add yolo v10 and modify pipeline
This commit is contained in:
王庆刚
@ -6,14 +6,17 @@ import torch.nn.functional as F
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from ultralytics.utils.metrics import OKS_SIGMA
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from ultralytics.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
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from ultralytics.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
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from .metrics import bbox_iou
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from ultralytics.utils.tal import RotatedTaskAlignedAssigner, TaskAlignedAssigner, dist2bbox, dist2rbox, make_anchors
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from .metrics import bbox_iou, probiou
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from .tal import bbox2dist
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class VarifocalLoss(nn.Module):
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"""Varifocal loss by Zhang et al. https://arxiv.org/abs/2008.13367."""
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"""
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Varifocal loss by Zhang et al.
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https://arxiv.org/abs/2008.13367.
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"""
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def __init__(self):
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"""Initialize the VarifocalLoss class."""
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@ -24,21 +27,25 @@ class VarifocalLoss(nn.Module):
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"""Computes varfocal loss."""
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weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
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with torch.cuda.amp.autocast(enabled=False):
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loss = (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction='none') *
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weight).mean(1).sum()
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loss = (
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(F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction="none") * weight)
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.mean(1)
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.sum()
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)
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return loss
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class FocalLoss(nn.Module):
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"""Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
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def __init__(self, ):
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def __init__(self):
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"""Initializer for FocalLoss class with no parameters."""
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super().__init__()
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@staticmethod
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def forward(pred, label, gamma=1.5, alpha=0.25):
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"""Calculates and updates confusion matrix for object detection/classification tasks."""
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loss = F.binary_cross_entropy_with_logits(pred, label, reduction='none')
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loss = F.binary_cross_entropy_with_logits(pred, label, reduction="none")
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# p_t = torch.exp(-loss)
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# loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
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@ -54,6 +61,7 @@ class FocalLoss(nn.Module):
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class BboxLoss(nn.Module):
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"""Criterion class for computing training losses during training."""
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def __init__(self, reg_max, use_dfl=False):
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"""Initialize the BboxLoss module with regularization maximum and DFL settings."""
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@ -79,42 +87,73 @@ class BboxLoss(nn.Module):
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@staticmethod
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def _df_loss(pred_dist, target):
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"""Return sum of left and right DFL losses."""
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# Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
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"""
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Return sum of left and right DFL losses.
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Distribution Focal Loss (DFL) proposed in Generalized Focal Loss
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https://ieeexplore.ieee.org/document/9792391
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"""
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tl = target.long() # target left
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tr = tl + 1 # target right
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wl = tr - target # weight left
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wr = 1 - wl # weight right
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return (F.cross_entropy(pred_dist, tl.view(-1), reduction='none').view(tl.shape) * wl +
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F.cross_entropy(pred_dist, tr.view(-1), reduction='none').view(tl.shape) * wr).mean(-1, keepdim=True)
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return (
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F.cross_entropy(pred_dist, tl.view(-1), reduction="none").view(tl.shape) * wl
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+ F.cross_entropy(pred_dist, tr.view(-1), reduction="none").view(tl.shape) * wr
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).mean(-1, keepdim=True)
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class RotatedBboxLoss(BboxLoss):
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"""Criterion class for computing training losses during training."""
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def __init__(self, reg_max, use_dfl=False):
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"""Initialize the BboxLoss module with regularization maximum and DFL settings."""
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super().__init__(reg_max, use_dfl)
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def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
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"""IoU loss."""
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weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1)
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iou = probiou(pred_bboxes[fg_mask], target_bboxes[fg_mask])
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loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum
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# DFL loss
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if self.use_dfl:
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target_ltrb = bbox2dist(anchor_points, xywh2xyxy(target_bboxes[..., :4]), self.reg_max)
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loss_dfl = self._df_loss(pred_dist[fg_mask].view(-1, self.reg_max + 1), target_ltrb[fg_mask]) * weight
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loss_dfl = loss_dfl.sum() / target_scores_sum
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else:
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loss_dfl = torch.tensor(0.0).to(pred_dist.device)
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return loss_iou, loss_dfl
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class KeypointLoss(nn.Module):
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"""Criterion class for computing training losses."""
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def __init__(self, sigmas) -> None:
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"""Initialize the KeypointLoss class."""
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super().__init__()
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self.sigmas = sigmas
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def forward(self, pred_kpts, gt_kpts, kpt_mask, area):
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"""Calculates keypoint loss factor and Euclidean distance loss for predicted and actual keypoints."""
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d = (pred_kpts[..., 0] - gt_kpts[..., 0]) ** 2 + (pred_kpts[..., 1] - gt_kpts[..., 1]) ** 2
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kpt_loss_factor = (torch.sum(kpt_mask != 0) + torch.sum(kpt_mask == 0)) / (torch.sum(kpt_mask != 0) + 1e-9)
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d = (pred_kpts[..., 0] - gt_kpts[..., 0]).pow(2) + (pred_kpts[..., 1] - gt_kpts[..., 1]).pow(2)
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kpt_loss_factor = kpt_mask.shape[1] / (torch.sum(kpt_mask != 0, dim=1) + 1e-9)
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# e = d / (2 * (area * self.sigmas) ** 2 + 1e-9) # from formula
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e = d / (2 * self.sigmas) ** 2 / (area + 1e-9) / 2 # from cocoeval
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return kpt_loss_factor * ((1 - torch.exp(-e)) * kpt_mask).mean()
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e = d / ((2 * self.sigmas).pow(2) * (area + 1e-9) * 2) # from cocoeval
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return (kpt_loss_factor.view(-1, 1) * ((1 - torch.exp(-e)) * kpt_mask)).mean()
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class v8DetectionLoss:
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"""Criterion class for computing training losses."""
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def __init__(self, model): # model must be de-paralleled
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def __init__(self, model, tal_topk=10): # model must be de-paralleled
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"""Initializes v8DetectionLoss with the model, defining model-related properties and BCE loss function."""
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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.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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@ -124,7 +163,7 @@ class v8DetectionLoss:
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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.assigner = TaskAlignedAssigner(topk=tal_topk, 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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@ -159,7 +198,8 @@ class v8DetectionLoss:
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loss = torch.zeros(3, device=self.device) # box, cls, dfl
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feats = preds[1] if isinstance(preds, tuple) else preds
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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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(self.reg_max * 4, self.nc), 1
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)
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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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@ -169,30 +209,36 @@ class v8DetectionLoss:
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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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targets = torch.cat((batch['batch_idx'].view(-1, 1), batch['cls'].view(-1, 1), batch['bboxes']), 1)
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# Targets
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targets = torch.cat((batch["batch_idx"].view(-1, 1), 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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# pboxes
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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, _ = 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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pred_scores.detach().sigmoid(),
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(pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
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anchor_points * stride_tensor,
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gt_labels,
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gt_bboxes,
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mask_gt,
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)
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target_scores_sum = max(target_scores.sum(), 1)
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# cls loss
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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[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
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# bbox loss
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# Bbox loss
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if fg_mask.sum():
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target_bboxes /= stride_tensor
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loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
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target_scores_sum, fg_mask)
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loss[0], loss[2] = self.bbox_loss(
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pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
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)
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loss[0] *= self.hyp.box # box gain
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loss[1] *= self.hyp.cls # cls gain
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@ -205,8 +251,8 @@ class v8SegmentationLoss(v8DetectionLoss):
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"""Criterion class for computing training losses."""
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def __init__(self, model): # model must be de-paralleled
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"""Initializes the v8SegmentationLoss class, taking a de-paralleled model as argument."""
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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 = model.args.overlap_mask
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def __call__(self, preds, batch):
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@ -215,9 +261,10 @@ class v8SegmentationLoss(v8DetectionLoss):
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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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(self.reg_max * 4, self.nc), 1
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)
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# b, grids, ..
