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trainer.py
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trainer.py
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import numpy as np
from statistics import mean
import torch.nn as nn
import torch.optim as optim
import torch
# from torch.utils.tensorboard import SummaryWriter
import os
from gan.network import AttrDiscriminator, Discriminator, DoppelGANgerGeneratorRNN, DoppelGANgerGeneratorAttention, \
TransformerDiscriminator
from gan.cgan import CGANGenerator, CGANDiscriminator
from gan.rcgan import RGANGenerator, RGANDiscriminator, RCGANGenerator2, RCGANDiscriminator2
from gan.timegan import TGEncoder, TGRecovery, TGGenerator, TGDiscriminator, TGSupervisor
from gan.naivegan import NaiveGanGenerator, NaiveGanDiscriminator
from gan.ttsgan import TTSGenerator, TTSDiscriminator
from gan.gan_util import gen_noise
from util import calculate_mmd_rbf, setup_logging
class DoppelGANger:
def __init__(self,
real_train_dl,
device,
checkpoint_dir='',
time_logging_file='',
config_logging_file='',
noise_dim=5,
sample_len=10,
batch_size=100,
dis_lambda_gp=10,
attr_dis_lambda_gp=10,
g_attr_d_coe=1,
d_rounds=1,
g_rounds=1,
gen_type='RNN',
dis_type='normal',
att_dim=50,
num_heads=5,
g_lr=0.00001,
g_beta1=0.5,
d_lr=0.00001,
d_beta1=0.5,
attr_d_lr=0.00001,
attr_d_beta1=0.5
):
self.config_logger, self.time_logger = setup_logging(time_logging_file, config_logging_file)
# setup models
if dis_type == 'MLP':
self.dis = Discriminator(real_train_dl.dataset.data_feature_shape,
real_train_dl.dataset.data_attribute_shape)
else:
self.dis = TransformerDiscriminator(input_feature_shape=real_train_dl.dataset.data_feature_shape,
input_attribute_shape=real_train_dl.dataset.data_attribute_shape)
self.config_logger.info("DISCRIMINATOR: {0}".format(self.dis))
self.attr_dis = AttrDiscriminator(real_train_dl.dataset.data_attribute_shape)
self.config_logger.info("ATTRIBUTE DISCRIMINATOR: {0}".format(self.attr_dis))
if gen_type == 'RNN':
noise_dim = noise_dim
else:
noise_dim = att_dim - real_train_dl.dataset.data_attribute.shape[1]
if gen_type == "RNN":
self.gen = DoppelGANgerGeneratorRNN(noise_dim=noise_dim,
feature_outputs=real_train_dl.dataset.data_feature_outputs,
attribute_outputs=real_train_dl.dataset.data_attribute_outputs,
real_attribute_mask=real_train_dl.dataset.real_attribute_mask,
device=device,
sample_len=sample_len)
else:
self.gen = DoppelGANgerGeneratorAttention(noise_dim=noise_dim,
feature_outputs=real_train_dl.dataset.data_feature_outputs,
attribute_outputs=real_train_dl.dataset.data_attribute_outputs,
real_attribute_mask=real_train_dl.dataset.real_attribute_mask,
device=device,
sample_len=sample_len, num_heads=num_heads, attn_dim=att_dim)
self.config_logger.info("GENERATOR: {0}".format(self.gen))
self.criterion = "Wasserstein GAN with Gradient Penalty"
self.config_logger.info("Criterion: {0}".format(self.criterion))
# setup optimizer
self.dis_opt = torch.optim.Adam(self.dis.parameters(), lr=d_lr, betas=(d_beta1, 0.999))
self.config_logger.info("DISCRIMINATOR OPTIMIZER: {0}".format(self.dis_opt))
self.attr_dis_opt = torch.optim.Adam(self.attr_dis.parameters(), lr=attr_d_lr, betas=(attr_d_beta1, 0.999))
self.config_logger.info("ATTRIBUTE DISCRIMINATOR OPTIMIZER: {0}".format(self.attr_dis_opt))
self.gen_opt = torch.optim.Adam(self.gen.parameters(), lr=g_lr, betas=(g_beta1, 0.999))
self.config_logger.info("GENERATOR OPTIMIZER: {0}".format(self.gen_opt))
self.real_train_dl = real_train_dl
self.data_feature_shape = self.real_train_dl.dataset.data_feature_shape
if self.data_feature_shape[1] % sample_len != 0:
raise Exception("length must be a multiple of sample_len")
self.sample_time = int(self.data_feature_shape[1] / sample_len)
self.batch_size = batch_size
self.config_logger.info("Batch Size: {0}".format(self.batch_size))
self.noise_dim = noise_dim
self.config_logger.info("Noise Dimension: {0}".format(self.noise_dim))
self.sample_len = sample_len
self.config_logger.info("Sample_Length: {0}".format(self.sample_len))
self.dis_lambda_gp = dis_lambda_gp
self.attr_dis_lambda_gp = attr_dis_lambda_gp
self.g_attr_d_coe = g_attr_d_coe
self.d_rounds = d_rounds
self.g_rounds = g_rounds
self.config_logger.info("d_rounds: {0}".format(self.d_rounds))
self.config_logger.info("g_rounds: {0}".format(self.g_rounds))
self.config_logger.info("d_lambda_gp_coefficient: {0}".format(self.dis_lambda_gp))
self.config_logger.info("attr_d_lambda_gp_coefficient: {0}".format(self.attr_dis_lambda_gp))
self.config_logger.info("g_attr_d_coe: {0}".format(self.g_attr_d_coe))
self.checkpoint_dir = checkpoint_dir
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.writer = None
self.device = device
self.config_logger.info("Device: {0}".format(self.device))
self.dis = self.dis.to(self.device)
self.attr_dis = self.attr_dis.to(self.device)
self.gen = self.gen.to(self.device)
self.EPS = 1e-8
# TODO: use helper function in gan_util
def gen_attribute_input_noise(self, num_sample):
return torch.randn(size=[num_sample, self.noise_dim])
def gen_feature_input_noise(self, num_sample, length):
return torch.randn(size=[num_sample, length, self.noise_dim])
def save(self, epoch):
if not os.path.exists("{0}/epoch_{1}".format(self.checkpoint_dir, epoch)):
