1a0400d736
ToDo: Visualization for variational spaces Trajectory Coloring Post Processing Metric Slurm Skript
138 lines
5.1 KiB
Python
138 lines
5.1 KiB
Python
from networks.auto_encoder import *
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import os
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import time
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from networks.variational_auto_encoder import *
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from networks.adverserial_auto_encoder import *
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from networks.seperating_adversarial_auto_encoder import *
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from networks.modules import LightningModule
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from torch.optim import Adam
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from torch.utils.data import DataLoader
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from pytorch_lightning import data_loader
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from dataset import DataContainer
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from torch.nn import BatchNorm1d
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from pytorch_lightning import Trainer
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from test_tube import Experiment
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from argparse import Namespace
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from argparse import ArgumentParser
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args = ArgumentParser()
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args.add_argument('step')
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args.add_argument('features')
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args.add_argument('size')
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args.add_argument('latent_dim')
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# ToDo: How to implement this better?
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# other_classes = [AutoEncoder, AutoEncoderLightningOverrides]
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class Model(AutoEncoderLightningOverrides, LightningModule):
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def __init__(self, latent_dim=0, size=0, step=0, features=0, **kwargs):
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assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
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self.size = args.size
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self.latent_dim = args.latent_dim
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self.features = args.features
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self.step = args.step
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super(Model, self).__init__()
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self.network = AutoEncoder(self.latent_dim, self.features)
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def configure_optimizers(self):
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return [Adam(self.parameters(), lr=0.02)]
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@data_loader
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def tng_dataloader(self):
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return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
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class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
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@property
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def name(self):
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return self.network.name
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def __init__(self, args: Namespace, **kwargs):
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assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
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self.size = args.size
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self.latent_dim = args.latent_dim
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self.features = args.features
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self.step = args.step
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super(AdversarialModel, self).__init__()
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self.normal = Normal(0, 1)
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self.network = AdversarialAutoEncoder(self.latent_dim, self.features)
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pass
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# This is Fucked up, why do i need to put an additional empty list here?
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def configure_optimizers(self):
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return [Adam(self.network.discriminator.parameters(), lr=0.02),
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Adam([*self.network.encoder.parameters(), *self.network.decoder.parameters()], lr=0.02)],\
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[]
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@data_loader
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def tng_dataloader(self):
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return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
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class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, LightningModule):
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def __init__(self, args: Namespace, **kwargs):
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assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
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self.size = args.size
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self.latent_dim = args.latent_dim
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self.features = args.features
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self.step = args.step
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super(SeparatingAdversarialModel, self).__init__()
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self.normal = Normal(0, 1)
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self.network = SeperatingAdversarialAutoEncoder(self.latent_dim, self.features, **kwargs)
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pass
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# This is Fucked up, why do i need to put an additional empty list here?
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def configure_optimizers(self):
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return [Adam([*self.network.spatial_discriminator.parameters(), *self.network.spatial_encoder.parameters()]
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, lr=0.02),
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Adam([*self.network.temporal_discriminator.parameters(), *self.network.temporal_encoder.parameters()]
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, lr=0.02),
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Adam([*self.network.temporal_encoder.parameters(),
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*self.network.spatial_encoder.parameters(),
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*self.network.decoder.parameters()]
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, lr=0.02)], []
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@data_loader
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def tng_dataloader(self):
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num_workers = os.cpu_count() // 2
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return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100, num_workers=num_workers)
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if __name__ == '__main__':
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features = 6
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tag_dict = dict(features=features, latent_dim=4, size=5, step=6, refresh=False,
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transforms=[BatchNorm1d(features)])
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arguments = args.parse_args()
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arguments.__dict__.update(tag_dict)
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model = SeparatingAdversarialModel(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=tag_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.ckpt'),
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save_best_only=True,
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verbose=True,
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monitor='tng_loss', # val_loss
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mode='min',
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)
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trainer = Trainer(experiment=exp, checkpoint_callback=checkpoint_callback, max_nb_epochs=15) # gpus=[0...LoL]
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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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