Merge branch 'autoencoder_2' into 'master'

Autoencoder 2

See merge request !3

BrainMapperAE.py

@@ -75,16 +75,7 @@ class BrainMapperAE3D(nn.Module):
         parameters['input_channels'] = parameters['output_channels']
         parameters['convolution_stride'] = original_stride
 
-        self.transformerBlock1 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock2 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock3 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock4 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock5 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock6 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock7 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock8 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock9 = modules.ResNetBlock3D(parameters)
-        self.transformerBlock10 = modules.ResNetBlock3D(parameters)
+        self.transformerBlocks = nn.ModuleList([modules.ResNetBlock3D(parameters) for i in range(parameters['number_of_transformer_blocks'])])
 
         # Decoder
 
@@ -124,16 +115,8 @@ class BrainMapperAE3D(nn.Module):
 
         # Transformer
 
-        X = self.transformerBlock1.forward(X)
-        X = self.transformerBlock2.forward(X)
-        X = self.transformerBlock3.forward(X)
-        X = self.transformerBlock4.forward(X)
-        X = self.transformerBlock5.forward(X)
-        X = self.transformerBlock6.forward(X)
-        X = self.transformerBlock7.forward(X)
-        X = self.transformerBlock8.forward(X)
-        X = self.transformerBlock9.forward(X)
-        X = self.transformerBlock10.forward(X)
+        for transformerBlock in self.transformerBlocks:
+            X = transformerBlock(X)
 
         # Decoder
 
@@ -207,26 +190,39 @@ class BrainMapperAE3D(nn.Module):
 
         return prediction
 
-    def reset_parameters(self):
+    def reset_parameters(self, custom_weight_reset_flag):
         """Parameter Initialization
 
         This function (re)initializes the parameters of the defined network.
         This function is a wrapper for the reset_parameters() function defined for each module. 
         More information can be found here: https://discuss.pytorch.org/t/what-is-the-default-initialization-of-a-conv2d-layer-and-linear-layer/16055 + https://discuss.pytorch.org/t/how-to-reset-model-weights-to-effectively-implement-crossvalidation/53859 
         An alternative (re)initialization method is described here: https://discuss.pytorch.org/t/how-to-reset-variables-values-in-nn-modules/32639 
+
+        Args:
+            custom_weight_reset_flag (bool): Flag indicating if the modified weight initialisation approach should be used. 
+
         """
 
         print("Initializing network parameters...")
 
         for _, module in self.named_children():
             for _, submodule in module.named_children():
+                if isinstance(submodule, (torch.nn.ConvTranspose3d, torch.nn.Conv3d, torch.nn.InstanceNorm3d)) == True:
+                    submodule.reset_parameters()
+                    if custom_weight_reset_flag == True:
+                        if isinstance(submodule, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):                        
+                            gain = np.sqrt(np.divide(2, 1 + np.power(0.25, 2)))
+                            fan, _ = calculate_fan(submodule.weight)
+                            std = np.divide(gain, np.sqrt(fan))
+                            submodule.weight.data.normal_(0, std)
                 for _, subsubmodule in submodule.named_children():
-                    if isinstance(subsubmodule, (torch.nn.PReLU, torch.nn.Dropout3d, torch.nn.MaxPool3d)) == False:
+                    if isinstance(subsubmodule, (torch.nn.ConvTranspose3d, torch.nn.Conv3d, torch.nn.InstanceNorm3d)) == True:
                         subsubmodule.reset_parameters()
-                        # if isinstance(subsubmodule, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):
-                        #     gain = np.sqrt(np.divide(2, 1 + np.power(0.25, 2)))
-                        #     fan, _ = calculate_fan(subsubmodule.weight)
-                        #     std = np.divide(gain, np.sqrt(fan))
-                        #     subsubmodule.weight.data.normal_(0, std)
+                        if custom_weight_reset_flag == True:
+                            if isinstance(subsubmodule, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):
+                                gain = np.sqrt(np.divide(2, 1 + np.power(0.25, 2)))
+                                fan, _ = calculate_fan(subsubmodule.weight)
+                                std = np.divide(gain, np.sqrt(fan))
+                                subsubmodule.weight.data.normal_(0, std)
 
         print("Initialized network parameters!")

