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use super::fastvit;
use super::openclip::text_model;
use candle::{Result, Tensor, D};
use candle_nn::{Func, VarBuilder};
#[derive(Clone, Debug)]
pub struct MobileClipModel {
text_model: text_model::OpenClipTextTransformer,
vision_model: Func<'static>,
text_projection: Tensor,
logit_scale: Tensor,
}
#[derive(Clone, Debug)]
pub struct MobileClipConfig {
pub text_config: text_model::Config,
pub vision_config: fastvit::Config,
pub image_size: usize,
}
impl MobileClipConfig {
pub fn s1() -> Self {
let text_config = text_model::Config::vit_base_patch32();
let vision_config = fastvit::Config::mci1();
Self {
text_config,
vision_config,
image_size: 256,
}
}
pub fn s2() -> Self {
let text_config = text_model::Config::vit_base_patch32();
let vision_config = fastvit::Config::mci2();
Self {
text_config,
vision_config,
image_size: 256,
}
}
}
impl MobileClipModel {
pub fn new(vs: VarBuilder, c: &MobileClipConfig) -> Result<Self> {
let vision_model = fastvit::fastvit(&c.vision_config, 512, vs.pp("visual.trunk"))?;
let text_model = text_model::OpenClipTextTransformer::new(vs.pp("text"), &c.text_config)?;
let text_projection = vs.get(
(c.text_config.embed_dim, c.text_config.projection_dim),
"text.text_projection",
)?;
let logit_scale = vs.get(&[], "logit_scale")?;
Ok(Self {
text_model,
vision_model,
text_projection,
logit_scale,
})
}
pub fn get_text_features(&self, input_ids: &Tensor) -> Result<Tensor> {
input_ids
.apply(&self.text_model)?
.matmul(&self.text_projection)
}
pub fn get_image_features(&self, pixel_values: &Tensor) -> Result<Tensor> {
pixel_values.apply(&self.vision_model)
}
pub fn forward(&self, pixel_values: &Tensor, input_ids: &Tensor) -> Result<(Tensor, Tensor)> {
let image_features = self.get_image_features(pixel_values)?;
let text_features = self.get_text_features(input_ids)?;
let image_features_normalized = div_l2_norm(&image_features)?;
let text_features_normalized = div_l2_norm(&text_features)?;
let logits_per_text = text_features_normalized.matmul(&image_features_normalized.t()?)?;
let logit_scale = self.logit_scale.exp()?;
let logits_per_text = logits_per_text.broadcast_mul(&logit_scale)?;
let logits_per_image = logits_per_text.t()?;
Ok((logits_per_text, logits_per_image))
}
}
pub fn div_l2_norm(v: &Tensor) -> Result<Tensor> {
let l2_norm = v.sqr()?.sum_keepdim(D::Minus1)?.sqrt()?;
v.broadcast_div(&l2_norm)
}
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