
| bboxes = batch['bboxes'] | |
| paths = batch['im_file'] | |
| batch_idx = batch['batch_idx'] | |
| plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f'train_batch{ni}.jpg') | |
| def plot_metrics(self): | |
| plot_results(file=self.csv, segment=True) # save results.png | |
| # Criterion class for computing training losses | |
| class SegLoss(Loss): | |
| def __init__(self, model, overlap=True): # model must be de-paralleled | |
| super().__init__(model) | |
| self.nm = model.model[-1].nm # number of masks | |
| self.overlap = overlap | |
| def __call__(self, preds, batch): | |
| loss = torch.zeros(4, device=self.device) # box, cls, dfl | |
| feats, pred_masks, proto = preds if len(preds) == 3 else preds[1] | |
| batch_size, _, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width | |
| 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) | |
| # b, grids, .. | |
| pred_scores = pred_scores.permute(0, 2, 1).contiguous() | |
| pred_distri = pred_distri.permute(0, 2, 1).contiguous() | |
| pred_masks = pred_masks.permute(0, 2, 1).contiguous() | |
| dtype = pred_scores.dtype | |
| 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 | |
| try: | |
| batch_idx = batch['batch_idx'].view(-1, 1) | |
| targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes'].to(dtype)), 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) | |
| except RuntimeError as e: | |
| raise TypeError('ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n' | |
| "This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, " | |
| "i.e. 'yolo train model=yolov8n-seg.pt data=coco128.yaml'.\nVerify your dataset is a " | |
| "correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' " | |
| 'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e | |
| # pboxes | |
| pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4) | |
| _, target_bboxes, target_scores, fg_mask, target_gt_idx = 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_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[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE | |
| if fg_mask.sum(): | |
| # bbox loss | |
| loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor, | |
| target_scores, target_scores_sum, fg_mask) | |
| # masks loss | |
| masks = batch['masks'].to(self.device).float() | |
| if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample | |
| masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0] | |
| for i in range(batch_size): | |
| if fg_mask[i].sum(): | |
| mask_idx = target_gt_idx[i][fg_mask[i]] | |
| if self.overlap: | |
| gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0) | |
| else: | |
| gt_mask = masks[batch_idx.view(-1) == i][mask_idx] | |
| xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]] | |
| marea = xyxy2xywh(xyxyn)[:, 2:].prod(1) | |
| mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device) | |
| loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy, marea) # seg | |
| # WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove | |
| else: | |
| loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss | |
| # WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove | |
| else: | |
| loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss | |
| loss[0] *= self.hyp.box # box gain | |
| loss[1] *= self.hyp.box / batch_size # seg gain | |
| loss[2] *= self.hyp.cls # cls gain | |
| loss[3] *= self.hyp.dfl # dfl gain | |
| return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) | |
| def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): | |
| # Mask loss for one image | |
| pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n, 32) @ (32,80,80) -> (n,80,80) | |
| loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none') | |
| return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() | |
| def train(cfg=DEFAULT_CFG, use_python=False): | |
| model = cfg.model or 'yolov8n-seg.pt' | |
| data = cfg.data or 'coco128-seg.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 = SegmentationTrainer(overrides=args) | |
| trainer.train() | |
| if __name__ == '__main__': | |
| train() | |