added minus one flag

settings.ini

@@ -9,8 +9,8 @@ crop_flag = True
 [TRAINING]
 experiment_name = "VA2-1"
 pre_trained_path = "saved_models/VA2-1.pth.tar"
-training_batch_size = 5
-validation_batch_size = 5
+training_batch_size = 3
+validation_batch_size = 3
 use_pre_trained = False
 learning_rate = 1e-5
 optimizer_beta = (0.9, 0.999)
@@ -18,9 +18,10 @@ optimizer_epsilon = 1e-8
 optimizer_weigth_decay = 1e-5
 number_of_epochs = 10
 loss_log_period = 50
-learning_rate_scheduler_step_size = 5
+learning_rate_scheduler_step_size = 6
 learning_rate_scheduler_gamma = 1e-1
 use_last_checkpoint = False
+adam_w_flag = False
 
 [NETWORK]
 kernel_heigth = 3
@@ -34,6 +35,7 @@ dropout = 0
 pool_kernel_size = 3
 pool_stride = 2
 up_mode = "upconv"
+final_activation = 'tanh'
 number_of_classes = 1
 number_of_transformer_blocks = 6
 custom_weight_reset_flag = False

settings_evaluation.ini

@@ -9,15 +9,16 @@ rsfmri_mean_mask_path = "utils/mean_dr_stage2.nii.gz"
 dmri_mean_mask_path = "utils/mean_tractsNormSummed_downsampled.nii.gz"
 mean_mask_path = "utils/mean_dr_stage2.nii.gz"
 regression_factors = "datasets/regression_weights.pkl"
-mean_regression_flag = False
-mean_regression_all_flag = False
-mean_subtraction_flag = True
+mean_regression_flag = True
+mean_regression_all_flag = True
+mean_subtraction_flag = False
 scale_volumes_flag = True
-normalize_flag = True
-negative_flag = True
+normalize_flag = False
+minus_one_scaling_flag = True
+negative_flag = False
 outlier_flag = True
 shrinkage_flag = False
 hard_shrinkage_flag = False
-crop_flag = False
+crop_flag = True
 device = 0
 exit_on_error = True
\ No newline at end of file

utils/data_evaluation_utils.py

@@ -43,6 +43,7 @@ def evaluate_mapping(trained_model_path,
                      mean_subtraction_flag,
                      scale_volumes_flag,
                      normalize_flag,
+                     minus_one_scaling_flag,
                      negative_flag, 
                      outlier_flag,
                      shrinkage_flag,
@@ -70,6 +71,7 @@ def evaluate_mapping(trained_model_path,
         mean_subtraction_flag (bool): Flag indicating if the targets should be de-meaned by subtraction using the mean_mask_path
         scale_volumes_flag (bool): Flag indicating if the volumes should be scaled.
         normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        minus_one_scaling_flag (bool): Flag signaling if the volume should be scaled to [-1,1] if True
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         outlier_flag (bool): Flag indicating if outliers should be set to the min/max values.
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
@@ -142,6 +144,7 @@ def evaluate_mapping(trained_model_path,
                                                                                                 mean_subtraction_flag,
                                                                                                 scale_volumes_flag,
                                                                                                 normalize_flag,
+                                                                                                minus_one_scaling_flag,
                                                                                                 negative_flag, 
                                                                                                 outlier_flag,
                                                                                                 shrinkage_flag,
@@ -212,6 +215,7 @@ def _generate_volume_map(file_path,
                          mean_subtraction_flag,
                          scale_volumes_flag,
                          normalize_flag,
+                         minus_one_scaling_flag,
                          negative_flag, 
                          outlier_flag,
                          shrinkage_flag,
@@ -237,6 +241,7 @@ def _generate_volume_map(file_path,
         mean_subtraction_flag (bool): Flag indicating if the targets should be de-meaned by subtraction using the mean_mask_path
         scale_volumes_flag (bool): Flag indicating if the volumes should be scaled.
         normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        minus_one_scaling_flag (bool): Flag signaling if the volume should be scaled to [-1,1] if True
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         outlier_flag (bool): Flag indicating if outliers should be set to the min/max values.
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
@@ -253,16 +258,16 @@ def _generate_volume_map(file_path,
     if mean_regression_flag == True:
         if mean_regression_all_flag == True:
             volume = _regress_input(volume, subject, dmri_mean_mask_path, regression_factors, crop_flag)
-            scaling_parameters = [-0.0539, 0.0969, -12.094, 14.6319]
+            scaling_parameters = [-0.0626, 0.1146, -14.18, 16.9475]
         else:
-            scaling_parameters = [0.0, 0.2, -12.094, 14.6319]
+            scaling_parameters = [0.0, 0.2, -14.18, 16.9475]
     elif mean_subtraction_flag == True:
         scaling_parameters = [0.0, 0.2, 0.0, 10.0]
 
