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46 lines
1.6 KiB
46 lines
1.6 KiB
3 years ago
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import numpy
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import torch
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from typing import NamedTuple
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from towhee.operator.base import NNOperator
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from towhee.utils.pil_utils import to_pil
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from towhee.types.image import Image as towheeImage
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from torch import nn
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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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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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"""
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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, image: 'towheeImage') -> NamedTuple('Outputs', [('vec', numpy.ndarray)]):
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img = self.tfms(to_pil(image)).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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Outputs = NamedTuple('Outputs', [('vec', numpy.ndarray)])
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return Outputs(feature_vector)
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