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66 lines
2.6 KiB
66 lines
2.6 KiB
2 years ago
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# Copyright 2021 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from transformers import BertForSequenceClassification, BertConfig, BertTokenizer
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from transformers import CLIPProcessor, CLIPModel
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@register(output_schema=['vec'])
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class Taiyi(NNOperator):
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"""
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Taiyi multi-modal embedding operator
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"""
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def __init__(self, model_name: str, modality: str):
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self.modality = modality
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.text_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese")
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self.text_encoder = BertForSequenceClassification.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese").eval()
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self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
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self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
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def inference_single_data(self, data):
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if self.modality == 'image':
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vec = self._inference_from_image(data)
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elif self.modality == 'text':
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vec = self._inference_from_text(data)
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else:
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raise ValueError("modality[{}] not implemented.".format(self._modality))
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return vec.detach().cpu().numpy().flatten()
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def __call__(self, data):
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if not isinstance(data, list):
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data = [data]
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else:
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data = data
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results = []
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for single_data in data:
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result = self.inference_single_data(single_data)
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results.append(result)
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if len(data) == 1:
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return results[0]
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else:
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return results
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def _inference_from_text(self, text):
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self.text = self.text_tokenizer(text, return_tensors='pt', padding=True)['input_ids'].to(self.device)
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text_features = text_encoder(text).logits
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return text_features
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@arg(1, to_image_color('RGB'))
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def _inference_from_image(self, img):
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image = to_pil(image)
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image = self.processor(images=image.raw), return_tensors="pt")
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image_features = clip_model.get_image_features(**image)
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return image_features
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