     print('volume range:', np.min(volume), np.max(volume))
 
     if scale_volumes_flag == True:
-        volume = _scale_input(volume, scaling_parameters, normalize_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag)
+        volume = _scale_input(volume, scaling_parameters, normalize_flag, minus_one_scaling_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag)
 
     if len(volume.shape) == 5:
         volume = volume
@@ -280,7 +285,7 @@ def _generate_volume_map(file_path,
 
     print('output range:', np.min(output), np.max(output))
 
-    output = _rescale_output(output, scaling_parameters, normalize_flag, negative_flag, shrinkage_flag, hard_shrinkage_flag)
+    output = _rescale_output(output, scaling_parameters, normalize_flag, minus_one_scaling_flag, negative_flag, shrinkage_flag, hard_shrinkage_flag)
 
     print('output rescaled:', np.min(output), np.max(output))
 
@@ -323,7 +328,7 @@ def _generate_volume_map(file_path,
     return predicted_complete_volume, predicted_volume, header, xform
 
 
-def _scale_input(volume, scaling_parameters, normalize_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag):
+def _scale_input(volume, scaling_parameters, normalize_flag, minus_one_scaling_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag):
     """Input Scaling
 
     This function reads the scaling factors from the saved file and then scales the data.
@@ -332,6 +337,7 @@ def _scale_input(volume, scaling_parameters, normalize_flag, negative_flag, outl
         volume (np.array): Numpy array representing the un-scalled volume. 
         scaling_parameters (list): List of scaling parameters.
         normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        minus_one_scaling_flag (bool): Flag signaling if the volume should be scaled to [-1,1] if True
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         outlier_flag (bool): Flag indicating if outliers should be set to the min/max values.
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
@@ -364,9 +370,10 @@ def _scale_input(volume, scaling_parameters, normalize_flag, negative_flag, outl
     if normalize_flag == True:
         # Normalization to [0, 1]
         scaled_volume = np.divide(np.subtract(volume, min_value), np.subtract(max_value, min_value))
-    else:
+    elif minus_one_scaling_flag == True:
         # Scaling between [-1, 1]
         scaled_volume = np.add(-1.0, np.multiply(2.0, np.divide(np.subtract(volume, min_value), np.subtract(max_value, min_value))))
+    # Else, no scaling occus, but the other flags can still hold true if the scaling flag is true! 
 
     return scaled_volume
 
@@ -401,7 +408,7 @@ def _regress_input(volume, subject, dmri_mean_mask_path, regression_factors, cro
     return regressed_volume
 
 
-def _rescale_output(volume, scaling_parameters, normalize_flag, negative_flag, shrinkage_flag, hard_shrinkage_flag):
+def _rescale_output(volume, scaling_parameters, normalize_flag, minus_one_scaling_flag, negative_flag, shrinkage_flag, hard_shrinkage_flag):
     """Output Rescaling
 
