commit for merge

.gitignore

@@ -30,4 +30,7 @@ conv_analysis.ipynb
 _MAX_FINAL_RUNS/
 Compute_Ensemble_Metrics.py
 _FINAL_EXP/
-model_load.ipynb 
\ No newline at end of file
+model_load.ipynb 
+max_runtimes.csv 
+min_runtimes.csv 
+stats.ipynb 
\ No newline at end of file

config.py

@@ -40,7 +40,7 @@ config['root_dir'] = '.'
 # Choose task and dataset
 ##################################################################
 
-config['preprocessing'] = 'min' # options: min and max 
+config['preprocessing'] = 'max' # options: min and max 
 
 #config['task'] = 'prosaccade-clf'
 config['task'] = 'gaze-reg'
@@ -137,7 +137,8 @@ def create_folder(config):
     if config['downsampled']:
         model_folder_name += '_downsampled'
     if config['ensemble']>1:
-        model_folder_name += '_ensemble'
+        num_models = str(config['ensemble'])
+        model_folder_name += f'_ensemble_{num_models}'
 
     config['model_dir'] = os.path.abspath(os.path.join(config['log_dir'], model_folder_name))
     if not os.path.exists(config['model_dir']):

main.py

@@ -34,7 +34,7 @@ def main():
     num_benchmarks = 1
 
     for i in range(num_benchmarks):
-        benchmark_task('prosaccade-clf')
+        #benchmark_task('prosaccade-clf')
         benchmark_task('gaze-reg')
         #benchmark_task('angle-reg')
         #benchmark_task('amplitude-reg')
@@ -48,8 +48,6 @@ def main():
 def benchmark_task(task):
     # 1 group: 1 learning rate with 5 models and 4 tasks = 20 
 
-    
-    
     ##################################################################
     # Task 1 Prosaccade
     ##################################################################

torch_models/BaseNetTorch.py

-from os import path
 from torch_models.torch_utils.plot import plot_metrics
 import torch
 import sys 
@@ -16,19 +15,18 @@ from memory_profiler import profile
 
 class Prediction_history:
     """
-    Collect predictions of the given validation set after each epoch 
-    predhis is a list of lists (one for each epoch) of tensors (one for each batch)
+    Collect predictions on the dataset in the given dataloader
+    Called after each epoch by BaseNet 
     """
     def __init__(self, dataloader, model) -> None:
+        """
+        predhis: a list of lists (one for each epoch) of tensors (one for each batch of length batch_size)
+        """
         self.dataloader = dataloader
         self.predhis = []
         self.model = model
 
-    #@timing_decorator
-    #@profile
     def on_epoch_end(self):
-        #print("Enter test on epoch end:")
-        #get_gpu_memory()
         with torch.no_grad():
             y_pred = []
             for batch, (x, y) in enumerate(self.dataloader):
@@ -50,7 +48,7 @@ class BaseNet(nn.Module):
     """
     def __init__(self, input_shape, epochs=50, verbose=True, model_number=0, batch_size=64):
         """
-        Initialize common variables of models based on BaseNet
+        Initialize common variables of models based on BaseNet, e.g. ConvNet or EEGNET 
         Create the common output layer dependent on the task to run 
         """
         super().__init__()
@@ -81,11 +79,13 @@ class BaseNet(nn.Module):
             self.output_layer = nn.Sequential(
                 nn.Linear(in_features=self.get_nb_features_output_layer(), out_features=1) 
             )
-        else: # elif config['task'] == 'amplitude-reg':
+        elif config['task'] == 'amplitude-reg':
             self.loss_fn = nn.MSELoss()
             self.output_layer = nn.Sequential(
                 nn.Linear(in_features=self.get_nb_features_output_layer(), out_features=1) 
             )
+        else:
+            raise ValueError("Choose a valid task")
 
         if verbose and self.model_number == 0:
             logging.info(f"Using loss fct: {self.loss_fn}")
@@ -94,6 +94,7 @@ class BaseNet(nn.Module):
     def forward(self, x):
         """
         Implements a forward pass of the network 
+        This method has to be implemented by models based on BaseNet 
         """
         pass
     
