142 lines
6.0 KiB
Python
142 lines
6.0 KiB
Python
# Imports
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# =============================================================================
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import os
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from distutils.util import strtobool
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from pathlib import Path
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from argparse import ArgumentParser, Namespace
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import warnings
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import torch
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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 torch.utils.data import DataLoader
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from ml_lib.modules.utils import LightningBaseModule
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from ml_lib.utils.config import Config
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from ml_lib.utils.logging import Logger
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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=UserWarning)
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_ROOT = Path(__file__).parent
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# Parameter Configuration
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# =============================================================================
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# Argument Parser
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main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
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# Main Parameters
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main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
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main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
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# Data Parameters
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main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
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main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
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main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
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main_arg_parser.add_argument("--data_normalized", 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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# Transformation Parameters
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main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
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# Training Parameters
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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_version", type=strtobool, required=False, help="")
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main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
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main_arg_parser.add_argument("--train_batch_size", type=int, default=200, help="")
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main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
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main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
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# Model Parameters
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main_arg_parser.add_argument("--model_type", type=str, default="BinaryClassifier", help="")
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main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
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main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
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main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
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main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="")
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main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=False, help="")
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main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
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# Project Parameters
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main_arg_parser.add_argument("--project_name", type=str, default=_ROOT.parent.name, help="")
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main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
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main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.getenv('NEPTUNE_KEY'), help="")
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# Parse it
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args: Namespace = main_arg_parser.parse_args()
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def run_lightning_loop(config_obj):
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# Logging
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# ================================================================================
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# Logger
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with Logger(config_obj) as logger:
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# Callbacks
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# =============================================================================
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# Checkpoint Saving
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checkpoint_callback = ModelCheckpoint(
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filepath=str(logger.log_dir / 'ckpt_weights'),
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verbose=True, save_top_k=0,
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)
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# =============================================================================
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# Early Stopping
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# TODO: For This to work, set a validation step and End Eval and Score
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early_stopping_callback = EarlyStopping(
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monitor='val_loss',
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min_delta=0.0,
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patience=0,
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)
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# Dataset and Dataloaders
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# =============================================================================
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# Train Dataset
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from datasets.binar_masks import BinaryMasks
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dataset = BinaryMasks(config_obj.data.root, setting='train')
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# Train Dataloader
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dataloader = DataLoader(dataset)
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# Model
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# =============================================================================
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# Build and Init its Weights
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model: LightningBaseModule = config_obj.build_and_init_model(weight_init_function=torch.nn.init.xavier_normal_)
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# Trainer
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# =============================================================================
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trainer = Trainer(max_epochs=config_obj.train.epochs,
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show_progress_bar=True,
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weights_save_path=logger.log_dir,
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gpus=[0] if torch.cuda.is_available() else None,
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check_val_every_n_epoch=10,
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# num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
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# row_log_interval=(model.n_train_batches * 0.1), # TODO: Better Value / Setting
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# log_save_interval=(model.n_train_batches * 0.2), # TODO: Better Value / Setting
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checkpoint_callback=checkpoint_callback,
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logger=logger,
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fast_dev_run=config_obj.main.debug,
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early_stop_callback=None
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)
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# Train It
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trainer.fit(model)
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# Save the last state & all parameters
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trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
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model.save_to_disk(logger.log_dir)
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# Evaluate It
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if config_obj.main.eval:
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trainer.test()
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return model
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if __name__ == "__main__":
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config = Config.read_namespace(args)
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trained_model = run_lightning_loop(config)
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