INNER CODE UNIT · Python

__len__

openfoodfacts/openfoodfacts-ai · logo-ann/benchmarks/embedding_models_benchmark/main.py:71

    def __len__(self):
        return len(self.image_paths)


def generate_embeddings(
    model: torch.nn.Module, data_loader: DataLoader, device: torch.device
) -> Tuple[torch.Tensor, torch.Tensor, float]:
    model.eval()
    embedding_all = []
    labels_all = []
    elapsed = 0.0

    with torch.inference_mode():
        for inputs, labels in tqdm.tqdm(data_loader):
            start_time = time.monotonic()
            output = model(inputs.to(device))
            elapsed += time.monotonic() - start_time
            embedding_all.append(output)

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