Fixed some small bugs

.gitignore

 /.idea/
 /venv/
 *.pyc
-.DS_Store
\ No newline at end of file
+.DS_Store
+/data/*
+all_EEGprocuesan.mat
+all_trialinfoprosan.mat

CNN/CNN.py

@@ -6,6 +6,8 @@ import torch.optim as optim
 import torch.utils.data
 from utils.utils import *
 
+from config import config
+
 
 class Net(nn.Module):
     def __init__(self):
@@ -45,9 +47,9 @@ def run(trainX, trainY):
     # PATH = '../cifar_net.pth'
     # torch.save(net.state_dict(), PATH)
     # Newly added lines below
-	save_logs(hist, config['model_dir'], config['model'], pytorch=True)
-	save_model_param(config['model_dir'], config['model'], pytorch=True)
-	plot_loss_torch(loss)
+	# save_logs(hist, config['model_dir'], config['model'], pytorch=True)
+	# save_model_param(config['model_dir'], config['model'], pytorch=True)
+	# plot_loss_torch(loss)
 
 def train(trainloader, net, optimizer, criterion, epoch=50):
     loss=[]

EEGNet/eegNet.py

@@ -18,8 +18,8 @@ def run(trainX, trainY):
     plot_loss(hist, 'EEGNet', True)
     plot_acc(hist, 'EEGNet', True)
     # Newly added lines below
-	save_logs(hist, config['model_dir'], config['model'], pytorch=False)
-	save_model_param(config['model_dir'], config['model'], pytorch=False)
+    save_logs(hist, config['model_dir'], config['model'], pytorch=False)
+    save_model_param(config['model_dir'], config['model'], pytorch=False)
 
 class Classifier_EEGNet:
     def __init__(self, output_directory, nb_classes=1, chans = 129, samples = 500, dropoutRate = 0.5, kernLength = 64, F1 = 8,

InceptionTime/inception.py

@@ -10,9 +10,8 @@ def run(trainX, trainY):
     hist = classifier.fit(trainX, trainY)
     plot_loss(hist, 'Inception', True)
     plot_acc(hist, 'Inception', True)
-    # Newly added lines below
-	save_logs(hist, config['model_dir'], config['model'], pytorch=False)
-	save_model_param(config['model_dir'], config['model'], pytorch=False)
+    save_logs(hist, config['model_dir'], config['model'], pytorch=False)
+    # save_model_param(config['model_dir'], config['model'], pytorch=False)
 
 class Classifier_INCEPTION:
     def __init__(self, output_directory, input_shape, verbose=False, build=True, batch_size=64, nb_filters=32,
@@ -96,5 +95,5 @@ class Classifier_INCEPTION:
         return model
 
     def fit(self, inception_x, y):
-        hist = self.model.fit(inception_x, y, verbose=1, validation_split=0.2, epochs=10)
+        hist = self.model.fit(inception_x, y, verbose=1, validation_split=0.2, epochs=1)
         return hist
\ No newline at end of file

config.py

@@ -15,7 +15,8 @@ config['log_dir'] = './runs/'
 config['tmp_dir'] = './tmp/'
 # Path to training, validation and test data folders.
 # PS: Note that we have to upload the data to the server!!!
-config['data_dir'] = '/cluster/home/your_username/data/'
+# config['data_dir'] = '/cluster/home/your_username/data/'
+config['data_dir'] = './'
 config['data_dir_server'] = '/cluster/project/infk/zigeng/preprocessed2/'
 # Path of root
 config['root_dir'] = '.'
@@ -35,7 +36,7 @@ deepeye: Our method
 """
 