@@ -101,6 +102,7 @@ class BaseNet(nn.Module):
     def get_nb_features_output_layer(self):
         """
         Return the number of features that the output layer should take as input
+        This method has to be implemented by models based on BaseNet to compute the number of hidden neurons that the output layer takes as input. 
         """
         pass
 
@@ -110,9 +112,15 @@ class BaseNet(nn.Module):
     
     #@profile
     #@timing_decorator
-    def fit(self, train_dataloader, validation_dataloader, test_dataloader=None, subjectID=None):
+    def fit(self, train_dataloader, validation_dataloader, test_dataloader, subjectID=None):
         """
         Fit the model on the dataset defined by data x and labels y 
+
+        Inputs:
+        training, validation and test dataloader containing the respective datasets
+
+        Output:
+        prediction_ensemble containing the predictions on the test data, prediction on test data when model stops is used to compute the ensemble metrics 
         """
         # Move the model to GPU
         if torch.cuda.is_available():
@@ -120,36 +128,28 @@ class BaseNet(nn.Module):
             logging.info(f"Model moved to cuda")
         # Create the optimizer
         optimizer = torch.optim.Adam(list(self.parameters()), lr=config['learning_rate'])
-        # Create a history to track ensemble performance 
+        # Create a history to track ensemble performance, similar to tensorflow.keras callbacks
         prediction_ensemble = Prediction_history(dataloader=test_dataloader, model=self)
-        # Train the model 
+        # Create datastructures to collect metrics and implement early stopping
         epochs = config['epochs']          
         metrics = {'train_loss':[], 'val_loss':[], 'train_acc':[], 'val_acc':[]} if config['task'] == 'prosaccade-clf' else {'train_loss':[], 'val_loss':[]}
         best_val_loss = sys.maxsize # For early stopping 
         patience = 0
+        # Train the model 
         for t in range(epochs):
             logging.info("-------------------------------")
             logging.info(f"Epoch {t+1}")
-            # print(f"Start EPOCH: Free GPU memory: {get_gpu_memory()}")
-            # print(f"memory {psutil.virtual_memory()}") 
-            # Run through training and test set 
+            # Run through training and validation set  
             if not self.early_stopped:
                 train_loss_epoch, train_acc_epoch = train_loop(train_dataloader, self.float(), self.loss_fn, optimizer)
-                # print(f"Free GPU mem after train loop: {get_gpu_memory()}")
-                # print(f"memory {psutil.virtual_memory()}") 
                 val_loss_epoch, val_acc_epoch = validation_loop(validation_dataloader, self.float(), self.loss_fn)
-                # Add them for later printout 
                 metrics['train_loss'].append(train_loss_epoch)
                 metrics['val_loss'].append(val_loss_epoch)
                 if config['task'] == 'prosaccade-clf':
                     metrics['train_acc'].append(train_acc_epoch)
-                    metrics['val_acc'].append(val_acc_epoch)
-                # print("Free GPU mem after test loop:")
-                # print(f"memory {psutil.virtual_memory()}") 
-            # Add the predictions on the validation set, even if model was early stopped 
+                    metrics['val_acc'].append(val_acc_epoch) 
+            # Add the predictions on the validation set, even if model was early stopped, later we will compute ensemble metrics on them 
             prediction_ensemble.on_epoch_end()
-            # print("Free GPU mem after prediction hist:")
-            # print(f"memory {psutil.virtual_memory()}") 
             # Impementation of early stopping 
             if config['early_stopping'] and not self.early_stopped:
                 if patience > config['patience']:
@@ -163,7 +163,7 @@ class BaseNet(nn.Module):
                     logging.info(f"Improved validation loss to: {best_val_loss}")
                     patience = 0 
                     
