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import numpy
import torch
from typing import NamedTuple
from towhee.operator.base import NNOperator
from towhee.utils.pil_utils import to_pil
from towhee.types.image import Image as towheeImage
from torch import nn
from timm.data.transforms_factory import create_transform
from timm.data import resolve_data_config
from timm.models.factory import create_model
import warnings
warnings.filterwarnings('ignore')
class TimmImage(NNOperator):
"""
Pytorch image embedding operator that uses the Pytorch Image Model (timm) collection.
Args:
model_name (`str`):
Which model to use for the embeddings.
"""
def __init__(self, model_name: str, num_classes: int = 1000) -> None:
super().__init__()
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = create_model(model_name, pretrained=True, num_classes=num_classes)
self.model.to(self.device)
self.model.eval()
config = resolve_data_config({}, model=self.model)
self.tfms = create_transform(**config)
def __call__(self, image: 'towheeImage') -> NamedTuple('Outputs', [('vec', numpy.ndarray)]):
img = self.tfms(to_pil(image)).unsqueeze(0)
img = img.to(self.device)
features = self.model.forward_features(img)
if features.dim() == 4:
global_pool = nn.AdaptiveAvgPool2d(1)
features = global_pool(features)
features = features.to('cpu')
feature_vector = features.flatten().detach().numpy()
Outputs = NamedTuple('Outputs', [('vec', numpy.ndarray)])
return Outputs(feature_vector)