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main.py
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#main.py
import timeit
from run_experiment import DukeCTModel
from models import custom_models_ctnet, custom_models_alternative, custom_models_ablation
from load_dataset import custom_datasets
#Note that here NUM_EPOCHS is set to 2 for the purposes of quickly demonstrating
#the code on the fake data. In all of the experiments reported in the paper,
#NUM_EPOCHS was set to 100. No model actually trained all the way to 100 epochs
#due to use of early stopping.
NUM_EPOCHS = 2
if __name__=='__main__':
####################################
# CTNet-83 Model on Whole Data Set #----------------------------------------
####################################
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'CTNet83',
custom_net = custom_models_ctnet.CTNetModel,
custom_net_args = {'n_outputs':83},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 2, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':'all',
'num_channels':3,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'','valid':''}})
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')
###################################
# CTNet-9 Model on Whole Data Set #-----------------------------------------
###################################
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'CTNet9',
custom_net = custom_models_ctnet.CTNetModel,
custom_net_args = {'n_outputs':9},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 2, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':['nodule','opacity','atelectasis','pleural_effusion','consolidation','mass','pericardial_effusion','cardiomegaly','pneumothorax'],
'num_channels':3,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'','valid':''}})
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')
####################################################
# CTNet-83 Model on 2000 Train and 1000 Val Subset #------------------------
####################################################
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'CTNet83_SmallData',
custom_net = custom_models_ctnet.CTNetModel,
custom_net_args = {'n_outputs':83},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 2, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':'all',
'num_channels':3,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgtrain_random2000.csv',
'valid':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgvalid_a_random1000.csv'}})
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')
######################################################################
# Alternative Arch: BodyConv Model on 2000 Train and 1000 Val Subset #------
######################################################################
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'BodyConv_SmallData',
custom_net = custom_models_alternative.BodyConv,
custom_net_args = {'n_outputs':83},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 2, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':'all',
'num_channels':3,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgtrain_random2000.csv',
'valid':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgvalid_a_random1000.csv'}})
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')
####################################################################
# Alternative Arch: 3DConv Model on 2000 Train and 1000 Val Subset #--------
####################################################################
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'ThreeDConv_SmallData',
custom_net = custom_models_alternative.ThreeDConv,
custom_net_args = {'n_outputs':83},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 4, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':'all',
'num_channels':1,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgtrain_random2000.csv',
'valid':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgvalid_a_random1000.csv'}})
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')
####################################################################
# Ablation Study: CTNet-83 (Pool) on 2000Train and 1000 Val Subset #--------
####################################################################
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'CTNet83AblatePool_SmallData',
custom_net = custom_models_ablation.CTNetModel_Ablate_PoolInsteadOf3D,
custom_net_args = {'n_outputs':83},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 2, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':'all',
'num_channels':3,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgtrain_random2000.csv',
'valid':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgvalid_a_random1000.csv'}})
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')
#####################################################################
# Ablation Study: CTNet-83 (Rand) on 2000 Train and 1000 Val Subset #-------
#####################################################################
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'CTNet83AblateRand_SmallData',
custom_net = custom_models_ablation.CTNetModel_Ablate_RandomInitResNet,
custom_net_args = {'n_outputs':83},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 2, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':'all',
'num_channels':3,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgtrain_random2000.csv',
'valid':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgvalid_a_random1000.csv'}})
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')
###################################################
# CTNet-1 Model on 2000 Train and 1000 Val Subset #-------------------------
###################################################
for abnormality in ['nodule', 'opacity', 'atelectasis', 'pleural_effusion',
'consolidation', 'mass', 'pericardial_effusion',
'cardiomegaly', 'pneumothorax']:
print('\n\n\n\n********** Working on',abnormality,'**********')
tot0 = timeit.default_timer()
DukeCTModel(descriptor = 'CTNet-'+abnormality,
custom_net = custom_models_ctnet.CTNetModel,
custom_net_args = {'n_outputs':1},
loss = 'bce', loss_args = {},
num_epochs=NUM_EPOCHS, patience = 15,
batch_size = 2, device = 'all', data_parallel = True,
use_test_set = False, task = 'train_eval',
old_params_dir = '',
dataset_class = custom_datasets.CTDataset_2019_10,
dataset_args = {'label_type_ld':'disease_new',
'label_meanings':[abnormality], #can be 'all' or a list of strings
'num_channels':3,
'pixel_bounds':[-1000,200],
'data_augment':True,
'crop_type':'single',
'selected_note_acc_files':{'train':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgtrain_random2000.csv',
'valid':'/load_dataset/fakedata/predefined_subsets/2020-01-10-imgvalid_a_random1000.csv'}}
)
tot1 = timeit.default_timer()
print('Total Time', round((tot1 - tot0)/60.0,2),'minutes')