-        # Plot and save metrics 
+        # Plot and save metrics
         plot_metrics(metrics['train_loss'], metrics['val_loss'], output_dir=config['model_dir'], metric='loss', model_number=self.model_number)
         if config['task'] == 'prosaccade-clf':
             plot_metrics(metrics['train_acc'], metrics['val_acc'], output_dir=config['model_dir'], metric='accuracy', model_number=self.model_number)

torch_models/CNN/CNN.py

@@ -8,6 +8,9 @@ class CNN(ConvNet):
     """
     def __init__(self, input_shape, kernel_size=64, epochs = 50, nb_filters=16, verbose=True, batch_size=64, 
                 use_residual=True, depth=12, regularization=0, model_number=0):
+        """
+        nb_features: specifies number of channels before the output layer 
+        """
         self.regularization = regularization
         self.nb_features = nb_filters # For CNN simply the number of filters inside the network 
         super().__init__(input_shape, kernel_size=kernel_size, epochs=epochs, 
@@ -16,8 +19,8 @@ class CNN(ConvNet):
 
     def _module(self, depth):
         """
-        The module of CNN is made of a simple convolution with batch normalization and ReLu activation. Finally, MaxPooling is also used.
-        We use two custom padding modules such that keras-like padding='same' is achieved, i.e. tensor shape stays constant.
+        The module of CNN is made of a simple convolution with batch normalization and ReLu activation. Finally, MaxPooling is used.
+        We use two custom padding modules such that keras-like padding='same' is achieved, i.e. tensor shape stays constant when passed through the module.
         """
         return nn.Sequential(
                 Pad_Conv(kernel_size=self.kernel_size, value=0),
@@ -27,12 +30,4 @@ class CNN(ConvNet):
                 nn.ReLU(),
                 Pad_Pool(left=0, right=1, value=0),
                 nn.MaxPool1d(kernel_size=2, stride=1)
-                )
-        """
-        Tensorflow code:
-        x = tf.keras.layers.Conv1D(filters=self.nb_filters, kernel_size=self.kernel_size, 
-                                    padding='same', use_bias=False)(input_tensor)
-        x = tf.keras.layers.BatchNormalization()(x)
-        x = tf.keras.layers.Activation(activation='relu')(x)
-        x = tf.keras.layers.MaxPool1D(pool_size=2, strides=1, padding='same')(x)
-        """       
\ No newline at end of file
+                )
\ No newline at end of file

torch_models/ConvNetTorch.py

@@ -13,7 +13,7 @@ class ConvNet(ABC, BaseNet):
     """
     This class defines all the common functionality for convolutional nets
     Inherit from this class and only implement _module() and _get_nb_features_output_layer() methods
-    Modules are then stacked in the forward() method 
+    Modules are then stacked in the forward() pass of the model 
     """
     def __init__(self, input_shape, kernel_size=32, nb_filters=32, verbose=True, batch_size=64, 
                 use_residual=False, depth=6, epochs=2, preprocessing = False, model_number=0):
@@ -25,7 +25,6 @@ class ConvNet(ABC, BaseNet):
 
         self.use_residual = use_residual
         self.depth = depth
-        self.callbacks = None
         self.kernel_size = kernel_size
         self.nb_filters = nb_filters
         self.preprocessing = preprocessing
@@ -52,43 +51,32 @@ class ConvNet(ABC, BaseNet):
     def forward(self, x):
         """
         Implements the forward pass of the network
-        Note that every layer used here must be defined in __init__ to be persistent and able to be trained 
+        Modules defined in a class implementing ConvNet are stacked and shortcut connections are used if specified. 
         """
         if self.preprocessing:
             x = self._preprocessing(x)
-        input_res = x # residual shortcut connection   
-
-        # Stack the modules 
+        input_res = x # set for the residual shortcut connection   
+        # Stack the modules and residual connection 
         shortcut_cnt = 0 
         for d in range(self.depth):
-            #print(f"x after block {d} {x.size()}")
             x = self.conv_blocks[d](x)            
             if self.use_residual and d % 3 == 2:
                 res = self.shortcuts[shortcut_cnt](input_res)
                 shortcut_cnt += 1 
-                #print(f"x before add {x.size()}")
-                #print(f"res before add {res.size()}")
                 x = torch.add(x, res)
                 x = nn.functional.relu(x)
                 input_res = x
-
         x = self.gap_layer_pad(x) 
         x = self.gap_layer(x)
-
-        #print(f"x after gap {x.size()}")
-
         x = x.view(self.batch_size, -1)
-
-        #print(f"x before output {x.size()}")
-
         output = self.output_layer(x) # Defined in BaseNet 
         return output 
     
