added masking to eval path, eliminated batch option, set min_max to none in loading

run.py

@@ -309,7 +309,6 @@ def evaluate_mapping(mapping_evaluation_parameters):
     mapping_data_file = mapping_evaluation_parameters['mapping_data_file']
     data_list = mapping_evaluation_parameters['data_list']
     prediction_output_path = mapping_evaluation_parameters['prediction_output_path']
-    batch_size = mapping_evaluation_parameters['batch_size']
     device = mapping_evaluation_parameters['device']
     exit_on_error = mapping_evaluation_parameters['exit_on_error']
 
@@ -318,7 +317,6 @@ def evaluate_mapping(mapping_evaluation_parameters):
                                  mapping_data_file,
                                  data_list,
                                  prediction_output_path,
-                                 batch_size,
                                  device=device,
                                  exit_on_error=exit_on_error)
 

settings_evaluation.ini

@@ -4,6 +4,5 @@ data_directory = "/well/win-biobank/projects/imaging/data/data3/subjectsAll/"
 mapping_data_file = "dMRI/autoptx_preproc/tractsNormSummed.nii.gz"
 data_list = "datasets/test.txt"
 prediction_output_path = "network_predictions"
-batch_size = 1
 device = 0
 exit_on_error = True
\ No newline at end of file

utils/data_evaluation_utils.py

@@ -206,7 +206,6 @@ def evaluate_mapping(trained_model_path,
                      mapping_data_file,
                      data_list,
                      prediction_output_path,
-                     batch_size,
                      device=0,
                      mode='evaluate',
                      exit_on_error=False):
@@ -220,7 +219,6 @@ def evaluate_mapping(trained_model_path,
         mapping_data_file (str): Path to the input file
         data_list (str): Path to a .txt file containing the input files for consideration
         prediction_output_path (str): Output prediction path
-        batch_size (int): Size of batch to be evaluated
         device (str/int): Device type used for training (int - GPU id, str- CPU)
         mode (str): Current run mode or phase
         exit_on_error (bool): Flag that triggers the raising of an exception
@@ -272,7 +270,7 @@ def evaluate_mapping(trained_model_path,
                 print("Mapping Volume {}/{}".format(volume_index+1, len(file_paths)))
                 # Generate volume & header
                 _, predicted_volume, header, xform = _generate_volume_map(
-                    file_path, model, batch_size, device, cuda_available)
+                    file_path, model, device, cuda_available)
 
                 # Generate New Header Affine
 
@@ -308,7 +306,7 @@ def evaluate_mapping(trained_model_path,
     log.info("rsfMRI Generation Complete")
 
 
-def _generate_volume_map(file_path, model, batch_size, device, cuda_available):
+def _generate_volume_map(file_path, model, device, cuda_available):
     """rsfMRI Volume Generator
 
     This function uses the trained model to generate a new volume
@@ -316,7 +314,6 @@ def _generate_volume_map(file_path, model, batch_size, device, cuda_available):
     Args:
         file_path (str): Path to the desired file
         model (class): BrainMapper model class
-        batch_size (int): Size of batch to be evaluated
         device (str/int): Device type used for training (int - GPU id, str- CPU)
         cuda_available (bool): Flag indicating if a cuda-enabled GPU is present
 
@@ -337,26 +334,16 @@ def _generate_volume_map(file_path, model, batch_size, device, cuda_available):
 
     output_volume = []
 
-    for i in range(0, len(volume), batch_size):
-        batch_x = volume[i: i+batch_size]
+    MNI152_T1_2mm_brain_mask = torch.from_numpy(Image('utils/MNI152_T1_2mm_brain_mask.nii.gz').data)
 
     if cuda_available and (type(device) == int):
-            batch_x = batch_x.cuda(device)
+        volume = volume.cuda(device)
+        MNI152_T1_2mm_brain_mask = MNI152_T1_2mm_brain_mask.cuda(device)
 
-        output = model(batch_x)
-        output_volume.append(output)
+    output = model(volume)
+    output = torch.mul(output, MNI152_T1_2mm_brain_mask)
 
-    output_volume = torch.cat(output_volume)
-
-    predicted_volume = output_volume
-
-    # _, predicted_volume = torch.max(output_volume, dim=1)
-
-    # This needs to be checked - torch.max returns max values and locations.
-    # For segmentations, we are interested in the locations
-    # For the functional data, we might be interested in the actual values.
-    # The strength of the value represents the strength of the activation
-    # A threshold might also be required!
+    predicted_volume = output
 
     predicted_volume = (predicted_volume.cpu().numpy()).astype('float32')
     predicted_volume = np.squeeze(predicted_volume)

utils/data_utils.py

@@ -439,7 +439,7 @@ def set_orientation(volume, label_map, orientation):
             "Orientation value is invalid. It must be either >>coronal<<, >>axial<< or >>sagital<< ")
 
 
-def load_and_preprocess_evaluation(file_path, min_max=False):
+def load_and_preprocess_evaluation(file_path, min_max=None):
     """Load & Preprocessing before evaluation
 
     This function loads a nifty file and returns its volume and header information