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| import torch | |
| import numpy as np | |
| from pathlib import Path | |
| from boxmot.utils import logger as LOGGER | |
| from boxmot.appearance.backends.base_backend import BaseModelBackend | |
| class TorchscriptBackend(BaseModelBackend): | |
| def __init__(self, weights, device, half): | |
| super().__init__(weights, device, half) | |
| self.nhwc = False | |
| self.half = half | |
| def load_model(self, w): | |
| LOGGER.info(f"Loading {w} for TorchScript inference...") | |
| self.model = torch.jit.load(w) | |
| self.model.half() if self.half else self.model.float() | |
| def forward(self, im_batch): | |
| features = self.model(im_batch) | |
| return features | |