From b0247acd287d7033e238c111a044e0888bfbe366 Mon Sep 17 00:00:00 2001
From: scap3yvt <149599669+scap3yvt@users.noreply.github.com>
Date: Thu, 18 Apr 2024 10:00:34 -0400
Subject: [PATCH 01/11] added standardized CLI options

---
 gandlf_anonymizer      | 5 +++--
 gandlf_configGenerator | 3 ++-
 gandlf_constructCSV    | 5 +++--
 gandlf_patchMiner      | 3 ++-
 gandlf_preprocess      | 3 ++-
 gandlf_recoverConfig   | 3 ++-
 6 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/gandlf_anonymizer b/gandlf_anonymizer
index 2d6141c89..294ab9aa5 100644
--- a/gandlf_anonymizer
+++ b/gandlf_anonymizer
@@ -38,6 +38,7 @@ def main():
     )
     parser.add_argument(
         "-o",
+        "--outputfile",
         "--outputFile",
         metavar="",
         type=str,
@@ -47,12 +48,12 @@ def main():
     args = parser.parse_args()
 
     # check for required parameters - this is needed here to keep the cli clean
-    for param_none_check in [args.inputDir, args.outputFile]:
+    for param_none_check in [args.inputDir, args.outputfile]:
         if param_none_check is None:
             sys.exit("ERROR: Missing required parameter:", param_none_check)
 
     inputDir = os.path.normpath(args.inputDir)
-    outputFile = os.path.normpath(args.outputFile)
+    outputFile = os.path.normpath(args.outputfile)
     if os.path.isfile(args.config):
         config = yaml.safe_load(open(args.config, "r"))
     else:
diff --git a/gandlf_configGenerator b/gandlf_configGenerator
index 26ddca70b..1c0ea7db3 100644
--- a/gandlf_configGenerator
+++ b/gandlf_configGenerator
@@ -28,6 +28,7 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "-o",
+        "--outputdir",
         "--output",
         metavar="",
         type=str,
@@ -37,6 +38,6 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
-    config_generator(args.config, args.strategy, args.output)
+    config_generator(args.config, args.strategy, args.outputdir)
 
     print("Finished.")
diff --git a/gandlf_constructCSV b/gandlf_constructCSV
index a61f322ea..bbad3deb8 100644
--- a/gandlf_constructCSV
+++ b/gandlf_constructCSV
@@ -40,6 +40,7 @@ def main():
     )
     parser.add_argument(
         "-o",
+        "--outputfile",
         "--outputFile",
         metavar="",
         type=str,
@@ -60,13 +61,13 @@ def main():
     for param_none_check in [
         args.inputDir,
         args.channelsID,
-        args.outputFile,
+        args.outputfile,
     ]:
         if param_none_check is None:
             sys.exit("ERROR: Missing required parameter:", param_none_check)
 
     inputDir = os.path.normpath(args.inputDir)
-    outputFile = os.path.normpath(args.outputFile)
+    outputFile = os.path.normpath(args.outputfile)
     channelsID = args.channelsID
     labelID = args.labelID
     relativizePathsToOutput = args.relativizePaths
diff --git a/gandlf_patchMiner b/gandlf_patchMiner
index 1cb63ec2d..daff4c4b8 100644
--- a/gandlf_patchMiner
+++ b/gandlf_patchMiner
@@ -24,6 +24,7 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "-o",
+        "--outputdir",
         "--output_path",
         dest="output_path",
         default=None,
@@ -41,6 +42,6 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
-    patch_extraction(args.input_path, args.output_path, args.config)
+    patch_extraction(args.input_path, args.outputdir, args.config)
 
     print("Finished.")
diff --git a/gandlf_preprocess b/gandlf_preprocess
index 1522f8607..991d737b2 100644
--- a/gandlf_preprocess
+++ b/gandlf_preprocess
@@ -31,6 +31,7 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "-o",
+        "--outputdir",
         "--output",
         metavar="",
         type=str,
@@ -70,7 +71,7 @@ if __name__ == "__main__":
     preprocess_and_save(
         args.inputdata,
         args.config,
-        args.output,
+        args.outputdir,
         args.labelPad,
         args.applyaugs,
         args.cropzero,
diff --git a/gandlf_recoverConfig b/gandlf_recoverConfig
index 37653fe04..464a97586 100644
--- a/gandlf_recoverConfig
+++ b/gandlf_recoverConfig
@@ -33,6 +33,7 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "-o",
+        "--outputfile",
         "--outputFile",
         metavar="",
         type=str,
@@ -46,5 +47,5 @@ if __name__ == "__main__":
     else:
         search_dir = args.modeldir
 
-    result = recover_config(search_dir, args.outputFile)
+    result = recover_config(search_dir, args.outputfile)
     assert result, "Config file recovery failed."

From ca49b4ca71988de850791f3691be3ef0e924e9d7 Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 09:57:43 -0500
Subject: [PATCH 02/11] putting comments and added parameter to get rmse

---
 GANDLF/metrics/synthesis.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/GANDLF/metrics/synthesis.py b/GANDLF/metrics/synthesis.py
index ba1b4113e..f29933da7 100644
--- a/GANDLF/metrics/synthesis.py
+++ b/GANDLF/metrics/synthesis.py
@@ -39,15 +39,21 @@ def structural_similarity_index(
     return ssim_idx.mean()
 
 
-def mean_squared_error(prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+def mean_squared_error(
+    prediction: torch.Tensor, target: torch.Tensor, squared: bool = True
+) -> torch.Tensor:
     """
     Computes the mean squared error between the target and prediction.
 
     Args:
         prediction (torch.Tensor): The prediction tensor.
         target (torch.Tensor): The target tensor.
+        squared (bool, optional): Whether to return squared error. Defaults to True.
+
+    Returns:
+        torch.Tensor: The mean squared error or its square root.
     """
-    mse = MeanSquaredError()
+    mse = MeanSquaredError(squared=squared)
     return mse(preds=prediction, target=target)
 
 
@@ -78,10 +84,9 @@ def peak_signal_noise_ratio(
         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
+        if data_range is None:  # compute data_range like torchmetrics if not given
+            # put the min value to 0 if all values are positive
+            min_v = 0 if torch.min(target) > 0 else torch.min(target)
             max_v = torch.max(target)
         else:
             min_v, max_v = data_range

From 1c4afd7306a2b766f8d71a5460bd9edc151412eb Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 09:58:05 -0500
Subject: [PATCH 03/11] ensure that the brain mask and void image are treated
 differently

---
 GANDLF/cli/generate_metrics.py | 83 ++++++++++++++++++++--------------
 1 file changed, 49 insertions(+), 34 deletions(-)

diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index d484b63a0..1821aa2dd 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -302,21 +302,22 @@ def __percentile_clip(
             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
-
+            # get p_min percentile and p_max percentile
+            v_min, v_max = np.percentile(reference_tensor, [p_min, p_max])
             # 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]
+            # clip values to percentiles from reference_tensor
+            output_tensor = np.clip(input_tensor, v_min, v_max)
+            # normalizes values to [0;1]
+            output_tensor = (output_tensor - v_min) / (v_max - v_min)
             return output_tensor
 
-        input_df = __update_header_location_case_insensitive(input_df, "Mask", False)
+        # these are additional columns that could be present for synthesis tasks
+        for column_to_make_case_insensitive in ["Mask", "VoidImage"]:
+            input_df = __update_header_location_case_insensitive(
+                input_df, column_to_make_case_insensitive, False
+            )
+
         for _, row in tqdm(input_df.iterrows(), total=input_df.shape[0]):
             current_subject_id = row["SubjectID"]
             overall_stats_dict[current_subject_id] = {}
@@ -332,6 +333,15 @@ def __percentile_clip(
                 )
             ).byte()
 
+            void_image_present = True if "VoidImage" in row else False
+            void_image = (
+                __fix_2d_tensor(torchio.ScalarImage(row["VoidImage"]).data)
+                if "VoidImage" in row
+                else torch.from_numpy(
+                    np.ones(target_image.numpy().shape, dtype=np.uint8)
+                )
+            )
+
             # Get Infill region (we really are only interested in the infill region)
             output_infill = (pred_image * mask).float()
             gt_image_infill = (target_image * mask).float()
@@ -339,9 +349,10 @@ def __percentile_clip(
             # Normalize to [0;1] based on GT (otherwise MSE will depend on the image intensity range)
             normalize = parameters.get("normalize", True)
             if normalize:
+                # use all the tissue that is not masked for normalization
                 reference_tensor = (
-                    target_image * ~mask
-                )  # use all the tissue that is not masked for normalization
+                    target_image * ~mask if not void_image_present else void_image
+                )
                 gt_image_infill = __percentile_clip(
                     gt_image_infill,
                     reference_tensor=reference_tensor,
@@ -357,9 +368,9 @@ def __percentile_clip(
                     strictlyPositive=True,
                 )
 
-            overall_stats_dict[current_subject_id][
-                "ssim"
-            ] = structural_similarity_index(output_infill, gt_image_infill, mask).item()
+            overall_stats_dict[current_subject_id]["ssim"] = (
+                structural_similarity_index(output_infill, gt_image_infill, mask).item()
+            )
 
             # ncc metrics
             compute_ncc = parameters.get("compute_ncc", True)
@@ -386,6 +397,10 @@ def __percentile_clip(
                 output_infill, gt_image_infill
             ).item()
 
+            overall_stats_dict[current_subject_id]["rmse"] = mean_squared_error(
+                output_infill, gt_image_infill, squared=False
+            ).item()
+
             overall_stats_dict[current_subject_id]["msle"] = mean_squared_log_error(
                 output_infill, gt_image_infill
             ).item()
@@ -400,30 +415,30 @@ def __percentile_clip(
             ).item()
 
             # same as above but with epsilon for robustness
-            overall_stats_dict[current_subject_id][
-                "psnr_eps"
-            ] = peak_signal_noise_ratio(
-                output_infill, gt_image_infill, epsilon=sys.float_info.epsilon
-            ).item()
+            overall_stats_dict[current_subject_id]["psnr_eps"] = (
+                peak_signal_noise_ratio(
+                    output_infill, gt_image_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(
-                    output_infill, gt_image_infill, data_range=(0, 1)
-                ).item()
+                overall_stats_dict[current_subject_id]["psnr_01"] = (
+                    peak_signal_noise_ratio(
+                        output_infill, gt_image_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(
-                    output_infill,
-                    gt_image_infill,
-                    data_range=(0, 1),
-                    epsilon=sys.float_info.epsilon,
-                ).item()
+                overall_stats_dict[current_subject_id]["psnr_01_eps"] = (
+                    peak_signal_noise_ratio(
+                        output_infill,
+                        gt_image_infill,
+                        data_range=(0, 1),
+                        epsilon=sys.float_info.epsilon,
+                    ).item()
+                )
 
     pprint(overall_stats_dict)
     if outputfile is not None:

From 05f71014e7d614e707203ea9e4984f80b4c3518f Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 11:09:16 -0500
Subject: [PATCH 04/11] updated dictionary for spell checker

---
 .spelling/.spelling/expect.txt | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.spelling/.spelling/expect.txt b/.spelling/.spelling/expect.txt
index fe6a792b1..b4f50c2f9 100644
--- a/.spelling/.spelling/expect.txt
+++ b/.spelling/.spelling/expect.txt
@@ -487,6 +487,7 @@ rgbatorgb
 rgbtorgba
 rigourous
 Ritesh
+rmse
 rmsprop
 rocm
 rocmdocs
@@ -561,7 +562,6 @@ thresholded
 thresholding
 Thu
 tiatoolbox
-tiffslide
 timepoints
 timm
 tio
@@ -597,7 +597,6 @@ unittests
 unitwise
 unsqueeze
 upenn
-Uploaing
 Uploded
 upsample
 upsampled
@@ -725,7 +724,6 @@ zsuokb
 zwezggl
 zzokqk
 thirdparty
-adopy
 Shohei
 crcrpar
 lrs

From ba3ea8329c89e188f9bd79038a8adf65ab90df20 Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 11:09:49 -0500
Subject: [PATCH 05/11] lint fix

---
 GANDLF/cli/generate_metrics.py | 42 +++++++++++++++++-----------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index 1821aa2dd..fc0b25ff1 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -368,9 +368,9 @@ def __percentile_clip(
                     strictlyPositive=True,
                 )
 
-            overall_stats_dict[current_subject_id]["ssim"] = (
-                structural_similarity_index(output_infill, gt_image_infill, mask).item()
-            )
+            overall_stats_dict[current_subject_id][
+                "ssim"
+            ] = structural_similarity_index(output_infill, gt_image_infill, mask).item()
 
             # ncc metrics
             compute_ncc = parameters.get("compute_ncc", True)
@@ -415,30 +415,30 @@ def __percentile_clip(
             ).item()
 
             # same as above but with epsilon for robustness
-            overall_stats_dict[current_subject_id]["psnr_eps"] = (
-                peak_signal_noise_ratio(
-                    output_infill, gt_image_infill, epsilon=sys.float_info.epsilon
-                ).item()
-            )
+            overall_stats_dict[current_subject_id][
+                "psnr_eps"
+            ] = peak_signal_noise_ratio(
+                output_infill, gt_image_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(
-                        output_infill, gt_image_infill, data_range=(0, 1)
-                    ).item()
-                )
+                overall_stats_dict[current_subject_id][
+                    "psnr_01"
+                ] = peak_signal_noise_ratio(
+                    output_infill, gt_image_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(
-                        output_infill,
-                        gt_image_infill,
-                        data_range=(0, 1),
-                        epsilon=sys.float_info.epsilon,
-                    ).item()
-                )
+                overall_stats_dict[current_subject_id][
+                    "psnr_01_eps"
+                ] = peak_signal_noise_ratio(
+                    output_infill,
+                    gt_image_infill,
+                    data_range=(0, 1),
+                    epsilon=sys.float_info.epsilon,
+                ).item()
 
     pprint(overall_stats_dict)
     if outputfile is not None:

From e4f68509c9a48086eb5aa8b642fde599225cc401 Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 11:24:21 -0500
Subject: [PATCH 06/11] typo fix and unnecessary word removed

---
 .spelling/.spelling/expect.txt           | 1 -
 GANDLF/entrypoints/hf_hub_integration.py | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/.spelling/.spelling/expect.txt b/.spelling/.spelling/expect.txt
index b4f50c2f9..0055fe5c9 100644
--- a/.spelling/.spelling/expect.txt
+++ b/.spelling/.spelling/expect.txt
@@ -597,7 +597,6 @@ unittests
 unitwise
 unsqueeze
 upenn
-Uploded
 upsample
 upsampled
 upsampling
diff --git a/GANDLF/entrypoints/hf_hub_integration.py b/GANDLF/entrypoints/hf_hub_integration.py
index 353f31dfb..d0f209ad4 100644
--- a/GANDLF/entrypoints/hf_hub_integration.py
+++ b/GANDLF/entrypoints/hf_hub_integration.py
@@ -96,7 +96,7 @@
 @click.option(
     "--hf-template",
     "-hft",
-    help="Adding the template path for the model card it is Required during Uploaing a model",
+    help="Adding the template path for the model card: it is required during model upload",
     default=huggingface_file_path,
     type=click.Path(exists=True, file_okay=True, dir_okay=False),
 )

From b1f0b54e9356550de4d65242247008fdf9572e97 Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 13:47:31 -0500
Subject: [PATCH 07/11] updated ncc metrics

