ultralytics 8.0.167 Tuner updates and HUB Pose and Classify fixes (#4656)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
GitHub
parent
8596ee241f
commit
d2cf7acce0
21 changed files with 174 additions and 144 deletions
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@ -84,35 +84,36 @@ class DETRLoss(nn.Module):
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loss[name_giou] = self.loss_gain['giou'] * loss[name_giou]
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return {k: v.squeeze() for k, v in loss.items()}
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def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
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# masks: [b, query, h, w], gt_mask: list[[n, H, W]]
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name_mask = f'loss_mask{postfix}'
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name_dice = f'loss_dice{postfix}'
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# This function is for future RT-DETR Segment models
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# def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
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# # masks: [b, query, h, w], gt_mask: list[[n, H, W]]
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# name_mask = f'loss_mask{postfix}'
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# name_dice = f'loss_dice{postfix}'
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#
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# loss = {}
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# if sum(len(a) for a in gt_mask) == 0:
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# loss[name_mask] = torch.tensor(0., device=self.device)
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# loss[name_dice] = torch.tensor(0., device=self.device)
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# return loss
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#
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# num_gts = len(gt_mask)
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# src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
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# src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
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# # TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
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# loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
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# torch.tensor([num_gts], dtype=torch.float32))
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# loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
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# return loss
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loss = {}
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if sum(len(a) for a in gt_mask) == 0:
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loss[name_mask] = torch.tensor(0., device=self.device)
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loss[name_dice] = torch.tensor(0., device=self.device)
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return loss
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num_gts = len(gt_mask)
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src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
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src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
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# TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
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loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
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torch.tensor([num_gts], dtype=torch.float32))
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loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
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return loss
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@staticmethod
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def _dice_loss(inputs, targets, num_gts):
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inputs = F.sigmoid(inputs)
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inputs = inputs.flatten(1)
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targets = targets.flatten(1)
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numerator = 2 * (inputs * targets).sum(1)
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denominator = inputs.sum(-1) + targets.sum(-1)
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loss = 1 - (numerator + 1) / (denominator + 1)
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return loss.sum() / num_gts
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# This function is for future RT-DETR Segment models
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# @staticmethod
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# def _dice_loss(inputs, targets, num_gts):
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# inputs = F.sigmoid(inputs).flatten(1)
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# targets = targets.flatten(1)
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# numerator = 2 * (inputs * targets).sum(1)
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# denominator = inputs.sum(-1) + targets.sum(-1)
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# loss = 1 - (numerator + 1) / (denominator + 1)
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# return loss.sum() / num_gts
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def _get_loss_aux(self,
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pred_bboxes,
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@ -110,34 +110,35 @@ class HungarianMatcher(nn.Module):
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return [(torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
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for k, (i, j) in enumerate(indices)]
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def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
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assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
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# all masks share the same set of points for efficient matching
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sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
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sample_points = 2.0 * sample_points - 1.0
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out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
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out_mask = out_mask.flatten(0, 1)
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tgt_mask = torch.cat(gt_mask).unsqueeze(1)
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sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
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tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
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with torch.cuda.amp.autocast(False):
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# binary cross entropy cost
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pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
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neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
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cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
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cost_mask /= self.num_sample_points
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# dice cost
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out_mask = F.sigmoid(out_mask)
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numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
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denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
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cost_dice = 1 - (numerator + 1) / (denominator + 1)
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C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
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return C
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# This function is for future RT-DETR Segment models
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# def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
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# assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
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# # all masks share the same set of points for efficient matching
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# sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
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# sample_points = 2.0 * sample_points - 1.0
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#
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# out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
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# out_mask = out_mask.flatten(0, 1)
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#
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# tgt_mask = torch.cat(gt_mask).unsqueeze(1)
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# sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
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# tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
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#
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# with torch.cuda.amp.autocast(False):
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# # binary cross entropy cost
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# pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
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# neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
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# cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
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# cost_mask /= self.num_sample_points
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#
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# # dice cost
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# out_mask = F.sigmoid(out_mask)
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# numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
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# denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
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# cost_dice = 1 - (numerator + 1) / (denominator + 1)
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#
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# C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
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# return C
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def get_cdn_group(batch,
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@ -55,7 +55,7 @@ class ClassificationValidator(BaseValidator):
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def update_metrics(self, preds, batch):
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"""Updates running metrics with model predictions and batch targets."""
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n5 = min(len(self.model.names), 5)
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n5 = min(len(self.names), 5)
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self.pred.append(preds.argsort(1, descending=True)[:, :n5])
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self.targets.append(batch['cls'])
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