ru-clip
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77 lines
2.8 KiB
77 lines
2.8 KiB
# 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 torchvision import transforms
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from towhee.types.image_utils import to_pil
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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 import register
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@register(output_schema=['vec'])
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class RuClip(NNOperator):
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"""
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Russian 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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self.modality = modality
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model = clip.create_model(model_name=model_name, pretrained=True, jit=True)
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self.tokenize = clip.tokenize
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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path = str(Path(__file__).parent)
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sys.path.append(path)
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import ruclip
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sys.path.pop()
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clip, processor = ruclip.load('ruclip-vit-base-patch32-384', device=self.device)
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templates = ['{}', 'это {}', 'на картинке {}', 'это {}, домашнее животное']
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self.predictor = ruclip.Predictor(clip, processor, device, bs=1, templates=templates)
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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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text_features = self.predictor.get_text_latents([text])
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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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img = to_pil(img)
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image_features = self.predictor.get_image_latents([img])
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return image_features
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