---
 GANDLF/cli/generate_metrics.py | 20 ++--------
 GANDLF/metrics/__init__.py     |  5 +--
 GANDLF/metrics/synthesis.py    | 72 +++++++---------------------------
 3 files changed, 20 insertions(+), 77 deletions(-)

diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index fc0b25ff1..e28ece92b 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -18,10 +18,7 @@
     peak_signal_noise_ratio,
     mean_squared_log_error,
     mean_absolute_error,
-    ncc_mean,
-    ncc_std,
-    ncc_max,
-    ncc_min,
+    ncc_metrics,
 )
 from GANDLF.losses.segmentation import dice
 from GANDLF.metrics.segmentation import (
@@ -375,18 +372,9 @@ def __percentile_clip(
             # ncc metrics
             compute_ncc = parameters.get("compute_ncc", True)
             if compute_ncc:
-                overall_stats_dict[current_subject_id]["ncc_mean"] = ncc_mean(
-                    output_infill, gt_image_infill
-                )
-                overall_stats_dict[current_subject_id]["ncc_std"] = ncc_std(
-                    output_infill, gt_image_infill
-                )
-                overall_stats_dict[current_subject_id]["ncc_max"] = ncc_max(
-                    output_infill, gt_image_infill
-                )
-                overall_stats_dict[current_subject_id]["ncc_min"] = ncc_min(
-                    output_infill, gt_image_infill
-                )
+                calculated_ncc_metrics = ncc_metrics(output_infill, gt_image_infill)
+                for key, value in calculated_ncc_metrics.items():
+                    overall_stats_dict[current_subject_id][key] = value.item()
 
             # only voxels that are to be inferred (-> flat array)
             # these are required for mse, psnr, etc.
diff --git a/GANDLF/metrics/__init__.py b/GANDLF/metrics/__init__.py
index 1fc21b3fb..08d1165c1 100644
--- a/GANDLF/metrics/__init__.py
+++ b/GANDLF/metrics/__init__.py
@@ -35,10 +35,7 @@
     peak_signal_noise_ratio,
     mean_squared_log_error,
     mean_absolute_error,
-    ncc_mean,
-    ncc_std,
-    ncc_max,
-    ncc_min,
+    ncc_metrics,
 )
 import GANDLF.metrics.classification as classification
 import GANDLF.metrics.regression as regression
diff --git a/GANDLF/metrics/synthesis.py b/GANDLF/metrics/synthesis.py
index f29933da7..a8db74bf1 100644
--- a/GANDLF/metrics/synthesis.py
+++ b/GANDLF/metrics/synthesis.py
@@ -163,69 +163,27 @@ def __convert_to_grayscale(image: sitk.Image) -> sitk.Image:
     return correlation_filter.Execute(target_image, pred_image)
 
 
-def ncc_mean(prediction: torch.Tensor, target: torch.Tensor) -> float:
+def ncc_metrics(prediction: torch.Tensor, target: torch.Tensor) -> dict:
     """
-    Computes normalized cross correlation mean between target and prediction.
+    Computes normalized cross correlation metrics between target and prediction.
 
     Args:
         prediction (torch.Tensor): The prediction tensor.
         target (torch.Tensor): The target tensor.
 
