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training.py
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training.py
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from utils.dataset import Dataset
from utils.trainer import Trainer
from utils.parser import training_parse
if __name__ == '__main__':
# Get params
args = training_parse()
# Normalization
normalize_params = {"mean": args['mean'], "std": args['std']}
# Create dataset
train_dataset = Dataset(args['train_dir'],
data_location=args['data_location'],
data_key=args['data_key'],
chunk_len=args['chunk_len'],
chunk_only_one=args['chunk_only_one'],
chunk_rate=args['chunk_rate'],
chunk_random_crop=args['chunk_random_crop'],
data_sampling_frequency=args['data_sampling_frequency'],
chunk_linear_subsample=args['chunk_linear_subsample'],
chunk_butterworth_lowpass=args['chunk_butterworth_lowpass'],
chunk_butterworth_highpass=args['chunk_butterworth_highpass'],
chunk_butterworth_order=args['chunk_butterworth_order'],
normalize_params=normalize_params,
channels_list=args['channels_list'],
channels_name=args['channels_name'],
provider=args['data_provider'],
labels=args['training_labels'])
val_dataset = Dataset(args['val_dir'],
data_location=args['data_location'],
data_key=args['data_key'],
chunk_len=args['chunk_len'],
chunk_only_one=args['chunk_only_one'],
chunk_rate=args['chunk_rate'],
chunk_random_crop=args['chunk_random_crop'],
data_sampling_frequency=args['data_sampling_frequency'],
chunk_linear_subsample=args['chunk_linear_subsample'],
chunk_butterworth_lowpass=args['chunk_butterworth_lowpass'],
chunk_butterworth_highpass=args['chunk_butterworth_highpass'],
chunk_butterworth_order=args['chunk_butterworth_order'],
normalize_params=normalize_params,
channels_list=args['channels_list'],
channels_name=args['channels_name'],
provider=args['data_provider'],
labels=args['validation_labels'])
# Save number of channels
args['data_channels'] = len(train_dataset.channels_list)
args['channels_list'] = train_dataset.channels_list
# Setup dataset dictionary
args['datasets'] = {'trainingSet': train_dataset,
'validationSet': val_dataset}
# Define trainer
trainer = Trainer(args)
# Run training
model, metrics = trainer.train()