Merge branch 'master' into master

mlcommons · Aug 11, 2023 · 4426030 · 4426030
2 parents 13b0cc0 + 616b37b
commit 4426030
Show file tree

Hide file tree

Showing 2 changed files with 7 additions and 6 deletions.
diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
@@ -301,14 +301,14 @@ def __percentile_clip(input_tensor, reference_tensor=None, p_min=0.5, p_max=99.5
                 overall_stats_dict[current_subject_id][
                     "psnr_01"
                 ] = peak_signal_noise_ratio(
-                    gt_image_infill, output_infill, data_range=1.0
+                    gt_image_infill, output_infill, data_range=(0,1)
                 ).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
+                    gt_image_infill, output_infill, data_range=(0,1), epsilon=sys.float_info.epsilon
                 ).item()
 
     pprint(overall_stats_dict)

diff --git a/GANDLF/metrics/synthesis.py b/GANDLF/metrics/synthesis.py
@@ -55,20 +55,21 @@ def peak_signal_noise_ratio(target, prediction, data_range=None, epsilon=None) -
     Args:
         target (torch.Tensor): The target tensor.
         prediction (torch.Tensor): The prediction tensor.
-        data_range (float, optional): If not None, this data range is used as enumerator instead of computing it from the given data. Defaults to None.
+        data_range (tuple, optional): If not None, this data range (min, max) is used as enumerator instead of computing it from the given data. Defaults to None.
         epsilon (float, optional): If not None, this epsilon is added to the denominator of the fraction to avoid infinity as output. Defaults to None.
     """
 
     if epsilon == None:
-        psnr = PeakSignalNoiseRatio(data_range=data_range)
+        psnr = PeakSignalNoiseRatio() if data_range == None else PeakSignalNoiseRatio(data_range=data_range[1]-data_range[0])
         return psnr(preds=prediction, target=target)
     else: # implementation of PSNR that does not give 'inf'/'nan' when 'mse==0'
         mse = mean_squared_error(target, prediction)
         if data_range == None: #compute data_range like torchmetrics if not given
             min_v = 0 if torch.min(target) > 0 else torch.min(target) #look at this line
             max_v = torch.max(target)
-            data_range = max_v - min_v
-        return 10.0 * torch.log10((data_range ** 2) / (mse + epsilon))
+        else:
+            min_v, max_v = data_range
+        return 10.0 * torch.log10(((max_v-min_v) ** 2) / (mse + epsilon))
 
 
 def mean_squared_log_error(target, prediction) -> torch.Tensor: