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157 lines
6.8 KiB
157 lines
6.8 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license |
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from copy import copy |
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import hydra |
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import torch |
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import torch.nn.functional as F |
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from ultralytics.nn.tasks import SegmentationModel |
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from ultralytics.yolo import v8 |
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from ultralytics.yolo.utils import DEFAULT_CONFIG |
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from ultralytics.yolo.utils.ops import crop_mask, xyxy2xywh |
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from ultralytics.yolo.utils.plotting import plot_images, plot_results |
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from ultralytics.yolo.utils.tal import make_anchors |
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from ultralytics.yolo.utils.torch_utils import de_parallel |
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from ultralytics.yolo.v8.detect.train import Loss |
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# BaseTrainer python usage |
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class SegmentationTrainer(v8.detect.DetectionTrainer): |
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def __init__(self, config=DEFAULT_CONFIG, overrides=None): |
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if overrides is None: |
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overrides = {} |
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overrides["task"] = "segment" |
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super().__init__(config, overrides) |
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def get_model(self, cfg=None, weights=None, verbose=True): |
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model = SegmentationModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose) |
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if weights: |
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model.load(weights) |
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return model |
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def get_validator(self): |
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self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss' |
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return v8.segment.SegmentationValidator(self.test_loader, |
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save_dir=self.save_dir, |
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logger=self.console, |
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args=copy(self.args)) |
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def criterion(self, preds, batch): |
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if not hasattr(self, 'compute_loss'): |
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self.compute_loss = SegLoss(de_parallel(self.model), overlap=self.args.overlap_mask) |
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return self.compute_loss(preds, batch) |
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def plot_training_samples(self, batch, ni): |
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images = batch["img"] |
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masks = batch["masks"] |
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cls = batch["cls"].squeeze(-1) |
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bboxes = batch["bboxes"] |
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paths = batch["im_file"] |
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batch_idx = batch["batch_idx"] |
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plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f"train_batch{ni}.jpg") |
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def plot_metrics(self): |
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plot_results(file=self.csv, segment=True) # save results.png |
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# Criterion class for computing training losses |
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class SegLoss(Loss): |
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def __init__(self, model, overlap=True): # model must be de-paralleled |
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super().__init__(model) |
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self.nm = model.model[-1].nm # number of masks |
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self.overlap = overlap |
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def __call__(self, preds, batch): |
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loss = torch.zeros(4, device=self.device) # box, cls, dfl |
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feats, pred_masks, proto = preds if len(preds) == 3 else preds[1] |
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batch_size, _, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width |
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pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split( |
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(self.reg_max * 4, self.nc), 1) |
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# b, grids, .. |
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pred_scores = pred_scores.permute(0, 2, 1).contiguous() |
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pred_distri = pred_distri.permute(0, 2, 1).contiguous() |
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pred_masks = pred_masks.permute(0, 2, 1).contiguous() |
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dtype = pred_scores.dtype |
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imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w) |
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anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5) |
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# targets |
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batch_idx = batch["batch_idx"].view(-1, 1) |
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targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1) |
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targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]]) |
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gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy |
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mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0) |
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masks = batch["masks"].to(self.device).float() |
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if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample |
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masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0] |
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# pboxes |
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pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4) |
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_, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner( |
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pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype), |
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anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt) |
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target_scores_sum = target_scores.sum() |
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# cls loss |
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# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way |
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loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE |
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# bbox loss |
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if fg_mask.sum(): |
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loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor, |
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target_scores, target_scores_sum, fg_mask) |
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for i in range(batch_size): |
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if fg_mask[i].sum(): |
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mask_idx = target_gt_idx[i][fg_mask[i]] + 1 |
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if self.overlap: |
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gt_mask = torch.where(masks[[i]] == mask_idx.view(-1, 1, 1), 1.0, 0.0) |
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else: |
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gt_mask = masks[batch_idx == i][mask_idx] |
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xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]] |
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marea = xyxy2xywh(xyxyn)[:, 2:].prod(1) |
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mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device) |
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loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy, |
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marea) # seg loss |
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# WARNING: Uncomment lines below in case of Multi-GPU DDP unused gradient errors |
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# else: |
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# loss[1] += proto.sum() * 0 |
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# else: |
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# loss[1] += proto.sum() * 0 |
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loss[0] *= self.hyp.box # box gain |
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loss[1] *= self.hyp.box / batch_size # seg gain |
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loss[2] *= self.hyp.cls # cls gain |
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loss[3] *= self.hyp.dfl # dfl gain |
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return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) |
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def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): |
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# Mask loss for one image |
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pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n, 32) @ (32,80,80) -> (n,80,80) |
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loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none") |
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return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() |
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) |
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def train(cfg): |
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cfg.model = cfg.model or "yolov8n-seg.pt" |
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cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist") |
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cfg.device = cfg.device if cfg.device is not None else '' |
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# trainer = SegmentationTrainer(cfg) |
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# trainer.train() |
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from ultralytics import YOLO |
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model = YOLO(cfg.model) |
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model.train(**cfg) |
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if __name__ == "__main__": |
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train()
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