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import os
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import torch
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from lib import utility
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def test(args):
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config = utility.get_config(args)
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config.device_id = args.device_ids[0]
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utility.set_environment(config)
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config.init_instance()
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if config.logger is not None:
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config.logger.info("Loaded configure file %s: %s" % (args.config_name, config.id))
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config.logger.info("\n" + "\n".join(["%s: %s" % item for item in config.__dict__.items()]))
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net = utility.get_net(config)
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model_path = os.path.join(config.model_dir,
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"train.pkl") if args.pretrained_weight is None else args.pretrained_weight
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if args.device_ids == [-1]:
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checkpoint = torch.load(model_path, map_location="cpu")
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else:
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checkpoint = torch.load(model_path)
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net.load_state_dict(checkpoint["net"])
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if config.logger is not None:
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config.logger.info("Loaded network")
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test_loader = utility.get_dataloader(config, "test")
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if config.logger is not None:
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config.logger.info("Loaded data from {:}".format(config.test_tsv_file))
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result, metrics = utility.forward(config, test_loader, net)
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if config.logger is not None:
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config.logger.info("Finished inference")
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for k, metric in enumerate(metrics):
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if config.logger is not None and len(metric) != 0:
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config.logger.info(
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"Tested {} dataset, the Size is {}, Metric: [NME {:.6f}, FR {:.6f}, AUC {:.6f}]".format(
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config.type, len(test_loader.dataset), metric[0], metric[1], metric[2]))
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