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Use shape with holes. (#771)

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This commit is contained in:
Laurent Mazare
2023-09-08 08:50:27 +01:00
committed by GitHub
parent 0e250aee4f
commit 989a4807b1
3 changed files with 7 additions and 7 deletions
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4
candle-examples/examples/segment-anything/model_image_encoder.rs
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@ -123,7 +123,7 @@ fn get_rel_pos(q_size: usize, k_size: usize, rel_pos: &Tensor) -> Result<Tensor>
let relative_coords = relative_coords.to_dtype(DType::U32)?;
rel_pos_resized
.index_select(&relative_coords.reshape(d1 * d2)?, 0)?
.reshape((d1, d2, rel_pos_resized.dim(1)?))
.reshape((d1, d2, ()))
}
impl Module for Attention {
@ -243,7 +243,7 @@ fn window_unpartition(
))?
.transpose(2, 3)?
.contiguous()?
.reshape((b, h_p, w_p, windows.elem_count() / b / h_p / w_p))?;
.reshape((b, h_p, w_p, ()))?;
let xs = if h_p > h { xs.narrow(1, 0, h)? } else { xs };
let xs = if w_p > w { xs.narrow(2, 0, w)? } else { xs };
Ok(xs)

2
candle-examples/examples/segment-anything/model_mask_decoder.rs
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@ -214,7 +214,7 @@ impl MaskDecoder {
let hyper_in = Tensor::stack(hyper_in_list.as_slice(), 1)?;
let (b, c, h, w) = upscaled_embedding.dims4()?;
let masks = hyper_in.matmul(&upscaled_embedding.reshape((b, c, h * w))?)?;
let masks = masks.reshape((b, masks.elem_count() / b / h / w, h, w))?;
let masks = masks.reshape((b, (), h, w))?;
// Generate mask quality predictions.
let iou_pred = self.iou_prediction_head.forward(&iou_token_out)?;

8
candle-examples/examples/segment-anything/model_prompt_encoder.rs
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@ -28,10 +28,10 @@ impl PostionEmbeddingRandom {
let x_embed = (Tensor::arange(0u32, w as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
let y_embed = (Tensor::arange(0u32, h as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
let x_embed = (x_embed / w as f64)?
.reshape((1, w))?
.reshape((1, ()))?
.broadcast_as((h, w))?;
let y_embed = (y_embed / h as f64)?
.reshape((h, 1))?
.reshape(((), 1))?
.broadcast_as((h, w))?;
let coords = Tensor::stack(&[&x_embed, &y_embed], D::Minus1)?;
self.pe_encoding(&coords)?.permute((2, 0, 1))
@ -163,7 +163,7 @@ impl PromptEncoder {
fn embed_boxes(&self, boxes: &Tensor) -> Result<Tensor> {
let boxes = (boxes + 0.5)?;
let coords = boxes.reshape((boxes.elem_count() / 4, 2, 2))?;
let coords = boxes.reshape(((), 2, 2))?;
let corner_embedding = self
.pe_layer
.forward_with_coords(&coords, self.input_image_size)?;
@ -200,7 +200,7 @@ impl PromptEncoder {
let dense_embeddings = match masks {
None => {
let emb = self.no_mask_embed.embeddings();
emb.reshape((1, emb.elem_count(), 1, 1))?.expand((
emb.reshape((1, (), 1, 1))?.expand((
1,
emb.elem_count(),
self.image_embedding_size.0,