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715 lines
32 KiB
715 lines
32 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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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.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 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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""" |
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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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super().__init__() |
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@staticmethod |
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def forward(pred_score, gt_score, label, alpha=0.75, gamma=2.0): |
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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 = ( |
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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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"""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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# 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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# TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py |
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pred_prob = pred.sigmoid() # prob from logits |
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p_t = label * pred_prob + (1 - label) * (1 - pred_prob) |
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modulating_factor = (1.0 - p_t) ** gamma |
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loss *= modulating_factor |
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if alpha > 0: |
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alpha_factor = label * alpha + (1 - label) * (1 - alpha) |
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loss *= alpha_factor |
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return loss.mean(1).sum() |
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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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super().__init__() |
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self.reg_max = reg_max |
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self.use_dfl = 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 = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True) |
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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, target_bboxes, 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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@staticmethod |
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def _df_loss(pred_dist, target): |
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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 ( |
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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]).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).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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"""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.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.nc + m.reg_max * 4 |
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self.reg_max = m.reg_max |
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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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"""Preprocesses the target counts and matches with the input batch size to output a 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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counts = counts.to(dtype=torch.int32) |
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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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"""Decode predicted object bounding box coordinates from anchor points and distribution.""" |
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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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"""Calculate the sum of the loss for box, cls and dfl multiplied by batch size.""" |
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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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) |
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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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dtype = pred_scores.dtype |
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batch_size = pred_scores.shape[0] |
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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 = 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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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(), |
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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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# 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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if fg_mask.sum(): |
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target_bboxes /= stride_tensor |
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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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loss[2] *= self.hyp.dfl # dfl gain |
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return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) |
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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.overlap = model.args.overlap_mask |
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def __call__(self, preds, batch): |
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"""Calculate and return the loss for the YOLO model.""" |
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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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) |
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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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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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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( |
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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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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(), |
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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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# 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( |
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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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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 # 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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@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). |
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area (torch.Tensor): Area of each ground truth bounding box of shape (n,). |
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Returns: |
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(torch.Tensor): The calculated mask loss for a single image. |
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Notes: |
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The function uses the equation pred_mask = torch.einsum('in,nhw->ihw', pred, proto) to produce the |
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predicted masks from the prototype masks and predicted mask coefficients. |
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""" |
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pred_mask = torch.einsum("in,nhw->ihw", pred, proto) # (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).sum() |
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def calculate_segmentation_loss( |
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self, |
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fg_mask: torch.Tensor, |
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masks: torch.Tensor, |
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target_gt_idx: torch.Tensor, |
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target_bboxes: torch.Tensor, |
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batch_idx: torch.Tensor, |
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proto: torch.Tensor, |
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pred_masks: torch.Tensor, |
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imgsz: torch.Tensor, |
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overlap: bool, |
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) -> torch.Tensor: |
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""" |
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Calculate the loss for instance segmentation. |
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Args: |
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fg_mask (torch.Tensor): A binary tensor of shape (BS, N_anchors) indicating which anchors are positive. |
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masks (torch.Tensor): Ground truth masks of shape (BS, H, W) if `overlap` is False, otherwise (BS, ?, H, W). |
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target_gt_idx (torch.Tensor): Indexes of ground truth objects for each anchor of shape (BS, N_anchors). |
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target_bboxes (torch.Tensor): Ground truth bounding boxes for each anchor of shape (BS, N_anchors, 4). |
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batch_idx (torch.Tensor): Batch indices of shape (N_labels_in_batch, 1). |
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proto (torch.Tensor): Prototype masks of shape (BS, 32, H, W). |
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pred_masks (torch.Tensor): Predicted masks for each anchor of shape (BS, N_anchors, 32). |
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imgsz (torch.Tensor): Size of the input image as a tensor of shape (2), i.e., (H, W). |
