text_encoder/model.py

"""CLIP text encoder - same interface as FrozenCLIPEmbedder (forward(text) returns last_hidden_state)."""

import torch
import torch.nn as nn
from transformers import CLIPTokenizer, CLIPTextModel


class CLIPTextEncoder(nn.Module):
    """CLIP text encoder wrapping transformers CLIPTokenizer + CLIPTextModel.
    Same interface as FrozenCLIPEmbedder: forward(text) returns last_hidden_state.
    """

    def __init__(
        self,
        version: str = "openai/clip-vit-large-patch14",
        max_length: int = 77,
        freeze: bool = True,
    ):
        super().__init__()
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
        self.transformer = CLIPTextModel.from_pretrained(version)
        self.max_length = max_length
        if freeze:
            self.transformer.eval()
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, text):
        """Encode text. Returns last_hidden_state (B, seq_len, dim)."""
        if isinstance(text, str):
            text = [text]
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(next(self.parameters()).device)
        outputs = self.transformer(input_ids=tokens)
        return outputs.last_hidden_state