def _plot_closure(self, step):
print('Iteration {:5d} Loss {:5f} PSRN_gt: {:f}'.format(
step, self.total_loss.item(), self.current_result.psnr),
'\r',
end='')
if step % self.show_every == self.show_every - 1:
plot_image_grid("left_right_{}".format(step), [
self.current_result.reflection,
self.current_result.transmission
])
def _plot_closure(self, step):
print('Iteration {:5d} Loss {:5f} Fidelity {:5f} Exclusion {:5f}'.
format(step, self.total_loss.item(), self.recon_loss,
self.exclusion.item()))
if step % self.show_every == self.show_every - 1:
plot_image_grid("left_right_{}".format(step), [
self.current_result.reflection,
self.current_result.transmission
],
output_path=self.output_folder)
save_tiff('reflection_{}.tiff'.format(step),
np.squeeze(self.current_result.reflection),
output_path=self.output_folder)
save_tiff('transmission_{}.tiff'.format(step),
np.squeeze(self.current_result.transmission),
output_path=self.output_folder)
save_tiff('sum_{}.tiff'.format(step),
np.squeeze(self.current_result.sum),
output_path=self.output_folder)
def _plot_closure(self, step):
print('Iteration {:5d} Loss {:5f} Exclusion {:5f} PSRN_gt: {:f}'.
format(step, self.total_loss.item(), self.exclusion.item(),
self.current_result.psnr),
'\r',
end='')
if self.plot_during_training and step % self.show_every == self.show_every - 1:
plot_image_grid("reflection_transmission_{}".format(step), [
self.current_result.reflection,
self.current_result.transmission
])
# plot_image_grid("learned_mask_{}".format(step),
# [self.current_result.alpha1, self.current_result.alpha2])
save_image(
"sum1_{}".format(step),
self.current_result.alpha1 * self.current_result.reflection +
(1 - self.current_result.alpha1) *
self.current_result.transmission)
save_image(
"sum2_{}".format(step),
self.current_result.alpha2 * self.current_result.reflection +
(1 - self.current_result.alpha2) *
self.current_result.transmission)