Introduced percentile normalization for synthesis challenge metrics

GANDLF/cli/generate_metrics.py

@@ -193,6 +193,30 @@ def __fix_2d_tensor(input_tensor):
  else:
  return input_tensor
 
+ def __percentile_clip(input_tensor, reference_tensor=None, p_min=0.5, p_max=99.5, strictlyPositive=True):
+ """Normalizes a tensor based on percentiles. Clips values below and above the percentile.
+ Percentiles for normalization can come from another tensor.
+
+ Args:
+ input_tensor (torch.Tensor): Tensor to be normalized based on the data from the reference_tensor.
+ If reference_tensor is None, the percentiles from this tensor will be used.
+ reference_tensor (torch.Tensor, optional): The tensor used for obtaining the percentiles.
+ p_min (float, optional): Lower end percentile. Defaults to 0.5.
+ p_max (float, optional): Upper end percentile. Defaults to 99.5.
+ strictlyPositive (bool, optional): Ensures that really all values are above 0 before normalization. Defaults to True.
+
+ Returns:
+ torch.Tensor: The input_tensor normalized based on the percentiles of the reference tensor.
+ """
+ reference_tensor = input_tensor if reference_tensor is None else reference_tensor
+ v_min, v_max = np.percentile(reference_tensor, [p_min,p_max]) #get p_min percentile and p_max percentile
+
+ # set lower bound to be 0 if strictlyPositive is enabled
+ v_min = max(v_min, 0.0) if strictlyPositive else v_min
+ output_tensor = np.clip(input_tensor,v_min,v_max) #clip values to percentiles from reference_tensor
+ output_tensor = (output_tensor - v_min)/(v_max-v_min) #normalizes values to [0;1]
+ return output_tensor
+
  for _, row in tqdm(input_df.iterrows(), total=input_df.shape[0]):
  current_subject_id = row[headers["subjectid"]]
  overall_stats_dict[current_subject_id] = {}
@@ -219,9 +243,9 @@ def __fix_2d_tensor(input_tensor):
  # Normalize to [0;1] based on GT (otherwise MSE will depend on the image intensity range)
  normalize = parameters.get("normalize", True)
  if normalize:
- v_max = gt_image_infill.max()
- output_infill /= v_max
- gt_image_infill /= v_max
+ reference_tensor = target_image * ~mask #use all the tissue that is not masked for normalization
+ gt_image_infill = __percentile_clip(gt_image_infill, reference_tensor=reference_tensor, p_min=0.5, p_max=99.5, strictlyPositive=True)
+ output_infill = __percentile_clip(output_infill, reference_tensor=reference_tensor, p_min=0.5, p_max=99.5, strictlyPositive=True)
 
  overall_stats_dict[current_subject_id][
  "ssim"
@@ -258,6 +282,7 @@ def __fix_2d_tensor(input_tensor):
  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
  overall_stats_dict[current_subject_id]["psnr"] = peak_signal_noise_ratio(
  gt_image_infill, output_infill