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overlap (bool): Whether the masks in `masks` tensor overlap. |
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Returns: |
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(torch.Tensor): The calculated loss for instance segmentation. |
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Notes: |
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The batch loss can be computed for improved speed at higher memory usage. |
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For example, pred_mask can be computed as follows: |
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pred_mask = torch.einsum('in,nhw->ihw', pred, proto) # (i, 32) @ (32, 160, 160) -> (i, 160, 160) |
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""" |
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_, _, mask_h, mask_w = proto.shape |
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loss = 0 |
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# Normalize to 0-1 |
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target_bboxes_normalized = target_bboxes / imgsz[[1, 0, 1, 0]] |
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# Areas of target bboxes |
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marea = xyxy2xywh(target_bboxes_normalized)[..., 2:].prod(2) |
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# Normalize to mask size |
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mxyxy = target_bboxes_normalized * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=proto.device) |
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for i, single_i in enumerate(zip(fg_mask, target_gt_idx, pred_masks, proto, mxyxy, marea, masks)): |
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fg_mask_i, target_gt_idx_i, pred_masks_i, proto_i, mxyxy_i, marea_i, masks_i = single_i |
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if fg_mask_i.any(): |
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mask_idx = target_gt_idx_i[fg_mask_i] |
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if overlap: |
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gt_mask = masks_i == (mask_idx + 1).view(-1, 1, 1) |
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gt_mask = gt_mask.float() |
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else: |
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gt_mask = masks[batch_idx.view(-1) == i][mask_idx] |
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loss += self.single_mask_loss( |
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gt_mask, pred_masks_i[fg_mask_i], proto_i, mxyxy_i[fg_mask_i], marea_i[fg_mask_i] |
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) |
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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 += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss |
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return loss / fg_mask.sum() |
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class v8PoseLoss(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 v8PoseLoss with model, sets keypoint variables and declares a keypoint loss instance.""" |
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super().__init__(model) |
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self.kpt_shape = model.model[-1].kpt_shape |
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self.bce_pose = nn.BCEWithLogitsLoss() |
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is_pose = self.kpt_shape == [17, 3] |
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nkpt = self.kpt_shape[0] # number of keypoints |
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sigmas = torch.from_numpy(OKS_SIGMA).to(self.device) if is_pose else torch.ones(nkpt, device=self.device) / nkpt |
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self.keypoint_loss = KeypointLoss(sigmas=sigmas) |
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def __call__(self, preds, batch): |
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"""Calculate the total loss and detach it.""" |
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loss = torch.zeros(5, device=self.device) # box, cls, dfl, kpt_location, kpt_visibility |
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feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1] |
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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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) |
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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_kpts = pred_kpts.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_size = pred_scores.shape[0] |
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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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# Pboxes |
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pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4) |
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pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape)) # (b, h*w, 17, 3) |
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_, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner( |
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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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# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way |
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loss[3] = 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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target_bboxes /= stride_tensor |
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loss[0], loss[4] = 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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keypoints = batch["keypoints"].to(self.device).float().clone() |
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keypoints[..., 0] *= imgsz[1] |
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keypoints[..., 1] *= imgsz[0] |
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loss[1], loss[2] = self.calculate_keypoints_loss( |
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fg_mask, target_gt_idx, keypoints, batch_idx, stride_tensor, target_bboxes, pred_kpts |
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) |
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loss[0] *= self.hyp.box # box gain |
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loss[1] *= self.hyp.pose # pose gain |
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loss[2] *= self.hyp.kobj # kobj gain |
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loss[3] *= self.hyp.cls # cls gain |
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loss[4] *= self.hyp.dfl # dfl gain |
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return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) |
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@staticmethod |
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def kpts_decode(anchor_points, pred_kpts): |
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"""Decodes predicted keypoints to image coordinates.""" |
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y = pred_kpts.clone() |
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y[..., :2] *= 2.0 |
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y[..., 0] += anchor_points[:, [0]] - 0.5 |
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y[..., 1] += anchor_points[:, [1]] - 0.5 |
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return y |
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def calculate_keypoints_loss( |
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self, masks, target_gt_idx, keypoints, batch_idx, stride_tensor, target_bboxes, pred_kpts |
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): |
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""" |
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Calculate the keypoints loss for the model. |
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This function calculates the keypoints loss and keypoints object loss for a given batch. The keypoints loss is |
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based on the difference between the predicted keypoints and ground truth keypoints. The keypoints object loss is |
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a binary classification loss that classifies whether a keypoint is present or not. |
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Args: |
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masks (torch.Tensor): Binary mask tensor indicating object presence, shape (BS, N_anchors). |
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target_gt_idx (torch.Tensor): Index tensor mapping anchors to ground truth objects, shape (BS, N_anchors). |
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keypoints (torch.Tensor): Ground truth keypoints, shape (N_kpts_in_batch, N_kpts_per_object, kpts_dim). |
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batch_idx (torch.Tensor): Batch index tensor for keypoints, shape (N_kpts_in_batch, 1). |
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stride_tensor (torch.Tensor): Stride tensor for anchors, shape (N_anchors, 1). |
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target_bboxes (torch.Tensor): Ground truth boxes in (x1, y1, x2, y2) format, shape (BS, N_anchors, 4). |
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pred_kpts (torch.Tensor): Predicted keypoints, shape (BS, N_anchors, N_kpts_per_object, kpts_dim). |
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Returns: |
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(tuple): Returns a tuple containing: |
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- kpts_loss (torch.Tensor): The keypoints loss. |
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- kpts_obj_loss (torch.Tensor): The keypoints object loss. |
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""" |
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batch_idx = batch_idx.flatten() |
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batch_size = len(masks) |
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# Find the maximum number of keypoints in a single image |
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max_kpts = torch.unique(batch_idx, return_counts=True)[1].max() |