os.makedirs("{0}/epoch_{1}".format(self.checkpoint_dir, epoch))
torch.save(self.dis, "{0}/epoch_{1}/discriminator.pth".format(self.checkpoint_dir, epoch))
torch.save(self.attr_dis, "{0}/epoch_{1}/attr_discriminator.pth".format(self.checkpoint_dir, epoch))
torch.save(self.gen, "{0}/epoch_{1}/generator.pth".format(self.checkpoint_dir, epoch))
def inference(self, epoch, model_dir=None):
if model_dir is None:
model_dir = "{0}/epoch_{1}".format(self.checkpoint_dir, epoch)
batch_size = self.batch_size
while self.real_train_dl.dataset.data_attribute_shape[0] % batch_size != 0:
batch_size -= 1
rounds = self.real_train_dl.dataset.data_attribute_shape[0] // batch_size
sampled_features = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2] - 2))
sampled_attributes = np.zeros((0, self.real_train_dl.dataset.data_attribute_shape[1]))
sampled_gen_flags = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1]))
sampled_lengths = np.zeros(0)
for i in range(rounds):
features, attributes, gen_flags, lengths = self.sample_from(batch_size=batch_size)
sampled_features = np.concatenate((sampled_features, features), axis=0)
sampled_attributes = np.concatenate((sampled_attributes, attributes), axis=0)
sampled_gen_flags = np.concatenate((sampled_gen_flags, gen_flags), axis=0)
sampled_lengths = np.concatenate((sampled_lengths, lengths), axis=0)
np.savez("{0}/generated_samples.npz".format(model_dir), sampled_features=sampled_features,
sampled_attributes=sampled_attributes, sampled_gen_flags=sampled_gen_flags,
sampled_lengths=sampled_lengths)
def load(self, model_dir=None):
if not os.path.exists(model_dir):
raise Exception("Directory to load pytorch model doesn't exist")
self.dis = torch.load("{0}/discriminator.pth".format(model_dir))
self.attr_dis = torch.load("{0}/attr_discriminator.pth".format(model_dir))
self.gen = torch.load("{0}/generator.pth".format(model_dir))
self.dis = self.dis.to(self.device)
self.attr_dis = self.attr_dis.to(self.device)
self.gen = self.gen.to(self.device)
self.dis.device = self.device
self.attr_dis.device = self.device
self.gen.device = self.device
def sample_from(self, batch_size, return_gen_flag_feature=False):
real_attribute_noise = self.gen_attribute_input_noise(batch_size).to(self.device)
addi_attribute_noise = self.gen_attribute_input_noise(batch_size).to(self.device)
feature_input_noise = self.gen_feature_input_noise(batch_size, self.sample_time).to(self.device)
self.dis.eval()
self.attr_dis.eval()
self.gen.eval()
with torch.no_grad():
attributes, features = self.gen(real_attribute_noise,
addi_attribute_noise,
feature_input_noise)
attributes = attributes.cpu().numpy()
features = features.cpu().numpy()
# TODO: possible without loop?!
gen_flags = np.zeros(features.shape[:-1])
lengths = np.zeros(features.shape[0])
for i in range(len(features)):
winner = (features[i, :, -1] > features[i, :, -2])
argmax = np.argmax(winner == True)
if argmax == 0:
gen_flags[i, :] = 1
else:
gen_flags[i, :argmax] = 1
lengths[i] = argmax
if not return_gen_flag_feature:
features = features[:, :, :-2]
return features, attributes, gen_flags, lengths
def calculate_gp_dis(self, batch_size, fake_feature, data_feature, fake_attribute, data_attribute):
alpha_dim2 = torch.FloatTensor(batch_size, 1).uniform_(1).to(self.device)
alpha_dim3 = torch.unsqueeze(alpha_dim2, 2).to(self.device)
differences_input_feature = (fake_feature -
data_feature)
interpolates_input_feature = (data_feature +
alpha_dim3 * differences_input_feature)
differences_input_attribute = (fake_attribute -
data_attribute)
interpolates_input_attribute = (data_attribute +
(alpha_dim2 *
differences_input_attribute))
mixed_scores = self.dis(interpolates_input_feature,
interpolates_input_attribute)
gradients = torch.autograd.grad(
inputs=[interpolates_input_feature, interpolates_input_attribute],
outputs=mixed_scores,
grad_outputs=torch.ones_like(mixed_scores),
create_graph=True,
retain_graph=True
)
slopes1 = torch.sum(torch.square(gradients[0]),
dim=(1, 2))
slopes2 = torch.sum(torch.square(gradients[1]),
dim=(1))
slopes = torch.sqrt(slopes1 + slopes2 + self.EPS)
loss_dis_gp = torch.mean((slopes - 1.) ** 2)
loss_dis_gp_unflattened = (slopes - 1.) ** 2
return loss_dis_gp, loss_dis_gp_unflattened
def calculate_gp_attr_dis(self, batch_size, fake_attribute, data_attribute):
alpha_dim2 = torch.FloatTensor(batch_size, 1).uniform_(1).to(self.device)
differences_input_attribute = (fake_attribute -
data_attribute)
interpolates_input_attribute = (data_attribute +
(alpha_dim2 *
differences_input_attribute))
mixed_scores = self.attr_dis(interpolates_input_attribute)
gradients = torch.autograd.grad(
inputs=interpolates_input_attribute,
outputs=mixed_scores,
grad_outputs=torch.ones_like(mixed_scores),
create_graph=True,
retain_graph=True
)
slopes1 = torch.sum(torch.square(gradients[0]),
dim=(1))
slopes = torch.sqrt(slopes1 + self.EPS)
loss_attr_dis_gp = torch.mean((slopes - 1.) ** 2)
loss_attr_dis_gp_unflattened = (slopes - 1.) ** 2
return loss_attr_dis_gp, loss_attr_dis_gp_unflattened
def add_losses(self, running_losses, writer_frequency, epoch, n_total_steps, batch_idx):
