japanese-clip
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80 lines
2.8 KiB
80 lines
2.8 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 sys
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from pathlib import Path
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import torch
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from towhee import register
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from towhee.operator.base import NNOperator, OperatorFlag
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from towhee.types.arg import arg, to_image_color
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from towhee.types.image_utils import from_pil, to_pil
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@register(output_schema=['vec'])
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class Jaclip(NNOperator):
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"""
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Japanese CLIP multi-modal embedding operator
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"""
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def __init__(self, model_name: str, modality: str):
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super().__init__()
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path = str(Path(__file__).parent)
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sys.path.append(path)
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import japanese_clip as ja_clip
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sys.path.pop()
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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model, preprocess = ja_clip.load("rinna/japanese-clip-vit-b-16", cache_dir="{}/weights/japanese_clip".format(path), device=self.device)
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self.model = model
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self.tfms = preprocess
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self.tokenizer = ja_clip.load_tokenizer()
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self.ja_clip = ja_clip
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def __call__(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 _inference_from_text(self, text):
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encodings = ja_clip.tokenize(
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texts=[text],
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max_seq_len=77,
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device=self.device,
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tokenizer=self.tokenizer, # this is optional. if you don't pass, load tokenizer each time
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)
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text_feature = model.get_text_features(**encodings)
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return text_feature
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@arg(1, to_image_color('RGB'))
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def _inference_from_image(self, img):
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img = self._preprocess(img)
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caption = ''
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image_feature = self.model.get_image_features(image)
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return image_feature
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def _preprocess(self, img):
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img = to_pil(img)
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processed_img = self.tfms(img).unsqueeze(0).to(self.device)
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return processed_img
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def _configs(self):
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config = {}
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config['blip_base'] = {}
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config['blip_base']['weights'] = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth'
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return config
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