1a0400d736
ToDo: Visualization for variational spaces Trajectory Coloring Post Processing Metric Slurm Skript
97 lines
4.0 KiB
Python
97 lines
4.0 KiB
Python
from networks.auto_encoder import AutoEncoder
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from torch.nn.functional import mse_loss
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from networks.modules import *
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import torch
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class SeperatingAdversarialAutoEncoder(Module):
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def __init__(self, latent_dim, features, **kwargs):
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assert latent_dim % 2 == 0, f'Your latent space needs to be even, not odd, but was: "{latent_dim}"'
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super(SeperatingAdversarialAutoEncoder, self).__init__()
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self.latent_dim = latent_dim
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self.features = features
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self.spatial_encoder = PoolingEncoder(self.latent_dim // 2)
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self.temporal_encoder = Encoder(self.latent_dim // 2)
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self.decoder = Decoder(self.latent_dim, self.features)
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self.spatial_discriminator = Discriminator(self.latent_dim // 2, self.features)
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self.temporal_discriminator = Discriminator(self.latent_dim // 2, self.features)
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def forward(self, batch):
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# Encoder
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# outputs, hidden (Batch, Timesteps aka. Size, Features / Latent Dim Size)
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z_spatial, z_temporal = self.spatial_encoder(batch), self.temporal_encoder(batch)
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# Decoder
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# First repeat the data accordingly to the batch size
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z_concat = torch.cat((z_spatial, z_temporal), dim=-1)
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z_repeatet = Repeater((batch.shape[0], batch.shape[1], -1))(z_concat)
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x_hat = self.decoder(z_repeatet)
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return z_spatial, z_temporal, x_hat
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class SeparatingAdversarialAELightningOverrides:
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@property
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def name(self):
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return self.__class__.__name__
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def forward(self, x):
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return self.network.forward(x)
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def training_step(self, batch, _, optimizer_i):
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spatial_latent_fake, temporal_latent_fake, batch_hat = self.network.forward(batch)
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if optimizer_i == 0:
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# ---------------------
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# Train temporal Discriminator
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# ---------------------
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# latent_fake, reconstruction
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temporal_latent_real = self.normal.sample(temporal_latent_fake.shape)
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# Evaluate the input
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temporal_real_prediction = self.network.temporal_discriminator.forward(temporal_latent_real)
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temporal_fake_prediction = self.network.temporal_discriminator.forward(temporal_latent_fake)
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# Train the discriminator
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temporal_loss_real = mse_loss(temporal_real_prediction, torch.zeros(temporal_real_prediction.shape))
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temporal_loss_fake = mse_loss(temporal_fake_prediction, torch.ones(temporal_fake_prediction.shape))
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# Calculate the mean over bot the real and the fake acc
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# ToDo: do i need to compute this seperate?
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d_loss = 0.5 * torch.add(temporal_loss_real, temporal_loss_fake)
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return {'loss': d_loss}
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if optimizer_i == 1:
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# ---------------------
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# Train spatial Discriminator
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# ---------------------
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# latent_fake, reconstruction
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spatial_latent_real = self.normal.sample(spatial_latent_fake.shape)
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# Evaluate the input
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spatial_real_prediction = self.network.spatial_discriminator.forward(spatial_latent_real)
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spatial_fake_prediction = self.network.spatial_discriminator.forward(spatial_latent_fake)
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# Train the discriminator
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spatial_loss_real = mse_loss(spatial_real_prediction, torch.zeros(spatial_real_prediction.shape))
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spatial_loss_fake = mse_loss(spatial_fake_prediction, torch.ones(spatial_fake_prediction.shape))
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# Calculate the mean over bot the real and the fake acc
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# ToDo: do i need to compute this seperate?
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d_loss = 0.5 * torch.add(spatial_loss_real, spatial_loss_fake)
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return {'loss': d_loss}
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elif optimizer_i == 2:
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# ---------------------
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# Train AutoEncoder
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# ---------------------
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loss = mse_loss(batch, batch_hat)
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return {'loss': loss}
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else:
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raise RuntimeError('This should not have happened, catch me if u can.')
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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