run.py

@@ -33,7 +33,6 @@ import os
 import shutil
 import argparse
 import logging
-from settings import Settings
 
 import torch
 import torch.utils.data as data
@@ -41,9 +40,11 @@ import numpy as np
 
 from solver import Solver
 from BrainMapperAE import BrainMapperAE3D
-from utils.data_utils import get_datasets, data_preparation, update_shuffling_flag, create_folder
+from utils.data_utils import get_datasets
+from utils.settings import Settings
 import utils.data_evaluation_utils as evaluations
 from utils.data_logging_utils import LogWriter
+from utils.common_utils import create_folder
 
 # Set the default floating point tensor type to FloatTensor
 
@@ -133,7 +134,6 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
             dataset=train_data,
             batch_size=training_parameters['training_batch_size'],
             shuffle=True,
-            num_workers=4,
             pin_memory=True
         )
 
@@ -141,7 +141,6 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
             dataset=validation_data,
             batch_size=training_parameters['validation_batch_size'],
             shuffle=False,
-            num_workers=4,
             pin_memory=True
         )
 
@@ -151,10 +150,14 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
         else:
             BrainMapperModel = BrainMapperAE3D(network_parameters)
 
-        BrainMapperModel.reset_parameters()
+        custom_weight_reset_flag = network_parameters['custom_weight_reset_flag']
 
+        BrainMapperModel.reset_parameters(custom_weight_reset_flag)
+
+        if training_parameters['adam_w_flag'] == True:
+            optimizer = torch.optim.AdamW
+        else:
             optimizer = torch.optim.Adam
-        # optimizer = torch.optim.AdamW
 
         solver = Solver(model=BrainMapperModel,
                         device=misc_parameters['device'],
@@ -177,7 +180,8 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
                         logs_directory=misc_parameters['logs_directory'],
                         checkpoint_directory=misc_parameters['checkpoint_directory'],
                         save_model_directory=misc_parameters['save_model_directory'],
-                        final_model_output_file=training_parameters['final_model_output_file']
+                        final_model_output_file=training_parameters['final_model_output_file'],
+                        crop_flag = data_parameters['crop_flag']
                         )
 
         validation_loss = solver.train(train_loader, validation_loader)
@@ -187,94 +191,8 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
 
         return validation_loss
 
-    if data_parameters['k_fold'] is None:
-
-        _ = _train_runner(data_parameters, training_parameters,
-                          network_parameters, misc_parameters)
-
-    else:
-        print("Training initiated using K-fold Cross Validation!")
-        k_fold_losses = []
-
-        for k in range(data_parameters['k_fold']):
-
-            print("K-fold Number: {}".format(k+1))
-
-            data_parameters['train_list'] = os.path.join(
-                data_parameters['data_folder_name'], 'train' + str(k+1)+'.txt')
-            data_parameters['validation_list'] = os.path.join(
-                data_parameters['data_folder_name'], 'validation' + str(k+1)+'.txt')
-            training_parameters['final_model_output_file'] = training_parameters['final_model_output_file'].replace(
-                ".pth.tar", str(k+1)+".pth.tar")
-
-            validation_loss = _train_runner(
-                data_parameters, training_parameters, network_parameters, misc_parameters)
-
-            k_fold_losses.append(validation_loss)
-
-        for k in range(data_parameters['k_fold']):
-            print("K-fold Number: {} Loss: {}".format(k+1, k_fold_losses[k]))
-        print("K-fold Cross Validation Avearge Loss: {}".format(np.mean(k_fold_losses)))
-
-
-def evaluate_score(training_parameters, network_parameters, misc_parameters, evaluation_parameters):
-    """Mapping Score Evaluator
-
-    This function evaluates a given trained model by calculating the it's dice score prediction.
-
-    Args:
-
-        training_parameters(dict): Dictionary containing relevant hyperparameters for training the network.
-        training_parameters = {
-            'experiment_name': 'experiment_name'
-        }
-
-        network_parameters (dict): Contains information relevant parameters
-        network_parameters= {
-            'number_of_classes': 1
-        }
-
-        misc_parameters (dict): Dictionary of aditional hyperparameters
-        misc_parameters = {
-            'logs_directory': 'log-directory'
-            'device': 1
-            'experiments_directory': 'experiments-directory'
-        }
 