 # Choosing model
-config['model'] = 'eegnet'
+config['model'] = 'inception'
 
 # CNN - 1
 config['cnn'] = {}
@@ -74,4 +75,6 @@ config['deepeye']['trainY_filename'] = "trialinfocuelocked"
 # Create a unique output directory for this experiment.
 timestamp = str(int(time.time()))
 model_folder_name = timestamp if config['model'] == '' else timestamp + "_" + config['model']
-config['model_dir'] = os.path.abspath(os.path.join(config['log_dir'], model_folder_name))
\ No newline at end of file
+config['model_dir'] = os.path.abspath(os.path.join(config['log_dir'], model_folder_name))
+if not os.path.exists(config['model_dir']):
+    os.makedirs(config['model_dir'])
\ No newline at end of file

main.py

@@ -10,7 +10,8 @@ import numpy as np
 def main():
     start_time = time.time()
     try:
-        trainX, trainY = IOHelper.get_data(config['data_dir'], verbose=True)
+        trainX, trainY = IOHelper.get_mat_data(config['data_dir'], verbose=True)
+        # trainX, trainY = IOHelper.get_pickle_data(config['data_dir'], verbose=True)
         # IOHelper.store(trainX, trainY)
     except:
         return
@@ -39,4 +40,4 @@ def main():
     print("--- Runtime: %s seconds ---" % (time.time() - start_time))
 
 if __name__=='__main__':
-    main()
+    main()
\ No newline at end of file

runs/1609157751_inception/inception_df_metrics.csv

+Accuracy,Loss
+0.5900683403015137,0.6709842085838318

runs/1609157751_inception/inception_history.csv

+loss,accuracy,val_loss,val_accuracy
+0.6709842085838318,0.5900683403015137,0.6837942004203796,0.6404416561126709

runs/1609159406_inception/inception_df_best_model.csv

+best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
+0.706407904624939,0.6844089031219482,0.5264661908149719,0.5702930688858032

runs/1609159406_inception/inception_df_metrics.csv

+Accuracy,Loss
+0.5264661908149719,0.706407904624939

runs/1609159406_inception/inception_history.csv

+loss,accuracy,val_loss,val_accuracy
+0.706407904624939,0.5264661908149719,0.6844089031219482,0.5702930688858032

utils/IOHelper.py

@@ -3,8 +3,30 @@ import numpy as np
 import scipy.io as sio
 import os
 import pickle
+import h5py
 
-def get_data(data_dir, verbose=True):
+def get_mat_data(data_dir, verbose=True):
+    with h5py.File(data_dir + 'all_EEGprocuesan.mat', 'r') as f:
+        X = f['all_EEGprocuesan'][:]
+        if verbose:
+            print("X training loaded.")
+            print(X.shape)
+
+    with h5py.File(data_dir + 'all_trialinfoprosan.mat', 'r') as f:
+        y = f['all_trialinfoprosan'][:]
+        if verbose:
+            print("y training loaded.")
+            print(y.shape)
+
+    if verbose: print("Setting the shapes")
+    X = np.transpose(X, (2, 1, 0))
+    y = np.transpose(y, (1, 0))
+    if verbose:
+        print(X.shape)
+        print(y.shape)
+    return X, y
+
+def get_pickle_data(data_dir, verbose=True):
     pkl_file_x = open(data_dir + 'x.pkl', 'rb')
     x = pickle.load(pkl_file_x)
     pkl_file_x.close()

utils/utils.py

@@ -74,14 +74,14 @@ def save_logs(hist, output_directory, model, pytorch=False):
 
         df_best_model['best_model_train_loss'] = row_best_model['loss']
         df_best_model['best_model_val_loss'] = row_best_model['val_loss']
-        df_best_model['best_model_train_acc'] = row_best_model['acc']
-        df_best_model['best_model_val_acc'] = row_best_model['val_acc']
+        df_best_model['best_model_train_acc'] = row_best_model['accuracy']
+        df_best_model['best_model_val_acc'] = row_best_model['val_accuracy']
         df_best_model.to_csv(output_directory + '/' + model + '_' + 'df_best_model.csv', index=False)
 
 # Save the model parameters (newly added without debugging)
-def save_model_param(output_directory, model, pytorch=False):
-    if pytorch:
-        torch.save(net.state_dict(), output_directory + '/' + model + '_' + 'model.pth')
-    else:
-        classifier.save(output_directory + '/' + model + '_' + 'model.h5')
+# def save_model_param(output_directory, model, pytorch=False):
+#    if pytorch:
+#        torch.save(net.state_dict(), output_directory + '/' + model + '_' + 'model.pth')
+#    else:
+#        classifier.save(output_directory + '/' + model + '_' + 'model.h5')