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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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@ -226,80 +273,168 @@ class v8SegmentationLoss(v8DetectionLoss):
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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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# Targets
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try:
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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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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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except RuntimeError as e:
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raise TypeError('ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n'
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"This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
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"i.e. 'yolo train model=yolov8n-seg.pt data=coco128.yaml'.\nVerify your dataset is a "
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"correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
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'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e
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raise TypeError(
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"ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n"
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"This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
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"i.e. 'yolo train model=yolov8n-seg.pt data=coco8.yaml'.\nVerify your dataset is a "
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"correctly formatted 'segment' dataset using 'data=coco8-seg.yaml' "
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"as an example.\nSee https://docs.ultralytics.com/datasets/segment/ for help."
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) from e
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# pboxes
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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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pred_scores.detach().sigmoid(),
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(pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
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anchor_points * stride_tensor,
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gt_labels,
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gt_bboxes,
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mask_gt,
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)
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target_scores_sum = max(target_scores.sum(), 1)
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# cls loss
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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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if fg_mask.sum():
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# bbox loss
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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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# masks loss
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masks = batch['masks'].to(self.device).float()
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# Bbox loss
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loss[0], loss[3] = self.bbox_loss(
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pred_distri,
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pred_bboxes,
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anchor_points,
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target_bboxes / stride_tensor,
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target_scores,
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target_scores_sum,
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fg_mask,
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)
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# Masks loss
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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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masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
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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]]
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if self.overlap:
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gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0)
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else:
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gt_mask = masks[batch_idx.view(-1) == 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, marea) # seg
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# WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
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else:
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loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
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loss[1] = self.calculate_segmentation_loss(
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fg_mask, masks, target_gt_idx, target_bboxes, batch_idx, proto, pred_masks, imgsz, self.overlap
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)
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# WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
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else:
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loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
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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[1] *= self.hyp.box # 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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@staticmethod
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def single_mask_loss(
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gt_mask: torch.Tensor, pred: torch.Tensor, proto: torch.Tensor, xyxy: torch.Tensor, area: torch.Tensor
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) -> torch.Tensor:
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"""
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Compute the instance segmentation loss for a single image.
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Args:
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gt_mask (torch.Tensor): Ground truth mask of shape (n, H, W), where n is the number of objects.
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pred (torch.Tensor): Predicted mask coefficients of shape (n, 32).
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proto (torch.Tensor): Prototype masks of shape (32, H, W).
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||||
xyxy (torch.Tensor): Ground truth bounding boxes in xyxy format, normalized to [0, 1], of shape (n, 4).
|
||||
area (torch.Tensor): Area of each ground truth bounding box of shape (n,).
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): The calculated mask loss for a single image.
|
||||
|
||||
Notes:
|
||||
The function uses the equation pred_mask = torch.einsum('in,nhw->ihw', pred, proto) to produce the
|
||||
predicted masks from the prototype masks and predicted mask coefficients.
|
||||
"""
|
||||
pred_mask = torch.einsum("in,nhw->ihw", pred, proto) # (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).sum()
|
||||
|
||||
def calculate_segmentation_loss(
|
||||
self,
|
||||
fg_mask: torch.Tensor,
|
||||
masks: torch.Tensor,
|
||||
target_gt_idx: torch.Tensor,
|
||||
target_bboxes: torch.Tensor,
|
||||
batch_idx: torch.Tensor,
|
||||
proto: torch.Tensor,
|
||||
pred_masks: torch.Tensor,
|
||||
imgsz: torch.Tensor,
|
||||
overlap: bool,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Calculate the loss for instance segmentation.
|
||||
|
||||
Args:
|
||||
fg_mask (torch.Tensor): A binary tensor of shape (BS, N_anchors) indicating which anchors are positive.
|
||||
masks (torch.Tensor): Ground truth masks of shape (BS, H, W) if `overlap` is False, otherwise (BS, ?, H, W).
|
||||
target_gt_idx (torch.Tensor): Indexes of ground truth objects for each anchor of shape (BS, N_anchors).
|
||||
target_bboxes (torch.Tensor): Ground truth bounding boxes for each anchor of shape (BS, N_anchors, 4).