     def _shortcut(self, depth):
         """
         Implements a shortcut with a convolution and batch norm 
-        This is the same for all our convolutional models
-        Padding before convolution for constant tensor shape, similar to Keras padding=same
+        This is the same for all models implementing ConvNet, therefore defined here 
+        Padding before convolution for constant tensor shape, similar to tensorflow.keras padding=same
         """
         return nn.Sequential(
                 Pad_Conv(kernel_size=self.kernel_size, value=0),

torch_models/EEGNet/eegNet.py

@@ -10,14 +10,21 @@ from torch_models.torch_utils.utils import get_gpu_memory
 class EEGNet(BaseNet):
     """
     The EEGNet architecture used as baseline. This is the architecture explained in the paper
-
     'EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces' with authors
     Vernon J. Lawhern, Amelia J. Solon, Nicholas R. Waytowich, Stephen M. Gordon, Chou P. Hung, Brent J. Lance
+
+    In our implementation it is built on BaseNet and can therefore use the same interface for training as models based on ConvNet. 
+    We only define the layers we need, the forward pass, and a method that returns the number of hidden units before the output layer, which is accessed by BaseNet to create the same output layer as for ConvNet models. 
+
+    
     """
     def __init__(self, input_shape, epochs=50, model_number=0, 
                 F1=16, F2=256, verbose=True, D=4, kernel_size=256,
                 dropout_rate=0.5, batch_size=64):
-
+        """
+        nb_features: specifies number of channels before the output layer 
+        Padding=same 
+        """
         self.kernel_size = kernel_size
         self.timesamples = input_shape[0]
         self.channels = input_shape[1]
@@ -80,6 +87,9 @@ class EEGNet(BaseNet):
             print(self)
 
     def forward(self, x):
+        """
+        Implements a forward pass of the eegnet.
+        """
         # Block 1
         x = self.padconv1(x)
         x = self.conv1(x)
@@ -108,28 +118,6 @@ class EEGNet(BaseNet):
 
         return x
 
-        """
-        Tensorflow code:
-         block1 = Conv2D(self.F1, (1, self.kernLength), padding='same',
-                        input_shape=(self.chans, self.samples, 1),
-                        use_bias=False)(input1)
-        block1 = BatchNormalization()(block1)
-        block1 = DepthwiseConv2D((self.chans, 1), use_bias=False,
-                                 depth_multiplier=self.D,
-                                 depthwise_constraint=max_norm(1.))(block1)
-        block1 = BatchNormalization()(block1)
-        block1 = Activation('elu')(block1)
-        block1 = AveragePooling2D((1, 16))(block1)
-        block1 = dropoutType(self.dropoutRate)(block1)
-
-        block2 = SeparableConv2D(self.F2, (1, 64),
-                                 use_bias=False, padding='same')(block1)
-        block2 = BatchNormalization()(block2)
-        block2 = Activation('elu')(block2)
-        block2 = AveragePooling2D((1, 6))(block2)
-        block2 = dropoutType(self.dropoutRate)(block2)
-        """
-
     def get_nb_features_output_layer(self):
         """
         Return number of features passed into the output layer of the network 

torch_models/Ensemble/Ensemble_torch.py

@@ -20,34 +20,34 @@ from torch_models.PyramidalCNN.PyramidalCNN import PyramidalCNN
 
 class Ensemble_torch:
     """
-    The Ensemble is a model itself, which contains a number of models whose prediction is averaged. 
-    Default: nb_models of the model_type model
-    Optional: Initialize with a model list, create a more versatile Ensemble model
+    The Ensemble is a model itself, which contains a number of models whose prediction is averaged (majority decision in case of a classifier). 
     """
-    def __init__(self, nb_models=1, model_type='cnn', model_list=[]):
+    def __init__(self, nb_models=1, model_type='cnn'):
         """
         nb_models: Number of models to run in the ensemble
-        model_type: Default model to run 
+        model_type: type of model that is used in the ensemble, e.g. 5 cnn models 
         model_list: optional, give a list of models that should be contained in the Ensemble 
         """
         self.nb_models = nb_models
         self.model_type = model_type
-        self.model_list = model_list 
-        self.models = []
 