self.writer.add_scalar('loss/d_wo_gp', running_losses["dis_wogp_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/d_total', running_losses["dis_total_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/d/fake', running_losses["dis_fake_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/d/real', running_losses["dis_real_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/d/gp', running_losses["dis_gp_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/attr_d', running_losses["attr_dis_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/attr_d/fake', running_losses["attr_dis_fake_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/attr_d/real', running_losses["attr_dis_real_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/attr_d/gp', running_losses["attr_dis_gp_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/g/d', running_losses["gen_d_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/g/attr_d', running_losses["gen_attr_d_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
self.writer.add_scalar('loss/g', running_losses["gen_rl"] / writer_frequency,
epoch * n_total_steps + batch_idx)
running_losses["dis_total_rl"] = 0
running_losses["dis_wogp_rl"] = 0
running_losses["dis_fake_rl"] = 0
running_losses["dis_real_rl"] = 0
running_losses["dis_gp_rl"] = 0
running_losses["attr_dis_rl"] = 0
running_losses["attr_dis_fake_rl"] = 0
running_losses["attr_dis_real_rl"] = 0
running_losses["attr_dis_gp_rl"] = 0
running_losses["gen_d_rl"] = 0
running_losses["gen_rl"] = 0
running_losses["gen_attr_d_rl"] = 0
return running_losses
def train(self, epochs, writer_frequency=1, saver_frequency=10):
self.dis.train()
self.attr_dis.train()
self.gen.train()
# add models to writer
# self.writer.add_graph(model=self.dis, input_to_model=[
# torch.randn([1, self.dis.input_feature_shape[1], self.dis.input_feature_shape[2]]).to(self.device),
# torch.randn([1, self.dis.input_attribute_shape[1]]).to(self.device)])
# self.writer.add_graph(self.attr_dis, input_to_model=torch.randn([1, self.attr_dis.input_size]).
# to(self.device))
# self.writer.add_graph(self.gen,
# input_to_model=[torch.randn(1, self.noise_dim).to(self.device),
# torch.randn(1, self.noise_dim).to(self.device),
# torch.randn(1, self.sample_time, self.noise_dim).to(self.device)])
# create all running losses (rl) dict
# running_losses = {
# "dis_total_rl": 0,
# "dis_wogp_rl": 0,
# "dis_fake_rl": 0,
# "dis_real_rl": 0,
# "dis_gp_rl": 0,
# "attr_dis_rl": 0,
# "attr_dis_fake_rl": 0,
# "attr_dis_real_rl": 0,
# "attr_dis_gp_rl": 0,
# "gen_rl": 0,
# "gen_d_rl": 0,
# "gen_attr_d_rl": 0
# }
avg_mmd = []
n_total_steps = len(self.real_train_dl)
for epoch in range(epochs):
self.dis.train()
self.attr_dis.train()
self.gen.train()
mmd = []
for batch_idx, (data_attribute, data_feature) in enumerate(self.real_train_dl):
data_attribute = data_attribute.to(self.device)
data_feature = data_feature.to(self.device)
batch_size = data_attribute.shape[0]
# Train Critic: max E[critic(real)] - E[critic(fake)]
for _ in range(self.d_rounds):
real_attribute_noise = self.gen_attribute_input_noise(batch_size).to(self.device)
addi_attribute_noise = self.gen_attribute_input_noise(batch_size).to(self.device)
feature_input_noise = self.gen_feature_input_noise(batch_size, self.sample_time).to(self.device)
fake_attribute, fake_feature = self.gen(real_attribute_noise,
addi_attribute_noise,
feature_input_noise)
mmd.append(calculate_mmd_rbf(torch.mean(fake_feature, dim=0).detach().cpu().numpy(),
torch.mean(data_feature, dim=0).detach().cpu().numpy()))
# discriminator
dis_real = self.dis(data_feature, data_attribute)
dis_fake = self.dis(fake_feature, fake_attribute)
loss_dis_fake = torch.mean(dis_fake)
loss_dis_real = -torch.mean(dis_real)
# running_losses["dis_wogp_rl"] += (loss_dis_fake + loss_dis_real).item()
# calculate gradient penalty
loss_dis_gp, loss_dis_gp_unflattened = self.calculate_gp_dis(batch_size, fake_feature, data_feature,
fake_attribute, data_attribute)
loss_dis = loss_dis_fake + loss_dis_real + self.dis_lambda_gp * loss_dis_gp
self.dis.zero_grad()
loss_dis.backward(retain_graph=True)
self.dis_opt.step()
# running_losses["dis_total_rl"] += loss_dis.item()
# running_losses["dis_fake_rl"] += loss_dis_fake.item()
# running_losses["dis_real_rl"] += loss_dis_real.item()
# running_losses["dis_gp_rl"] += loss_dis_gp.item()
# attribute discriminator
attr_dis_real = self.attr_dis(data_attribute)
attr_dis_fake = self.attr_dis(fake_attribute)
loss_attr_dis_real = -torch.mean(attr_dis_real)
loss_attr_dis_fake = torch.mean(attr_dis_fake)
# calculate gradient penalty
loss_attr_dis_gp, loss_attr_dis_gp_unflattened = self.calculate_gp_attr_dis(batch_size,
fake_attribute,
data_attribute)
loss_attr_dis = loss_attr_dis_fake + loss_attr_dis_real + self.attr_dis_lambda_gp * loss_attr_dis_gp
self.attr_dis.zero_grad()
loss_attr_dis.backward(retain_graph=True)
self.attr_dis_opt.step()
# running_losses["attr_dis_rl"] += loss_attr_dis.item()
# running_losses["attr_dis_fake_rl"] += loss_attr_dis_fake.item()
# running_losses["attr_dis_real_rl"] += loss_attr_dis_real.item()
# running_losses["attr_dis_gp_rl"] += loss_attr_dis_gp.item()
# Train Generator: max E[critic(gen_fake)] <-> min -E[critic(gen_fake)]
for _ in range(self.g_rounds):
gen_d_fake = self.dis(fake_feature, fake_attribute)
gen_attr_d_fake = self.attr_dis(fake_attribute)
loss_gen_d = -torch.mean(gen_d_fake)
loss_gen_attr_d = -torch.mean(gen_attr_d_fake)
