import torch import sys sys.path.append("..") # =================== from OCT_train import trainModels torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False trainModels(model='SOASNet_single', data_set='duke', input_dim=1, epochs=250, width=64, depth=4, depth_limit=6, repeat=5, l_r=1e-3, l_r_s=True, train_batch=4, shuffle=True, loss='ce', norm='bn', log='MICCAI_Duke_Results', class_no=8, cluster=True, data_augmentation_train='all', data_augmentation_test='none')
import torch import sys sys.path.append("..") # =================== from OCT_train import trainModels torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False if __name__ == '__main__': # trainModels(model='SOASNet', data_set='ours', input_dim=1, epochs=50, width=16, depth=4, depth_limit=6, repeat=3, l_r=1e-3, l_r_s=True, train_batch=4, shuffle=True, loss='ce', norm='bn', log='MICCAI_Our_Data_Results', class_no=2, cluster=True, data_augmentation_train='all', data_augmentation_test='all') print('Finished.')
# cluster=False) # # # ==================================== # SegNet based # ==================================== # trainModels(model='SOASNet_very_large_kernel', data_set='ours', input_dim=1, epochs=1, width=16, depth=4, depth_limit=6, repeat=1, l_r=1e-3, l_r_s=True, train_batch=4, shuffle=True, loss='ce', norm='bn', log='Test', class_no=2, cluster=False, data_augmentation_train='all', data_augmentation_test='none') # # trainModels(model='RelayNet', # input_dim=1, # epochs=250, # width=64, # depth=4,