-        evaluation_parameters (dict): Dictionary of parameters useful during evaluation.
-        evaluation_parameters = {
-            'trained_model_path': 'path/to/model'
-            'data_directory': 'path/to/data'
-            'targets_directory': 'path/to/targets'
-            'data_list': 'path/to/datalist.txt/
-            'orientation': 'coronal'
-            'saved_predictions_directory': 'directory-of-saved-predictions'
-        }
-    """
-
-    # TODO - NEED TO UPDATE THE DATA FUNCTIONS!
-
-    prediction_output_path = os.path.join(misc_parameters['experiments_directory'],
-                                          training_parameters['experiment_name'],
-                                          evaluation_parameters['saved_predictions_directory']
-                                          )
-
-    evaluations.evaluate_correlation(trained_model_path=evaluation_parameters['trained_model_path'],
-                                     data_directory=evaluation_parameters['data_directory'],
-                                     mapping_data_file=mapping_evaluation_parameters['mapping_data_file'],
-                                     target_data_file=evaluation_parameters['targets_directory'],
-                                     data_list=evaluation_parameters['data_list'],
-                                     prediction_output_path=prediction_output_path,
-                                     brain_mask_path=mapping_evaluation_parameters['brain_mask_path'],
-                                     rsfmri_mean_mask_path=mapping_evaluation_parameters[
-                                         'rsfmri_mean_mask_path'],
-                                     dmri_mean_mask_path=mapping_evaluation_parameters[
-                                         'dmri_mean_mask_path'],
-                                     mean_regression=mapping_evaluation_parameters['mean_regression'],
-                                     scaling_factors=mapping_evaluation_parameters['scaling_factors'],
-                                     regression_factors=mapping_evaluation_parameters['regression_factors'],
-                                     device=misc_parameters['device'],
-                                     )
+    _ = _train_runner(data_parameters, training_parameters, network_parameters, misc_parameters)
 
 
 def evaluate_mapping(mapping_evaluation_parameters):
@@ -305,10 +223,18 @@ def evaluate_mapping(mapping_evaluation_parameters):
     device = mapping_evaluation_parameters['device']
     exit_on_error = mapping_evaluation_parameters['exit_on_error']
     brain_mask_path = mapping_evaluation_parameters['brain_mask_path']
-    mean_regression = mapping_evaluation_parameters['mean_regression']
-    mean_subtraction = mapping_evaluation_parameters['mean_subtraction']
-    scaling_factors = mapping_evaluation_parameters['scaling_factors']
     regression_factors = mapping_evaluation_parameters['regression_factors']
+    mean_regression_flag = mapping_evaluation_parameters['mean_regression_flag']
+    mean_regression_all_flag = mapping_evaluation_parameters['mean_regression_all_flag']
+    mean_subtraction_flag = mapping_evaluation_parameters['mean_subtraction_flag']
+    scale_volumes_flag = mapping_evaluation_parameters['scale_volumes_flag']
+    normalize_flag = mapping_evaluation_parameters['normalize_flag']
+    minus_one_scaling_flag = mapping_evaluation_parameters['minus_one_scaling_flag']
+    negative_flag = mapping_evaluation_parameters['negative_flag']
+    outlier_flag = mapping_evaluation_parameters['outlier_flag']
+    shrinkage_flag = mapping_evaluation_parameters['shrinkage_flag']
+    hard_shrinkage_flag = mapping_evaluation_parameters['hard_shrinkage_flag']
+    crop_flag = mapping_evaluation_parameters['crop_flag']
 
     evaluations.evaluate_mapping(trained_model_path,
                                  data_directory,
@@ -318,13 +244,20 @@ def evaluate_mapping(mapping_evaluation_parameters):
                                  brain_mask_path,
                                  dmri_mean_mask_path,
                                  rsfmri_mean_mask_path,
-                                 mean_regression,
-                                 mean_subtraction,
-                                 scaling_factors,
                                  regression_factors,
-                                 device=device,
-                                 exit_on_error=exit_on_error)
-
+                                 mean_regression_flag,
+                                 mean_regression_all_flag, 
+                                 mean_subtraction_flag,
+                                 scale_volumes_flag,
+                                 normalize_flag,
+                                 minus_one_scaling_flag,
+                                 negative_flag, 
+                                 outlier_flag,
+                                 shrinkage_flag,
+                                 hard_shrinkage_flag,
+                                 crop_flag,
+                                 device, 
+                                 exit_on_error)
 
