Question about code of position embedding in rt-detr about ultralytics

[glenn-jocher]

@Rbrq03 hello,

Thank you for your kind words and for bringing up this interesting question about the position embedding in the TransformerEncoderLayer.

The implementation of position embeddings in the TransformerEncoderLayer indeed differs slightly from the original DETR approach. In our implementation, the position embeddings are added only to the query (q) and key (k) tensors, but not to the value (v) tensor. This design choice can be attributed to different architectural preferences and optimizations.

Here's a brief explanation of the current implementation:

def forward_post(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
    """Performs forward pass with post-normalization."""
    q = k = self.with_pos_embed(src, pos)
    src2 = self.ma(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
    src = src + self.dropout1(src2)
    src = self.norm1(src)
    src2 = self.fc2(self.dropout(self.act(self.fc1(src))))
    src = src + self.dropout2(src2)
    return self.norm2(src)

In this code, the position embeddings are added to the q and k tensors, which helps the model to learn spatial relationships more effectively. The value tensor (v), however, remains unchanged. This approach can sometimes lead to better performance in certain tasks by focusing the positional information on the attention mechanism rather than the entire input.

Your suggested modification aligns more closely with the original DETR implementation, where the position embeddings are added to all three tensors (q, k, and v). This can be beneficial in scenarios where the positional context is crucial for all aspects of the attention mechanism.

If you would like to experiment with this approach, you can modify the forward_post method as follows:

def forward_post(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
    """Performs forward pass with post-normalization."""
    q = k = v = self.with_pos_embed(src, pos)
    src2 = self.ma(q, k, value=v, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
    src = src + self.dropout1(src2)
    src = self.norm1(src)
    src2 = self.fc2(self.dropout(self.act(self.fc1(src))))
    src = src + self.dropout2(src2)
    return self.norm2(src)

Feel free to test this modification and observe how it impacts your model's performance. If you encounter any issues or have further questions, please don't hesitate to ask.

For more detailed information on the transformer modules, you can refer to our documentation.

[Rbrq03]

Thanks @glenn-jocher,
My further question is :which paper/work points out this modification can benefit the performance of model?

[glenn-jocher]

Hello @Rbrq03,

Thank you for your follow-up question!

The modification of adding positional embeddings only to the query (q) and key (k) tensors, while leaving the value (v) tensor unchanged, is not directly derived from a specific paper. Instead, it is an architectural choice that can be influenced by various research works and practical considerations in the field of transformer models.

This approach can be seen as a variation to potentially improve performance by focusing the positional information on the attention mechanism. While the original DETR paper (https://arxiv.org/abs/2005.12872) adds positional embeddings to all three tensors (q, k, and v), other works in the transformer space have explored different ways of incorporating positional information.

For instance, the Vision Transformer (ViT) paper (https://arxiv.org/abs/2010.11929) and subsequent research have experimented with various positional encoding strategies. These variations often aim to balance computational efficiency and model performance.

If you are interested in further exploring this topic, I recommend reviewing the following papers:

• "Attention is All You Need" (https://arxiv.org/abs/1706.03762) – foundational work on transformers.
• "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" (https://arxiv.org/abs/2010.11929) – introduces Vision Transformers (ViT).

Experimenting with different positional encoding strategies in your models can provide insights into what works best for your specific use case. If you have any more questions or need further assistance, feel free to ask!

[Rbrq03]

Thanks for your kind response!

[glenn-jocher]

You're welcome! If you have any further questions or need additional assistance, feel free to ask. We're here to help! 😊