     This function reads the scaling factors from the saved file and then scales the data.
@@ -410,6 +417,7 @@ def _rescale_output(volume, scaling_parameters, normalize_flag, negative_flag, s
         volume (np.array): Unscalled volume
         scaling_parameters (list): List of scaling parameters.
         normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        minus_one_scaling_flag (bool): Flag signaling if the volume should be scaled to [-1,1] if True
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
         hard_shrinkage_flag (bool): Flag indicating if hard shrinkage should be applied. If False, soft shrinkage is applied. 
@@ -434,9 +442,10 @@ def _rescale_output(volume, scaling_parameters, normalize_flag, negative_flag, s
     if normalize_flag == True:
         # Normalization to [0, 1]
         rescaled_volume = np.add(np.multiply(volume, np.subtract(max_value, min_value)), min_value)
-    else:
+    elif minus_one_scaling_flag == True:
         # Scaling between [-1, 1]
         rescaled_volume = np.add(np.multiply(np.divide(np.add(volume, 1), 2), np.subtract(max_value, min_value)), min_value)
+    # Else, no rescaling occus, but the other flags can still hold true if the scaling flag is true! 
 
     return rescaled_volume
 

utils/hdf5_generator.py

@@ -80,6 +80,7 @@ def convert_hdf5(data_parameters, file_information):
                                                         mean_subtraction_flag = data_parameters['mean_subtraction_flag'], 
                                                         scale_volumes_flag = data_parameters['scale_volumes_flag'], 
                                                         normalize_flag = data_parameters['normalize_flag'],
+                                                        minus_one_scaling_flag = data_parameters['minus_one_scaling_flag'],
                                                         negative_flag = data_parameters['negative_flag'],
                                                         outlier_flag = data_parameters['outlier_flag'], 
                                                         shrinkage_flag = data_parameters['shrinkage_flag'], 
@@ -105,6 +106,7 @@ def convert_hdf5(data_parameters, file_information):
                                                                     mean_subtraction_flag = data_parameters['mean_subtraction_flag'], 
                                                                     scale_volumes_flag = data_parameters['scale_volumes_flag'], 
                                                                     normalize_flag = data_parameters['normalize_flag'], 
+                                                                    minus_one_scaling_flag = data_parameters['minus_one_scaling_flag'],
                                                                     negative_flag = data_parameters['negative_flag'],
                                                                     outlier_flag = data_parameters['outlier_flag'], 
                                                                     shrinkage_flag = data_parameters['shrinkage_flag'], 

utils/hdf5_settings.ini

@@ -14,12 +14,13 @@ train_data_file = "dMRI/autoptx_preproc/tractsNormSummed.nii.gz"
 train_output_targets = "fMRI/rfMRI_25.dr/dr_stage2.nii.gz"
 rsfmri_mean_mask_path = "utils/mean_dr_stage2.nii.gz"
 dmri_mean_mask_path = "utils/mean_tractsNormSummed_downsampled.nii.gz"
-mean_regression_flag = False
-mean_regression_all_flag = False
-mean_subtraction_flag = True
+mean_regression_flag = True
+mean_regression_all_flag = True
+mean_subtraction_flag = False
 scale_volumes_flag = True
-normalize_flag = True
-negative_flag = True
+normalize_flag = False
+minus_one_scaling_flag = True
+negative_flag = False
 outlier_flag = True
 shrinkage_flag = False
 hard_shrinkage_flag = False

utils/preprocessor.py

@@ -273,7 +273,7 @@ def weight_calculator(data_directory, subject, train_inputs, train_targets, rsfM
 
 
 def load_datasets(subjects, data_directory, input_file, output_target, mean_regression_flag, mean_regression_all_flag, regression_weights_path,
-                    dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, negative_flag,
+                    dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, minus_one_scaling_flag, negative_flag,
                     outlier_flag, shrinkage_flag, hard_shrinkage_flag, crop_flag):
     """ Dataset loader and pre-processor
 
