Merge pull request #11 from ivadomed/nk/faster-metrics-computation

Speed up metrics computation by parallelizing across subjects

compute_metrics_reloaded.py

@@ -32,7 +32,7 @@
 The script is compatible with both binary and multi-class segmentation tasks (e.g., nnunet region-based).
 The metrics are computed for each unique label (class) in the reference (ground truth) image.
 
-Authors: Jan Valosek
+Authors: Jan Valosek, Naga Karthik
 """
 
 
@@ -41,6 +41,8 @@
 import numpy as np
 import nibabel as nib
 import pandas as pd
+from multiprocessing import Pool, cpu_count
+from functools import partial
 
 from MetricsReloaded.metrics.pairwise_measures import BinaryPairwiseMeasures as BPM
 
@@ -81,6 +83,8 @@ def get_parser():
                              'see: https://metricsreloaded.readthedocs.io/en/latest/reference/metrics/metrics.html.')
     parser.add_argument('-output', type=str, default='metrics.csv', required=False,
                         help='Path to the output CSV file to save the metrics. Default: metrics.csv')
+    parser.add_argument('-jobs', type=int, default=cpu_count()//8, required=False,
+                        help='Number of CPU cores to use in parallel. Default: cpu_count()//8.')
 
     return parser
 
@@ -130,9 +134,7 @@ def compute_metrics_single_subject(prediction, reference, metrics):
     :param metrics: list of metrics to compute
     """
     # load nifti images
-    print(f'Processing...')
-    print(f'\tPrediction: {os.path.basename(prediction)}')
-    print(f'\tReference: {os.path.basename(reference)}')
+    print(f'\nProcessing:\n\tPrediction: {os.path.basename(prediction)}\n\tReference: {os.path.basename(reference)}')
     prediction_data = load_nifti_image(prediction)
     reference_data = load_nifti_image(reference)
 
@@ -159,7 +161,6 @@ def compute_metrics_single_subject(prediction, reference, metrics):
     # by doing this, we can compute metrics for each label separately, e.g., separately for spinal cord and lesions
     for label in unique_labels:
         # create binary masks for the current label
-        print(f'\tLabel {label}')
         prediction_data_label = np.array(prediction_data == label, dtype=float)
         reference_data_label = np.array(reference_data == label, dtype=float)
 
@@ -171,12 +172,9 @@ def compute_metrics_single_subject(prediction, reference, metrics):
         # add the metrics to the output dictionary
         metrics_dict[label] = dict_seg
 
-        if label == max(unique_labels):
-            break       # break to loop to avoid processing the background label ("else" block)
     # Special case when both the reference and prediction images are empty
     else:
         label = 1
-        print(f'\tLabel {label} -- both the reference and prediction are empty')
         bpm = BPM(prediction_data, reference_data, measures=metrics)
         dict_seg = bpm.to_dict_meas()
 
@@ -216,8 +214,14 @@ def build_output_dataframe(output_list):
     return df
 
 
-def main():
+def process_subject(prediction_file, reference_file, metrics):
+    """
+    Wrapper function to process a single subject.
+    """
+    return compute_metrics_single_subject(prediction_file, reference_file, metrics)
+
 
+def main():
     # parse command line arguments
     parser = get_parser()
     args = parser.parse_args()
@@ -227,19 +231,22 @@ def main():
 
     # Print the metrics to be computed
     print(f'Computing metrics: {args.metrics}')
+    print(f'Using {args.jobs} CPU cores in parallel ...')
 
     # Args.prediction and args.reference are paths to folders with multiple nii.gz files (i.e., MULTIPLE subjects)
     if os.path.isdir(args.prediction) and os.path.isdir(args.reference):
         # Get all files in the directories
         prediction_files, reference_files = get_images_in_folder(args.prediction, args.reference)
-        # Loop over the subjects
-        for i in range(len(prediction_files)):
-            # Compute metrics for each subject
-            metrics_dict = compute_metrics_single_subject(prediction_files[i], reference_files[i], args.metrics)
-            # Append the output dictionary (representing a single reference-prediction pair per subject) to the
-            # output_list
-            output_list.append(metrics_dict)
-    # Args.prediction and args.reference are paths nii.gz files from a SINGLE subject
+
+        # Use multiprocessing to parallelize the computation
+        with Pool(args.jobs) as pool:
+            # Create a partial function to pass the metrics argument to the process_subject function
+            func = partial(process_subject, metrics=args.metrics)
+            # Compute metrics for each subject in parallel
+            results = pool.starmap(func, zip(prediction_files, reference_files))
+
+            # Collect the results
+            output_list.extend(results)
     else:
         metrics_dict = compute_metrics_single_subject(args.prediction, args.reference, args.metrics)
         # Append the output dictionary (representing a single reference-prediction pair per subject) to the output_list

test/test_metrics/test_pairwise_measures_neuropoly.py

@@ -287,11 +287,11 @@ def test_non_empty_ref_and_pred_multi_class(self):
         Multi-class (i.e., voxels with values 1 and 2, e.g., region-based nnUNet training)
         """
 
-        expected_metrics = {1.0: {'dsc': 0.25,
-                                  'fbeta': 0.2500000055879354,
-                                  'nsd': 0.5,
-                                  'vol_diff': 2.0,
-                                  'rel_vol_error': 200.0,
+        expected_metrics = {1.0: {'dsc': 0.6521739130434783,
+                                  'fbeta': 0.5769230751596257,
+                                  'nsd': 0.23232323232323232,
+                                  'vol_diff': 2.6,
+                                  'rel_vol_error': 260.0,
                                   'EmptyRef': False,
                                   'EmptyPred': False,
                                   'lesion_ppv': 1.0,