 def delete_files(folder):
     """ Clear Folder Contents
@@ -351,49 +284,32 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--mode', '-m', required=True,
                         help='run mode, valid values are train or evaluate')
-    parser.add_argument('--settings_path', '-sp', required=False,
-                        help='optional argument, set path to settings_evaluation.ini')
+    parser.add_argument('--model_name', '-n', required=True,
+                        help='model name, required for identifying the settings file modelName.ini & modelName_eval.ini')
+    parser.add_argument('--use_last_checkpoint', '-c', required=False,
+                        help='flag indicating if the last checkpoint should be used if 1; useful when wanting to time-limit jobs.')
+    parser.add_argument('--number_of_epochs', '-e', required=False,
+                        help='flag indicating how many epochs the network will train for; should be limited to ~3 hours or 2/3 epochs')
 
     arguments = parser.parse_args()
 
-    settings = Settings('settings.ini')
+    settings_file_name = arguments.model_name + '.ini'
+    evaluation_settings_file_name = arguments.model_name + '_eval.ini'
+
+    settings = Settings(settings_file_name)
     data_parameters = settings['DATA']
     training_parameters = settings['TRAINING']
     network_parameters = settings['NETWORK']
     misc_parameters = settings['MISC']
     evaluation_parameters = settings['EVALUATION']
 
-    # Here we shuffle the data!
-
-    if data_parameters['data_split_flag'] == True:
-        print('Data is shuffling... This could take a few minutes!')
-
-        if data_parameters['use_data_file'] == True:
-            data_preparation(data_parameters['data_folder_name'],
-                             data_parameters['test_percentage'],
-                             data_parameters['subject_number'],
-                             data_directory=data_parameters['data_directory'],
-                             train_inputs=data_parameters['train_data_file'],
-                             train_targets=data_parameters['train_output_targets'],
-                             rsfMRI_mean_mask_path=data_parameters['rsfmri_mean_mask_path'],
-                             dMRI_mean_mask_path=data_parameters['dmri_mean_mask_path'],
-                             data_file=data_parameters['data_file'],
-                             K_fold=data_parameters['k_fold']
-                             )
-        else:
-            data_preparation(data_parameters['data_folder_name'],
-                             data_parameters['test_percentage'],
-                             data_parameters['subject_number'],
-                             data_directory=data_parameters['data_directory'],
-                             train_inputs=data_parameters['train_data_file'],
-                             train_targets=data_parameters['train_output_targets'],
-                             rsfMRI_mean_mask_path=data_parameters['rsfmri_mean_mask_path'],
-                             dMRI_mean_mask_path=data_parameters['dmri_mean_mask_path'],
-                             K_fold=data_parameters['k_fold']
-                             )
-        update_shuffling_flag('settings.ini')
+    if arguments.use_last_checkpoint == '1':
+        training_parameters['use_last_checkpoint'] = True
+    elif arguments.use_last_checkpoint == '0':
+        training_parameters['use_last_checkpoint'] = False
 
-        print('Data is shuffling... Complete!')
+    if arguments.number_of_epochs is not None:
+        training_parameters['number_of_epochs'] = int(arguments.number_of_epochs)
 
     if arguments.mode == 'train':
         train(data_parameters, training_parameters,
@@ -401,15 +317,9 @@ if __name__ == '__main__':
 
     # NOTE: THE EVAL FUNCTIONS HAVE NOT YET BEEN DEBUGGED (16/04/20)
 
-    elif arguments.mode == 'evaluate-score':
-        evaluate_score(training_parameters,
-                       network_parameters, misc_parameters, evaluation_parameters)
     elif arguments.mode == 'evaluate-mapping':
         logging.basicConfig(filename='evaluate-mapping-error.log')
-        if arguments.settings_path is not None:
-            settings_evaluation = Settings(arguments.settings_path)
-        else:
-            settings_evaluation = Settings('settings_evaluation.ini')
+        settings_evaluation = Settings(evaluation_settings_file_name)
         mapping_evaluation_parameters = settings_evaluation['MAPPING']
         evaluate_mapping(mapping_evaluation_parameters)
     elif arguments.mode == 'clear-experiments':
@@ -423,18 +333,12 @@ if __name__ == '__main__':
         delete_files(misc_parameters['logs_directory'])
         print('Cleared the current experiments and logs directory successfully!')
     elif arguments.mode == 'train-and-evaluate-mapping':
-        if arguments.settings_path is not None:
-            settings_evaluation = Settings(arguments.settings_path)
-        else:
-            settings_evaluation = Settings('settings_evaluation.ini')
+        settings_evaluation = Settings(evaluation_settings_file_name)
         mapping_evaluation_parameters = settings_evaluation['MAPPING']
         train(data_parameters, training_parameters,
               network_parameters, misc_parameters)
         logging.basicConfig(filename='evaluate-mapping-error.log')
         evaluate_mapping(mapping_evaluation_parameters)
-    elif arguments.mode == 'prepare-data':
-        print('Ensure you have updated the settings.ini file accordingly! This call does nothing but pass after data was shuffled!')
-        pass
     else:
         raise ValueError(
-            'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, train-and-evaluate-mapping, prepare-data, clear-experiments and clear-everything')
+            'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, train-and-evaluate-mapping, clear-experiments and clear-everything')