+        # Set the loss function depending on the task we run 
         if config['task'] == 'prosaccade-clf':            
             self.loss_fn = nn.BCELoss()
         elif config['task'] == 'angle-reg':
             self.loss_fn = angle_loss
         elif config['task'] == 'gaze-reg':
             self.loss_fn = nn.MSELoss()
-        else: # amplitude-task
-            self.loss_fn = nn.MSELoss() # can also try MAE
+        elif config['task'] == 'amplitude-task': 
+            self.loss_fn = nn.MSELoss()
+        else:
+            raise ValueError("Choose a valid task")
 
     
     def run(self, x, y):
         """
         Fit all the models in the ensemble and save them to the run directory 
+        Also save and plot metrics metrics computed accross the ensemble 
         Inputs:
         x: dataset as np.array
         y: labels as np.array
@@ -55,16 +55,18 @@ class Ensemble_torch:
         # Create a split
         x = np.transpose(x, (0, 2, 1)) # (batch_size, samples, channels) to (bs, ch, samples) as torch conv layers want it 
         X_train, y_train, X_val, y_val, X_test, y_test = generate_split(x, y)
-        # Remove the subject counter from the labels and extract only angle for direction task 
+
+        # Remove the subject counter from the labels and extract the proper label for angle and amplitude task. structure of direction data: [subjectid, amplitude, angle] 
         if config['task'] == 'amplitude-reg':
             y_train = y_train[:, 1:2]
-            y_val = y_val[:, 1:2] # only extract the amplitude, not the subjectid or angle 
+            y_val = y_val[:, 1:2]  
             y_test = y_test[:, 1:2]
         else:
-            offset = 2 if config['task'] == 'angle-reg' else 1
+            offset = 2 if config['task'] == 'angle-reg' else 1 # for the other datasets it is [subjectid, label]
             y_train = y_train[:, offset:]
             y_val = y_val[:, offset:]
             y_test = y_test[:, offset:]
+
         # Log shapes
         logging.info(f"Training data shapes X, y: {X_train.shape, y_train.shape}")
         logging.info(f"Test data shapes X, y: {X_test.shape, y_test.shape}")
@@ -76,37 +78,22 @@ class Ensemble_torch:
         # Metrics to save across the ensemble 
         loss=[]
         accuracy=[]
-        prediction_list = [] # saves the predicted batches in the test dataloader across models
         # Fit the models 
         for i in range(config['ensemble']):
             logging.info("------------------------------------------------------------------------------------")
             logging.info('Start training model number {}/{} ...'.format(i+1, self.nb_models))
             model = create_model(model_type=self.model_type, model_number=i)
             pred_ensemble = model.fit(train_dataloader, validation_dataloader, test_dataloader)
-            # Collect the prediction on the test set
-            #prediction_list = sum_predictions(test_dataloader, model, model_number=i, prediction_list=prediction_list)
-        
             if i == 0:
                 pred = pred_ensemble.predhis
             else:
                 for j, pred_epoch in enumerate(pred_ensemble.predhis):
                     for batch, predictions in enumerate(pred_epoch):
                         pred[j][batch] = pred[j][batch] + predictions
-
             logging.info('Finished training model number {}/{} ...'.format(i+1, self.nb_models))
         
         logging.info("------------------------------------------------------------------------------------")
-        
-        """
-        #Divide prediction list by number of models and compute the final loss and accuracy of the ensemble 
-        ensemble_loss = compute_loss(loss_fn=self.loss_fn, dataloader=test_dataloader, pred_list=prediction_list, nb_models=self.nb_models)
-        if config['task'] == 'prosaccade-clf':
-            ensemble_acc = compute_accuracy(dataloader=test_dataloader, pred_list=prediction_list, nb_models=self.nb_models)
-            logging.info(f"ENSEMBLE ACCURACY ON TEST SET: {ensemble_acc}")
-        logging.info(f"ENSEMBLE LOSS ON TEST SET: {ensemble_loss}")
-        """
-
-        # Create the ensemble metrics 
+        logging.info("Finished training the Ensemble, computing ensemble metrics...")
         for j, pred_epoch in enumerate(pred):
             loss.append(compute_loss(loss_fn=self.loss_fn, dataloader=test_dataloader, pred_list=pred_epoch, nb_models=config['ensemble']))
             if config['task'] == 'prosaccade-clf':
@@ -127,7 +114,7 @@ class Ensemble_torch:
 
