Models/CLIP.py

import flax
import jax.numpy as jnp


class Image(flax.linen.Module):
    out_units: int = 1024

    @flax.linen.compact
    def __call__(self, x, training=False):
        x = flax.linen.Dropout(0.1)(x, deterministic=not training)
        return x


class Text(flax.linen.Module):
    out_units: int = 1024

    @flax.linen.compact
    def __call__(self, x, training=False):
        x = flax.linen.Dense(features=self.out_units)(x)

        res = flax.linen.silu(x)
        res = flax.linen.Dense(features=self.out_units)(res)
        res = flax.linen.Dropout(0.1)(res, deterministic=not training)

        x = x + res
        return x


class CLIP(flax.linen.Module):
    out_units: int = 1024

    def setup(self):
        self.image_enc = Image(self.out_units)
        self.text_enc = Text(self.out_units)

        self.logit_scale = self.variable(
            "params",
            "logit_scale",
            lambda x: jnp.log(10) * jnp.ones((1,)),
            None,
        ).value

    @flax.linen.compact
    def __call__(self, image, text, training=False):
        image_emb = self.image_enc(image, training=training)
        text_emb = self.text_enc(text, training=training)

        # Normalize
        image_emb = image_emb / jnp.linalg.norm(image_emb, axis=-1, keepdims=True)
        text_emb = text_emb / jnp.linalg.norm(text_emb, axis=-1, keepdims=True)

        image_sim = jnp.exp(self.logit_scale) * image_emb @ text_emb.T
        text_sim = jnp.exp(self.logit_scale) * text_emb @ image_emb.T
        return image_sim, text_sim

    def encode_text(self, text):
        text_emb = self.text_enc(text, training=False)
        return text_emb