epos-ai/platform-demo-scripts
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extended pipeline arguments

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This commit is contained in:
Krystyna Milian 2024-02-26 23:43:31 +01:00
parent bb2e136d42
commit 318a344c15
1 changed files with 19 additions and 15 deletions
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scripts/pipeline.py
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@ -79,26 +79,30 @@ def main():
parser.add_argument("--basic_phase_ae_config", type=str, required=False) parser.add_argument("--basic_phase_ae_config", type=str, required=False)
parser.add_argument("--eqtransformer_config", type=str, required=False) parser.add_argument("--eqtransformer_config", type=str, required=False)
parser.add_argument("--dataset", type=str, required=False) parser.add_argument("--dataset", type=str, required=False)
available_models = ["GPD", "PhaseNet", "BasicPhaseAE", "EQTransformer"]
parser.add_argument("--models", nargs='*', required=False, choices=available_models, default=available_models,
help="Models to train and evaluate (default: all)")
parser.add_argument("--collect_results", action="store_true", help="Collect and log results without training")
args = parser.parse_args() args = parser.parse_args()
if args.dataset is not None: if not args.collect_results:
util.set_dataset(args.dataset)
importlib.reload(config_loader)
logger.info(f"Started pipeline for the {config_loader.dataset_name} dataset.") if args.dataset is not None:
util.set_dataset(args.dataset)
importlib.reload(config_loader)
# generate labels logger.info(f"Started pipeline for the {config_loader.dataset_name} dataset.")
logger.info("Started generating labels for the dataset.")
generate_eval_targets.main(config_loader.data_path, config_loader.targets_path, "2,3", config_loader.sampling_rate,
None)
# find the best hyperparams for the models # generate labels
logger.info("Started training the models.") logger.info("Started generating labels for the dataset.")
for model_name in ["GPD", "PhaseNet", "BasicPhaseAE", "EQTransformer"]: generate_eval_targets.main(config_loader.data_path, config_loader.targets_path, "2,3", config_loader.sampling_rate,
if config_loader.dataset_name == "lumineos" and model_name == "EQTransformer": None)
break
sweep_id = find_the_best_params(model_name, args) # find the best hyperparams for the models
generate_predictions(sweep_id, model_name) logger.info("Started training the models.")
for model_name in args.models:
sweep_id = find_the_best_params(model_name, args)
generate_predictions(sweep_id, model_name)
# collect results # collect results
logger.info("Collecting results.") logger.info("Collecting results.")