diff --git a/dataset.py b/dataset.py
index 6d160dc..ccf5a6c 100644
--- a/dataset.py
+++ b/dataset.py
@@ -47,8 +47,8 @@ class AbstractDataset(ConcatDataset, ABC):
# maps = ['hotel', 'tum','gallery', 'queens', 'oet']
@property
def maps(self):
- return ['test', 'test2']
- # return ['hotel', 'tum','gallery', 'queens', 'oet']
+ # return ['test', 'test2']
+ return ['hotel', 'tum','gallery', 'queens', 'oet']
@property
@abstractmethod
diff --git a/networks/adverserial_auto_encoder.py b/networks/adverserial_auto_encoder.py
index faaeee8..7cc55bd 100644
--- a/networks/adverserial_auto_encoder.py
+++ b/networks/adverserial_auto_encoder.py
@@ -6,6 +6,9 @@ from networks.modules import *
import torch
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
class AdversarialAutoEncoder(AutoEncoder):
def __init__(self, *args, **kwargs):
@@ -32,18 +35,18 @@ class AdversarialAELightningOverrides(LightningModuleOverrides):
if optimizer_i == 0:
# ---------------------
# Train Discriminator
- # ---------------------
+ # ---------------------p
# latent_fake, reconstruction
- latent_fake, _ = self.network.forward(batch)
- latent_real = self.normal.sample(latent_fake.shape)
+ latent_fake = self.network.encoder.forward(batch)
+ latent_real = self.normal.sample(latent_fake.shape).to(device)
# Evaluate the input
d_real_prediction = self.network.discriminator.forward(latent_real)
d_fake_prediction = self.network.discriminator.forward(latent_fake)
# Train the discriminator
- d_loss_real = mse_loss(d_real_prediction, torch.zeros(d_real_prediction.shape))
- d_loss_fake = mse_loss(d_fake_prediction, torch.ones(d_fake_prediction.shape))
+ d_loss_real = mse_loss(d_real_prediction, torch.zeros(d_real_prediction.shape, device=device))
+ d_loss_fake = mse_loss(d_fake_prediction, torch.ones(d_fake_prediction.shape, device=device))
# Calculate the mean over both the real and the fake acc
# ToDo: do i need to compute this seperate?
diff --git a/networks/modules.py b/networks/modules.py
index 81a02bb..17ba250 100644
--- a/networks/modules.py
+++ b/networks/modules.py
@@ -25,9 +25,9 @@ class LightningModuleOverrides:
@data_loader
def tng_dataloader(self):
- num_workers = os.cpu_count() // 2
+ num_workers = 0 # os.cpu_count() // 2
return DataLoader(DataContainer('data', self.size, self.step),
- shuffle=True, batch_size=100, num_workers=num_workers)
+ shuffle=True, batch_size=10000, num_workers=num_workers)
class AbstractNeuralNetwork(Module):
diff --git a/networks/seperating_adversarial_auto_encoder.py b/networks/seperating_adversarial_auto_encoder.py
index b0872f4..556da2c 100644
--- a/networks/seperating_adversarial_auto_encoder.py
+++ b/networks/seperating_adversarial_auto_encoder.py
@@ -1,21 +1,22 @@
from torch.optim import Adam
-
-from networks.auto_encoder import AutoEncoder
from torch.nn.functional import mse_loss
from networks.modules import *
import torch
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
class SeperatingAdversarialAutoEncoder(Module):
- def __init__(self, latent_dim, features, **kwargs):
+ def __init__(self, latent_dim, features):
super(SeperatingAdversarialAutoEncoder, self).__init__()
self.latent_dim = latent_dim
self.features = features
self.spatial_encoder = PoolingEncoder(self.latent_dim)
self.temporal_encoder = Encoder(self.latent_dim)
- self.decoder = Decoder(self.latent_dim, self.features)
+ self.decoder = Decoder(self.latent_dim * 2, self.features)
self.spatial_discriminator = Discriminator(self.latent_dim, self.features)
self.temporal_discriminator = Discriminator(self.latent_dim, self.features)
@@ -43,15 +44,17 @@ class SeparatingAdversarialAELightningOverrides(LightningModuleOverrides):
# Train temporal Discriminator
# ---------------------
# latent_fake, reconstruction
- temporal_latent_real = self.normal.sample(temporal_latent_fake.shape)
+ temporal_latent_real = self.normal.sample(temporal_latent_fake.shape).to(device)
# Evaluate the input
temporal_real_prediction = self.network.temporal_discriminator.forward(temporal_latent_real)
temporal_fake_prediction = self.network.temporal_discriminator.forward(temporal_latent_fake)
# Train the discriminator
- temporal_loss_real = mse_loss(temporal_real_prediction, torch.zeros(temporal_real_prediction.shape))
- temporal_loss_fake = mse_loss(temporal_fake_prediction, torch.ones(temporal_fake_prediction.shape))
+ temporal_loss_real = mse_loss(temporal_real_prediction,
+ torch.zeros(temporal_real_prediction.shape, device=device))
+ temporal_loss_fake = mse_loss(temporal_fake_prediction,
+ torch.ones(temporal_fake_prediction.shape, device=device))
# Calculate the mean over bot the real and the fake acc
# ToDo: do i need to compute this seperate?
@@ -63,15 +66,17 @@ class SeparatingAdversarialAELightningOverrides(LightningModuleOverrides):
# Train spatial Discriminator
# ---------------------
# latent_fake, reconstruction
- spatial_latent_real = self.normal.sample(spatial_latent_fake.shape)
+ spatial_latent_real = self.normal.sample(spatial_latent_fake.shape).to(device)
# Evaluate the input
spatial_real_prediction = self.network.spatial_discriminator.forward(spatial_latent_real)
spatial_fake_prediction = self.network.spatial_discriminator.forward(spatial_latent_fake)
# Train the discriminator
- spatial_loss_real = mse_loss(spatial_real_prediction, torch.zeros(spatial_real_prediction.shape))
- spatial_loss_fake = mse_loss(spatial_fake_prediction, torch.ones(spatial_fake_prediction.shape))
+ spatial_loss_real = mse_loss(spatial_real_prediction,
+ torch.zeros(spatial_real_prediction.shape, device=device))
+ spatial_loss_fake = mse_loss(spatial_fake_prediction,
+ torch.ones(spatial_fake_prediction.shape, device=device))
# Calculate the mean over bot the real and the fake acc
# ToDo: do i need to compute this seperate?
diff --git a/run_models.py b/run_models.py
index 228430d..8fafd41 100644
--- a/run_models.py
+++ b/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'))
diff --git a/viz/viz_latent.py b/viz/viz_latent.py
index 15e61f0..84ba2a2 100644
--- a/viz/viz_latent.py
+++ b/viz/viz_latent.py
@@ -62,7 +62,7 @@ def load_and_predict(path_like_element):
plot.savefig(os.path.join(os.path.dirname(path_like_element), f'latent_space_{idx}.png'))
-def viz_latent(prediction, title=f'Latent Space '):
+def viz_latent(prediction):
if prediction.shape[-1] <= 1:
raise ValueError('How did this happen?')
elif prediction.shape[-1] == 2: