All models running.

run_models.py

@@ -1,37 +1,33 @@
 from torch.distributions import Normal
-
 from networks.auto_encoder import *
-import os
+
 import time
 from networks.variational_auto_encoder import *
 from networks.adverserial_auto_encoder import *
 from networks.seperating_adversarial_auto_encoder import *
 from networks.modules import LightningModule
-from torch.optim import Adam
-from torch.utils.data import DataLoader
-from pytorch_lightning import data_loader
-from dataset import DataContainer
-
-from torch.nn import BatchNorm1d
 from pytorch_lightning import Trainer
 from test_tube import Experiment
 
 from argparse import Namespace
 from argparse import ArgumentParser
+from distutils.util import strtobool
 
 args = ArgumentParser()
-args.add_argument('--step', default=0)
-args.add_argument('--features', default=0)
-args.add_argument('--size', default=0)
-args.add_argument('--latent_dim', default=0)
+args.add_argument('--step', default=6)
+args.add_argument('--features', default=6)
+args.add_argument('--size', default=9)
+args.add_argument('--latent_dim', default=4)
 args.add_argument('--model', default='Model')
+args.add_argument('--refresh', type=strtobool, default=False)
+
 
 
 # ToDo: How to implement this better?
 # other_classes = [AutoEncoder, AutoEncoderLightningOverrides]
 class Model(AutoEncoderLightningOverrides, LightningModule):
 
-    def __init__(self, parameters, **kwargs):
+    def __init__(self, parameters):
         assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
         self.size = parameters.size
         self.latent_dim = parameters.latent_dim
@@ -43,7 +39,7 @@ class Model(AutoEncoderLightningOverrides, LightningModule):
 
 class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
 
-    def __init__(self, parameters: Namespace, **kwargs):
+    def __init__(self, parameters: Namespace):
         assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
         self.size = parameters.size
         self.latent_dim = parameters.latent_dim
@@ -57,7 +53,7 @@ class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
 
 class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, LightningModule):
 
-    def __init__(self, parameters: Namespace, **kwargs):
+    def __init__(self, parameters: Namespace):
         assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
         self.size = parameters.size
         self.latent_dim = parameters.latent_dim
@@ -65,16 +61,12 @@ class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, Ligh
         self.step = parameters.step
         super(SeparatingAdversarialModel, self).__init__()
         self.normal = Normal(0, 1)
-        self.network = SeperatingAdversarialAutoEncoder(self.latent_dim, self.features, **kwargs)
+        self.network = SeperatingAdversarialAutoEncoder(self.latent_dim, self.features)
         pass
 
 
 if __name__ == '__main__':
-    features = 6
-    tag_dict = dict(features=features, latent_dim=4, size=5, step=6, refresh=False,
-                     transforms=[BatchNorm1d(features)])
     arguments = args.parse_args()
-    arguments.__dict__.update(tag_dict)
 
     model = globals()[arguments.model](arguments)
 
@@ -82,19 +74,19 @@ if __name__ == '__main__':
     outpath = os.path.join(os.getcwd(), 'output', model.name, time.asctime().replace(' ', '_').replace(':', '-'))
     os.makedirs(outpath, exist_ok=True)
     exp = Experiment(save_dir=outpath)
-    exp.tag(tag_dict=tag_dict)
+    exp.tag(tag_dict=arguments.__dict__)
 
     from pytorch_lightning.callbacks import ModelCheckpoint
 
     checkpoint_callback = ModelCheckpoint(
         filepath=os.path.join(outpath, 'weights.ckpt'),
-        save_best_only=True,
+        save_best_only=False,
         verbose=True,
-        monitor='val_loss',  # val_loss
-        mode='min',
+        period=4
     )
 
-    trainer = Trainer(experiment=exp, checkpoint_callback=checkpoint_callback, max_nb_epochs=15)  # gpus=[0...LoL]
+    trainer = Trainer(experiment=exp, max_nb_epochs=250, gpus=[0],
+                      add_log_row_interval=1000, checkpoint_callback=checkpoint_callback)
     trainer.fit(model)
     trainer.save_checkpoint(os.path.join(outpath, 'weights.ckpt'))