(self, forward_params)
| 30 | self._tokenizer, self._img_processor = processors |
| 31 | |
| 32 | def forward(self, forward_params): |
| 33 | rets = {} |
| 34 | if "text" in forward_params: |
| 35 | text = forward_params.get("text") |
| 36 | txt_encoding = self._tokenizer( |
| 37 | text, |
| 38 | padding="max_length", |
| 39 | truncation=True, |
| 40 | max_length=self.model.hparams.config["max_text_len"], |
| 41 | return_special_tokens_mask=True, |
| 42 | ) |
| 43 | txt_data = { |
| 44 | "text_ids": torch.tensor(txt_encoding["input_ids"]).to(self._device), |
| 45 | "text_masks": torch.tensor(txt_encoding["attention_mask"]).to( |
| 46 | self._device |
| 47 | ), |
| 48 | "text_labels": None, |
| 49 | } |
| 50 | txt_feats = self.model.infer_text(txt_data)["cls_vlffn_feats"] |
| 51 | rets.update({"text_embedding": txt_feats.detach()}) |
| 52 | if "img" in forward_params: |
| 53 | input_img = forward_params["img"] |
| 54 | img = self._img_processor(input_img).unsqueeze(0) |
| 55 | img_data = {"image": [img.to(self._device)]} |
| 56 | img_feats = self.model.infer_image(img_data)["cls_vlffn_feats"] |
| 57 | rets.update({"img_embedding": img_feats.detach()}) |
| 58 | |
| 59 | return rets |
| 60 | |
| 61 | |
| 62 | @PREPROCESSORS.register_module( |
no test coverage detected