##########
## Test ##
##########
print('Testing...')
with torch.no_grad():
l = 0.0
for i, x in enumerate(test_loader):
loss = -model.log_prob(x.to(device)).sum() / (math.log(2) * x.numel())
l += loss.detach().cpu().item()
print('Iter: {}/{}, Bits/dim: {:.3f}'.format(i + 1, len(test_loader),
l / (i + 1)),
end='\r')
print('')
############
## Sample ##
############
print('Sampling...')
img = torch.from_numpy(data.test.data[:64]).permute([0, 3, 1, 2])
samples = model.sample(64)
vutils.save_image(img.cpu().float() / 255,
'./examples/results/cifar10_data.png',
nrow=8)
vutils.save_image(samples.cpu().float() / 255,
'./examples/results/cifar10_aug_flow.png',
nrow=8)
with open(
'results/{}_test_loglik{}.txt'.format(
run_name, args.iwbo_k if args.iwbo_k else ''), 'w') as f:
f.write(str(test_ppll))
##############
## Sampling ##
##############
if args.dataset in {'spatial_mnist'}:
bounds = [[0, 28], [0, 28]]
else:
raise NotImplementedError()
model = model.eval()
samples = model.sample(args.rowcol**2)
samples = samples.cpu().numpy()
fig, ax = plt.subplots(args.rowcol,
args.rowcol,
figsize=(args.pixels / args.dpi,
args.pixels / args.dpi),
dpi=args.dpi)
for i in range(args.rowcol):
for j in range(args.rowcol):
idx = i + args.rowcol * j
ax[i][j].scatter(samples[idx, :, 0], samples[idx, :, 1])
ax[i][j].set_xlim(bounds[0])
ax[i][j].set_ylim(bounds[1])
ax[i][j].axis('off')
plt.savefig('figures/{}.png'.format(run_name),
bbox_inches='tight',
loss = -model.log_prob(x).mean()
loss.backward()
optimizer.step()
l += loss.detach().cpu().item()
print('Epoch: {}/{}, Loglik: {:.3f}'.format(epoch + 1, 10,
l / (i + 1)),
end='\r')
print('')
############
## Sample ##
############
print('Sampling...')
data = test.data.numpy()
samples = model.sample(100000).numpy()
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].set_title('Data')
ax[0].hist2d(data[..., 0], data[..., 1], bins=256, range=[[-4, 4], [-4, 4]])
ax[0].set_xlim([-4, 4])
ax[0].set_ylim([-4, 4])
ax[0].axis('off')
ax[1].set_title('Samples')
ax[1].hist2d(samples[..., 0],
samples[..., 1],
bins=256,
range=[[-4, 4], [-4, 4]])
ax[1].set_xlim([-4, 4])
ax[1].set_ylim([-4, 4])
ax[1].axis('off')
end='\r')
print('')
final_test_nats = loss_sum / len(test_loader)
##############
## Sampling ##
##############
print('Sampling...')
if args.dataset == 'face_einstein':
bounds = [[0, 1], [0, 1]]
else:
bounds = [[-4, 4], [-4, 4]]
# Plot samples
samples = model.sample(args.num_samples)
samples = samples.detach().cpu().numpy()
plt.figure(figsize=(args.pixels / args.dpi, args.pixels / args.dpi),
dpi=args.dpi)
plt.hist2d(samples[..., 0], samples[..., 1], bins=256, range=bounds)
plt.xlim(bounds[0])
plt.ylim(bounds[1])
plt.axis('off')
plt.savefig('figures/{}_aug_flow_samples.png'.format(args.dataset),
bbox_inches='tight',
pad_inches=0)
# Plot density
xv, yv = torch.meshgrid([
torch.linspace(bounds[0][0], bounds[0][1], args.grid_size),
torch.linspace(bounds[1][0], bounds[1][1], args.grid_size)
x = next(iter(train_loader))
x = x['X']
x = x.unsqueeze(1)
x = torch.cat([x, torch.zeros_like(x[..., [0] * 101])], dim=-1)
print(model.log_prob(x))
print('back')
print('back')
print('back')
print('back')
print('back')
print('back')
print('back')
print('back')
model.sample(64).shape
#%%
###########
## Optim ##
###########
optimizer = Adam(model.parameters(), lr=1e-3)
###########
## Train ##
###########
print('Training...')
for epoch in range(1000):
l = [0.0]
for i, x in enumerate(train_loader):