 def create_model(model_type, model_number):
     """
-    Returns the specified torch model as nn.Module built on BaseNet
+    Returns the specified torch model as nn.Module built on BaseNet, BaseNet is the common denominator of ConvNet models and the EEGNET model
     """
     if model_type == 'cnn':
         model = CNN(input_shape=config['input_shape'], kernel_size=64, epochs = config['epochs'], nb_filters=16, batch_size=config['batch_size'],

torch_models/InceptionTime/InceptionTime.py

@@ -10,13 +10,16 @@ from torch_models.Modules import Pad_Conv, Pad_Pool
 class Inception(ConvNet):
     """
     The InceptionTime architecture used as baseline. This is the architecture explained in the paper
-
     'InceptionTime: Finding AlexNet for Time Series Classification' with authors
     Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier,
     Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean
+    
     """
     def __init__(self, input_shape, kernel_size=64, epochs = 50, nb_filters=16, verbose=True, 
                             batch_size=64, use_residual=True, depth=12, bottleneck_size=16, model_number=0):
+        """
+        nb_features: specifies number of channels before the output layer 
+        """
         self.bottleneck_size = bottleneck_size
         self.nb_features = 4 * nb_filters # these are the 4 concatenated parallel convolutions, width of the inner tensort passed through network 
         logging.info('--------------- bottleneck_size : ' + str(self.bottleneck_size))
@@ -31,35 +34,16 @@ class Inception(ConvNet):
         Then it uses 3 filters with different kernel sizes (Default values are 40, 20, and 10)
         In parallel it uses a simple convolution with kernel size 1 with max pooling for stability during training.
         The outputs of each convolution are concatenated, followed by batch normalization and a ReLu activation.
+
+        Padding=same 
         """
         return Inception_module(self.kernel_size, self.nb_features, self.nb_channels, 
                                 self.nb_filters, self.bottleneck_size, depth)
-        """
-        Tensorflow code:
-        if int(input_tensor.shape[-1]) > 1:
-            input_inception = tf.keras.layers.Conv1D(filters=self.bottleneck_size, kernel_size=1, padding='same', use_bias=False)(input_tensor)
-        else:
-            input_inception = input_tensor
-
-        # kernel_size_s = [3, 5, 8, 11, 17]
-        kernel_size_s = [self.kernel_size // (2 ** i) for i in range(3)]
-        conv_list = []
-
-        for i in range(len(kernel_size_s)):
-            conv_list.append(
-                tf.keras.layers.Conv1D(filters=self.nb_filters, kernel_size=kernel_size_s[i], padding='same', use_bias=False)(input_inception))
-
-        max_pool_1 = tf.keras.layers.MaxPool1D(pool_size=3, strides=1, padding='same')(input_tensor)
-        conv_6 = tf.keras.layers.Conv1D(filters=self.nb_filters, kernel_size=1, padding='same', use_bias=False)(max_pool_1)
-
-        conv_list.append(conv_6)
-        x = tf.keras.layers.Concatenate(axis=2)(conv_list)
-        x = tf.keras.layers.BatchNormalization()(x)
-        x = tf.keras.layers.Activation(activation='relu')(x)
-        return x
-        """
 
 class Inception_module(nn.Module):
+    """
+    This class implements one inception module descirbed above as torch.nn.Module, which can then be stacked into a model by ConvNet 
+    """
     def __init__(self, kernel_size, nb_features, nb_channels, nb_filters, bottleneck_size, depth):
         super().__init__()
         kernel_size_s = [kernel_size // (2 ** i) for i in range(3)]

torch_models/Modules.py

@@ -87,15 +87,15 @@ class TCSConv1d(nn.Module):
 
     def forward(self, x):
         x = self.pad_depthwise(x)
-        #print(f"tensor after pad depthwise: {x.size()}")
         x = self.depthwise(x)
-        #print(f"tensor after conv depthwise: {x.size()}")
         x = self.pointwise(x)
-        #print(f"tensor after conv pointwise: {x.size()}")
         return x
 
