if __name__ == '__main__':
ensure_dir(out_dir + '/')
if args.net == 'made':
net = MADE(**vars(args))
elif args.net == 'pixelcnn':
net = PixelCNN(**vars(args))
else:
raise ValueError('Unknown net: {}'.format(args.net))
net.to(args.device)
print('{}\n'.format(net))
print(state_filename)
state = torch.load(state_filename, map_location=args.device)
net.load_state_dict(state['net'])
sample = torch.zeros([num_channel, 1, args.L, args.L], requires_grad=True)
nn.init.normal_(sample)
optimizer = torch.optim.Adam([sample], lr=1e-3, weight_decay=1)
start_time = time.time()
for step in range(args.max_step + 1):
optimizer.zero_grad()
x = sample
for idx, layer in enumerate(net.net):
x = layer(x)
if idx == target_layer:
break
def main():
start_time = time.time()
init_out_dir()
if args.clear_checkpoint:
clear_checkpoint()
last_step = get_last_checkpoint_step()
if last_step >= 0:
my_log('\nCheckpoint found: {}\n'.format(last_step))
else:
clear_log()
print_args()
if args.net == 'made':
net = MADE(**vars(args))
elif args.net == 'pixelcnn':
net = PixelCNN(**vars(args))
elif args.net == 'bernoulli':
net = BernoulliMixture(**vars(args))
else:
raise ValueError('Unknown net: {}'.format(args.net))
net.to(args.device)
my_log('{}\n'.format(net))
params = list(net.parameters())
params = list(filter(lambda p: p.requires_grad, params))
nparams = int(sum([np.prod(p.shape) for p in params]))
my_log('Total number of trainable parameters: {}'.format(nparams))
named_params = list(net.named_parameters())
if args.optimizer == 'sgd':
optimizer = torch.optim.SGD(params, lr=args.lr)
elif args.optimizer == 'sgdm':
optimizer = torch.optim.SGD(params, lr=args.lr, momentum=0.9)
elif args.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(params, lr=args.lr, alpha=0.99)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.9, 0.999))
elif args.optimizer == 'adam0.5':
optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.5, 0.999))
else:
raise ValueError('Unknown optimizer: {}'.format(args.optimizer))
if args.lr_schedule:
# 0.92**80 ~ 1e-3
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, factor=0.92, patience=100, threshold=1e-4, min_lr=1e-6)
if last_step >= 0:
state = torch.load('{}_save/{}.state'.format(args.out_filename,
last_step))
ignore_param(state['net'], net)
net.load_state_dict(state['net'])
if state.get('optimizer'):
optimizer.load_state_dict(state['optimizer'])
if args.lr_schedule and state.get('scheduler'):
scheduler.load_state_dict(state['scheduler'])
init_time = time.time() - start_time
my_log('init_time = {:.3f}'.format(init_time))
my_log('Training...')
sample_time = 0
train_time = 0
start_time = time.time()
for step in range(last_step + 1, args.max_step + 1):
optimizer.zero_grad()
sample_start_time = time.time()
with torch.no_grad():
sample, x_hat = net.sample(args.batch_size)
assert not sample.requires_grad
assert not x_hat.requires_grad
sample_time += time.time() - sample_start_time
train_start_time = time.time()
log_prob = net.log_prob(sample)
# 0.998**9000 ~ 1e-8
beta = args.beta * (1 - args.beta_anneal**step)
with torch.no_grad():
energy = ising.energy(sample, args.ham, args.lattice,
args.boundary)
loss = log_prob + beta * energy
assert not energy.requires_grad
assert not loss.requires_grad
loss_reinforce = torch.mean((loss - loss.mean()) * log_prob)
loss_reinforce.backward()
if args.clip_grad:
nn.utils.clip_grad_norm_(params, args.clip_grad)
optimizer.step()
if args.lr_schedule:
scheduler.step(loss.mean())
train_time += time.time() - train_start_time
if args.print_step and step % args.print_step == 0:
