Merge pull request #703 from FelixSteinbauer/patch-4

Additional PSNR evaluations for the normalized synthesis case

GANDLF/cli/generate_metrics.py

@@ -283,16 +283,34 @@ def __percentile_clip(input_tensor, reference_tensor=None, p_min=0.5, p_max=99.5
  gt_image_infill, output_infill
  ).item()
 
- #TODO: use data_range=1.0 as parameter for PSNR when the Pull request is accepted that introduces the data_range parameter!
- # PSNR - similar to pytorch PeakSignalNoiseRatio until 4 digits after decimal point
+ # torchmetrics PSNR using "max"
  overall_stats_dict[current_subject_id]["psnr"] = peak_signal_noise_ratio(
  gt_image_infill, output_infill
  ).item()
 
- overall_stats_dict[current_subject_id]["psnr_eps"] = peak_signal_noise_ratio(
+ # same as above but with epsilon for robustness
+ overall_stats_dict[current_subject_id][
+ "psnr_eps"
+ ] = peak_signal_noise_ratio(
  gt_image_infill, output_infill, epsilon=sys.float_info.epsilon
  ).item()
 
+ # only use fix data range to [0;1] if the data was normalized before
+ if normalize:
+ # torchmetrics PSNR but with fixed data range of 0 to 1
+ overall_stats_dict[current_subject_id][
+ "psnr_01"
+ ] = peak_signal_noise_ratio(
+ gt_image_infill, output_infill, data_range=1.0
+ ).item()
+
+ # same as above but with epsilon for robustness
+ overall_stats_dict[current_subject_id][
+ "psnr_01_eps"
+ ] = peak_signal_noise_ratio(
+ gt_image_infill, output_infill, epsilon=sys.float_info.epsilon
+ ).item()
+
  pprint(overall_stats_dict)
  if outputfile is not None:
  with open(outputfile, "w") as outfile: