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87 lines
2.8 KiB
87 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 logging
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import numpy
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from towhee.operator.base import NNOperator, OperatorFlag
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from towhee.types import Image as towheeImage
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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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import torch
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from torch import nn
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from PIL import Image as PILImage
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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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skip_preprocess (`bool = False`):
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Whether skip image transforms.
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"""
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def __init__(self, model_name: str, num_classes: int = 1000, skip_preprocess: bool = False) -> 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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self.skip_tfms = skip_preprocess
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@arg(1, to_image_color('RGB'))
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def __call__(self, img: towheeImage) -> numpy.ndarray:
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img = PILImage.fromarray(img.astype('uint8'), 'RGB')
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if not self.skip_tfms:
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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 import ops
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#
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# path = '/image/path/or/link'
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#
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# decoder = ops.image_decode.cv2()
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# img = decoder(path)
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#
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# op = TimmImage('resnet50')
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# out = op(img)
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# print(out)
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