loss_gen = loss_gen_d + self.g_attr_d_coe * loss_gen_attr_d
self.gen.zero_grad()
loss_gen.backward()
self.gen_opt.step()
# running_losses["gen_d_rl"] += loss_gen_d.item()
# running_losses["gen_attr_d_rl"] += loss_gen_attr_d.item()
# running_losses["gen_rl"] += loss_gen.item()
# write losses to summary writer
# if (batch_idx + 1) % writer_frequency == 0:
# running_losses = self.add_losses(running_losses, writer_frequency, epoch, n_total_steps,
# batch_idx)
self.time_logger.info('END OF EPOCH {0}'.format(epoch))
avg_mmd.append(np.asarray(mmd).mean())
# save model
if epoch % saver_frequency == 0:
self.save(epoch)
self.inference(epoch)
np.save("{}/mmd.npy".format(self.checkpoint_dir), np.asarray(avg_mmd))
# self.writer.close()
class CGAN:
@property
def name(self):
return 'CGAN'
def __init__(self,
train_loader,
device,
batch_size=28,
lr=0.001,
noise_dim=30,
num_units_dis=100,
num_units_gen=200,
num_layers=3,
beta1=0.5,
alpha=0.1,
checkpoint_dir='',
time_logging_file='',
config_logging_file=''):
self.config_logger, self.time_logger = setup_logging(time_logging_file, config_logging_file)
# setup models
self.device = device
self.real_train_dl = train_loader
self.checkpoint_dir = checkpoint_dir
self.noise_dim = noise_dim
self.generator = CGANGenerator(input_feature_shape=train_loader.dataset.data_feature_shape,
input_attribute_shape=train_loader.dataset.data_attribute_shape,
noise_dim=noise_dim, num_units=num_units_gen, num_layers=num_layers, alpha=alpha)
self.discriminator = CGANDiscriminator(input_feature_shape=train_loader.dataset.data_feature_shape,
input_attribute_shape=train_loader.dataset.data_attribute_shape,
num_units=num_units_dis, num_layers=num_layers, alpha=alpha)
self.generator = self.generator.to(self.device)
self.discriminator = self.discriminator.to(self.device)
self.config_logger.info("DISCRIMINATOR: {0}".format(self.discriminator))
self.config_logger.info("GENERATOR: {0}".format(self.generator))
# loss
self.criterion = nn.BCELoss()
self.config_logger.info("Criterion: {0}".format(self.criterion))
# Setup optimizer
self.optimizer_gen = optim.Adam(self.generator.parameters(), lr=lr, betas=(beta1, 0.999))
self.optimizer_dis = optim.Adam(self.discriminator.parameters(), lr=lr, betas=(beta1, 0.999))
self.config_logger.info("DISCRIMINATOR OPTIMIZER: {0}".format(self.optimizer_dis))
self.config_logger.info("GENERATOR OPTIMIZER: {0}".format(self.optimizer_gen))
self.batch_size = batch_size
self.config_logger.info("Batch Size: {0}".format(self.batch_size))
self.config_logger.info("Noise Dimension: {0}".format(self.noise_dim))
self.config_logger.info("d_rounds: {0}".format("1"))
self.config_logger.info("g_rounds: {0}".format("1"))
self.config_logger.info("Device: {0}".format(self.device))
def save(self, epoch):
if not os.path.exists("{0}/epoch_{1}".format(self.checkpoint_dir, epoch)):
os.makedirs("{0}/epoch_{1}".format(self.checkpoint_dir, epoch))
torch.save(self.generator, "{0}/epoch_{1}/generator.pth".format(self.checkpoint_dir, epoch))
torch.save(self.discriminator, "{0}/epoch_{1}/discriminator.pth".format(self.checkpoint_dir, epoch))
def load(self, model_dir=None):
if not os.path.exists(model_dir):
raise Exception("Directory to load pytorch model doesn't exist")
self.generator = torch.load("{0}/generator.pth".format(model_dir))
self.discriminator = torch.load("{0}/discriminator.pth".format(model_dir))
self.generator = self.generator.to(self.device)
self.discriminator = self.discriminator.to(self.device)
def inference(self, epoch, model_dir=None):
if model_dir is None:
model_dir = "{0}/epoch_{1}".format(self.checkpoint_dir, epoch)
batch_size = self.batch_size
while self.real_train_dl.dataset.data_attribute_shape[0] % batch_size != 0:
batch_size -= 1
rounds = self.real_train_dl.dataset.data_attribute_shape[0] // batch_size
sampled_features = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2] - 2))
sampled_attributes = np.zeros((0, self.real_train_dl.dataset.data_attribute_shape[1]))
sampled_gen_flags = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1]))
sampled_lengths = np.zeros(0)
for i in range(rounds):
features, attributes, gen_flags, lengths = self.sample_from(batch_size=batch_size)
sampled_features = np.concatenate((sampled_features, features), axis=0)
sampled_attributes = np.concatenate((sampled_attributes, attributes), axis=0)
sampled_gen_flags = np.concatenate((sampled_gen_flags, gen_flags), axis=0)
sampled_lengths = np.concatenate((sampled_lengths, lengths), axis=0)
np.savez("{0}/generated_samples.npz".format(model_dir), sampled_features=sampled_features,
sampled_attributes=sampled_attributes, sampled_gen_flags=sampled_gen_flags,
sampled_lengths=sampled_lengths)
def sample_from(self, batch_size, return_gen_flag_feature=False):
self.discriminator.eval()
self.generator.eval()
noise = gen_noise((batch_size, self.noise_dim)).to(self.device)
attributes, data_feature = next(iter(self.real_train_dl))
attributes = attributes.to(self.device)
attributes = attributes[:batch_size, :]
input_gen = torch.cat((attributes, noise), dim=1)
with torch.no_grad():
features = self.generator(input_gen)
features = torch.reshape(features, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
features = features.cpu().numpy()
gen_flags = np.zeros(features.shape[:-1])
