More segment-anything. (#763)

* More segment-anything.

* Split the model in multiple files.

* Start adding the transformer.

* Add the attention block.

* Move the MLP Block.

candle-examples/examples/segment-anything/model_image_encoder.rs

@@ -0,0 +1,257 @@
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+#[derive(Debug)]
+struct PatchEmbed {
+    proj: candle_nn::Conv2d,
+}
+
+impl PatchEmbed {
+    fn new(
+        in_chans: usize,
+        embed_dim: usize,
+        k_size: usize,
+        stride: usize,
+        padding: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let cfg = candle_nn::Conv2dConfig {
+            stride,
+            padding,
+            ..Default::default()
+        };
+        let proj = candle_nn::conv2d(in_chans, embed_dim, k_size, cfg, vb.pp("proj"))?;
+        Ok(Self { proj })
+    }
+}
+
+impl Module for PatchEmbed {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.proj)?.permute((0, 2, 3, 1))
+    }
+}
+
+#[derive(Debug)]
+struct Attention {
+    qkv: Linear,
+    proj: Linear,
+    num_heads: usize,
+    scale: f64,
+    use_rel_pos: bool,
+    rel_pos_hw: Option<(Tensor, Tensor)>,
+}
+
+impl Attention {
+    fn new(
+        dim: usize,
+        num_heads: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let qkv = crate::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
+        let proj = crate::linear(vb.pp("proj"), dim, dim, true)?;
+        let head_dim = dim / num_heads;
+        let scale = 1. / (head_dim as f64).sqrt();
+        let rel_pos_hw = if use_rel_pos {
+            let h = vb.get((2 * window_size - 1, head_dim), "rel_pos_h")?;
+            let w = vb.get((2 * window_size - 1, head_dim), "rel_pos_w")?;
+            Some((h, w))
+        } else {
+            None
+        };
+        Ok(Self {
+            qkv,
+            proj,
+            num_heads,
+            scale,
+            use_rel_pos,
+            rel_pos_hw,
+        })
+    }
+}
+
+impl Module for Attention {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (b, h, w, c) = xs.dims4()?;
+        let qkv = self
+            .qkv
+            .forward(xs)?
+            .reshape((b, h * w, 3, self.num_heads, c / self.num_heads))?
+            .permute((2, 0, 3, 1, 4))?
+            .reshape((3, b * self.num_heads, h * w, c / self.num_heads))?;
+        let q = qkv.i(0)?;
+        let k = qkv.i(1)?;
+        let v = qkv.i(2)?;
+        let attn = (q * self.scale)?.matmul(&k.t()?)?;
+        if self.use_rel_pos {
+            todo!()
+        }
+        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
+        let attn = attn
+            .matmul(&v)?
+            .reshape((b, self.num_heads, h, w, c / self.num_heads))?
+            .permute((0, 2, 3, 1, 4))?
+            .reshape((b, h, w, c / self.num_heads))?;
+        self.proj.forward(&attn)
+    }
+}
+
+#[derive(Debug)]
+struct Block {
+    norm1: LayerNorm,
+    attn: Attention,
+    norm2: LayerNorm,
+    mlp: crate::MlpBlock,
+    window_size: usize,
+}
+
+impl Block {
+    fn new(
+        dim: usize,
+        num_heads: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
+        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
+        let attn = Attention::new(
+            dim,
+            num_heads,
+            qkv_bias,
+            use_rel_pos,
+            window_size,
+            vb.pp("attn"),
+        )?;
+        let mlp = crate::MlpBlock::new(dim, dim * 4, vb.pp("mlp"))?;
+        Ok(Self {
+            norm1,
+            attn,
+            norm2,
+            mlp,
+            window_size,
+        })
+    }
+}
+
+impl Module for Block {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let shortcut = xs;
+        let xs = self.norm1.forward(xs)?;
+        if self.window_size > 0 {
+            todo!()
+        }
+        let xs = self.attn.forward(&xs)?;
+        if self.window_size > 0 {
+            todo!()
+        }
+        let xs = (xs + shortcut)?;
+        &xs + xs.apply(&self.norm2)?.apply(&self.mlp)?
+    }
+}
+
+#[derive(Debug)]
+struct ImageEncoderViT {
+    img_size: usize,
+    patch_embed: PatchEmbed,
+    blocks: Vec<Block>,
+    neck_conv1: candle_nn::Conv2d,
+    neck_ln1: LayerNorm,
+    neck_conv2: candle_nn::Conv2d,
+    neck_ln2: LayerNorm,
+    pos_embed: Option<Tensor>,
+}
+
+impl ImageEncoderViT {
+    #[allow(clippy::too_many_arguments)]
+    fn new(
+        img_size: usize,
+        patch_size: usize,
+        in_chans: usize,
+        embed_dim: usize,
+        depth: usize,
+        num_heads: usize,
+        out_chans: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        use_abs_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let patch_embed = PatchEmbed::new(
+            in_chans,
+            embed_dim,
+            patch_size,
+            patch_size,
+            0,
+            vb.pp("patch_embed"),
+        )?;
+        let mut blocks = Vec::with_capacity(depth);
+        let vb_b = vb.pp("blocks");
+        for i in 0..depth {
+            let block = Block::new(
+                embed_dim,
+                num_heads,
+                qkv_bias,
+                use_rel_pos,
+                window_size,
+                vb_b.pp(i),
+            )?;
+            blocks.push(block)
+        }
+        let neck_conv1 = candle_nn::conv2d_no_bias(
+            embed_dim,
+            out_chans,
+            1,
+            Default::default(),
+            vb.pp("neck.0"),
+        )?;
+        let neck_ln1 = layer_norm(out_chans, 1e-6, vb.pp("neck.1"))?;
+        let cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
+        let neck_ln2 = layer_norm(out_chans, 1e-6, vb.pp("neck.3"))?;
+        let pos_embed = if use_abs_pos {
+            let p = vb.get(
+                (1, img_size / patch_size, img_size / patch_size, embed_dim),
+                "pos_embed",
+            )?;
+            Some(p)
+        } else {
+            None
+        };
+        Ok(Self {
+            img_size,
+            patch_embed,
+            blocks,
+            neck_conv1,
+            neck_ln1,
+            neck_conv2,
+            neck_ln2,
+            pos_embed,
+        })
+    }
+}
+
+impl Module for ImageEncoderViT {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.patch_embed.forward(xs)?;
+        let mut xs = match &self.pos_embed {
+            Some(pos_embed) => (xs + pos_embed)?,
+            None => xs,
+        };
+        for block in self.blocks.iter() {
+            xs = block.forward(&xs)?
+        }
+        xs.permute((0, 3, 1, 2))?
+            .apply(&self.neck_conv1)?
+            .apply(&self.neck_ln1)?
+            .apply(&self.neck_conv2)?
+            .apply(&self.neck_ln2)
+    }
+}