|
||||
batch_idx (torch.Tensor): Batch indices of shape (N_labels_in_batch, 1).
|
||||
proto (torch.Tensor): Prototype masks of shape (BS, 32, H, W).
|
||||
pred_masks (torch.Tensor): Predicted masks for each anchor of shape (BS, N_anchors, 32).
|
||||
imgsz (torch.Tensor): Size of the input image as a tensor of shape (2), i.e., (H, W).
|
||||
overlap (bool): Whether the masks in `masks` tensor overlap.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): The calculated loss for instance segmentation.
|
||||
|
||||
Notes:
|
||||
The batch loss can be computed for improved speed at higher memory usage.
|
||||
For example, pred_mask can be computed as follows:
|
||||
pred_mask = torch.einsum('in,nhw->ihw', pred, proto) # (i, 32) @ (32, 160, 160) -> (i, 160, 160)
|
||||
"""
|
||||
_, _, mask_h, mask_w = proto.shape
|
||||
loss = 0
|
||||
|
||||
# Normalize to 0-1
|
||||
target_bboxes_normalized = target_bboxes / imgsz[[1, 0, 1, 0]]
|
||||
|
||||
# Areas of target bboxes
|
||||
marea = xyxy2xywh(target_bboxes_normalized)[..., 2:].prod(2)
|
||||
|
||||
# Normalize to mask size
|
||||
mxyxy = target_bboxes_normalized * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=proto.device)
|
||||
|
||||
for i, single_i in enumerate(zip(fg_mask, target_gt_idx, pred_masks, proto, mxyxy, marea, masks)):
|
||||
fg_mask_i, target_gt_idx_i, pred_masks_i, proto_i, mxyxy_i, marea_i, masks_i = single_i
|
||||
if fg_mask_i.any():
|
||||
mask_idx = target_gt_idx_i[fg_mask_i]
|
||||
if overlap:
|
||||
gt_mask = masks_i == (mask_idx + 1).view(-1, 1, 1)
|
||||
gt_mask = gt_mask.float()
|
||||
else:
|
||||
gt_mask = masks[batch_idx.view(-1) == i][mask_idx]
|
||||
|
||||
loss += self.single_mask_loss(
|
||||
gt_mask, pred_masks_i[fg_mask_i], proto_i, mxyxy_i[fg_mask_i], marea_i[fg_mask_i]
|
||||
)
|
||||
|
||||
# WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
|
||||
else:
|
||||
loss += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
|
||||
|
||||
return loss / fg_mask.sum()
|
||||
|
||||
|
||||
class v8PoseLoss(v8DetectionLoss):
|
||||
"""Criterion class for computing training losses."""
|
||||
|
||||
def __init__(self, model): # model must be de-paralleled
|
||||
"""Initializes v8PoseLoss with model, sets keypoint variables and declares a keypoint loss instance."""
|
||||
super().__init__(model)
|
||||
self.kpt_shape = model.model[-1].kpt_shape
|
||||
self.bce_pose = nn.BCEWithLogitsLoss()
|
||||
@ -313,9 +448,10 @@ class v8PoseLoss(v8DetectionLoss):
|
||||
loss = torch.zeros(5, device=self.device) # box, cls, dfl, kpt_location, kpt_visibility
|
||||
feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1]
|
||||
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)
|
||||
(self.reg_max * 4, self.nc), 1
|
||||
)
