def train_iwae(algorithm,
generative_model,
inference_network,
data_loader,
num_iterations,
num_particles,
optim_kwargs,
callback=None):
parameters = itertools.chain.from_iterable(
[x.parameters() for x in [generative_model, inference_network]])
optimizer = torch.optim.Adam(parameters, **optim_kwargs)
iteration = 0
while iteration < num_iterations:
for obs in iter(data_loader):
optimizer.zero_grad()
if algorithm == 'vimco':
loss, elbo = losses.get_vimco_loss(generative_model,
inference_network, obs,
num_particles)
elif algorithm == 'reinforce':
loss, elbo = losses.get_reinforce_loss(generative_model,
inference_network, obs,
num_particles)
loss.backward()
optimizer.step()
if callback is not None:
callback(iteration, loss.item(), elbo.item(), generative_model,
inference_network, optimizer)
iteration += 1
# by this time, we have gone through `iteration` iterations
if iteration == num_iterations:
break
def train_iwae(algorithm,
generative_model,
inference_network,
obss_data_loader,
num_iterations,
num_particles,
callback=None):
"""Train using IWAE objective.
Args:
algorithm: reinforce, vimco or concrete
"""
parameters = itertools.chain.from_iterable(
[x.parameters() for x in [generative_model, inference_network]])
optimizer = torch.optim.Adam(parameters)
obss_iter = iter(obss_data_loader)
for iteration in range(num_iterations):
# get obss
try:
obss = next(obss_iter)
except StopIteration:
obss_iter = iter(obss_data_loader)
obss = next(obss_iter)
# wake theta
optimizer.zero_grad()
if algorithm == 'vimco':
loss, elbo = losses.get_vimco_loss(generative_model,
inference_network, obss,
num_particles)
elif algorithm == 'reinforce':
loss, elbo = losses.get_reinforce_loss(generative_model,
inference_network, obss,
num_particles)
elif algorithm == 'concrete':
loss, elbo = losses.get_concrete_loss(generative_model,
inference_network, obss,
num_particles)
loss.backward()
optimizer.step()
if callback is not None:
callback(iteration, loss.item(), elbo.item(), generative_model,
inference_network, optimizer)
return optimizer
def __call__(self, iteration, loss, elbo, generative_model,
inference_network, optimizer):
if iteration % self.logging_interval == 0:
util.print_with_time(
'Iteration {} loss = {:.3f}, elbo = {:.3f}'.format(
iteration, loss, elbo))
self.loss_history.append(loss)
self.elbo_history.append(elbo)
if iteration % self.checkpoint_interval == 0:
stats_path = util.get_stats_path(self.save_dir)
util.save_object(self, stats_path)
util.save_checkpoint(self.save_dir,
iteration,
generative_model=generative_model,
inference_network=inference_network)
if iteration % self.eval_interval == 0:
log_p, kl = eval_gen_inf(generative_model, inference_network,
self.test_data_loader,
self.eval_num_particles)
self.log_p_history.append(log_p)
self.kl_history.append(kl)
stats = util.OnlineMeanStd()
for _ in range(10):
generative_model.zero_grad()
inference_network.zero_grad()
if self.train_mode == 'vimco':
loss, elbo = losses.get_vimco_loss(generative_model,
inference_network,
self.test_obs,
self.num_particles)
elif self.train_mode == 'reinforce':
loss, elbo = losses.get_reinforce_loss(
generative_model, inference_network, self.test_obs,
self.num_particles)
loss.backward()
stats.update([p.grad for p in generative_model.parameters()] +
[p.grad for p in inference_network.parameters()])
self.grad_std_history.append(stats.avg_of_means_stds()[1].item())
util.print_with_time(
'Iteration {} log_p = {:.3f}, kl = {:.3f}'.format(
iteration, self.log_p_history[-1], self.kl_history[-1]))
def train_iwae(algorithm,
generative_model,
inference_network,
true_generative_model,
batch_size,
num_iterations,
num_particles,
callback=None):
"""Train using IWAE objective.
