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@ -270,7 +270,7 @@ class RTDETRDecoder(nn.Module):
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anchors = torch.cat(anchors, 1) # (1, h*w*nl, 4)
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anchors = torch.cat(anchors, 1) # (1, h*w*nl, 4)
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valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) # 1, h*w*nl, 1
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valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) # 1, h*w*nl, 1
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anchors = torch.log(anchors / (1 - anchors))
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anchors = torch.log(anchors / (1 - anchors))
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anchors = torch.where(valid_mask, anchors, torch.inf)
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anchors = anchors.masked_fill(~valid_mask, float('inf'))
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return anchors, valid_mask
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return anchors, valid_mask
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def _get_encoder_input(self, x):
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def _get_encoder_input(self, x):
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@ -294,7 +294,7 @@ class RTDETRDecoder(nn.Module):
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bs = len(feats)
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bs = len(feats)
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# prepare input for decoder
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# prepare input for decoder
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anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
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anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
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features = self.enc_output(torch.where(valid_mask, feats, 0)) # bs, h*w, 256
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features = self.enc_output(valid_mask * feats) # bs, h*w, 256
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enc_outputs_scores = self.enc_score_head(features) # (bs, h*w, nc)
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enc_outputs_scores = self.enc_score_head(features) # (bs, h*w, nc)
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# dynamic anchors + static content
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# dynamic anchors + static content
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