def train(self, epochs, load=False):
print('TRAINING MODEL:'
' BATCH_SIZE = ' + str(Trainer.BATCH_SIZE) + ', PARTICLE_DIM: ' +
str(PARTICLE_DIM) + ', EPOCHS: ' + str(epochs) +
', PRTCL_LATENT_SPACE_SIZE: ' +
str(self.PRTCL_LATENT_SPACE_SIZE))
encoder, decoder, discriminator = self.create_model()
enc_optim = torch.optim.Adam(encoder.parameters(), lr=self.LR)
dec_optim = torch.optim.Adam(decoder.parameters(), lr=self.LR)
dis_optim = torch.optim.Adam(discriminator.parameters(), lr=self.LR)
embedder = self.create_embedder()
deembedder = self.create_deembedder()
if load:
print('LOADING MODEL STATES...')
encoder.load_state_dict(torch.load(Trainer.ENCODER_SAVE_PATH))
decoder.load_state_dict(torch.load(Trainer.DECODER_SAVE_PATH))
discriminator.load_state_dict(
torch.load(Trainer.DISCRIMINATOR_SAVE_PATH))
embedder.load_state_dict(torch.load(Trainer.PDG_EMBED_SAVE_PATH))
deembedder.load_state_dict(
torch.load(Trainer.PDG_DEEMBED_SAVE_PATH))
print('AUTOENCODER')
print(encoder)
print(decoder)
print(discriminator)
print('EMBEDDER')
print(embedder)
print('DEEMBEDDER')
print(deembedder)
_data = load_data()
data_train, data_valid = self.prep_data(_data,
batch_size=Trainer.BATCH_SIZE,
valid=0.1)
particles = torch.tensor(particle_idxs(), device=self.device)
particles.requires_grad = False
for epoch in range(epochs):
for n_batch, batch in enumerate(data_train):
encoder.zero_grad()
decoder.zero_grad()
discriminator.zero_grad()
real_data: torch.Tensor = batch.to(self.device)
emb_data = self.embed_data(real_data, [embedder]).detach()
batch_size = len(batch)
zeros = torch.zeros(batch_size,
device=self.device,
requires_grad=False)
ones = torch.ones(batch_size,
device=self.device,
requires_grad=False)
# ======== Train Discriminator ======== #
decoder.freeze(True)
encoder.freeze(True)
discriminator.freeze(False)
lat_fake = torch.randn(batch_size,
self.PRTCL_LATENT_SPACE_SIZE,
device=self.device)
disc_fake = discriminator(lat_fake)
lat_real = encoder(emb_data)
disc_real = discriminator(lat_real)
loss_fake = MSELoss()(disc_fake, zeros)
loss_real = MSELoss()(disc_real, ones)
loss_fake.backward()
loss_real.backward()
dis_optim.step()
# ======== Train Generator ======== #
decoder.freeze(False)
encoder.freeze(False)
discriminator.freeze(True)
lat_real = encoder(emb_data)
recon_data = decoder(lat_real)
d_real = discriminator(encoder(emb_data))
recon_loss = MSELoss()(emb_data, recon_data)
d_loss = MSELoss()(d_real, zeros)
recon_loss.backward()
d_loss.backward()
enc_optim.step()
dec_optim.step()
self.train_deembeders([
(particles, embedder, deembedder),
],
epochs=2)
if n_batch % 100 == 0:
self.print_deemb_quality(particles, embedder, deembedder)
self.show_heatmaps(emb_data[:30, :],
recon_data[:30, :],
reprod=False,
save=True,
epoch=epoch,
batch=n_batch)
err_kld, err_wass = self.gen_show_comp_hists(
decoder,
_data,
attr_idxs=[
FEATURES - 8, FEATURES - 7, FEATURES - 6,
FEATURES - 5
],
embedders=[embedder],
emb=False,
deembedder=deembedder,
save=True,
epoch=epoch,
batch=n_batch)
self.errs_kld.append(err_kld)
self.errs_wass.append(err_wass)
valid_loss = self._valid_loss(encoder, decoder, embedder,
data_valid)
print(
f'Epoch: {str(epoch)}/{epochs} :: '
f'Batch: {str(n_batch)}/{str(len(data_train))} :: '
f'train loss: {"{:.6f}".format(round(recon_loss.item(), 6))} :: '
f'valid loss: {"{:.6f}".format(round(valid_loss, 6))} :: '
f'err kld: {"{:.6f}".format(round(err_kld, 6))} :: '
f'err wass: {"{:.6f}".format(round(err_wass, 6))}')
self._save_models(encoder, decoder, discriminator, embedder,
deembedder)
with open(self.ERRS_SAVE_PATH, 'wb') as handle:
pickle.dump((self.errs_kld, self.errs_wass),
handle,
protocol=pickle.HIGHEST_PROTOCOL)
return encoder, decoder, discriminator, embedder, deembedder
def load_trans_data(self):
return load_data()
from common.consts import FEATURES
from common.load_event_data import sgnlog_rev
from i_trainer.load_data import load_data, sgnlog
from single_prtcl_generator_wgan_pytorch.trainer import Trainer
### TRAINING
trainer = Trainer()
#generator, discriminator, embedder, deembedder = trainer.train(epochs=15, load=False)
wgan_gen, wgan_disc = trainer.create_model()
wgan_gen.load_state_dict(torch.load(trainer.GENERATOR_SAVE_PATH))
embedder = trainer.create_embedder()
deembedder = trainer.create_deembedder()
_data = load_data()
_data = _data[:10000, :]
trainer.gen_show_comp_hists(
wgan_gen,
_data,
