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190 lines
8.5 KiB
190 lines
8.5 KiB
import hydra
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import torch
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import torch.nn as nn
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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.loss import BboxLoss
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from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy, 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 TaskAlignedAssigner, dist2bbox, make_anchors
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from ultralytics.yolo.utils.torch_utils import de_parallel
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# BaseTrainer python usage
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class SegmentationTrainer(v8.detect.DetectionTrainer):
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def load_model(self, model_cfg=None, weights=None, verbose=True):
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model = SegmentationModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], verbose=verbose)
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if weights:
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model.load(weights, verbose)
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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=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:
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def __init__(self, model, overlap=True): # model must be de-paralleled
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device = next(model.parameters()).device # get model device
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h = model.args # hyperparameters
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m = model.model[-1] # Detect() module
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self.bce = nn.BCEWithLogitsLoss(reduction='none')
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self.hyp = h
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self.stride = m.stride # model strides
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self.nc = m.nc # number of classes
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self.no = m.no
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self.nm = m.nm # number of masks
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self.reg_max = m.reg_max
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self.overlap = overlap
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self.device = device
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self.use_dfl = m.reg_max > 1
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self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
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self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
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self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
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def preprocess(self, targets, batch_size, scale_tensor):
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if targets.shape[0] == 0:
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out = torch.zeros(batch_size, 0, 5, device=self.device)
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else:
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i = targets[:, 0] # image index
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_, counts = i.unique(return_counts=True)
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out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
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for j in range(batch_size):
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matches = i == j
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n = matches.sum()
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if n:
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out[j, :n] = targets[matches, 1:]
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out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
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return out
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def bbox_decode(self, anchor_points, pred_dist):
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if self.use_dfl:
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b, a, c = pred_dist.shape # batch, anchors, channels
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pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# 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)
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return dist2bbox(pred_dist, anchor_points, xywh=False)
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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] *= 7.5 # box gain
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loss[1] *= 7.5 / batch_size # seg gain
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loss[2] *= 0.5 # cls gain
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loss[3] *= 1.5 # 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.yaml"
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cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
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trainer = SegmentationTrainer(cfg)
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trainer.train()
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if __name__ == "__main__":
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"""
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CLI usage:
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python ultralytics/yolo/v8/segment/train.py model=yolov8n-seg.yaml data=coco128-segments epochs=100 imgsz=640
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TODO:
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Direct cli support, i.e, yolov8 classify_train args.epochs 10
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"""
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train()
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