Done: AE, VAE, AAE

ToDo: Double AAE, Visualization
All Modularized

run_models.py

@@ -0,0 +1,60 @@
+from networks.auto_encoder import *
+from networks.variational_auto_encoder import *
+from networks.adverserial_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
+
+
+# ToDo: How to implement this better?
+# other_classes = [AutoEncoder, AutoEncoderLightningOverrides]
+class Model(VariationalAutoEncoderLightningOverrides, LightningModule):
+
+    def __init__(self, dataParams: dict):
+        super(Model, self).__init__()
+        self.dataParams = dataParams
+        self.network = VariationalAutoEncoder(self.dataParams)
+
+    def configure_optimizers(self):
+        return [Adam(self.parameters(), lr=0.02)]
+
+    @data_loader
+    def tng_dataloader(self):
+        return DataLoader(DataContainer('data', **self.dataParams), shuffle=True, batch_size=100)
+
+
+class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
+
+    def __init__(self, dataParams: dict):
+        super(AdversarialModel, self).__init__()
+        self.dataParams = dataParams
+        self.normal = Normal(0, 1)
+        self.network = AdversarialAutoEncoder(self.dataParams)
+        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.dataParams), shuffle=True, batch_size=100)
+
+
+if __name__ == '__main__':
+    features = 6
+    ae = AdversarialModel(
+        dataParams=dict(refresh=False, size=5, step=5,
+                        features=features, transforms=[BatchNorm1d(features)]
+                        )
+    )
+
+    trainer = Trainer()
+    trainer.fit(ae)