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61 lines
2.1 KiB
61 lines
2.1 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 numpy
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import towhee
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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 Clip(NNOperator):
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"""
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CLIP multi-modal embedding operator
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"""
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def __init__(self, name: str, modality: str):
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sys.path.append(str(Path(__file__).parent))
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#from clip_impl import load
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import clip_impl
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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, self.preprocess = clip_impl.load(name, self.device)
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self.tokenize = clip_impl.tokenize
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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
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def _inference_from_text(self, text):
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text = self.tokenize(text).to(self.device)
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text_features = self._model.encode_text(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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image = self.preprocess(to_pil(img)).unsqueeze(0).to(self.device)
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image_features = self._model.encode_image(image)
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return image_features
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