towhee
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vit-image-embedding
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52 lines
1.8 KiB
52 lines
1.8 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 typing import NamedTuple
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from pathlib import Path
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from PIL import Image
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import torch
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import numpy
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from towhee.operator import Operator
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class VitImageEmbedding(Operator):
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"""
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Embedding extractor using ViT.
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Args:
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model_name (`string`):
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Model name.
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weights_path (`string`):
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Path to local weights.
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"""
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def __init__(self, model_name: str = 'vit_large_patch16_224',
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framework: str = 'pytorch', weights_path: str = None) -> None:
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super().__init__()
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sys.path.append(str(Path(__file__).parent))
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if framework == 'pytorch':
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import pytorch
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from pytorch.model import Model
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self.model = Model(model_name, weights_path)
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config = pytorch.resolve_data_config({}, model=self.model._model)
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self.tfms = pytorch.create_transform(**config)
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def __call__(self, img_path: str) -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
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Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
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img = self.tfms(Image.open(img_path)).unsqueeze(0)
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features = self.model(img)
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return Outputs(features.flatten().detach().numpy())
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