attr_idxs=[7 - 4, 8 - 4, 9 - 4, FEATURES - 5],
embedders=[embedder],
emb=False,
sample_cnt=1000,
deembedder=deembedder,
)
'''
import time
def train(self, epochs, load=False):
print('TRAINING MODEL:'
' BATCH_SIZE = ' + str(self.BATCH_SIZE) + ', PARTICLE_DIM: ' +
str(PARTICLE_DIM) + ', EPOCHS: ' + str(epochs) +
', PRTCL_LATENT_SPACE_SIZE: ' +
str(self.PRTCL_LATENT_SPACE_SIZE))
generator, discriminator = self.create_model()
gen_optim = Adam(generator.parameters(), lr=self.LR, betas=(.1, .9))
dis_optim = Adam(discriminator.parameters(),
lr=self.LR,
betas=(.1, .9))
embedder = self.create_embedder()
deembedder = self.create_deembedder()
if load:
print('LOADING MODEL STATES...')
try:
generator.load_state_dict(
torch.load(Trainer.GENERATOR_SAVE_PATH))
except Exception:
print('Problem loading generator!')
pass
try:
discriminator.load_state_dict(
torch.load(Trainer.CRITIC_SAVE_PATH))
except Exception:
print('Problem loading critic!')
pass
try:
embedder.load_state_dict(
torch.load(Trainer.PDG_EMBED_SAVE_PATH))
except Exception:
print('Problem loading embeder!')
pass
try:
deembedder.load_state_dict(
torch.load(Trainer.PDG_DEEMBED_SAVE_PATH))
except Exception:
print('Problem loading deembeder!')
pass
print('GENERATOR')
print(generator)
print('DISCRIMINATOR')
print(discriminator)
print('EMBEDDER')
print(embedder)
print('DEEMBEDDER')
print(deembedder)
_data = load_data()
data_train, data_valid = self.prep_data(_data,
batch_size=self.BATCH_SIZE,
valid=0.1)
particles = torch.tensor(particle_idxs(), device=self.device)
particles.requires_grad = False
for epoch in range(epochs):
for n_batch, batch in enumerate(data_train):
real_data: torch.Tensor = batch.to(self.device)
emb_data = self.embed_data(real_data, [embedder]).detach()
batch_size = len(batch)
# ======== Train Generator ======== #
gen_optim.zero_grad()
for p in discriminator.parameters():
p.requires_grad = False # to avoid computation
# Sample noise as generator input
lat_fake = torch.randn(batch_size,
self.PRTCL_LATENT_SPACE_SIZE,
device=self.device)
gen_data = generator(lat_fake)
output = discriminator(gen_data)
gen_loss = -torch.mean(output)
gen_loss.backward()
gen_optim.step()
for p in discriminator.parameters():
p.requires_grad = True # to avoid computation
# ======== Train Discriminator ======== #
for _ in range(self.CRITIC_ITERATIONS):
critic_real = discriminator(emb_data)
critic_fake = discriminator(gen_data.detach())
gp = self.calc_gradient_penalty(discriminator, emb_data,
gen_data, batch_size)
critic_loss = -(torch.mean(critic_real) -
torch.mean(critic_fake)) + gp
critic_loss.backward()
dis_optim.step()
dis_optim.zero_grad()
self.train_deembeders([
(particles, embedder, deembedder),
],
epochs=2)
if n_batch % 100 == 0:
self.print_deemb_quality(particles, embedder, deembedder)
self.show_heatmaps(emb_data[:30, :],
gen_data[:30, :],
reprod=False,
save=True,
epoch=epoch,
batch=n_batch)
err_kld, err_wass = self.gen_show_comp_hists(
generator,
_data,
attr_idxs=[
FEATURES - 8, FEATURES - 7, FEATURES - 6,
FEATURES - 5
],
embedders=[embedder],
emb=False,
deembedder=deembedder,
save=True,
epoch=epoch,
batch=n_batch)
self.errs_kld.append(err_kld)
self.errs_wass.append(err_wass)
print(
f'Epoch: {str(epoch)}/{epochs} :: '
f'Batch: {str(n_batch)}/{str(len(data_train))} :: '
f'generator loss: {"{:.6f}".format(round(gen_loss.item(), 6))} :: '
f'critic loss: {"{:.6f}".format(round(critic_loss.item(), 6))} :: '
f'err kld: {"{:.6f}".format(round(err_kld, 6))} :: '
f'err wass: {"{:.6f}".format(round(err_wass, 6))}')
self._save_models(generator, discriminator, embedder, deembedder)
with open(self.ERRS_SAVE_PATH, 'wb') as handle:
pickle.dump((self.errs_kld, self.errs_wass),
handle,
protocol=pickle.HIGHEST_PROTOCOL)
return generator, discriminator, embedder, deembedder
def train(self, epochs, load=False):
print('TRAINING MODEL:'
' BATCH_SIZE = ' + str(self.BATCH_SIZE) + ', PARTICLE_DIM: ' +
str(PARTICLE_DIM) + ', EPOCHS: ' + str(epochs) +
', PRTCL_LATENT_SPACE_SIZE: ' +
str(self.PRTCL_LATENT_SPACE_SIZE))
generator, discriminator = self.create_model()
gen_optim = torch.optim.Adam(generator.parameters(),
lr=self.LR,
betas=(0, .9))
dis_optim = torch.optim.Adam(discriminator.parameters(),
lr=self.LR,
betas=(0, .9))
embedder = self.create_embedder()
deembedder = self.create_deembedder()
particles = torch.tensor(particle_idxs(), device=self.device)
if load:
print('LOADING MODEL STATES...')