lengths = np.zeros(features.shape[0])
for i in range(len(features)):
winner = (features[i, :, -1] > features[i, :, -2])
argmax = np.argmax(winner == True)
if argmax == 0:
gen_flags[i, :] = 1
else:
gen_flags[i, :argmax + 1] = 1
lengths[i] = argmax
if not return_gen_flag_feature:
features = features[:, :, :-2]
return features, attributes.cpu().numpy(), gen_flags, lengths
def train(self, epochs, writer_frequency=1, saver_frequency=20):
avg_mmd = []
for epoch in range(epochs):
mmd = []
for batch_idx, (data_attribute, data_feature) in enumerate(self.real_train_dl):
data_attribute = data_attribute.to(self.device)
data_feature = data_feature.to(self.device)
data_feature = torch.flatten(data_feature, start_dim=1, end_dim=2)
batch_size = data_attribute.shape[0]
real = torch.cat((data_attribute, data_feature), dim=1)
noise = gen_noise((batch_size, self.noise_dim)).to(self.device)
input_gen = torch.cat((data_attribute, noise), dim=1)
### Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
fake = self.generator(input_gen)
mmd_fake = torch.reshape(fake, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
mmd_real = torch.reshape(data_feature, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
mmd.append(calculate_mmd_rbf(torch.mean(mmd_fake, dim=0).detach().cpu().numpy(),
torch.mean(mmd_real, dim=0).detach().cpu().numpy()))
fake = torch.cat((data_attribute, fake), dim=1)
disc_real = self.discriminator(real).view(-1)
lossD_real = self.criterion(disc_real, torch.ones_like(disc_real))
disc_fake = self.discriminator(fake).view(-1)
lossD_fake = self.criterion(disc_fake, torch.zeros_like(disc_fake))
lossD = (lossD_real + lossD_fake) / 2
self.discriminator.zero_grad()
lossD.backward(retain_graph=True)
self.optimizer_dis.step()
### Train Generator: min log(1 - D(G(z))) <-> max log(D(G(z))
output = self.discriminator(fake).view(-1)
lossG = self.criterion(output, torch.ones_like(output))
self.generator.zero_grad()
lossG.backward()
self.optimizer_gen.step()
avg_mmd.append(mean(mmd))
self.time_logger.info('END OF EPOCH {0}'.format(epoch))
if epoch % saver_frequency == 0:
self.save(epoch)
self.inference(epoch)
np.save("{}/mmd.npy".format(self.checkpoint_dir), np.asarray(avg_mmd))
class TTSGAN:
@property
def name(self):
return 'TTSGAN'
def __init__(self,
real_train_dl,
device,
lr=0.001,
noise_dim=100,
batch_size=20,
sample_len=10,
beta1=0.5,
alpha=0.1,
checkpoint_dir="",
time_logging_file='',
config_logging_file=''):
# setup models
self.config_logger, self.time_logger = setup_logging(time_logging_file, config_logging_file)
self.device = device
self.real_train_dl = real_train_dl
self.checkpoint_dir = checkpoint_dir
self.noise_dim = noise_dim
self.seq_len = self.real_train_dl.dataset.data_feature_shape[1]
self.channels = self.real_train_dl.dataset.data_feature_shape[2]
self.generator = TTSGenerator(seq_len=self.seq_len, latent_dim=self.noise_dim,
channels=self.channels)
self.discriminator = TTSDiscriminator(in_channels=self.channels, seq_length=self.seq_len, patch_size=sample_len)
self.generator = self.generator.to(self.device)
self.discriminator = self.discriminator.to(self.device)
# loss
self.criterion = nn.MSELoss()
# Setup optimizer
self.optimizer_gen = optim.Adam(self.generator.parameters(), lr=lr, betas=(beta1, 0.999))
self.optimizer_dis = optim.Adam(self.discriminator.parameters(), lr=lr, betas=(beta1, 0.999))
self.config_logger.info("DISCRIMINATOR: {0}".format(self.discriminator))
self.config_logger.info("GENERATOR: {0}".format(self.generator))
self.config_logger.info("Criterion: {0}".format(self.criterion))
self.config_logger.info("DISCRIMINATOR OPTIMIZER: {0}".format(self.optimizer_dis))
self.config_logger.info("GENERATOR OPTIMIZER: {0}".format(self.optimizer_gen))
self.batch_size = batch_size
self.config_logger.info("Batch Size: {0}".format(self.batch_size))
self.config_logger.info("Noise Dimension: {0}".format(self.noise_dim))
self.config_logger.info("d_rounds: {0}".format("1"))
self.config_logger.info("g_rounds: {0}".format("1"))
self.config_logger.info("Device: {0}".format(self.device))
def save(self, epoch):
if not os.path.exists("{0}/epoch_{1}".format(self.checkpoint_dir, epoch)):
os.makedirs("{0}/epoch_{1}".format(self.checkpoint_dir, epoch))
torch.save(self.generator, "{0}/epoch_{1}/generator.pth".format(self.checkpoint_dir, epoch))
torch.save(self.discriminator, "{0}/epoch_{1}/discriminator.pth".format(self.checkpoint_dir, epoch))
def load(self, model_dir=None):
if not os.path.exists(model_dir):
raise Exception("Directory to load pytorch model doesn't exist")
self.generator = torch.load("{0}/generator.pth".format(model_dir))
self.discriminator = torch.load("{0}/discriminator.pth".format(model_dir))
self.generator = self.generator.to(self.device)
self.discriminator = self.discriminator.to(self.device)
def inference(self, epoch, model_dir=None):
if model_dir is None:
model_dir = "{0}/epoch_{1}".format(self.checkpoint_dir, epoch)
batch_size = self.batch_size
while self.real_train_dl.dataset.data_attribute_shape[0] % batch_size != 0:
batch_size -= 1
rounds = self.real_train_dl.dataset.data_attribute_shape[0] // batch_size
sampled_features = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2] - 2))