|
||||
|
||||
# b, grids, ..
|
||||
# B, grids, ..
|
||||
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
|
||||
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
|
||||
pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()
|
||||
@ -324,53 +460,50 @@ class v8PoseLoss(v8DetectionLoss):
|
||||
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
|
||||
batch_size = pred_scores.shape[0]
|
||||
batch_idx = batch['batch_idx'].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
|
||||
batch_idx = batch["batch_idx"].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, 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
|
||||
# Pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape)) # (b, h*w, 17, 3)
|
||||
|
||||
_, 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)
|
||||
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
|
||||
# Cls loss
|
||||
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
|
||||
loss[3] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
# bbox loss
|
||||
# Bbox loss
|
||||
if fg_mask.sum():
|
||||
target_bboxes /= stride_tensor
|
||||
loss[0], loss[4] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
|
||||
target_scores_sum, fg_mask)
|
||||
keypoints = batch['keypoints'].to(self.device).float().clone()
|
||||
loss[0], loss[4] = self.bbox_loss(
|
||||
pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
|
||||
)
|
||||
keypoints = batch["keypoints"].to(self.device).float().clone()
|
||||
keypoints[..., 0] *= imgsz[1]
|
||||
keypoints[..., 1] *= imgsz[0]
|
||||
for i in range(batch_size):
|
||||
if fg_mask[i].sum():
|
||||
idx = target_gt_idx[i][fg_mask[i]]
|
||||
gt_kpt = keypoints[batch_idx.view(-1) == i][idx] # (n, 51)
|
||||
gt_kpt[..., 0] /= stride_tensor[fg_mask[i]]
|
||||
gt_kpt[..., 1] /= stride_tensor[fg_mask[i]]
|
||||
area = xyxy2xywh(target_bboxes[i][fg_mask[i]])[:, 2:].prod(1, keepdim=True)
|
||||
pred_kpt = pred_kpts[i][fg_mask[i]]
|
||||
kpt_mask = gt_kpt[..., 2] != 0
|
||||
loss[1] += self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss
|
||||
# kpt_score loss
|
||||
if pred_kpt.shape[-1] == 3:
|
||||
loss[2] += self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss
|
||||
|
||||
loss[1], loss[2] = self.calculate_keypoints_loss(
|
||||
fg_mask, target_gt_idx, keypoints, batch_idx, stride_tensor, target_bboxes, pred_kpts
|
||||
)
|
||||
|
||||
loss[0] *= self.hyp.box # box gain
|
||||
loss[1] *= self.hyp.pose / batch_size # pose gain
|
||||
loss[2] *= self.hyp.kobj / batch_size # kobj gain
|
||||
loss[1] *= self.hyp.pose # pose gain
|
||||
loss[2] *= self.hyp.kobj # kobj gain
|
||||
loss[3] *= self.hyp.cls # cls gain
|
||||
loss[4] *= self.hyp.dfl # dfl gain
|
||||
|
||||
@ -385,12 +518,210 @@ class v8PoseLoss(v8DetectionLoss):
|
||||
y[..., 1] += anchor_points[:, [1]] - 0.5
|
||||
return y
|
||||
|
||||
def calculate_keypoints_loss(
|
||||
self, masks, target_gt_idx, keypoints, batch_idx, stride_tensor, target_bboxes, pred_kpts
|
||||
):
|
||||
"""
|
||||
Calculate the keypoints loss for the model.
|
||||
|
||||
This function calculates the keypoints loss and keypoints object loss for a given batch. The keypoints loss is
|
||||
based on the difference between the predicted keypoints and ground truth keypoints. The keypoints object loss is
|
||||
a binary classification loss that classifies whether a keypoint is present or not.
|
||||
|
||||
Args:
|
||||
masks (torch.Tensor): Binary mask tensor indicating object presence, shape (BS, N_anchors).
|
||||
target_gt_idx (torch.Tensor): Index tensor mapping anchors to ground truth objects, shape (BS, N_anchors).
|
||||
keypoints (torch.Tensor): Ground truth keypoints, shape (N_kpts_in_batch, N_kpts_per_object, kpts_dim).
|
||||
batch_idx (torch.Tensor): Batch index tensor for keypoints, shape (N_kpts_in_batch, 1).
|
||||
stride_tensor (torch.Tensor): Stride tensor for anchors, shape (N_anchors, 1).
|
||||
target_bboxes (torch.Tensor): Ground truth boxes in (x1, y1, x2, y2) format, shape (BS, N_anchors, 4).
|
||||
pred_kpts (torch.Tensor): Predicted keypoints, shape (BS, N_anchors, N_kpts_per_object, kpts_dim).
