117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
from torch.distributions import Normal
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from torch.cuda import is_available
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import time
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import os
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from argparse import Namespace
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from argparse import ArgumentParser
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from distutils.util import strtobool
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from networks.auto_encoder import AutoEncoder, AutoEncoder_LO
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from networks.variational_auto_encoder import VariationalAE, VAE_LO
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from networks.adverserial_auto_encoder import AdversarialAE_LO, AdversarialAE
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from networks.seperating_adversarial_auto_encoder import SeperatingAAE, SeparatingAAE_LO
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from networks.modules import LightningModule
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from pytorch_lightning import Trainer
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from test_tube import Experiment
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args = ArgumentParser()
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args.add_argument('--step', default=5)
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args.add_argument('--features', default=6)
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args.add_argument('--size', default=9)
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args.add_argument('--latent_dim', default=2)
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args.add_argument('--model', default='AE_Model')
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args.add_argument('--refresh', type=strtobool, default=False)
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args.add_argument('--future_predictions', type=strtobool, default=False)
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args.add_argument('--use_norm', type=strtobool, default=True)
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class AE_Model(AutoEncoder_LO, LightningModule):
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def __init__(self, parameters):
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assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
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self.size = parameters.size
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self.latent_dim = parameters.latent_dim
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self.features = parameters.features
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self.step = parameters.step
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super(AE_Model, self).__init__(train_on_predictions=parameters.future_predictions)
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self.network = AutoEncoder(self.latent_dim, self.features, use_norm=parameters.use_norm)
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class VAE_Model(VAE_LO, LightningModule):
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def __init__(self, parameters):
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assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
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self.size = parameters.size
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self.latent_dim = parameters.latent_dim
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self.features = parameters.features
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self.step = parameters.step
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super(VAE_Model, self).__init__(train_on_predictions=parameters.future_predictions)
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self.network = VariationalAE(self.latent_dim, self.features, use_norm=parameters.use_norm)
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class AAE_Model(AdversarialAE_LO, LightningModule):
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def __init__(self, parameters: Namespace):
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assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
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self.size = parameters.size
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self.latent_dim = parameters.latent_dim
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self.features = parameters.features
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self.step = parameters.step
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super(AAE_Model, self).__init__(train_on_predictions=parameters.future_predictions)
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self.normal = Normal(0, 1)
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self.network = AdversarialAE(self.latent_dim, self.features, use_norm=parameters.use_norm)
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pass
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class SAAE_Model(SeparatingAAE_LO, LightningModule):
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def __init__(self, parameters: Namespace):
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assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
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self.size = parameters.size
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self.latent_dim = parameters.latent_dim
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self.features = parameters.features
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self.step = parameters.step
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super(SAAE_Model, self).__init__(train_on_predictions=parameters.future_predictions)
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self.normal = Normal(0, 1)
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self.network = SeperatingAAE(self.latent_dim, self.features, use_norm=parameters.use_norm)
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pass
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if __name__ == '__main__':
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arguments = args.parse_args()
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model = globals()[arguments.model](arguments)
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# PyTorch summarywriter with a few bells and whistles
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outpath = os.path.join(os.getcwd(), 'output', model.name, time.asctime().replace(' ', '_').replace(':', '-'))
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os.makedirs(outpath, exist_ok=True)
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exp = Experiment(save_dir=outpath)
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exp.tag(tag_dict=arguments.__dict__)
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from pytorch_lightning.callbacks import ModelCheckpoint
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checkpoint_callback = ModelCheckpoint(
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filepath=os.path.join(outpath, 'weights'),
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save_best_only=False,
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verbose=True,
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period=4
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)
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trainer = Trainer(experiment=exp,
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max_nb_epochs=60,
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gpus=[0] if is_available() else None,
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row_log_interval=1000,
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checkpoint_callback=checkpoint_callback
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)
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trainer.fit(model)
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trainer.save_checkpoint(os.path.join(outpath, 'weights.ckpt'))
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# view tensorflow logs
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print(f'View tensorboard logs by running\ntensorboard --logdir {outpath}')
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print('and going to http://localhost:6006 on your browser')
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