Merge pull request #271 from developer0hye/patch-1

modify variable name related to multi scale features

rtdetr_pytorch/src/zoo/rtdetr/hybrid_encoder.py

@@ -303,20 +303,20 @@ def forward(self, feats):
         # broadcasting and fusion
         inner_outs = [proj_feats[-1]]
         for idx in range(len(self.in_channels) - 1, 0, -1):
-            feat_heigh = inner_outs[0]
+            feat_high = inner_outs[0]
             feat_low = proj_feats[idx - 1]
-            feat_heigh = self.lateral_convs[len(self.in_channels) - 1 - idx](feat_heigh)
-            inner_outs[0] = feat_heigh
-            upsample_feat = F.interpolate(feat_heigh, scale_factor=2., mode='nearest')
+            feat_high = self.lateral_convs[len(self.in_channels) - 1 - idx](feat_high)
+            inner_outs[0] = feat_high
+            upsample_feat = F.interpolate(feat_high, scale_factor=2., mode='nearest')
             inner_out = self.fpn_blocks[len(self.in_channels)-1-idx](torch.concat([upsample_feat, feat_low], dim=1))
             inner_outs.insert(0, inner_out)
 
         outs = [inner_outs[0]]
         for idx in range(len(self.in_channels) - 1):
             feat_low = outs[-1]
-            feat_height = inner_outs[idx + 1]
+            feat_high = inner_outs[idx + 1]
             downsample_feat = self.downsample_convs[idx](feat_low)
-            out = self.pan_blocks[idx](torch.concat([downsample_feat, feat_height], dim=1))
+            out = self.pan_blocks[idx](torch.concat([downsample_feat, feat_high], dim=1))
             outs.append(out)
 
         return outs