latest params and relative paths
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@ -27,8 +27,8 @@ main_arg_parser.add_argument("--data_additional_resource_root", type=str, defaul
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main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
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# Transformations
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# Transformations
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main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
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# main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
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main_arg_parser.add_argument("--transformations_normalize", type=strtobool, default=False, help="")
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# main_arg_parser.add_argument("--transformations_normalize", type=strtobool, default=False, help="")
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# Transformations
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# Transformations
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main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
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main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
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10
main.py
10
main.py
@ -7,10 +7,10 @@ import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
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from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
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from modules.utils import LightningBaseModule
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from ml_lib.modules.util import LightningBaseModule
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from utils.config import Config
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from ml_lib.utils.config import Config
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from utils.logging import Logger
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from ml_lib.utils.logging import Logger
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from utils.model_io import SavedLightningModels
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from ml_lib.utils.model_io import SavedLightningModels
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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@ -76,6 +76,6 @@ def run_lightning_loop(config_obj):
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if __name__ == "__main__":
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if __name__ == "__main__":
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from _templates.new_project._parameters import args
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from ._parameters import args
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config = Config.read_namespace(args)
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config = Config.read_namespace(args)
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trained_model = run_lightning_loop(config)
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trained_model = run_lightning_loop(config)
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