def get_debiased_prompt()

in threestudio/models/prompt_processors/base.py [0:0]

• 50 lines of code
• 12 McCabe index (conditional complexity)

    def get_debiased_prompt(self, prompt: str) -> List[str]:
        os.environ["TOKENIZERS_PARALLELISM"] = "false"

        tokenizer = AutoTokenizer.from_pretrained(
            self.cfg.pretrained_model_name_or_path_prompt_debiasing
        )
        model = BertForMaskedLM.from_pretrained(
            self.cfg.pretrained_model_name_or_path_prompt_debiasing
        )

        views = [d.name for d in self.directions]
        view_ids = tokenizer(" ".join(views), return_tensors="pt").input_ids[0]
        view_ids = view_ids[1:5]

        def modulate(prompt):
            prompt_vd = f"This image is depicting a [MASK] view of {prompt}"
            tokens = tokenizer(
                prompt_vd,
                padding="max_length",
                truncation=True,
                add_special_tokens=True,
                return_tensors="pt",
            )
            mask_idx = torch.where(tokens.input_ids == tokenizer.mask_token_id)[1]

            logits = model(**tokens).logits
            logits = F.softmax(logits[0, mask_idx], dim=-1)
            logits = logits[0, view_ids]
            probes = logits / logits.sum()
            return probes

        prompts = [prompt.split(" ") for _ in range(4)]
        full_probe = modulate(prompt)
        n_words = len(prompt.split(" "))
        prompt_debiasing_mask_ids = (
            self.cfg.prompt_debiasing_mask_ids
            if self.cfg.prompt_debiasing_mask_ids is not None
            else list(range(n_words))
        )
        words_to_debias = [prompt.split(" ")[idx] for idx in prompt_debiasing_mask_ids]
        threestudio.info(f"Words that can potentially be removed: {words_to_debias}")
        for idx in prompt_debiasing_mask_ids:
            words = prompt.split(" ")
            prompt_ = " ".join(words[:idx] + words[(idx + 1) :])
            part_probe = modulate(prompt_)

            pmi = full_probe / torch.lerp(part_probe, full_probe, 0.5)
            for i in range(pmi.shape[0]):
                if pmi[i].item() < 0.95:
                    prompts[i][idx] = ""

        debiased_prompts = [" ".join([word for word in p if word]) for p in prompts]
        for d, debiased_prompt in zip(views, debiased_prompts):
            threestudio.info(f"Debiased prompt of the {d} view is [{debiased_prompt}]")

        del tokenizer, model
        cleanup()

        return debiased_prompts