     Returns:
-        float: The normalized cross correlation mean.
+        dict: The normalized cross correlation metrics.
     """
-    stats_filter = sitk.StatisticsImageFilter()
     corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetMean()
-
-
-def ncc_std(prediction: torch.Tensor, target: torch.Tensor) -> float:
-    """
-    Computes normalized cross correlation standard deviation between target and prediction.
-
-    Args:
-        prediction (torch.Tensor): The prediction tensor.
-        target (torch.Tensor): The target tensor.
-
-    Returns:
-        float: The normalized cross correlation standard deviation.
-    """
-    stats_filter = sitk.StatisticsImageFilter()
-    corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetSigma()
-
-
-def ncc_max(prediction: torch.Tensor, target: torch.Tensor) -> float:
-    """
-    Computes normalized cross correlation maximum between target and prediction.
-
-    Args:
-        prediction (torch.Tensor): The prediction tensor.
-        target (torch.Tensor): The target tensor.
-
-    Returns:
-        float: The normalized cross correlation maximum.
-    """
-    stats_filter = sitk.StatisticsImageFilter()
-    corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetMaximum()
-
-
-def ncc_min(prediction: torch.Tensor, target: torch.Tensor) -> float:
-    """
-    Computes normalized cross correlation minimum between target and prediction.
-
-    Args:
-        prediction (torch.Tensor): The prediction tensor.
-        target (torch.Tensor): The target tensor.
-
-    Returns:
-        float: The normalized cross correlation minimum.
-    """
-    stats_filter = sitk.StatisticsImageFilter()
-    corr_image = _get_ncc_image(target, prediction)
-    stats_filter.Execute(corr_image)
-    return stats_filter.GetMinimum()
+    stats_filter = sitk.LabelStatisticsImageFilter()
+    stats_filter.UseHistogramsOn()
+    # ensure that we are not considering zeros
+    onesImage = corr_image == corr_image
+    stats_filter.Execute(corr_image, onesImage)
+    return {
+        "mean": stats_filter.GetMean(1),
+        "std": stats_filter.GetSigma(1),
+        "max": stats_filter.GetMaximum(1),
+        "min": stats_filter.GetMinimum(1),
+        "median": stats_filter.GetMedian(1),
+    }

From c12c37d429b585896b9cc10a56798b70480d80cf Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 13:52:41 -0500
Subject: [PATCH 08/11] addressing comment

---
 GANDLF/cli/generate_metrics.py |  5 +++--
 GANDLF/metrics/__init__.py     |  1 +
 GANDLF/metrics/synthesis.py    | 22 ++++++++++++++++++----
 3 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index e28ece92b..72b572e4c 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -15,6 +15,7 @@
     overall_stats,
     structural_similarity_index,
     mean_squared_error,
+    root_mean_squared_error,
     peak_signal_noise_ratio,
     mean_squared_log_error,
     mean_absolute_error,
@@ -385,8 +386,8 @@ def __percentile_clip(
                 output_infill, gt_image_infill
             ).item()
 
-            overall_stats_dict[current_subject_id]["rmse"] = mean_squared_error(
-                output_infill, gt_image_infill, squared=False
+            overall_stats_dict[current_subject_id]["rmse"] = root_mean_squared_error(
+                output_infill, gt_image_infill
             ).item()
 
             overall_stats_dict[current_subject_id]["msle"] = mean_squared_log_error(
diff --git a/GANDLF/metrics/__init__.py b/GANDLF/metrics/__init__.py
index 08d1165c1..824d38512 100644
--- a/GANDLF/metrics/__init__.py
+++ b/GANDLF/metrics/__init__.py
@@ -32,6 +32,7 @@
 from .synthesis import (
     structural_similarity_index,
     mean_squared_error,
+    root_mean_squared_error,
     peak_signal_noise_ratio,
     mean_squared_log_error,
     mean_absolute_error,
diff --git a/GANDLF/metrics/synthesis.py b/GANDLF/metrics/synthesis.py
index a8db74bf1..973ca6ff8 100644
--- a/GANDLF/metrics/synthesis.py
+++ b/GANDLF/metrics/synthesis.py
@@ -39,8 +39,23 @@ def structural_similarity_index(
     return ssim_idx.mean()
 
 
-def mean_squared_error(
-    prediction: torch.Tensor, target: torch.Tensor, squared: bool = True
+def mean_squared_error(prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    """
+    Computes the mean squared error between the target and prediction.
+
+    Args:
+        prediction (torch.Tensor): The prediction tensor.
+        target (torch.Tensor): The target tensor.
+
+    Returns:
+        torch.Tensor: The mean squared error or its square root.
+    """
+    mse = MeanSquaredError(squared=True)
+    return mse(preds=prediction, target=target)
+
+
+def root_mean_squared_error(
+    prediction: torch.Tensor, target: torch.Tensor
 ) -> torch.Tensor:
     """
     Computes the mean squared error between the target and prediction.
@@ -48,12 +63,11 @@ def mean_squared_error(
     Args:
         prediction (torch.Tensor): The prediction tensor.
         target (torch.Tensor): The target tensor.
-        squared (bool, optional): Whether to return squared error. Defaults to True.
 
