Done: Latent Space Viz
ToDo: Visualization for variational spaces Trajectory Coloring Post Processing Metric Slurm Skript
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Si11ium
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67
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@ -163,7 +163,7 @@ class Trajectories(Dataset):
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self.data = self.__init_data_(**kwargs)
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pass
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def __init_data_(self, **kwargs):
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def __init_data_(self, **kwargs: dict):
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dataDict = dict()
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for key, val in kwargs.items():
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if key in self.isovistMeasures:
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@ -177,6 +177,7 @@ class Trajectories(Dataset):
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return data
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def __iter__(self):
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# FixMe: is that correct?
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for i in range(len(self)):
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yield self[i]
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@ -10,7 +10,7 @@ class AdversarialAutoEncoder(AutoEncoder):
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def __init__(self, *args, **kwargs):
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super(AdversarialAutoEncoder, self).__init__(*args, **kwargs)
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self.discriminator = Discriminator(self.latent_dim, self.dataParams)
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self.discriminator = Discriminator(self.latent_dim, self.features)
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def forward(self, batch):
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# Encoder
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@ -18,7 +18,7 @@ class AdversarialAutoEncoder(AutoEncoder):
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z = self.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_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
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z_repeatet = Repeater((batch.shape[0], batch.shape[1], -1))(z)
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x_hat = self.decoder(z_repeatet)
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return z, x_hat
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@ -7,12 +7,13 @@ from torch import Tensor
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# Basic AE-Implementation
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class AutoEncoder(AbstractNeuralNetwork, ABC):
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def __init__(self, latent_dim: int, dataParams: dict, **kwargs):
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def __init__(self, latent_dim: int=0, features: int = 0, **kwargs):
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assert latent_dim and features
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super(AutoEncoder, self).__init__()
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self.dataParams = dataParams
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self.latent_dim = latent_dim
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self.features = features
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self.encoder = Encoder(self.latent_dim)
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self.decoder = Decoder(self.latent_dim, self.dataParams['features'])
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self.decoder = Decoder(self.latent_dim, self.features)
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def forward(self, batch: Tensor):
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# Encoder
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@ -20,7 +21,7 @@ class AutoEncoder(AbstractNeuralNetwork, ABC):
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z = self.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_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
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z_repeatet = Repeater((batch.shape[0], batch.shape[1], -1))(z)
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x_hat = self.decoder(z_repeatet)
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return z, x_hat
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@ -131,13 +131,13 @@ class AvgDimPool(Module):
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# Generators, Decoders, Encoders, Discriminators
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class Discriminator(Module):
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def __init__(self, latent_dim, dataParams, dropout=.0, activation=ReLU):
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def __init__(self, latent_dim, features, dropout=.0, activation=ReLU):
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super(Discriminator, self).__init__()
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self.dataParams = dataParams
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self.features = features
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self.latent_dim = latent_dim
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self.l1 = Linear(self.latent_dim, self.dataParams['features'] * 10)
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self.l2 = Linear(self.dataParams['features'] * 10, self.dataParams['features'] * 20)
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self.lout = Linear(self.dataParams['features'] * 20, 1)
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self.l1 = Linear(self.latent_dim, self.features * 10)
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self.l2 = Linear(self.features * 10, self.features * 20)
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self.lout = Linear(self.features * 20, 1)
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self.dropout = Dropout(dropout)
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self.activation = activation()
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self.sigmoid = Sigmoid()
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@ -6,17 +6,17 @@ import torch
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class SeperatingAdversarialAutoEncoder(Module):
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def __init__(self, latent_dim, dataParams, **kwargs):
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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.dataParams = dataParams
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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.dataParams['features'])
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self.spatial_discriminator = Discriminator(self.latent_dim // 2, self.dataParams)
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self.temporal_discriminator = Discriminator(self.latent_dim // 2, self.dataParams)
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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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@ -25,7 +25,7 @@ class SeperatingAdversarialAutoEncoder(Module):