sampled_attributes = np.zeros((0, self.real_train_dl.dataset.data_attribute_shape[1]))
sampled_gen_flags = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1]))
sampled_lengths = np.zeros(0)
for i in range(rounds):
features, attributes, gen_flags, lengths = self.sample_from(batch_size=batch_size)
sampled_features = np.concatenate((sampled_features, features), axis=0)
sampled_attributes = np.concatenate((sampled_attributes, attributes), axis=0)
sampled_gen_flags = np.concatenate((sampled_gen_flags, gen_flags), axis=0)
sampled_lengths = np.concatenate((sampled_lengths, lengths), axis=0)
np.savez("{0}/generated_samples.npz".format(model_dir), sampled_features=sampled_features,
sampled_attributes=sampled_attributes, sampled_gen_flags=sampled_gen_flags,
sampled_lengths=sampled_lengths)
def sample_from(self, batch_size, return_gen_flag_feature=False):
self.discriminator.eval()
self.generator.eval()
noise = gen_noise((batch_size, self.noise_dim)).to(self.device)
attributes = torch.zeros((batch_size, self.real_train_dl.dataset.data_attribute.shape[1]))
with torch.no_grad():
features = self.generator(noise)
features = torch.reshape(features, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
features = features.cpu().numpy()
gen_flags = np.zeros(features.shape[:-1])
lengths = np.zeros(features.shape[0])
for i in range(len(features)):
winner = (features[i, :, -1] > features[i, :, -2])
argmax = np.argmax(winner == True)
if argmax == 0:
gen_flags[i, :] = 1
else:
gen_flags[i, :argmax + 1] = 1
lengths[i] = argmax
if not return_gen_flag_feature:
features = features[:, :, :-2]
return features, attributes.cpu().numpy(), gen_flags, lengths
def train(self, epochs, writer_frequency=1, saver_frequency=20):
avg_mmd = []
for epoch in range(epochs):
mmd = []
for batch_idx, (data_attribute, data_feature) in enumerate(self.real_train_dl):
# data_attribute = data_attribute.to(self.device)
data_feature = data_feature.to(self.device)
data_feature = data_feature.view(-1, self.channels, 1, self.seq_len)
batch_size = data_attribute.shape[0]
# real = torch.cat((data_attribute, data_feature), dim=1)
noise = gen_noise((batch_size, self.noise_dim)).to(self.device)
# input_gen = torch.cat((data_attribute, noise), dim=1)
# Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
fake = self.generator(noise)
mmd_fake = torch.reshape(fake, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
mmd_real = torch.reshape(data_feature, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
mmd.append(calculate_mmd_rbf(torch.mean(mmd_fake, dim=0).detach().cpu().numpy(),
torch.mean(mmd_real, dim=0).detach().cpu().numpy()))
# fake = torch.cat((data_attribute, fake), dim=1)
disc_fake = self.discriminator(fake).view(-1)
lossD_fake = self.criterion(disc_fake, torch.zeros_like(disc_fake))
disc_real = self.discriminator(data_feature).view(-1)
lossD_real = self.criterion(disc_real, torch.ones_like(disc_real))
lossD = lossD_real + lossD_fake
self.discriminator.zero_grad()
lossD.backward(retain_graph=True)
self.optimizer_dis.step()
# Train Generator: min log(1 - D(G(z))) <-> max log(D(G(z))
output = self.discriminator(fake).view(-1)
lossG = self.criterion(output, torch.ones_like(output))
self.generator.zero_grad()
lossG.backward()
self.optimizer_gen.step()
avg_mmd.append(mean(mmd))
self.time_logger.info('END OF EPOCH {0}'.format(epoch))
if epoch % saver_frequency == 0:
self.save(epoch)
self.inference(epoch)
np.save("{}/mmd.npy".format(self.checkpoint_dir), np.asarray(avg_mmd))
class NAIVEGAN:
@property
def name(self):
return 'NAIVEGAN'
def __init__(self,
train_loader,
device,
lr=0.001,
noise_dim=5,
batch_size=20,
num_units=200,
num_layers=5,
beta1=0.5,
alpha=0.1,
checkpoint_dir="",
time_logging_file='',
config_logging_file=''):
# setup models
self.config_logger, self.time_logger = setup_logging(time_logging_file, config_logging_file)
self.device = device
self.real_train_dl = train_loader
self.checkpoint_dir = checkpoint_dir
self.noise_dim = noise_dim
self.generator = NaiveGanGenerator(input_feature_shape=train_loader.dataset.data_feature_shape,
input_attribute_shape=train_loader.dataset.data_attribute_shape,
noise_dim=noise_dim, num_units=num_units, num_layers=num_layers, alpha=alpha)
self.discriminator = NaiveGanDiscriminator(input_feature_shape=train_loader.dataset.data_feature_shape,
input_attribute_shape=train_loader.dataset.data_attribute_shape,
num_units=num_units, num_layers=num_layers, alpha=alpha)
self.generator = self.generator.to(self.device)
self.discriminator = self.discriminator.to(self.device)
# loss
self.criterion = nn.BCELoss()
# Setup optimizer
self.optimizer_gen = optim.Adam(self.generator.parameters(), lr=lr, betas=(beta1, 0.999))
self.optimizer_dis = optim.Adam(self.discriminator.parameters(), lr=lr, betas=(beta1, 0.999))
self.config_logger.info("DISCRIMINATOR: {0}".format(self.discriminator))
self.config_logger.info("GENERATOR: {0}".format(self.generator))
self.config_logger.info("Criterion: {0}".format(self.criterion))
self.config_logger.info("DISCRIMINATOR OPTIMIZER: {0}".format(self.optimizer_dis))
self.config_logger.info("GENERATOR OPTIMIZER: {0}".format(self.optimizer_gen))
self.batch_size = batch_size
self.config_logger.info("Batch Size: {0}".format(self.batch_size))
self.config_logger.info("Noise Dimension: {0}".format(self.noise_dim))