generator.load_state_dict(torch.load(Trainer.GENERATOR_SAVE_PATH))
discriminator.load_state_dict(
torch.load(Trainer.DISCRIMINATOR_SAVE_PATH))
embedder.load_state_dict(torch.load(Trainer.PDG_EMBED_SAVE_PATH))
deembedder.load_state_dict(
torch.load(Trainer.PDG_DEEMBED_SAVE_PATH))
print('GENERATOR')
print(generator)
print('DISCRIMINATOR')
print(discriminator)
print('EMBEDDER')
print(embedder)
print('DEEMBEDDER')
print(deembedder)
_data = load_data()
data_train, data_valid = self.prep_data(_data,
batch_size=self.BATCH_SIZE,
valid=0.1)
for epoch in range(epochs):
for n_batch, batch in enumerate(data_train):
real_data: torch.Tensor = batch.to(self.device)
emb_data = self.embed_data(real_data, [embedder]).detach()
batch_size = len(batch)
valid = torch.ones(batch_size,
device=self.device,
requires_grad=False)
fake = torch.zeros(batch_size,
device=self.device,
requires_grad=False)
# ======== Train Generator ======== #
gen_optim.zero_grad()
# Sample noise as generator input
lat_fake = torch.randn(batch_size,
self.PRTCL_LATENT_SPACE_SIZE,
device=self.device)
lat_fake = Variable(
torch.tensor(np.random.normal(
0, 1, (batch_size, self.PRTCL_LATENT_SPACE_SIZE)),
device=self.device).float())
# Generate a batch of images
gen_data = generator(lat_fake)
# Loss measures generator's ability to fool the discriminator
g_loss = MSELoss()(discriminator(gen_data), valid)
g_loss.backward()
gen_optim.step()
# ======== Train Discriminator ======== #
dis_optim.zero_grad()
real_loss = MSELoss()(discriminator(emb_data), valid)
fake_loss = MSELoss()(discriminator(gen_data.detach()), fake)
d_loss = (real_loss + fake_loss) / 2
d_loss.backward()
dis_optim.step()
self.train_deembeders([
(particles, embedder, deembedder),
],
epochs=2)
if n_batch % 100 == 0:
self.print_deemb_quality(
torch.tensor(particle_idxs(), device=self.device),
embedder, deembedder)
self.show_heatmaps(emb_data[:30, :],
gen_data[:30, :],
reprod=False,
save=True,
epoch=epoch,
batch=n_batch)
err_kld, err_wass = self.gen_show_comp_hists(
generator,
_data,
attr_idxs=[
FEATURES - 8, FEATURES - 7, FEATURES - 6,
FEATURES - 5
],
embedders=[embedder],
emb=False,
deembedder=deembedder,
save=True,
epoch=epoch,
batch=n_batch)
self.errs_kld.append(err_kld)
self.errs_wass.append(err_wass)
print(
f'Epoch: {str(epoch)}/{epochs} :: '
f'Batch: {str(n_batch)}/{str(len(data_train))} :: '
f'generator loss: {"{:.6f}".format(round(g_loss.item(), 6))} :: '
f'discriminator loss: {"{:.6f}".format(round(d_loss.item(), 6))} :: '
f'err kld: {"{:.6f}".format(round(err_kld, 6))} :: '
f'err wass: {"{:.6f}".format(round(err_wass, 6))}')
self._save_models(generator, discriminator, embedder, deembedder)
with open(self.ERRS_SAVE_PATH, 'wb') as handle:
pickle.dump((self.errs_kld, self.errs_wass),
handle,
protocol=pickle.HIGHEST_PROTOCOL)
return generator, discriminator, embedder, deembedder