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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], self.dataParams['size'], -1))(z_concat)
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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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@ -10,12 +10,13 @@ class VariationalAutoEncoder(AbstractNeuralNetwork, ABC):
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def name(self):
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return self.__class__.__name__
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def __init__(self, dataParams, **kwargs):
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def __init__(self, latent_dim=0, features=0, **kwargs):
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assert latent_dim and features
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super(VariationalAutoEncoder, self).__init__()
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self.dataParams = dataParams
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self.latent_dim = kwargs.get('latent_dim', 2)
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self.features = features
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self.latent_dim = latent_dim
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self.encoder = Encoder(self.latent_dim, variational=True)
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self.decoder = Decoder(self.latent_dim, self.dataParams['features'], variational=True)
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self.decoder = Decoder(self.latent_dim, self.features, variational=True)
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@staticmethod
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def reparameterize(mu, logvar):
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@ -27,7 +28,7 @@ class VariationalAutoEncoder(AbstractNeuralNetwork, ABC):
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def forward(self, batch):
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mu, logvar = self.encoder(batch)
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z = self.reparameterize(mu, logvar)
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repeat = Repeater((batch.shape[0], self.dataParams['size'], -1))
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repeat = Repeater((batch.shape[0], batch.shape[1], -1))
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x_hat = self.decoder(repeat(z))
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return x_hat, mu, logvar
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@ -14,21 +14,35 @@ 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(VariationalAutoEncoderLightningOverrides, LightningModule):
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class Model(AutoEncoderLightningOverrides, LightningModule):
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def __init__(self, dataParams: dict):
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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.dataParams = dataParams
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self.network = VariationalAutoEncoder(self.dataParams)
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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.dataParams), shuffle=True, batch_size=100)
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return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
|
||||
|
||||
|
||||
class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
|
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@ -37,11 +51,15 @@ class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
|
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def name(self):
|
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return self.network.name
|
||||
|
||||
def __init__(self, dataParams: dict):
|
||||
def __init__(self, args: Namespace, **kwargs):
|
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assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
|
||||
self.size = args.size
|
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self.latent_dim = args.latent_dim
|
||||
self.features = args.features
|
||||
self.step = args.step
|
||||
super(AdversarialModel, self).__init__()
|
||||
self.dataParams = dataParams
|
||||
self.normal = Normal(0, 1)
|
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self.network = AdversarialAutoEncoder(self.dataParams)
|
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self.network = AdversarialAutoEncoder(self.latent_dim, self.features)
|
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pass
|
||||
|
||||
# This is Fucked up, why do i need to put an additional empty list here?
|
||||
@ -52,17 +70,20 @@ class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
|
||||
|
||||
@data_loader
|
||||
def tng_dataloader(self):
|
||||
return DataLoader(DataContainer('data', **self.dataParams), shuffle=True, batch_size=100)
|
||||
return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
|
||||
|
||||
|
||||
class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, LightningModule):
|
||||
|
||||
def __init__(self, latent_dim, dataParams: dict):
|
||||
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
|
||||
super(SeparatingAdversarialModel, self).__init__()
|
||||
self.latent_dim = latent_dim
|
||||
self.dataParams = dataParams
|
||||
self.normal = Normal(0, 1)
|
||||
self.network = SeperatingAdversarialAutoEncoder(self.latent_dim, self.dataParams)
|
||||
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?
|
||||
@ -78,22 +99,24 @@ class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, Ligh
|
||||
|
||||
@data_loader
|
||||
def tng_dataloader(self):
|
||||
return DataLoader(DataContainer('data', **self.dataParams), shuffle=True, batch_size=100)
|
||||
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
|
||||
latent_dim = 4
|
||||
model = SeparatingAdversarialModel(latent_dim=latent_dim, dataParams=dict(refresh=False, size=5, step=5,
|
||||
features=features, transforms=[BatchNorm1d(features)]
|
||||
)
|
||||
)
|
||||
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 = SeparatingAdversarialModel(arguments)
|
||||
|
||||
# PyTorch summarywriter with a few bells and whistles
|
||||
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)
|
||||
|
||||
from pytorch_lightning.callbacks import ModelCheckpoint
|
||||
|
||||
@ -101,9 +124,8 @@ if __name__ == '__main__':
|
||||
filepath=os.path.join(outpath, 'weights.ckpt'),
|
||||
save_best_only=True,
|
||||
verbose=True,
|
||||
monitor='val_loss',
|
||||
monitor='tng_loss', # val_loss
|
||||
mode='min',
|
||||
|
||||
)
|
||||
|
||||
trainer = Trainer(experiment=exp, checkpoint_callback=checkpoint_callback, max_nb_epochs=15) # gpus=[0...LoL]
|
||||
|
||||
@ -4,6 +4,8 @@ import torch
|
||||
from torch.utils.data import DataLoader
|
||||
from pytorch_lightning import data_loader
|
||||
from dataset import DataContainer
|
||||
from collections import defaultdict
|
||||
from tqdm import tqdm
|
||||
import os
|
||||
|
||||
from sklearn.manifold import TSNE
|
||||
@ -12,30 +14,28 @@ from sklearn.decomposition import PCA
|
||||
import seaborn as sns; sns.set()
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from run_models import SeparatingAdversarialModel
|
||||
|
||||
path = 'output'
|
||||
mylightningmodule = 'weired name, loaded from disk'
