Fixed the Model classes, Visualization

run_models.py

@@ -1,3 +1,5 @@
+from torch.distributions import Normal
+
 from networks.auto_encoder import *
 import os
 import time
@@ -18,90 +20,54 @@ from argparse import Namespace
 from argparse import ArgumentParser
 
 args = ArgumentParser()
-args.add_argument('step')
-args.add_argument('features')
-args.add_argument('size')
-args.add_argument('latent_dim')
+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('--model', default='Model')
 
 
 # ToDo: How to implement this better?
 # other_classes = [AutoEncoder, AutoEncoderLightningOverrides]
 class Model(AutoEncoderLightningOverrides, LightningModule):
 
-    def __init__(self, latent_dim=0, size=0, step=0, features=0, **kwargs):
-        assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
-        self.size = args.size
-        self.latent_dim = args.latent_dim
-        self.features = args.features
-        self.step = args.step
+    def __init__(self, parameters, **kwargs):
+        assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
+        self.size = parameters.size
+        self.latent_dim = parameters.latent_dim
+        self.features = parameters.features
+        self.step = parameters.step
         super(Model, self).__init__()
         self.network = AutoEncoder(self.latent_dim, self.features)
 
-    def configure_optimizers(self):
-        return [Adam(self.parameters(), lr=0.02)]
-
-    @data_loader
-    def tng_dataloader(self):
-        return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
-
 
 class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
 
-    @property
-    def name(self):
-        return self.network.name
-
-    def __init__(self, args: Namespace, **kwargs):
-        assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
-        self.size = args.size
-        self.latent_dim = args.latent_dim
-        self.features = args.features
-        self.step = args.step
+    def __init__(self, parameters: Namespace, **kwargs):
+        assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
+        self.size = parameters.size
+        self.latent_dim = parameters.latent_dim
+        self.features = parameters.features
+        self.step = parameters.step
         super(AdversarialModel, self).__init__()
         self.normal = Normal(0, 1)
         self.network = AdversarialAutoEncoder(self.latent_dim, self.features)
         pass
 
-    # This is Fucked up, why do i need to put an additional empty list here?
-    def configure_optimizers(self):
-        return [Adam(self.network.discriminator.parameters(), lr=0.02),
-                Adam([*self.network.encoder.parameters(), *self.network.decoder.parameters()], lr=0.02)],\
-               []
-
-    @data_loader
-    def tng_dataloader(self):
-        return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
-
 
 class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, LightningModule):
 
-    def __init__(self, args: Namespace, **kwargs):
-        assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
-        self.size = args.size
-        self.latent_dim = args.latent_dim
-        self.features = args.features
-        self.step = args.step
+    def __init__(self, parameters: Namespace, **kwargs):
+        assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
+        self.size = parameters.size
+        self.latent_dim = parameters.latent_dim
+        self.features = parameters.features
+        self.step = parameters.step
         super(SeparatingAdversarialModel, self).__init__()
         self.normal = Normal(0, 1)
         self.network = SeperatingAdversarialAutoEncoder(self.latent_dim, self.features, **kwargs)
         pass
 
-    # This is Fucked up, why do i need to put an additional empty list here?
-    def configure_optimizers(self):
-        return [Adam([*self.network.spatial_discriminator.parameters(), *self.network.spatial_encoder.parameters()]
-                     , lr=0.02),
-                Adam([*self.network.temporal_discriminator.parameters(), *self.network.temporal_encoder.parameters()]
-                     , lr=0.02),
-                Adam([*self.network.temporal_encoder.parameters(),
-                      *self.network.spatial_encoder.parameters(),
-                      *self.network.decoder.parameters()]
-                     , lr=0.02)], []
-
-    @data_loader
-    def tng_dataloader(self):
-        num_workers = os.cpu_count() // 2
-        return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100, num_workers=num_workers)
-
 
 if __name__ == '__main__':
     features = 6
@@ -110,7 +76,7 @@ if __name__ == '__main__':
     arguments = args.parse_args()
     arguments.__dict__.update(tag_dict)
 
-    model = SeparatingAdversarialModel(arguments)
+    model = globals()[arguments.model](arguments)
 
     # PyTorch summarywriter with a few bells and whistles
     outpath = os.path.join(os.getcwd(), 'output', model.name, time.asctime().replace(' ', '_').replace(':', '-'))
@@ -124,7 +90,7 @@ if __name__ == '__main__':
         filepath=os.path.join(outpath, 'weights.ckpt'),
         save_best_only=True,
         verbose=True,
-        monitor='tng_loss',  # val_loss
+        monitor='val_loss',  # val_loss
         mode='min',
     )