     Returns:
         torch.Tensor: The mean squared error or its square root.
     """
-    mse = MeanSquaredError(squared=squared)
+    mse = MeanSquaredError(squared=False)
     return mse(preds=prediction, target=target)
 
 

From 08dc42a6068d8117df1b7116c28eace7577b3bd9 Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 13:53:51 -0500
Subject: [PATCH 09/11] ensure `ncc` gets picked up correctly

---
 GANDLF/cli/generate_metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index 72b572e4c..591b1d0ff 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -375,7 +375,7 @@ def __percentile_clip(
             if compute_ncc:
                 calculated_ncc_metrics = ncc_metrics(output_infill, gt_image_infill)
                 for key, value in calculated_ncc_metrics.items():
-                    overall_stats_dict[current_subject_id][key] = value.item()
+                    overall_stats_dict[current_subject_id][f"ncc_{key}"] = value.item()
 
             # only voxels that are to be inferred (-> flat array)
             # these are required for mse, psnr, etc.

From 8b9a797418e98e73d32599b5e460cb557869edcc Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 14:01:23 -0500
Subject: [PATCH 10/11] putting the `ncc` in the metric calculation itself for
 clarity

---
 GANDLF/cli/generate_metrics.py |  2 +-
 GANDLF/metrics/synthesis.py    | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index 591b1d0ff..72b572e4c 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -375,7 +375,7 @@ def __percentile_clip(
             if compute_ncc:
                 calculated_ncc_metrics = ncc_metrics(output_infill, gt_image_infill)
                 for key, value in calculated_ncc_metrics.items():
-                    overall_stats_dict[current_subject_id][f"ncc_{key}"] = value.item()
+                    overall_stats_dict[current_subject_id][key] = value.item()
 
             # only voxels that are to be inferred (-> flat array)
             # these are required for mse, psnr, etc.
diff --git a/GANDLF/metrics/synthesis.py b/GANDLF/metrics/synthesis.py
index 973ca6ff8..a7d6a4523 100644
--- a/GANDLF/metrics/synthesis.py
+++ b/GANDLF/metrics/synthesis.py
@@ -195,9 +195,9 @@ def ncc_metrics(prediction: torch.Tensor, target: torch.Tensor) -> dict:
     onesImage = corr_image == corr_image
     stats_filter.Execute(corr_image, onesImage)
     return {
-        "mean": stats_filter.GetMean(1),
-        "std": stats_filter.GetSigma(1),
-        "max": stats_filter.GetMaximum(1),
-        "min": stats_filter.GetMinimum(1),
-        "median": stats_filter.GetMedian(1),
+        "ncc_mean": stats_filter.GetMean(1),
+        "ncc_std": stats_filter.GetSigma(1),
+        "ncc_max": stats_filter.GetMaximum(1),
+        "ncc_min": stats_filter.GetMinimum(1),
+        "ncc_median": stats_filter.GetMedian(1),
     }

From 1e9447cf10c8aade588fa40cc7a4f21c6f3232a4 Mon Sep 17 00:00:00 2001
From: sarthakpati <sarthak.pati@hotmail.com>
Date: Thu, 19 Dec 2024 14:42:55 -0500
Subject: [PATCH 11/11] fixing error

---
 GANDLF/cli/generate_metrics.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index 72b572e4c..7e7fdda49 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -375,7 +375,8 @@ def __percentile_clip(
             if compute_ncc:
                 calculated_ncc_metrics = ncc_metrics(output_infill, gt_image_infill)
                 for key, value in calculated_ncc_metrics.items():
-                    overall_stats_dict[current_subject_id][key] = value.item()
+                    # we don't need the ".item()" here, since the values are already scalars
+                    overall_stats_dict[current_subject_id][key] = value
 
             # only voxels that are to be inferred (-> flat array)
             # these are required for mse, psnr, etc.