extended pipeline arguments
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bb2e136d42
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@ -79,8 +79,14 @@ def main():
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parser.add_argument("--basic_phase_ae_config", type=str, required=False)
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parser.add_argument("--basic_phase_ae_config", type=str, required=False)
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parser.add_argument("--eqtransformer_config", type=str, required=False)
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parser.add_argument("--eqtransformer_config", type=str, required=False)
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parser.add_argument("--dataset", type=str, required=False)
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parser.add_argument("--dataset", type=str, required=False)
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available_models = ["GPD", "PhaseNet", "BasicPhaseAE", "EQTransformer"]
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parser.add_argument("--models", nargs='*', required=False, choices=available_models, default=available_models,
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help="Models to train and evaluate (default: all)")
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parser.add_argument("--collect_results", action="store_true", help="Collect and log results without training")
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args = parser.parse_args()
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args = parser.parse_args()
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if not args.collect_results:
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if args.dataset is not None:
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if args.dataset is not None:
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util.set_dataset(args.dataset)
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util.set_dataset(args.dataset)
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importlib.reload(config_loader)
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importlib.reload(config_loader)
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@ -94,9 +100,7 @@ def main():
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# find the best hyperparams for the models
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# find the best hyperparams for the models
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logger.info("Started training the models.")
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logger.info("Started training the models.")
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for model_name in ["GPD", "PhaseNet", "BasicPhaseAE", "EQTransformer"]:
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for model_name in args.models:
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if config_loader.dataset_name == "lumineos" and model_name == "EQTransformer":
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break
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sweep_id = find_the_best_params(model_name, args)
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sweep_id = find_the_best_params(model_name, args)
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generate_predictions(sweep_id, model_name)
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generate_predictions(sweep_id, model_name)
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