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ultralytics 8.0.167 Tuner updates and HUB Pose and Classify fixes (#4656)

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Glenn Jocher 2023-08-31 01:38:42 +02:00 • committed by GitHub
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57
ultralytics/models/utils/loss.py
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@ -84,35 +84,36 @@ class DETRLoss(nn.Module):
loss[name_giou] = self.loss_gain['giou'] * loss[name_giou]
return {k: v.squeeze() for k, v in loss.items()}
def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
# masks: [b, query, h, w], gt_mask: list[[n, H, W]]
name_mask = f'loss_mask{postfix}'
name_dice = f'loss_dice{postfix}'
# This function is for future RT-DETR Segment models
# def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
# # masks: [b, query, h, w], gt_mask: list[[n, H, W]]
# name_mask = f'loss_mask{postfix}'
# name_dice = f'loss_dice{postfix}'
#
# loss = {}
# if sum(len(a) for a in gt_mask) == 0:
# loss[name_mask] = torch.tensor(0., device=self.device)
# loss[name_dice] = torch.tensor(0., device=self.device)
# return loss
#
# num_gts = len(gt_mask)
# src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
# src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
# # TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
# loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
# torch.tensor([num_gts], dtype=torch.float32))
# loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
# return loss
loss = {}
if sum(len(a) for a in gt_mask) == 0:
loss[name_mask] = torch.tensor(0., device=self.device)
loss[name_dice] = torch.tensor(0., device=self.device)
return loss
num_gts = len(gt_mask)
src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
# TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
torch.tensor([num_gts], dtype=torch.float32))
loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
return loss
@staticmethod
def _dice_loss(inputs, targets, num_gts):
inputs = F.sigmoid(inputs)
inputs = inputs.flatten(1)
targets = targets.flatten(1)
numerator = 2 * (inputs * targets).sum(1)
denominator = inputs.sum(-1) + targets.sum(-1)
loss = 1 - (numerator + 1) / (denominator + 1)
return loss.sum() / num_gts
# This function is for future RT-DETR Segment models
# @staticmethod
# def _dice_loss(inputs, targets, num_gts):
# inputs = F.sigmoid(inputs).flatten(1)
# targets = targets.flatten(1)
# numerator = 2 * (inputs * targets).sum(1)
# denominator = inputs.sum(-1) + targets.sum(-1)
# loss = 1 - (numerator + 1) / (denominator + 1)
# return loss.sum() / num_gts
def _get_loss_aux(self,
pred_bboxes,