|
||||
|
||||
|
||||
# FIXME: How to store hyperparamters in testtube element?
|
||||
|
||||
from run_models import *
|
||||
|
||||
def search_for_weights(folder):
|
||||
while not os.path.exists(folder):
|
||||
if len(os.path.split(folder)) >= 50:
|
||||
raise FileNotFoundError(f'The folder "{folder}" could not be found')
|
||||
folder = os.path.join(os.pardir, folder)
|
||||
for element in os.scandir(folder):
|
||||
if os.path.exists(element):
|
||||
if element.is_dir():
|
||||
search_for_weights(element.path)
|
||||
elif element.is_file() and element.name.endswith('.ckpt'):
|
||||
load_and_viz(element)
|
||||
load_and_predict(element)
|
||||
else:
|
||||
continue
|
||||
|
||||
|
||||
def load_and_viz(path_like_element):
|
||||
def load_and_predict(path_like_element):
|
||||
|
||||
# Define Loop to search for models and folder with visualizations
|
||||
pretrained_model = SeparatingAdversarialModel.load_from_metrics(
|
||||
model = globals()[path_like_element.path.split(os.sep)[-3]]
|
||||
pretrained_model = model.load_from_metrics(
|
||||
weights_path=path_like_element.path,
|
||||
tags_csv=os.path.join(os.path.dirname(path_like_element), 'default', 'version_0', 'meta_tags.csv'),
|
||||
on_gpu=True if torch.cuda.is_available() else False,
|
||||
@ -46,19 +46,26 @@ def load_and_viz(path_like_element):
|
||||
pretrained_model.eval()
|
||||
pretrained_model.freeze()
|
||||
|
||||
# Load the data fpr prediction
|
||||
dataset = DataContainer('data', 5, 5)
|
||||
# Load the data for prediction
|
||||
dataset = DataContainer(os.path.join(os.pardir, 'data'), 5, 5)
|
||||
|
||||
# Do the inference
|
||||
predictions = []
|
||||
for i in range(len(dataset)):
|
||||
z, _ = pretrained_model(dataset[i])
|
||||
predictions.append(z)
|
||||
predictions = torch.cat(predictions)
|
||||
if predictions.shape[-1] <= 1:
|
||||
prediction_dict = defaultdict(list)
|
||||
for i in tqdm(range(len(dataset)), total=len(dataset)):
|
||||
p_X = pretrained_model(dataset[i].unsqueeze(0))
|
||||
for idx in range(len(p_X) - 1):
|
||||
prediction_dict[idx].append(p_X[idx])
|
||||
|
||||
predictions = [torch.cat(prediction).detach().numpy() for prediction in prediction_dict.values()]
|
||||
for prediction in predictions:
|
||||
viz_latent(prediction)
|
||||
|
||||
|
||||
def viz_latent(prediction):
|
||||
if prediction.shape[-1] <= 1:
|
||||
raise ValueError('How did this happen?')
|
||||
elif predictions.shape[-1] == 2:
|
||||
ax = sns.scatterplot(x=predictions[:, 0], y=predictions[:, 1])
|
||||
elif prediction.shape[-1] == 2:
|
||||
ax = sns.scatterplot(x=prediction[:, 0], y=prediction[:, 1])
|
||||
plt.show()
|
||||
return ax
|
||||
else:
|
||||
@ -69,3 +76,7 @@ def load_and_viz(path_like_element):
|
||||
tsne_plot = sns.scatterplot(x=predictions_tsne[:, 0], y=predictions_tsne[:, 1], ax=axs[1])
|
||||
plt.show()
|
||||
return fig, axs, pca_plot, tsne_plot
|
||||
|
||||
if __name__ == '__main__':
|
||||
path = 'output'
|
||||
search_for_weights(path)
|
||||
Loading…
x
Reference in New Issue
Block a user