SimpleDownsample: weighted average over groups of `ds` frames using learned softmax weights.
(src: &Tensor, ds: usize, bias: &Tensor)
| 52 | |
| 53 | /// SimpleDownsample: weighted average over groups of `ds` frames using learned softmax weights. |
| 54 | fn simple_downsample(src: &Tensor, ds: usize, bias: &Tensor) -> Result<Tensor> { |
| 55 | // src: [batch, seq_len, dim], bias: [ds] |
| 56 | let (batch, seq_len, dim) = src.dims3()?; |
| 57 | let d_seq_len = seq_len.div_ceil(ds); |
| 58 | let padded_len = d_seq_len * ds; |
| 59 | |
| 60 | // Pad by repeating last frame if needed |
| 61 | let src = if padded_len > seq_len { |
| 62 | let last_frame = src.narrow(1, seq_len - 1, 1)?; // [batch, 1, dim] |
| 63 | let pad_count = padded_len - seq_len; |
| 64 | let padding = last_frame.expand((batch, pad_count, dim))?; |
| 65 | Tensor::cat(&[src, &padding], 1)? |
| 66 | } else { |
| 67 | src.clone() |
| 68 | }; |
| 69 | |
| 70 | // Reshape to [batch, d_seq_len, ds, dim] |
| 71 | let src = src.reshape((batch, d_seq_len, ds, dim))?; |
| 72 | |
| 73 | // Softmax weights: bias [ds] -> softmax -> [1, 1, ds, 1] |
| 74 | let b = bias.unsqueeze(0)?; |
| 75 | let max = b.max_keepdim(1)?; |
| 76 | let exp = b.broadcast_sub(&max)?.exp()?; |
| 77 | let sum = exp.sum_keepdim(1)?; |
| 78 | let weights = exp.broadcast_div(&sum)?; // [1, ds] |
| 79 | let weights = weights.reshape((1, 1, ds, 1))?; |
| 80 | |
| 81 | // Weighted sum over ds dimension |
| 82 | let weighted = src.broadcast_mul(&weights)?; // [batch, d_seq_len, ds, dim] |
| 83 | let result = weighted.sum(2)?; // [batch, d_seq_len, dim] |
| 84 | |
| 85 | Ok(result) |
| 86 | } |
| 87 | |
| 88 | /// SimpleUpsample: repeat each frame `ds` times. |
| 89 | fn simple_upsample(src: &Tensor, ds: usize) -> Result<Tensor> { |