@@ -292,6 +292,7 @@ def load_datasets(subjects, data_directory, input_file, output_target, mean_regr
         mean_subtraction_flag (bool): Flag indicating if the targets should be de-meaned by subtraction using the mean_mask_path
         scale_volumes_flag (bool): Flag indicating if the volumes should be scaled.
         normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        minus_one_scaling_flag
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         outlier_flag (bool): Flag indicating if outliers should be set to the min/max values.
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
@@ -307,22 +308,23 @@ def load_datasets(subjects, data_directory, input_file, output_target, mean_regr
 
     input_volumes, target_volumes = [], []
 
+    len_subjects = len(subjects)
+
     for index, subject in enumerate(subjects):
 
         input_volume, target_volume = load_and_preprocess(subject, data_directory, input_file, output_target, mean_regression_flag, mean_regression_all_flag, regression_weights_path,
-                                                        dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, negative_flag,
+                                                        dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, minus_one_scaling_flag, negative_flag,
                                                         outlier_flag, shrinkage_flag, hard_shrinkage_flag, crop_flag)
 
         input_volumes.append(input_volume)
         target_volumes.append(target_volume)
 
-        print("#", end='', flush=True)
-    print("100%", flush=True)
+        print("\r Processed {:.3f}%".format(index/len_subjects * 100.0), end='')
 
-    return input_volumes, target_volumes
+    return input_volumes, target_volume
 
 def load_and_preprocess(subject, data_directory, input_file, output_target, mean_regression_flag, mean_regression_all_flag, regression_weights_path,
-                        dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, negative_flag,
+                        dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, minus_one_scaling_flag, negative_flag,
                         outlier_flag, shrinkage_flag, hard_shrinkage_flag, crop_flag):
     """ Subject loader and pre-processor
 
@@ -341,6 +343,7 @@ def load_and_preprocess(subject, data_directory, input_file, output_target, mean
         mean_subtraction_flag (bool): Flag indicating if the targets should be de-meaned by subtraction using the mean_mask_path
         scale_volumes_flag (bool): Flag indicating if the volumes should be scaled.
         normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        minus_one_scaling_flag
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         outlier_flag (bool): Flag indicating if outliers should be set to the min/max values.
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
@@ -353,7 +356,7 @@ def load_and_preprocess(subject, data_directory, input_file, output_target, mean
     """
     input_volume, target_volume = load_data(subject, data_directory, input_file, output_target, crop_flag)
     input_volume, target_volume = preprocess(input_volume, target_volume, subject, mean_regression_flag, mean_regression_all_flag, regression_weights_path,
-                                            dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, negative_flag,
+                                            dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, minus_one_scaling_flag, negative_flag,
                                             outlier_flag, shrinkage_flag, hard_shrinkage_flag, crop_flag)
 
     return input_volume, target_volume
@@ -391,7 +394,7 @@ def load_data(subject, data_directory, input_file, output_target, crop_flag=Fals
 
 
 def preprocess(input_volume, target_volume, subject, mean_regression_flag, mean_regression_all_flag, regression_weights_path,
-               dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, negative_flag,
+               dMRI_mean_mask_path, rsfMRI_mean_mask_path, mean_subtraction_flag, scale_volumes_flag, normalize_flag, minus_one_scaling_flag, negative_flag,
                outlier_flag, shrinkage_flag, hard_shrinkage_flag, crop_flag):
     """Conducts pre-processing based on arguments
 