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# Create a tensor to hold batched keypoints |
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batched_keypoints = torch.zeros( |
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(batch_size, max_kpts, keypoints.shape[1], keypoints.shape[2]), device=keypoints.device |
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) |
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# TODO: any idea how to vectorize this? |
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# Fill batched_keypoints with keypoints based on batch_idx |
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for i in range(batch_size): |
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keypoints_i = keypoints[batch_idx == i] |
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batched_keypoints[i, : keypoints_i.shape[0]] = keypoints_i |
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# Expand dimensions of target_gt_idx to match the shape of batched_keypoints |
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target_gt_idx_expanded = target_gt_idx.unsqueeze(-1).unsqueeze(-1) |
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# Use target_gt_idx_expanded to select keypoints from batched_keypoints |
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selected_keypoints = batched_keypoints.gather( |
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1, target_gt_idx_expanded.expand(-1, -1, keypoints.shape[1], keypoints.shape[2]) |
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) |
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# Divide coordinates by stride |
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selected_keypoints /= stride_tensor.view(1, -1, 1, 1) |
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kpts_loss = 0 |
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kpts_obj_loss = 0 |
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if masks.any(): |
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gt_kpt = selected_keypoints[masks] |
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area = xyxy2xywh(target_bboxes[masks])[:, 2:].prod(1, keepdim=True) |
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pred_kpt = pred_kpts[masks] |
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kpt_mask = gt_kpt[..., 2] != 0 if gt_kpt.shape[-1] == 3 else torch.full_like(gt_kpt[..., 0], True) |
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kpts_loss = self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss |
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if pred_kpt.shape[-1] == 3: |
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kpts_obj_loss = self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss |
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return kpts_loss, kpts_obj_loss |
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class v8ClassificationLoss: |
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"""Criterion class for computing training losses.""" |
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def __call__(self, preds, batch): |
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"""Compute the classification loss between predictions and true labels.""" |
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loss = torch.nn.functional.cross_entropy(preds, batch["cls"], reduction="mean") |
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loss_items = loss.detach() |
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return loss, loss_items |
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class v8OBBLoss(v8DetectionLoss): |
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def __init__(self, model): |
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""" |
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Initializes v8OBBLoss with model, assigner, and rotated bbox loss. |
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Note model must be de-paralleled. |
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""" |
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super().__init__(model) |
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self.assigner = RotatedTaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0) |
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self.bbox_loss = RotatedBboxLoss(self.reg_max - 1, use_dfl=self.use_dfl).to(self.device) |
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def preprocess(self, targets, batch_size, scale_tensor): |
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"""Preprocesses the target counts and matches with the input batch size to output a tensor.""" |
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if targets.shape[0] == 0: |
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out = torch.zeros(batch_size, 0, 6, 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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counts = counts.to(dtype=torch.int32) |
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out = torch.zeros(batch_size, counts.max(), 6, 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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bboxes = targets[matches, 2:] |
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bboxes[..., :4].mul_(scale_tensor) |
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out[j, :n] = torch.cat([targets[matches, 1:2], bboxes], dim=-1) |
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return out |
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def __call__(self, preds, batch): |
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"""Calculate and return the loss for the YOLO model.""" |
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loss = torch.zeros(3, device=self.device) # box, cls, dfl |
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feats, pred_angle = preds if isinstance(preds[0], list) else preds[1] |
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batch_size = pred_angle.shape[0] # 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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) |
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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_angle = pred_angle.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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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"].view(-1, 5)), 1) |
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rw, rh = targets[:, 4] * imgsz[0].item(), targets[:, 5] * imgsz[1].item() |
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targets = targets[(rw >= 2) & (rh >= 2)] # filter rboxes of tiny size to stabilize training |
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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, 5), 2) # cls, xywhr |
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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( |
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"ERROR ❌ OBB dataset incorrectly formatted or not a OBB dataset.\n" |
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"This error can occur when incorrectly training a 'OBB' model on a 'detect' dataset, " |
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"i.e. 'yolo train model=yolov8n-obb.pt data=dota8.yaml'.\nVerify your dataset is a " |
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"correctly formatted 'OBB' dataset using 'data=dota8.yaml' " |
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"as an example.\nSee https://docs.ultralytics.com/datasets/obb/ for help." |
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) from e |
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# Pboxes |
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pred_bboxes = self.bbox_decode(anchor_points, pred_distri, pred_angle) # xyxy, (b, h*w, 4) |
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bboxes_for_assigner = pred_bboxes.clone().detach() |
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# Only the first four elements need to be scaled |
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bboxes_for_assigner[..., :4] *= stride_tensor |
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_, target_bboxes, target_scores, fg_mask, _ = self.assigner( |
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pred_scores.detach().sigmoid(), |
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bboxes_for_assigner.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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# 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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if fg_mask.sum(): |
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target_bboxes[..., :4] /= stride_tensor |
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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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else: |
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loss[0] += (pred_angle * 0).sum() |
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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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loss[2] *= 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 bbox_decode(self, anchor_points, pred_dist, pred_angle): |
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""" |
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Decode predicted object bounding box coordinates from anchor points and distribution. |
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Args: |
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anchor_points (torch.Tensor): Anchor points, (h*w, 2). |
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pred_dist (torch.Tensor): Predicted rotated distance, (bs, h*w, 4). |
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pred_angle (torch.Tensor): Predicted angle, (bs, h*w, 1). |
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Returns: |
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(torch.Tensor): Predicted rotated bounding boxes with angles, (bs, h*w, 5). |
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""" |
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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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return torch.cat((dist2rbox(pred_dist, pred_angle, anchor_points), pred_angle), dim=-1)
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