|
||||
|
||||
Returns:
|
||||
(tuple): Returns a tuple containing:
|
||||
- kpts_loss (torch.Tensor): The keypoints loss.
|
||||
- kpts_obj_loss (torch.Tensor): The keypoints object loss.
|
||||
"""
|
||||
batch_idx = batch_idx.flatten()
|
||||
batch_size = len(masks)
|
||||
|
||||
# Find the maximum number of keypoints in a single image
|
||||
max_kpts = torch.unique(batch_idx, return_counts=True)[1].max()
|
||||
|
||||
# Create a tensor to hold batched keypoints
|
||||
batched_keypoints = torch.zeros(
|
||||
(batch_size, max_kpts, keypoints.shape[1], keypoints.shape[2]), device=keypoints.device
|
||||
)
|
||||
|
||||
# TODO: any idea how to vectorize this?
|
||||
# Fill batched_keypoints with keypoints based on batch_idx
|
||||
for i in range(batch_size):
|
||||
keypoints_i = keypoints[batch_idx == i]
|
||||
batched_keypoints[i, : keypoints_i.shape[0]] = keypoints_i
|
||||
|
||||
# Expand dimensions of target_gt_idx to match the shape of batched_keypoints
|
||||
target_gt_idx_expanded = target_gt_idx.unsqueeze(-1).unsqueeze(-1)
|
||||
|
||||
# Use target_gt_idx_expanded to select keypoints from batched_keypoints
|
||||
selected_keypoints = batched_keypoints.gather(
|
||||
1, target_gt_idx_expanded.expand(-1, -1, keypoints.shape[1], keypoints.shape[2])
|
||||
)
|
||||
|
||||
# Divide coordinates by stride
|
||||
selected_keypoints /= stride_tensor.view(1, -1, 1, 1)
|
||||
|
||||
kpts_loss = 0
|
||||
kpts_obj_loss = 0
|
||||
|
||||
if masks.any():
|
||||
gt_kpt = selected_keypoints[masks]
|
||||
area = xyxy2xywh(target_bboxes[masks])[:, 2:].prod(1, keepdim=True)
|
||||
pred_kpt = pred_kpts[masks]
|
||||
kpt_mask = gt_kpt[..., 2] != 0 if gt_kpt.shape[-1] == 3 else torch.full_like(gt_kpt[..., 0], True)
|
||||
kpts_loss = self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss
|
||||
|
||||
if pred_kpt.shape[-1] == 3:
|
||||
kpts_obj_loss = self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss
|
||||
|
||||
return kpts_loss, kpts_obj_loss
|
||||
|
||||
|
||||
class v8ClassificationLoss:
|
||||
"""Criterion class for computing training losses."""
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Compute the classification loss between predictions and true labels."""
|
||||
loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / 64
|
||||
loss = torch.nn.functional.cross_entropy(preds, batch["cls"], reduction="mean")
|
||||
loss_items = loss.detach()
|
||||
return loss, loss_items
|
||||
|
||||
|
||||
class v8OBBLoss(v8DetectionLoss):
|
||||
def __init__(self, model):
|
||||
"""
|
||||
Initializes v8OBBLoss with model, assigner, and rotated bbox loss.
|
||||
|
||||
Note model must be de-paralleled.
|
||||
"""
|
||||
super().__init__(model)
|
||||
self.assigner = RotatedTaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
|
||||
self.bbox_loss = RotatedBboxLoss(self.reg_max - 1, use_dfl=self.use_dfl).to(self.device)
|
||||
|
||||
def preprocess(self, targets, batch_size, scale_tensor):
|
||||
"""Preprocesses the target counts and matches with the input batch size to output a tensor."""
