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109 lines
3.5 KiB
109 lines
3.5 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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import logging
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import os
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import numpy
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from typing import Union, List
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from pathlib import Path
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import towhee
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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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from towhee.models import isc
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import torch
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from torch import nn
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from torchvision import transforms
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from PIL import Image as PILImage
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import timm
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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 Isc(NNOperator):
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"""
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The operator uses pretrained ISC model to extract features for an image input.
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Args:
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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, timm_backbone: str = 'tf_efficientnetv2_m_in21ft1k',
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skip_preprocess: bool = False, checkpoint_path: str = None, device: str = None) -> None:
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super().__init__()
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if device is None:
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.device = device
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self.skip_tfms = skip_preprocess
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if checkpoint_path is None:
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checkpoint_path = os.path.join(str(Path(__file__).parent), 'checkpoints', timm_backbone + '.pth')
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backbone = timm.create_model(timm_backbone, features_only=True, pretrained=False)
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self.model = isc.create_model(pretrained=True, checkpoint_path=checkpoint_path, device=self.device,
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backbone=backbone, p=3.0, eval_p=1.0)
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self.model.eval()
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self.tfms = transforms.Compose([
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transforms.Resize((512, 512)),
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transforms.ToTensor(),
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transforms.Normalize(mean=backbone.default_cfg['mean'],
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std=backbone.default_cfg['std'])
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])
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def __call__(self, data: Union[List[towhee._types.Image], towhee._types.Image]):
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if isinstance(data, towhee._types.Image):
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imgs = [data]
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else:
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imgs = data
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img_list = []
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for img in imgs:
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img = self.convert_img(img)
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img = img if self.skip_tfms else self.tfms(img)
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img_list.append(img)
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inputs = torch.stack(img_list)
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inputs = inputs.to(self.device)
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features = self.model(inputs)
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features = features.to('cpu').flatten(1)
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if isinstance(data, list):
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vecs = list(features.detach().numpy())
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else:
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vecs = features.squeeze(0).detach().numpy()
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return vecs
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@arg(1, to_image_color('RGB'))
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def convert_img(self, img: towhee._types.Image):
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img = PILImage.fromarray(img.astype('uint8'), 'RGB')
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return img
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# if __name__ == '__main__':
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# from towhee import ops
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#
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# path = 'https://github.com/towhee-io/towhee/raw/main/towhee_logo.png'
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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 = Isc()
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# out = op(img)
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# assert out.shape == (256,)
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