@@ -409,6 +412,7 @@ def preprocess(input_volume, target_volume, subject, mean_regression_flag, mean_
         mean_subtraction_flag (bool): Flag indicating if the targets should be de-meaned by subtraction using the mean_mask_path
         scale_volumes_flag (bool): Flag indicating if the volumes should be scaled.
         normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        minus_one_scaling_flag
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         outlier_flag (bool): Flag indicating if outliers should be set to the min/max values.
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
@@ -426,12 +430,12 @@ def preprocess(input_volume, target_volume, subject, mean_regression_flag, mean_
             input_volume = linear_regress_mean(input_volume, subject, regression_weights_path, crop_flag, target_flag=False, dMRI_mean_mask_path=dMRI_mean_mask_path)
             target_volume = linear_regress_mean(target_volume, subject, regression_weights_path, crop_flag, target_flag=True, rsfMRI_mean_mask_path=rsfMRI_mean_mask_path)
             # Set scaling parameters to Andrei Scaling
-            scaling_parameters = [-0.0539, 0.0969, -12.094, 14.6319]
+            scaling_parameters = [-0.0626, 0.1146, -14.18, 16.9475]
         else:
             # Regress only targets, leave inputs as they are
             target_volume = linear_regress_mean(target_volume, subject, regression_weights_path, crop_flag, target_flag=True, rsfMRI_mean_mask_path=rsfMRI_mean_mask_path)
             # Set scaling parameters to Mixed Scaling
-            scaling_parameters = [0.0, 0.2, -12.094, 14.6319]
+            scaling_parameters = [0.0, 0.2, -14.18, 16.9475]
     elif mean_subtraction_flag == True:
         # Subtract the mean from targets, leave inputs as they are
         target_volume = subtract_mean(target_volume, crop_flag, rsfMRI_mean_mask_path)
@@ -441,8 +445,8 @@ def preprocess(input_volume, target_volume, subject, mean_regression_flag, mean_
         pass
 
     if scale_volumes_flag == True:
-        input_volume = volume_scaling(input_volume, scaling_parameters, normalize_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag, target_flag=False)
-        target_volume = volume_scaling(target_volume, scaling_parameters, normalize_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag, target_flag=True)
+        input_volume = volume_scaling(input_volume, scaling_parameters, normalize_flag, minus_one_scaling_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag, target_flag=False)
+        target_volume = volume_scaling(target_volume, scaling_parameters, normalize_flag, minus_one_scaling_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag, target_flag=True)
     
     return input_volume, target_volume
 
@@ -506,7 +510,7 @@ def subtract_mean(volume, crop_flag, rsfMRI_mean_mask_path):
         return volume
 
 
-def volume_scaling(volume, scaling_parameters, normalize_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag, target_flag):
+def volume_scaling(volume, scaling_parameters, normalize_flag, minus_one_scaling_flag, negative_flag, outlier_flag, shrinkage_flag, hard_shrinkage_flag, target_flag):
     """ Volume Scaling Function
 
     This function applies various scaling operations to the volumes, based on their nature and the employed scaling strategy.
@@ -515,7 +519,8 @@ def volume_scaling(volume, scaling_parameters, normalize_flag, negative_flag, ou
         volume (np.array): Numpy array representing the un-scalled volume. 
         scaling_parameters (list): List of scaling parameters.
         target_flag (bool): Flag signaling if the file is a target or an input.
-        normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True) or scaled to [-1,1] if False.
+        normalize_flag (bool): Flag signaling if the volume should be normalized ([0,1] if True).
+        minus_one_scaling_flag (bool): Flag signaling if the volume should be scaled to [-1,1] if True
         negative_flag (bool): Flag indicating if all the negative values should be 0-ed. 
         outlier_flag (bool): Flag indicating if outliers should be set to the min/max values.
         shrinkage_flag (bool): Flag indicating if shrinkage should be applied.
@@ -559,9 +564,10 @@ def volume_scaling(volume, scaling_parameters, normalize_flag, negative_flag, ou
     if normalize_flag == True:
         # Normalization to [0, 1]
         volume = np.divide(np.subtract(volume, min_value), np.subtract(max_value, min_value))
-    else:
+    elif minus_one_scaling_flag == True:
         # Scaling between [-1, 1]
         volume = np.add(-1.0, np.multiply(2.0, np.divide(np.subtract(volume, min_value), np.subtract(max_value, min_value))))
+    # Else, no scaling occus, but the other flags can still hold true if the scaling flag is true! 
 
     return volume