self.config_logger.info("d_rounds: {0}".format("1"))
self.config_logger.info("g_rounds: {0}".format("1"))
self.config_logger.info("Device: {0}".format(self.device))
def save(self, epoch):
if not os.path.exists("{0}/epoch_{1}".format(self.checkpoint_dir, epoch)):
os.makedirs("{0}/epoch_{1}".format(self.checkpoint_dir, epoch))
torch.save(self.generator, "{0}/epoch_{1}/generator.pth".format(self.checkpoint_dir, epoch))
torch.save(self.discriminator, "{0}/epoch_{1}/discriminator.pth".format(self.checkpoint_dir, epoch))
def load(self, model_dir=None):
if not os.path.exists(model_dir):
raise Exception("Directory to load pytorch model doesn't exist")
self.generator = torch.load("{0}/generator.pth".format(model_dir))
self.discriminator = torch.load("{0}/discriminator.pth".format(model_dir))
self.generator = self.generator.to(self.device)
self.discriminator = self.discriminator.to(self.device)
def inference(self, epoch, model_dir=None):
if model_dir is None:
model_dir = "{0}/epoch_{1}".format(self.checkpoint_dir, epoch)
batch_size = self.batch_size
while self.real_train_dl.dataset.data_attribute_shape[0] % batch_size != 0:
batch_size -= 1
rounds = self.real_train_dl.dataset.data_attribute_shape[0] // batch_size
sampled_features = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2] - 2))
sampled_attributes = np.zeros((0, self.real_train_dl.dataset.data_attribute_shape[1]))
sampled_gen_flags = np.zeros((0, self.real_train_dl.dataset.data_feature_shape[1]))
sampled_lengths = np.zeros(0)
for i in range(rounds):
features, attributes, gen_flags, lengths = self.sample_from(batch_size=batch_size)
sampled_features = np.concatenate((sampled_features, features), axis=0)
sampled_attributes = np.concatenate((sampled_attributes, attributes), axis=0)
sampled_gen_flags = np.concatenate((sampled_gen_flags, gen_flags), axis=0)
sampled_lengths = np.concatenate((sampled_lengths, lengths), axis=0)
np.savez("{0}/generated_samples.npz".format(model_dir), sampled_features=sampled_features,
sampled_attributes=sampled_attributes, sampled_gen_flags=sampled_gen_flags,
sampled_lengths=sampled_lengths)
def sample_from(self, batch_size, return_gen_flag_feature=False):
self.discriminator.eval()
self.generator.eval()
noise = gen_noise((batch_size, self.noise_dim)).to(self.device)
attributes = torch.zeros((batch_size, self.real_train_dl.dataset.data_attribute.shape[1]))
with torch.no_grad():
features = self.generator(noise)
features = torch.reshape(features, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
features = features.cpu().numpy()
gen_flags = np.zeros(features.shape[:-1])
lengths = np.zeros(features.shape[0])
for i in range(len(features)):
winner = (features[i, :, -1] > features[i, :, -2])
argmax = np.argmax(winner == True)
if argmax == 0:
gen_flags[i, :] = 1
else:
gen_flags[i, :argmax + 1] = 1
lengths[i] = argmax
if not return_gen_flag_feature:
features = features[:, :, :-2]
return features, attributes.cpu().numpy(), gen_flags, lengths
def train(self, epochs, writer_frequency=1, saver_frequency=20):
avg_mmd = []
for epoch in range(epochs):
mmd = []
for batch_idx, (data_attribute, data_feature) in enumerate(self.real_train_dl):
# data_attribute = data_attribute.to(self.device)
data_feature = data_feature.to(self.device)
data_feature = torch.flatten(data_feature, start_dim=1, end_dim=2)
batch_size = data_attribute.shape[0]
# real = torch.cat((data_attribute, data_feature), dim=1)
noise = gen_noise((batch_size, self.noise_dim)).to(self.device)
# input_gen = torch.cat((data_attribute, noise), dim=1)
### Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
fake = self.generator(noise)
mmd_fake = torch.reshape(fake, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
mmd_real = torch.reshape(data_feature, (batch_size,
self.real_train_dl.dataset.data_feature_shape[1],
self.real_train_dl.dataset.data_feature_shape[2]))
mmd.append(calculate_mmd_rbf(torch.mean(mmd_fake, dim=0).detach().cpu().numpy(),
torch.mean(mmd_real, dim=0).detach().cpu().numpy()))
# fake = torch.cat((data_attribute, fake), dim=1)
disc_real = self.discriminator(data_feature).view(-1)
lossD_real = self.criterion(disc_real, torch.ones_like(disc_real))
disc_fake = self.discriminator(fake).view(-1)
lossD_fake = self.criterion(disc_fake, torch.zeros_like(disc_fake))
lossD = (lossD_real + lossD_fake) / 2
self.discriminator.zero_grad()
lossD.backward(retain_graph=True)
self.optimizer_dis.step()
### Train Generator: min log(1 - D(G(z))) <-> max log(D(G(z))
output = self.discriminator(fake).view(-1)
lossG = self.criterion(output, torch.ones_like(output))
self.generator.zero_grad()
lossG.backward()
self.optimizer_gen.step()
avg_mmd.append(mean(mmd))
self.time_logger.info('END OF EPOCH {0}'.format(epoch))
if epoch % saver_frequency == 0:
self.save(epoch)
self.inference(epoch)
np.save("{}/mmd.npy".format(self.checkpoint_dir), np.asarray(avg_mmd))
class TimeGAN:
# TimeGAN Class
@property
def name(self):
return 'TimeGAN'
def __init__(self,
train_loader,
device,
lr=0.001,
z_dim=6,
hidden_dim=24,
num_layer=3,
batch_size=128,
beta1=0.9,
w_lambda=1,
w_eta=0.1,
checkpoint_dir="",
config_logging_file="",
time_logging_file=""):
self.config_logger, self.time_logger = setup_logging(time_logging_file, config_logging_file)
