flow.sample(int(1e6), batch_size=int(1e5)))
axes[2].hist2d(samples[:, 0],
samples[:, 1],
range=bounds,
bins=512,
cmap=cmap,
rasterized=False)
axes[2].set_xlim(bounds[0])
axes[2].set_ylim(bounds[1])
axes[2].set_xticks([])
axes[2].set_yticks([])
plt.tight_layout()
path = os.path.join(
cutils.get_output_root(),
'{}_{}_{}.png'.format(args.dataset_name, args.base_transform_type,
step + 1))
plt.savefig(path, dpi=300)
writer.add_figure(tag='viz', figure=figure, global_step=step)
plt.close()
if (step + 1) % args.save_interval == 0:
path = os.path.join(cutils.get_checkpoint_root(),
'{}.t'.format(args.dataset_name))
torch.save(flow.state_dict(), path)
path = os.path.join(cutils.get_checkpoint_root(),
'{}-{}.t'.format(args.dataset_name, timestamp))
torch.save(flow.state_dict(), path)
axes[1].set_xlim(bounds[0])
axes[1].set_ylim(bounds[1])
axes[1].set_xticks([])
axes[1].set_yticks([])
with torch.no_grad():
samples = utils.tensor2numpy(
flow.sample(num_samples=int(1e6), batch_size=int(1e5)))
axes[2].hist2d(samples[:, 0], samples[:, 1],
range=bounds, bins=512, cmap=cmap, rasterized=False)
axes[2].set_xlim(bounds[0])
axes[2].set_ylim(bounds[1])
axes[2].set_xticks([])
axes[2].set_yticks([])
plt.tight_layout()
path = os.path.join(cutils.get_output_root(), '{}_{}.png'.format(args.dataset_name, step))
plt.savefig(path, dpi=300)
writer.add_figure(tag='viz', figure=figure, global_step=step)
plt.close()
if (step + 1) % args.save_interval == 0:
path = os.path.join(cutils.get_checkpoint_root(),
'{}.t'.format(args.dataset_name))
torch.save(flow.state_dict(), path)
path = os.path.join(cutils.get_checkpoint_root(),
'{}-{}.t'.format(args.dataset_name, timestamp))
torch.save(flow.state_dict(), path)
(log_density_np, utils.tensor2numpy(log_density))
)
vmax = np.exp(log_density_np).max() * 0.7
cmap = cm.magma
# plot data
figure, axes = plt.subplots(1, 1, figsize=(3.125, 3.125))
axes.hist2d(utils.tensor2numpy(train_dataset.data[:, 0]),
utils.tensor2numpy(train_dataset.data[:, 1]),
range=bounds, bins=512, rasterized=False, normed=True, cmap='inferno')
axes.set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0,0)
axes.xaxis.set_major_locator(ticker.NullLocator())
axes.yaxis.set_major_locator(ticker.NullLocator())
path = os.path.join(cutils.get_output_root(), '{}-data.png'.format(dataset_name))
plt.savefig(path, bbox_inches='tight', pad_inches=0, dpi=320)
plt.close()
# plot density
figure, axes = plt.subplots(1, 1, figsize=(3.125, 3.125))
# axes.pcolormesh(grid_dataset.X, grid_dataset.Y,
# np.exp(log_density_np).reshape(grid_dataset.X.shape),
# cmap=cmap, vmin=0)
axes.imshow(np.exp(log_density_np).reshape(grid_dataset.X.shape), cmap='inferno')
axes.set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0,0)
axes.xaxis.set_major_locator(ticker.NullLocator())
axes.yaxis.set_major_locator(ticker.NullLocator())
path = os.path.join(cutils.get_output_root(), '{}-density.png'.format(dataset_name))
# plot data
figure, axes = plt.subplots(1, 1, figsize=(3.125, 3.125))
axes.hist2d(utils.tensor2numpy(train_dataset.data[:, 0]),
utils.tensor2numpy(train_dataset.data[:, 1]),
range=bounds,
bins=512,
cmap=cmap,
rasterized=False,
normed=True,
vmax=1.1 * vmax)
axes.set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0, 0)
axes.xaxis.set_major_locator(ticker.NullLocator())
axes.yaxis.set_major_locator(ticker.NullLocator())
path = os.path.join(cutils.get_output_root(),
'{}-data.png'.format(dataset_name))
plt.savefig(path, bbox_inches='tight', pad_inches=0, dpi=320)
plt.close()
# plot density
figure, axes = plt.subplots(1, 1, figsize=(3.125, 3.125))
axes.pcolormesh(grid_dataset.X,
grid_dataset.Y,
np.exp(log_density_np).reshape(grid_dataset.X.shape),
cmap=cmap,
vmin=0,
vmax=vmax)
axes.set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0, 0)