settings.ini

 [DATA]
 data_folder_name = "datasets"
-use_data_file = False
-data_directory = "/well/win-biobank/projects/imaging/data/data3/subjectsAll/"
-data_file = "/well/win-biobank/projects/imaging/data/data3/subjectsAll/subj_22k.txt"
-k_fold = None
-data_split_flag = False
-test_percentage = 5
-subject_number = 12000
-train_list = "datasets/train.txt"
-validation_list = "datasets/validation.txt"
-test_list = "datasets/test.txt"
-scaling_factors = "datasets/scaling_factors.pkl"
-regression_weights = "datasets/regression_weights.pkl"
-train_data_file = "dMRI/autoptx_preproc/tractsNormSummed.nii.gz"
-train_output_targets = "fMRI/rfMRI_25.dr/dr_stage2.nii.gz"
-validation_data_file = "dMRI/autoptx_preproc/tractsNormSummed.nii.gz"
-validation_target_file = "fMRI/rfMRI_25.dr/dr_stage2.nii.gz"
-brain_mask_path = "utils/MNI152_T1_2mm_brain_mask.nii.gz"
-rsfmri_mean_mask_path = "utils/mean_dr_stage2.nii.gz"
-dmri_mean_mask_path = "utils/mean_tractsNormSummed_downsampled.nii.gz"
-mean_regression = False
-mean_subtraction = True
+input_data_train = "input_data_train.h5"
+target_data_train = "target_data_train.h5"
+input_data_validation = "input_data_validation.h5"
+target_data_validation = "target_data_validation.h5"
+crop_flag = False
 
 [TRAINING]
 experiment_name = "VA2-1"
 pre_trained_path = "saved_models/VA2-1.pth.tar"
 final_model_output_file = "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)
@@ -35,9 +19,10 @@ 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
@@ -45,13 +30,16 @@ kernel_width = 3
 kernel_depth = 3
 kernel_classification = 7
 input_channels = 1
-output_channels = 64
+output_channels = 32
 convolution_stride = 1
 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
 
 [MISC]
 save_model_directory = "saved_models"

settings_evaluation.ini

 [MAPPING]
-trained_model_path = "saved_models/VA2.pth.tar"
-prediction_output_path = "VA2_predictions"
+trained_model_path = "saved_models/VA2-1.pth.tar"
+prediction_output_path = "VA2-1_predictions"
 data_directory = "/well/win-biobank/projects/imaging/data/data3/subjectsAll/"
 mapping_data_file = "dMRI/autoptx_preproc/tractsNormSummed.nii.gz"
 data_list = "datasets/test.txt"
@@ -8,9 +8,17 @@ brain_mask_path = "utils/MNI152_T1_2mm_brain_mask.nii.gz"
 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"
-scaling_factors = "datasets/scaling_factors.pkl"
 regression_factors = "datasets/regression_weights.pkl"
-mean_regression = False
-mean_subtraction = True
+mean_regression_flag = True
+mean_regression_all_flag = True
+mean_subtraction_flag = False
+scale_volumes_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 = True
 device = 0
 exit_on_error = True
\ No newline at end of file

setup.py

@@ -16,5 +16,7 @@ setup(
         'fslpy',
         'tensorboardX',
         'sklearn',
+        'nibabel',
+        'h5py',
         ],
 )

solver.py

@@ -17,9 +17,10 @@ import torch
 import glob
 
 from fsl.data.image import Image
+from fsl.utils.image.roi import roi
 from datetime import datetime
 from utils.losses import MSELoss
-from utils.data_utils import create_folder
+from utils.common_utils import create_folder
 from utils.data_logging_utils import LogWriter
 from utils.early_stopping import EarlyStopping
 from torch.optim import lr_scheduler
@@ -49,6 +50,7 @@ class Solver():
         use_last_checkpoint (bool): Flag for loading the previous checkpoint
         experiment_directory (str): Experiment output directory name
         logs_directory (str): Directory for outputing training logs
+        crop_flag (bool): Flag indicating if the volumes should be cropped from 91x109x91 to 72x90x77 to reduce storage space and speed-up training
 