# Initalize variables.
self.real_train_dl = train_loader
self.device = device
self.lr = lr
self.z_dim = self.real_train_dl.dataset.data_feature_shape[2]
self.hidden_dim = 4 * self.real_train_dl.dataset.data_feature_shape[2]
self.num_layer = num_layer
self.beta1 = beta1
self.w_lambda = w_lambda
self.w_eta = w_eta
self.checkpoint_dir = checkpoint_dir
self.batch_size = batch_size
# Create and initialize networks.
# determine input size for encoder
num_features = self.real_train_dl.dataset.data_feature.shape[2]
self.nete = TGEncoder(input_size=num_features, hidden_dim=self.hidden_dim, num_layer=self.num_layer).to(
self.device)
self.netr = TGRecovery(output_size=num_features, hidden_dim=self.hidden_dim, num_layer=self.num_layer).to(
self.device)
self.netg = TGGenerator(z_dim=self.z_dim, hidden_dim=self.hidden_dim, num_layer=self.num_layer).to(self.device)
self.netd = TGDiscriminator(hidden_dim=self.hidden_dim, num_layer=self.num_layer).to(
self.device)
self.nets = TGSupervisor(hidden_dim=self.hidden_dim, num_layer=self.num_layer).to(self.device)
# loss
self.l_mse = nn.MSELoss()
self.l_r = nn.L1Loss()
self.l_bce = nn.BCELoss()
# Setup optimizer
self.optimizer_er = optim.Adam(list(self.nete.parameters()) + list(self.netr.parameters()), lr=self.lr,
betas=(self.beta1, 0.999))
self.optimizer_d = optim.Adam(self.netd.parameters(), lr=self.lr, betas=(self.beta1, 0.999))
self.optimizer_gs = optim.Adam(list(self.netg.parameters()) + list(self.nets.parameters()), lr=self.lr,
betas=(self.beta1, 0.999))
self.config_logger.info("EMBEDDER: {0}".format(self.nete))
self.config_logger.info("RECOVERY: {0}".format(self.netr))
self.config_logger.info("DISCRIMINATOR: {0}".format(self.netd))
self.config_logger.info("GENERATOR: {0}".format(self.netg))
self.config_logger.info("SUPERVISOR: {0}".format(self.nets))
self.config_logger.info("EMBEDDER / RECOVERY OPTIMIZER: {0}".format(self.optimizer_er))
self.config_logger.info("DISCRIMINATOR OPTIMIZER: {0}".format(self.optimizer_d))
self.config_logger.info("GENERATOR / SUPERVISOR OPTIMIZER: {0}".format(self.optimizer_gs))
self.config_logger.info("Reconstruction Loss: {0}".format(self.l_mse))
self.config_logger.info("Unsupervised Loss: {0}".format(self.l_mse))
self.config_logger.info("Generator Adversarial Loss: {0}".format(self.l_bce))
self.config_logger.info("Discriminator Adversarial Loss: {0}".format(self.l_bce))
self.config_logger.info("Batch Size: {0}".format(self.batch_size))
self.config_logger.info("Z-Dimension: {0}".format(self.z_dim))
self.config_logger.info("Hidden Dimension: {0}".format(self.hidden_dim))
self.config_logger.info("Beta 1: {0}".format(self.beta1))
self.config_logger.info("w_gamma: {0}".format(self.w_lambda))
self.config_logger.info("w_eta: {0}".format(self.w_eta))
def save(self, epoch):
if not os.path.exists("{0}/epoch_{1}".format(self.checkpoint_dir, epoch)):
os.makedirs("{0}/epoch_{1}".format(self.checkpoint_dir, epoch))
torch.save(self.nete, "{0}/epoch_{1}/nete.pth".format(self.checkpoint_dir, epoch))
torch.save(self.netr, "{0}/epoch_{1}/netr.pth".format(self.checkpoint_dir, epoch))
torch.save(self.netg, "{0}/epoch_{1}/netg.pth".format(self.checkpoint_dir, epoch))
torch.save(self.netd, "{0}/epoch_{1}/netd.pth".format(self.checkpoint_dir, epoch))
torch.save(self.nets, "{0}/epoch_{1}/nets.pth".format(self.checkpoint_dir, epoch))
def load(self, model_dir=None):
if not os.path.exists(model_dir):
raise Exception("Directory to load pytorch model doesn't exist")
self.nete = torch.load("{0}/nete.pth".format(model_dir))
self.netr = torch.load("{0}/netr.pth".format(model_dir))
self.netg = torch.load("{0}/netg.pth".format(model_dir))
self.netd = torch.load("{0}/netd.pth".format(model_dir))
self.nets = torch.load("{0}/nets.pth".format(model_dir))
self.nete = self.nete.to(self.device)
self.netr = self.netr.to(self.device)
self.netg = self.netg.to(self.device)
self.netd = self.netd.to(self.device)
self.nets = self.nets.to(self.device)
def inference(self, epoch, model_dir=None):
if model_dir is None:
model_dir = "{0}/epoch_{1}".format(self.checkpoint_dir, epoch)
batch_size = self.batch_size
while self.real_train_dl.dataset.data_attribute_shape[0] % batch_size != 0:
batch_size -= 1