Args:
algorithm: reinforce or vimco
"""
parameters = itertools.chain.from_iterable(
[x.parameters() for x in [generative_model, inference_network]])
optimizer = torch.optim.Adam(parameters)
for iteration in range(num_iterations):
# generate synthetic data
obss = [true_generative_model.sample_obs() for _ in range(batch_size)]
# wake theta
optimizer.zero_grad()
if algorithm == 'vimco':
loss, elbo = losses.get_vimco_loss(generative_model,
inference_network, obss,
num_particles)
elif algorithm == 'reinforce':
loss, elbo = losses.get_reinforce_loss(generative_model,
inference_network, obss,
num_particles)
loss.backward()
optimizer.step()
if callback is not None:
callback(iteration, loss.item(), elbo.item(), generative_model,
inference_network, optimizer)
return optimizer
def __call__(self, iteration, loss, elbo, generative_model,
inference_network, optimizer):
if iteration % self.logging_interval == 0:
util.print_with_time(
'Iteration {} loss = {:.3f}, elbo = {:.3f}'.format(
iteration, loss, elbo))
self.loss_history.append(loss)
self.elbo_history.append(elbo)
if iteration % self.checkpoint_interval == 0:
stats_filename = util.get_stats_path(self.model_folder)
util.save_object(self, stats_filename)
util.save_models(generative_model, inference_network,
self.model_folder, iteration)
if iteration % self.eval_interval == 0:
self.p_error_history.append(
util.get_p_error(self.true_generative_model, generative_model))
self.q_error_history.append(
util.get_q_error(self.true_generative_model, inference_network,
self.test_obss))
stats = util.OnlineMeanStd()
for _ in range(10):
inference_network.zero_grad()
if self.train_mode == 'vimco':
loss, elbo = losses.get_vimco_loss(generative_model,
inference_network,
self.test_obss,
self.num_particles)
elif self.train_mode == 'reinforce':
loss, elbo = losses.get_reinforce_loss(
generative_model, inference_network, self.test_obss,
self.num_particles)
loss.backward()
stats.update([p.grad for p in inference_network.parameters()])
self.grad_std_history.append(stats.avg_of_means_stds()[1])
util.print_with_time(
'Iteration {} p_error = {:.3f}, q_error_to_true = '
'{:.3f}'.format(iteration, self.p_error_history[-1],
self.q_error_history[-1]))
def train_vimco(
generative_model,
inference_network,
data_loader,
num_iterations,
num_particles,
true_cluster_cov,
test_data_loader,
test_num_particles,
true_generative_model,
checkpoint_path,
):
optimizer = torch.optim.Adam(
itertools.chain(generative_model.parameters(),
inference_network.parameters()))
(
theta_losses,
phi_losses,
cluster_cov_distances,
test_log_ps,
test_log_ps_true,
test_kl_qps,
test_kl_pqs,
test_kl_qps_true,
test_kl_pqs_true,
train_log_ps,
train_log_ps_true,
train_kl_qps,
train_kl_pqs,
train_kl_qps_true,
train_kl_pqs_true,
reweighted_train_kl_qps,
reweighted_train_kl_qps_true,
) = ([], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [])
data_loader_iter = iter(data_loader)
for iteration in range(num_iterations):
# get obs
try:
obs = next(data_loader_iter)
except StopIteration:
data_loader_iter = iter(data_loader)
obs = next(data_loader_iter)
# loss
optimizer.zero_grad()
loss, elbo = losses.get_vimco_loss(generative_model, inference_network,
obs, num_particles)
loss.backward(retain_graph=True)
optimizer.step()
theta_losses.append(loss.item())
phi_losses.append(loss.item())
cluster_cov_distances.append(
torch.norm(true_cluster_cov -
generative_model.get_cluster_cov()).item())
if iteration % 100 == 0: # test every 100 iterations
(
test_log_p,
test_log_p_true,
test_kl_qp,
test_kl_pq,
test_kl_qp_true,
test_kl_pq_true,
_,
_,
) = models.eval_gen_inf(true_generative_model, generative_model,
inference_network, None, test_data_loader)
test_log_ps.append(test_log_p)
test_log_ps_true.append(test_log_p_true)
test_kl_qps.append(test_kl_qp)
test_kl_pqs.append(test_kl_pq)
test_kl_qps_true.append(test_kl_qp_true)
test_kl_pqs_true.append(test_kl_pq_true)
(
train_log_p,
train_log_p_true,
train_kl_qp,
train_kl_pq,
train_kl_qp_true,
train_kl_pq_true,
_,
_,
reweighted_train_kl_qp,
reweighted_train_kl_qp_true,
) = models.eval_gen_inf(
true_generative_model,
generative_model,
inference_network,
None,
data_loader,
num_particles=num_particles,
reweighted_kl=True,
)
train_log_ps.append(train_log_p)
train_log_ps_true.append(train_log_p_true)
train_kl_qps.append(train_kl_qp)
train_kl_pqs.append(train_kl_pq)
train_kl_qps_true.append(train_kl_qp_true)
train_kl_pqs_true.append(train_kl_pq_true)
reweighted_train_kl_qps.append(reweighted_train_kl_qp)
reweighted_train_kl_qps_true.append(reweighted_train_kl_qp_true)
util.save_checkpoint(
checkpoint_path,
generative_model,
inference_network,
theta_losses,
phi_losses,
cluster_cov_distances,
test_log_ps,
test_log_ps_true,
test_kl_qps,
test_kl_pqs,
test_kl_qps_true,
test_kl_pqs_true,
train_log_ps,
train_log_ps_true,
train_kl_qps,
train_kl_pqs,
train_kl_qps_true,
train_kl_pqs_true,
None,
None,
None,
reweighted_train_kl_qps,
reweighted_train_kl_qps_true,
)
util.print_with_time("it. {} | theta loss = {:.2f}".format(
iteration, loss))
# if iteration % 200 == 0:
# z = inference_network.get_latent_dist(obs).sample()
# util.save_plot("images/rws/iteration_{}.png".format(iteration),
# obs[:3], z[:3])
return (
theta_losses,
phi_losses,
cluster_cov_distances,
test_log_ps,
test_log_ps_true,
test_kl_qps,
test_kl_pqs,
test_kl_qps_true,
test_kl_pqs_true,
train_log_ps,
train_log_ps_true,
train_kl_qps,
train_kl_pqs,
train_kl_qps_true,
train_kl_pqs_true,
None,
None,
None,
reweighted_train_kl_qps,
reweighted_train_kl_qps_true,
)