     Returns:
         trained model - working on this!
@@ -76,7 +78,8 @@ class Solver():
                  logs_directory='logs',
                  checkpoint_directory='checkpoints',
                  save_model_directory='saved_models',
-                 final_model_output_file='finetuned_alldata.pth.tar'
+                 final_model_output_file='finetuned_alldata.pth.tar',
+                 crop_flag = False
                  ):
 
         self.model = model
@@ -125,15 +128,17 @@ class Solver():
         self.EarlyStopping = EarlyStopping(patience=10, min_delta=0)
         self.early_stop = False
 
-        if use_last_checkpoint:
-            self.load_checkpoint()
-
-        self.MNI152_T1_2mm_brain_mask = torch.from_numpy(
-            Image('utils/MNI152_T1_2mm_brain_mask.nii.gz').data)
+        if crop_flag == False:
+            self.MNI152_T1_2mm_brain_mask = torch.from_numpy(Image('utils/MNI152_T1_2mm_brain_mask.nii.gz').data)
+        elif crop_flag == True:
+            self.MNI152_T1_2mm_brain_mask = torch.from_numpy(roi(Image('utils/MNI152_T1_2mm_brain_mask.nii.gz'),((9,81),(10,100),(0,77))).data)
 
         self.save_model_directory = save_model_directory
         self.final_model_output_file = final_model_output_file
 
+        if use_last_checkpoint:
+            self.load_checkpoint()
+
     def train(self, train_loader, validation_loader):
         """Training Function
 
@@ -371,14 +376,14 @@ class Solver():
         self.start_epoch = checkpoint['epoch']
         self.start_iteration = checkpoint['start_iteration']
         # We are not loading the model_name as we might want to pre-train a model and then use it.
-        self.model.load_state_dict = checkpoint['state_dict']
-        self.optimizer.load_state_dict = checkpoint['optimizer']
-        self.learning_rate_scheduler.load_state_dict = checkpoint['scheduler']
+        self.model.load_state_dict(checkpoint['state_dict'])
+        self.optimizer.load_state_dict(checkpoint['optimizer'])
 
         for state in self.optimizer.state.values():
             for key, value in state.items():
                 if torch.is_tensor(value):
                     state[key] = value.to(self.device)
 
+        self.learning_rate_scheduler.load_state_dict(checkpoint['scheduler'])
         self.LogWriter.log(
             "Checkpoint Loaded {} - epoch {}".format(checkpoint_file_path, checkpoint['epoch']))

utils/DSbiobank.py

@@ -18,7 +18,7 @@ import numpy as np
 from fsl.data.image import Image
 from fsl.utils.image.resample import resampleToPixdims
 import matplotlib.pyplot as plt
-from data_utils import directory_reader, regression_weight_calculator
+from preprocessor import directory_reader, regression_weight_calculator
 from tempfile import TemporaryFile
 from datetime import datetime
 import pandas as pd

utils/common_utils.py

+""" Commom utilities
+
+Description:
+
+    This file contains several functions which can be useful througout the project.
+
+Usage:
+
+    To use content from this folder, import the functions and instantiate them as you wish to use them:
+
+        from utils.common_utils import function_name
+
+"""
+
+import os
+
+def create_folder(path):
+    """Folder Creator
+
+    A function which creates a folder at a given path if one does not exist
+
+    Args:
+        path (str): destination to check for folder existance
+    """
+
+    if not os.path.exists(path):
+        os.mkdir(path)
\ No newline at end of file

utils/data_logging_utils.py

@@ -18,8 +18,6 @@ import matplotlib.pyplot as plt
 import shutil
 import logging
 import numpy as np
-import re
-from textwrap import wrap
 import torch
 
 # The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it.

utils/group_mean.py

@@ -17,7 +17,7 @@ import os