libs/clip.py

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


class AbstractEncoder(nn.Module):
    def __init__(self):
        super().__init__()

    def encode(self, *args, **kwargs):
        raise NotImplementedError


class FrozenCLIPEmbedder(AbstractEncoder):
    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
        super().__init__()
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
        self.transformer = CLIPTextModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
        tokens = batch_encoding["input_ids"].to(self.device)
        outputs = self.transformer(input_ids=tokens)

        z = outputs.last_hidden_state
        return z, {'token_embedding': outputs.last_hidden_state, 'pooler_output': outputs.pooler_output, 'token_mask': batch_encoding['attention_mask'].to(self.device), 'tokens': batch_encoding["input_ids"].to(self.device)}

    def encode_from_token(self, tokens):
        tokens = tokens.to(self.device)
        outputs = self.transformer(input_ids=tokens)

        z = outputs.last_hidden_state
        return z
    
    def encode(self, text):
        return self(text)


class FrozenCLIPTokenizer(AbstractEncoder):
    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
        super().__init__()
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
        self.max_length = max_length
        self.freeze()

    def freeze(self):
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
        tokens = batch_encoding["input_ids"]
        return tokens

    def encode(self, text):
        return self(text)