Method extract_mean_emb

semantic_sam/language/encoder.py:86–93

(txts)

        else:
            with torch.no_grad():
                def extract_mean_emb(txts):
                    tokens = self.tokenizer(
                        txts, padding='max_length', truncation=True, max_length=self.max_token_num, return_tensors='pt'
                    )
                    clss_embedding = self.forward_language((tokens['input_ids'].cuda(), tokens['attention_mask'].cuda()), norm=norm)
                    clss_embedding = clss_embedding.mean(dim=0)
                    clss_embedding /= clss_embedding.norm()
                    return clss_embedding

                templates = get_prompt_templates()
                clss_embeddings = []