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: