resolve conflicts. Delete unused code

src/otx/algo/object_detection_3d/heads/depthaware_transformer.py

@@ -4,7 +4,6 @@
 """depth aware transformer head for 3d object detection."""
 from __future__ import annotations
 
-import math
 from typing import Any, Callable, ClassVar
 
 import torch
@@ -101,30 +100,6 @@ def _reset_parameters(self) -> None:
         constant_(self.reference_points.bias.data, 0.0)
         normal_(self.level_embed)
 
-    def get_proposal_pos_embed(self, proposals: Tensor) -> Tensor:
-        """Generate position embeddings for proposal tensor.
-
-        Args:
-            proposals (Tensor): Proposal tensor of shape (N, L, 6).
-
-        TODO (Kirill): Not used. Remove this function?
-
-        Returns:
-            Tensor: Position embeddings for proposal tensor of shape (N, L, embedding_dim).
-        """
-        num_pos_feats = 128
-        temperature = 10000
-        scale = 2 * math.pi
-
-        dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
-        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
-        # N, L, 6
-        proposals = proposals.sigmoid() * scale
-        # N, L, 6, 128
-        pos = proposals[:, :, :, None] / dim_t
-        # N, L, 6, 64, 2
-        return torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
-
     def get_valid_ratio(self, mask: Tensor) -> Tensor:
         """Calculate the valid ratio of the mask.
 

tests/unit/algo/object_detection_3d/heads/test_depthaware_transformer.py

@@ -7,7 +7,6 @@
 import torch
 from otx.algo.object_detection_3d.heads.depthaware_transformer import (
     DepthAwareTransformerBuilder,
-    gen_sineembed_for_position,
 )
 
 
@@ -57,16 +56,6 @@ def test_depth_aware_transformer_forward(self, depth_aware_transformer):
         assert output[2].shape == (1, 550, 2)
         assert output[4] is None
 
-    def test_depth_aware_transformer_get_proposal_pos_embed(self, depth_aware_transformer):
-        # Create dummy input tensor
-        proposals = torch.randn(2, 10, 6)
-
-        # Get proposal position embeddings
-        pos_embed = depth_aware_transformer.get_proposal_pos_embed(proposals)
-
-        # Check output shape
-        assert pos_embed.shape == (2, 10, 768)
-
     def test_depth_aware_transformer_get_valid_ratio(self, depth_aware_transformer):
         # Create dummy input tensor
         mask = torch.randn(2, 32, 32) > 0
@@ -76,13 +65,3 @@ def test_depth_aware_transformer_get_valid_ratio(self, depth_aware_transformer):
 
         # Check output shape
         assert valid_ratio.shape == (2, 2)
-
-    def test_gen_sineembed_for_position(self):
-        # Create dummy input tensor
-        pos_tensor = torch.randn(2, 4, 6)
-
-        # Generate sine embeddings for position tensor
-        pos_embed = gen_sineembed_for_position(pos_tensor)
-
-        # Check output shape
-        assert pos_embed.shape == (2, 4, 768)