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53 lines
1.5 KiB
53 lines
1.5 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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@register(output_schema=['vec'])
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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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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, modality: str):
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self._modality = modality
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def __call__(self, data):
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if self._modality == 'image'
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emb = self._inference_from_image(data)
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elif self._modality == 'text'
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emb = 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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def _inference_from_text(self, text):
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return text
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@arg(1, to_image_color('RGB'))
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def _inference_from_image(self, img):
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return img
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