Automatic mask generator + point base mask (#773)

* Add more to the automatic mask generator.

* Add the target point.

* Fix.

* Remove the allow-unused.

* Mask post-processing.

1 files changed, 1 insertions, 5 deletions

diff --git a/candle-examples/examples/segment-anything/model_transformer.rs b/candle-examples/examples/segment-anything/model_transformer.rs
index 044dce9b..e4de27cb 100644
--- a/candle-examples/examples/segment-anything/model_transformer.rs
+++ b/candle-examples/examples/segment-anything/model_transformer.rs
@@ -1,4 +1,4 @@
-use candle::{DType, IndexOp, Result, Tensor, D};
+use candle::{Result, Tensor};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
 
 #[derive(Debug)]
@@ -7,7 +7,6 @@ struct Attention {
     k_proj: Linear,
     v_proj: Linear,
     out_proj: Linear,
-    internal_dim: usize,
     num_heads: usize,
 }
 
@@ -28,7 +27,6 @@ impl Attention {
             k_proj,
             v_proj,
             out_proj,
-            internal_dim,
             num_heads,
         })
     }
@@ -85,7 +83,6 @@ impl TwoWayAttentionBlock {
         skip_first_layer_pe: bool,
         vb: VarBuilder,
     ) -> Result<Self> {
-        let self_attn = Attention::new(embedding_dim, num_heads, 1, vb.pp("self_attn"))?;
         let norm1 = layer_norm(embedding_dim, 1e-5, vb.pp("norm1"))?;
         let norm2 = layer_norm(embedding_dim, 1e-5, vb.pp("norm2"))?;
         let norm3 = layer_norm(embedding_dim, 1e-5, vb.pp("norm3"))?;
@@ -204,7 +201,6 @@ impl TwoWayTransformer {
         image_pe: &Tensor,
         point_embedding: &Tensor,
     ) -> Result<(Tensor, Tensor)> {
-        let (bs, c, h, w) = image_embedding.dims4()?;
         let image_embedding = image_embedding.flatten_from(2)?.permute((0, 2, 1))?;
         let image_pe = image_pe.flatten_from(2)?.permute((0, 2, 1))?;