inference/sample.py [35:66]:
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class Tester:
    def __init__(self, config):
        self.config = vars(config) if not isinstance(config, dict) else config

    def __call__(self) -> float:
        # Seed RNG
        biggan_utils.seed_rng(self.config["seed"])

        import torch

        # Setup cudnn.benchmark for free speed
        torch.backends.cudnn.benchmark = True

        self.config = biggan_utils.update_config_roots(
            self.config, change_weight_folder=False
        )
        # Prepare root folders if necessary
        biggan_utils.prepare_root(self.config)

        # Load model
        self.G, self.config = inference_utils.load_model_inference(self.config)
        biggan_utils.count_parameters(self.G)

        # Get sampling function and reference statistics for FID
        print("Eval reference set is ", self.config["eval_reference_set"])
        sample, im_reference_filename = inference_utils.get_sampling_funct(
            self.config,
            self.G,
            instance_set=self.config["eval_instance_set"],
            reference_set=self.config["eval_reference_set"],
            which_dataset=self.config["which_dataset"],
        )
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inference/test.py [35:66]:
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class Tester:
    def __init__(self, config):
        self.config = vars(config) if not isinstance(config, dict) else config

    def __call__(self) -> float:
        # Seed RNG
        biggan_utils.seed_rng(self.config["seed"])

        import torch

        # Setup cudnn.benchmark for free speed
        torch.backends.cudnn.benchmark = True

        self.config = biggan_utils.update_config_roots(
            self.config, change_weight_folder=False
        )
        # Prepare root folders if necessary
        biggan_utils.prepare_root(self.config)

        # Load model
        self.G, self.config = inference_utils.load_model_inference(self.config)
        biggan_utils.count_parameters(self.G)

        # Get sampling function and reference statistics for FID
        print("Eval reference set is ", self.config["eval_reference_set"])
        sample, im_reference_filename = inference_utils.get_sampling_funct(
            self.config,
            self.G,
            instance_set=self.config["eval_instance_set"],
            reference_set=self.config["eval_reference_set"],
            which_dataset=self.config["which_dataset"],
        )
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