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import torch
import torch.nn.functional as F
from torch.autograd import Variable
from .encoder import Encoder
from .decoder import Decoder
from .discriminator import Discriminator
from .common import *
def dummy_distribution(*args):
raise NotImplementedError("Give a distribution")
class AdversarialAutoencoder:
"""# Adversarial Autoencoder Model"""
def __init__(
self,
data_dim,
latent_dim,
enc_dec_size,
disc_size,
negative_slope=0.3,
dropout=0.0,
distribution=dummy_distribution,
):
self.encoder = Encoder(
data_dim,
enc_dec_size,
latent_dim,
negative_slope=negative_slope,
dropout=dropout,
)
self.decoder = Decoder(
data_dim,
enc_dec_size,
latent_dim,
negative_slope=negative_slope,
dropout=dropout,
)
self.discriminator = Discriminator(
disc_size, latent_dim, negative_slope=negative_slope, dropout=dropout
)
self.data_dim = data_dim
self.latent_dim = latent_dim
self.p = dropout
self.negative_slope = negative_slope
self.distribution = distribution
return
def parameters(self):
return (
*self.encoder.parameters(),
*self.decoder.parameters(),
*self.discriminator.parameters(),
)
def train(self):
self.encoder.train(True)
self.decoder.train(True)
self.discriminator.train(True)
return self
def eval(self):
self.encoder.train(False)
self.decoder.train(False)
self.discriminator.train(False)
return self
def encode(self, data):
return self.encoder(data)
def decode(self, data):
return self.decoder(data)
def __call__(self, data):
return self.decode(self.encode(data))
def save(self, base="./"):
torch.save(self.encoder.state_dict(), base + "weights_encoder.pt")
torch.save(self.decoder.state_dict(), base + "weights_decoder.pt")
torch.save(self.discriminator.state_dict(), base + "weights_discriminator.pt")
return self
def load(self, base="./"):
self.encoder.load_state_dict(torch.load(base + "weights_encoder.pt"))
self.encoder.eval()
self.decoder.load_state_dict(torch.load(base + "weights_decoder.pt"))
self.decoder.eval()
self.discriminator.load_state_dict(
torch.load(base + "weights_discriminator.pt")
)
self.discriminator.eval()
return self
def to(self, device):
self.encoder.to(device)
self.decoder.to(device)
self.discriminator.to(device)
return self
def cuda(self):
if torch.cuda.is_available():
self.encoder = self.encoder.cuda()
self.decoder = self.decoder.cuda()
self.discriminator = self.discriminator.cuda()
return self
def cpu(self):
self.encoder = self.encoder.cpu()
self.decoder = self.decoder.cpu()
self.discriminator = self.discriminator.cpu()
return self
def init_optimizers(self, recon_lr=1e-4, adv_lr=5e-5):
self.optim_E_gen = torch.optim.Adam(self.encoder.parameters(), lr=adv_lr)
self.optim_E_enc = torch.optim.Adam(self.encoder.parameters(), lr=recon_lr)
self.optim_D_dec = torch.optim.Adam(self.decoder.parameters(), lr=recon_lr)
self.optim_D_dis = torch.optim.Adam(self.discriminator.parameters(), lr=adv_lr)
return self
def init_losses(self, recon_loss_fn=F.binary_cross_entropy):
self.recon_loss_fn = recon_loss_fn
return self
def train_step(self, raw_data, scale=1.0):
data = to_var(raw_data.view(raw_data.size(0), -1))
self.encoder.zero_grad()
self.decoder.zero_grad()
self.discriminator.zero_grad()
z = self.encoder(data)
X = self.decoder(z)
self.recon_loss = self.recon_loss_fn(X + EPS, data + EPS)
self.recon_loss.backward()
self.optim_E_enc.step()
self.optim_D_dec.step()
self.encoder.eval()
z_gaussian = to_var(self.distribution(data.size(0), self.latent_dim) * scale)
z_gaussian_fake = self.encoder(data)
y_gaussian = self.discriminator(z_gaussian)
y_gaussian_fake = self.discriminator(z_gaussian_fake)
self.D_loss = -torch.mean(
torch.log(y_gaussian + EPS) + torch.log(1 - y_gaussian_fake + EPS)
)
self.D_loss.backward()
self.optim_D_dis.step()
self.encoder.train()
z_gaussian = self.encoder(data)
y_gaussian = self.discriminator(z_gaussian)
self.G_loss = -torch.mean(torch.log(y_gaussian + EPS))
self.G_loss.backward()
self.optim_E_gen.step()
return
def losses(self):
try:
return self.recon_loss, self.D_loss, self.G_loss
except:
...
return
|
