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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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21 changed files with 174 additions and 144 deletions
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ultralytics/models/utils/ops.py
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@ -110,34 +110,35 @@ class HungarianMatcher(nn.Module):
return [(torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
for k, (i, j) in enumerate(indices)]
def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
# all masks share the same set of points for efficient matching
sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
sample_points = 2.0 * sample_points - 1.0
out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
out_mask = out_mask.flatten(0, 1)
tgt_mask = torch.cat(gt_mask).unsqueeze(1)
sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
with torch.cuda.amp.autocast(False):
# binary cross entropy cost
pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
cost_mask /= self.num_sample_points
# dice cost
out_mask = F.sigmoid(out_mask)
numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
cost_dice = 1 - (numerator + 1) / (denominator + 1)
C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
return C
# This function is for future RT-DETR Segment models
# def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
# assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
# # all masks share the same set of points for efficient matching
# sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
# sample_points = 2.0 * sample_points - 1.0
#
# out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
# out_mask = out_mask.flatten(0, 1)
#
# tgt_mask = torch.cat(gt_mask).unsqueeze(1)
# sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
# tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
#
# with torch.cuda.amp.autocast(False):
# # binary cross entropy cost
# pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
# neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
# cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
# cost_mask /= self.num_sample_points
#
# # dice cost
# out_mask = F.sigmoid(out_mask)
# numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
# denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
# cost_dice = 1 - (numerator + 1) / (denominator + 1)
#
# C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
# return C
def get_cdn_group(batch,