timm
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91 lines
3.0 KiB
91 lines
3.0 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 logging
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import numpy
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from towhee.operator.base import NNOperator, OperatorFlag
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from towhee import register
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import torch
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from torch import nn
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from PIL import Image as PILImage
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import cv2
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from timm.data.transforms_factory import create_transform
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from timm.data import resolve_data_config
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from timm.models.factory import create_model
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import warnings
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warnings.filterwarnings('ignore')
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log = logging.getLogger()
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@register(output_schema=['vec'])
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class TimmImage(NNOperator):
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"""
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Pytorch image embedding operator that uses the Pytorch Image Model (timm) collection.
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Args:
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model_name (`str`):
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Which model to use for the embeddings.
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num_classes (`int = 1000`):
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Number of classes for classification.
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"""
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def __init__(self, model_name: str, num_classes: int = 1000) -> None:
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super().__init__()
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.model = create_model(model_name, pretrained=True, num_classes=num_classes)
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self.model.to(self.device)
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self.model.eval()
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config = resolve_data_config({}, model=self.model)
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self.tfms = create_transform(**config)
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def __call__(self, img: numpy.ndarray) -> numpy.ndarray:
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if hasattr(img, 'mode'):
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if img.mode not in ['RGB', 'BGR']:
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log.error(f'Invalid image mode: expect "RGB" or "BGR" but receive "{img.mode}".')
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raise AssertionError(f'Invalid image mode "{img.mode}".')
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elif img.mode == 'BGR':
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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log.warning('Converting image mode from "BGR" to "RGB" ...')
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else:
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log.warning(f'Image mode is not specified. Using "RGB" now.')
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img = PILImage.fromarray(img.astype('uint8'), 'RGB')
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img = self.tfms(img).unsqueeze(0)
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img = img.to(self.device)
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features = self.model.forward_features(img)
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if features.dim() == 4:
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global_pool = nn.AdaptiveAvgPool2d(1)
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features = global_pool(features)
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features = features.to('cpu')
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feature_vector = features.flatten().detach().numpy()
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return feature_vector
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# if __name__ == '__main__':
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# from towhee._types import Image
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#
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#
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# path = '/path/to/image'
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# img = cv2.imread(path)
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# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# img = Image(img)
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#
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# op = TimmImage('resnet50')
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# out = op(img)
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