All models running.
This commit is contained in:
Si11ium
parent
7b0b96eaa3
commit
18305a9e7e
@ -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
|
||||
|
||||
@ -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?
|
||||
|
||||
@ -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):
|
||||
|
||||
@ -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?
|
||||
|
||||
@ -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'))
|
||||
|
||||
|
||||
@ -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:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user