try:
mesh, stats_dict = out
except TypeError:
mesh, stats_dict = out, {}
mesh.export(
os.path.join(
out_dir, 'vis',
'{}_{}_{}.off'.format(it, data_vis['category'],
data_vis['it'])))
# Save checkpoint
if (checkpoint_every > 0 and (it % checkpoint_every) == 0):
print('Saving checkpoint')
checkpoint_io.save('model.pt',
epoch_it=epoch_it,
it=it,
loss_val_best=metric_val_best)
# Backup if necessary
if (backup_every > 0 and (it % backup_every) == 0):
print('Backup checkpoint')
checkpoint_io.save('model_%d.pt' % it,
epoch_it=epoch_it,
it=it,
loss_val_best=metric_val_best)
# Run validation
if validate_every == -1:
#if (validate_every > 0 and (it % validate_every) == 0) or (it0 + 1 == it):
eval_dict = trainer.evaluate(val_loader)
metric_val = eval_dict[model_selection_metric]
print('Validation metric (%s): %.4f' %
try:
mesh, stats_dict = out
except TypeError:
mesh, stats_dict = out, {}
mesh.export(
os.path.join(
out_dir, 'vis',
'{}_{}_{}.off'.format(it, data_vis['category'],
data_vis['it'])))
# Save checkpoint
if (checkpoint_every > 0 and (it % checkpoint_every) == 0):
print('Saving checkpoint')
checkpoint_io.save(f'{model_name}.pt',
epoch_it=epoch_it,
it=it,
loss_val_best=metric_val_best)
# Backup if necessary
if (backup_every > 0 and (it % backup_every) == 0):
print('Backup checkpoint')
checkpoint_io.save(f'{model_name}_{it}.pt',
epoch_it=epoch_it,
it=it,
loss_val_best=metric_val_best)
# Run validation
if (validate_every > 0 and
(it % validate_every) == 0) or (it0 + 1 == it):
eval_dict = trainer.evaluate(val_loader)
metric_val = eval_dict[model_selection_metric]
print('Validation metric (%s): %.4f' %