 class SeparableConv2d(nn.Module):
-
+    """
+    Implements a 2d separable convolution
+    Not used in our default models 
+    """
     def __init__(self, in_channels, out_channels, kernel_size, padding=0, bias=False):
         super(SeparableConv2d, self).__init__()
         self.depthwise = nn.Conv2d(in_channels, 

torch_models/PyramidalCNN/PyramidalCNN.py

@@ -7,10 +7,12 @@ class PyramidalCNN(ConvNet):
     """
     The Classifier_PyramidalCNN is one of the simplest classifiers. It implements the class ConvNet, which is made of modules with a
     specific depth, where for each depth the number of filters is increased.
-    nb_features: specifies number of channels before the output layer 
     """
     def __init__(self, input_shape, kernel_size=16, epochs = 50, nb_filters=16, verbose=True, batch_size=64, 
                     use_residual=False, depth=6, model_number=0, regularization=0):
+        """
+        nb_features: specifies number of channels before the output layer 
+        """
         self.regularization = regularization
         self.nb_features = depth * nb_filters # For pyramidal we increase the nbfilters each depth layer
         super().__init__(input_shape, kernel_size=kernel_size, epochs=epochs, nb_filters=nb_filters,
@@ -20,6 +22,8 @@ class PyramidalCNN(ConvNet):
     def _module(self, depth):
         """
         The module of CNN is made of a simple convolution with batch normalization and ReLu activation. Finally, MaxPooling is also used.
+        The number of filters / output channels is increases with the depth of the model.
+        Padding=same 
         """
         return nn.Sequential(
             Pad_Conv(kernel_size=self.kernel_size),
@@ -31,11 +35,4 @@ class PyramidalCNN(ConvNet):
             nn.ReLU(),
             Pad_Pool(left=0, right=1),
             nn.MaxPool1d(kernel_size=2, stride=1)
-        )
-        """
-        Tensorflow code:
-        x = tf.keras.layers.Conv1D(filters=self.nb_filters*(current_depth + 1), kernel_size=self.kernel_size, padding='same', use_bias=False)(input_tensor)
-        x = tf.keras.layers.BatchNormalization()(x)
-        x = tf.keras.layers.Activation(activation='relu')(x)
-        x = tf.keras.layers.MaxPool1D(pool_size=2, strides=2)(x)
-        """
+        )
\ No newline at end of file

torch_models/Xception/Xception.py

@@ -13,6 +13,9 @@ class XCEPTION(ConvNet):
     """
     def __init__(self, input_shape, kernel_size=40, nb_filters=64, verbose=True, epochs=1, batch_size=64, 
                         use_residual=True, depth=6, model_number=0, regularization=0):
+        """
+        nb_features: specifies number of channels before the output layer 
+        """
         self.regularization = regularization
         self.nb_features = nb_filters # Exception passes a tensor of shape (timesamples, nb_filters) through the network
         super(XCEPTION, self).__init__(input_shape, kernel_size=kernel_size, nb_filters=nb_filters,
@@ -22,16 +25,10 @@ class XCEPTION(ConvNet):
     def _module(self, depth):
         """
         The module of Xception. Consists of a separable convolution followed by batch normalization and a ReLu activation function.
+        Padding=same 
         """
         return nn.Sequential(
             TCSConv1d(mother=self, depth=depth, bias=False),
             nn.BatchNorm1d(num_features=self.nb_features),
             nn.ReLU()
-        )
-        """
-        Tensorflow code:
-        x = tf.keras.layers.SeparableConv1D(filters=self.nb_filters, kernel_size=self.kernel_size, padding='same', 
-                                                use_bias=False, depth_multiplier=1)(input_tensor)
-        x = tf.keras.layers.BatchNormalization()(x)
-        x = tf.keras.layers.Activation(activation='relu')(x)
-        """
\ No newline at end of file
+        )
\ No newline at end of file

torch_models/torch_utils/custom_losses.py

@@ -9,5 +9,11 @@ def angle_loss(a, b):
     Angles -pi and pi should lead to 0 loss, since this is actually the same angle on the unit circle
     Angles pi/2 and -pi/2 should lead to a large loss, since this is a difference by pi on the unit circle
     Therefore we compute the absolute error of the "shorter" direction on the unit circle
+
+    Inputs:
+    a: predicted angle in rad
+    b: target angle in rad 
+