free_energy_mean = loss.mean() / args.beta / args.L**2
free_energy_std = loss.std() / args.beta / args.L**2
entropy_mean = -log_prob.mean() / args.L**2
energy_mean = energy.mean() / args.L**2
mag = sample.mean(dim=0)
mag_mean = mag.mean()
mag_sqr_mean = (mag**2).mean()
if step > 0:
sample_time /= args.print_step
train_time /= args.print_step
used_time = time.time() - start_time
my_log(
'step = {}, F = {:.8g}, F_std = {:.8g}, S = {:.8g}, E = {:.8g}, M = {:.8g}, Q = {:.8g}, lr = {:.3g}, beta = {:.8g}, sample_time = {:.3f}, train_time = {:.3f}, used_time = {:.3f}'
.format(
step,
free_energy_mean.item(),
free_energy_std.item(),
entropy_mean.item(),
energy_mean.item(),
mag_mean.item(),
mag_sqr_mean.item(),
optimizer.param_groups[0]['lr'],
beta,
sample_time,
train_time,
used_time,
))
sample_time = 0
train_time = 0
if args.save_sample:
state = {
'sample': sample,
'x_hat': x_hat,
'log_prob': log_prob,
'energy': energy,
'loss': loss,
}
torch.save(state, '{}_save/{}.sample'.format(
args.out_filename, step))
if (args.out_filename and args.save_step
and step % args.save_step == 0):
state = {
'net': net.state_dict(),
'optimizer': optimizer.state_dict(),
}
if args.lr_schedule:
state['scheduler'] = scheduler.state_dict()
torch.save(state, '{}_save/{}.state'.format(
args.out_filename, step))
if (args.out_filename and args.visual_step
and step % args.visual_step == 0):
torchvision.utils.save_image(
sample,
'{}_img/{}.png'.format(args.out_filename, step),
nrow=int(sqrt(sample.shape[0])),
padding=0,
normalize=True)
if args.print_sample:
x_hat_np = x_hat.view(x_hat.shape[0], -1).cpu().numpy()
x_hat_std = np.std(x_hat_np, axis=0).reshape([args.L] * 2)
x_hat_cov = np.cov(x_hat_np.T)
x_hat_cov_diag = np.diag(x_hat_cov)
x_hat_corr = x_hat_cov / (
sqrt(x_hat_cov_diag[:, None] * x_hat_cov_diag[None, :]) +
args.epsilon)
x_hat_corr = np.tril(x_hat_corr, -1)
x_hat_corr = np.max(np.abs(x_hat_corr), axis=1)
x_hat_corr = x_hat_corr.reshape([args.L] * 2)
energy_np = energy.cpu().numpy()
energy_count = np.stack(
np.unique(energy_np, return_counts=True)).T
my_log(
'\nsample\n{}\nx_hat\n{}\nlog_prob\n{}\nenergy\n{}\nloss\n{}\nx_hat_std\n{}\nx_hat_corr\n{}\nenergy_count\n{}\n'
.format(
sample[:args.print_sample, 0],
x_hat[:args.print_sample, 0],
log_prob[:args.print_sample],
energy[:args.print_sample],
loss[:args.print_sample],
x_hat_std,
x_hat_corr,
energy_count,
))
if args.print_grad:
my_log('grad max_abs min_abs mean std')
for name, param in named_params:
if param.grad is not None:
grad = param.grad
grad_abs = torch.abs(grad)
my_log('{} {:.3g} {:.3g} {:.3g} {:.3g}'.format(
name,
torch.max(grad_abs).item(),
torch.min(grad_abs).item(),
torch.mean(grad).item(),
torch.std(grad).item(),
))
else:
my_log('{} None'.format(name))
my_log('')
best_epoch = epoch
params['best_epoch'] = epoch
params['best_loss'] = loss
save_dict_to_json_file(path + '.json', params)
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': opt.state_dict(),
'loss': loss
}, path + ".tar")
else:
epochs_no_improve += 1
last_loss = loss
if epochs_no_improve >= args.patience:
print("Early Stopping: No improvement for %d epochs" %
(args.patience))
break
print("optimization done. full test set eval:")
checkpoint = torch.load(path + ".tar")
model.load_state_dict(checkpoint['model_state_dict'])
opt.load_state_dict(checkpoint['optimizer_state_dict'])
test_loss = run_epoch(x=xte, split='test')
params['test_loss'] = test_loss
save_dict_to_json_file(path + '.json', params)
checkpoint['test_loss'] = test_loss
torch.save(checkpoint, path + ".tar")