|
||||
if targets.shape[0] == 0:
|
||||
out = torch.zeros(batch_size, 0, 6, device=self.device)
|
||||
else:
|
||||
i = targets[:, 0] # image index
|
||||
_, counts = i.unique(return_counts=True)
|
||||
counts = counts.to(dtype=torch.int32)
|
||||
out = torch.zeros(batch_size, counts.max(), 6, device=self.device)
|
||||
for j in range(batch_size):
|
||||
matches = i == j
|
||||
n = matches.sum()
|
||||
if n:
|
||||
bboxes = targets[matches, 2:]
|
||||
bboxes[..., :4].mul_(scale_tensor)
|
||||
out[j, :n] = torch.cat([targets[matches, 1:2], bboxes], dim=-1)
|
||||
return out
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Calculate and return the loss for the YOLO model."""
|
||||
loss = torch.zeros(3, device=self.device) # box, cls, dfl
|
||||
feats, pred_angle = preds if isinstance(preds[0], list) else preds[1]
|
||||
batch_size = pred_angle.shape[0] # 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_angle = pred_angle.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"].view(-1, 5)), 1)
|
||||
rw, rh = targets[:, 4] * imgsz[0].item(), targets[:, 5] * imgsz[1].item()
|
||||
targets = targets[(rw >= 2) & (rh >= 2)] # filter rboxes of tiny size to stabilize training
|
||||
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
|
||||
gt_labels, gt_bboxes = targets.split((1, 5), 2) # cls, xywhr
|
||||
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
|
||||
except RuntimeError as e:
|
||||
raise TypeError(
|
||||
"ERROR ❌ OBB dataset incorrectly formatted or not a OBB dataset.\n"
|
||||
"This error can occur when incorrectly training a 'OBB' model on a 'detect' dataset, "
|
||||
"i.e. 'yolo train model=yolov8n-obb.pt data=dota8.yaml'.\nVerify your dataset is a "
|
||||
"correctly formatted 'OBB' dataset using 'data=dota8.yaml' "
|
||||
"as an example.\nSee https://docs.ultralytics.com/datasets/obb/ for help."
|
||||
) from e
|
||||
|
||||
# Pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri, pred_angle) # xyxy, (b, h*w, 4)
|
||||
|
||||
bboxes_for_assigner = pred_bboxes.clone().detach()
|
||||
# Only the first four elements need to be scaled
|
||||
bboxes_for_assigner[..., :4] *= stride_tensor
|
||||
_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
|
||||
pred_scores.detach().sigmoid(),
|
||||
bboxes_for_assigner.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[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
# Bbox loss
|
||||
if fg_mask.sum():
|
||||
target_bboxes[..., :4] /= stride_tensor
|
||||
loss[0], loss[2] = self.bbox_loss(
|
||||
pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
|
||||
)
|
||||
else:
|
||||
loss[0] += (pred_angle * 0).sum()
|
||||
|
||||
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 bbox_decode(self, anchor_points, pred_dist, pred_angle):
|
||||
"""
|
||||
Decode predicted object bounding box coordinates from anchor points and distribution.
|
||||
|
||||
Args:
|
||||
anchor_points (torch.Tensor): Anchor points, (h*w, 2).
|
||||
pred_dist (torch.Tensor): Predicted rotated distance, (bs, h*w, 4).
|
||||
pred_angle (torch.Tensor): Predicted angle, (bs, h*w, 1).
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): Predicted rotated bounding boxes with angles, (bs, h*w, 5).
|
||||
"""
|
||||
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))
|
||||
return torch.cat((dist2rbox(pred_dist, pred_angle, anchor_points), pred_angle), dim=-1)
|
||||
|
||||
class v10DetectLoss:
|
||||
def __init__(self, model):
|
||||
self.one2many = v8DetectionLoss(model, tal_topk=10)
|
||||
self.one2one = v8DetectionLoss(model, tal_topk=1)
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
one2many = preds["one2many"]
|
||||
loss_one2many = self.one2many(one2many, batch)
|
||||
one2one = preds["one2one"]
|
||||
loss_one2one = self.one2one(one2one, batch)
|
||||
return loss_one2many[0] + loss_one2one[0], torch.cat((loss_one2many[1], loss_one2one[1]))
|
||||
|
||||
Reference in New Issue
Block a user