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config.py
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# author: github/zabir-nabil
class HyperP:
def __init__(self, model_type):
# hyperparameters
if model_type == "slope_train":
self.seed = 1997
self.data_folder = '..' # one level up
self.ct_tab_feature_csv = 'train_data_ct_tab.csv' # some extra features
self.strip_ct = .15 # strip this amount of ct slices before randomly choosing
self.n_tab = 5 # number of tabular features used
self.cnn_dim = 32 # compressed cnn feature dim
self.fc_dim = 16
# select which models to train
self.train_models = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50', 'resnext101', 'efnb0', 'efnb1', 'efnb2', 'efnb3', 'efnb4', 'efnb5', 'efnb6', 'efnb7']
self.gpu_index = 0
self.num_workers = 0 # 0 for bug fix/docker
self.results_dir = "results_slopes"
self.nfold = 5
self.n_epochs = 40
self.batch_size = 16
self.final_lr = 0.0002
elif model_type == "slope_test":
pass
elif model_type == "qreg_train":
self.seed = 1997
self.data_folder = '..'
self.ct_tab_feature_csv = 'train_data_ct_tab.csv' # some extra features
self.strip_ct = .15 # strip this amount of ct slices before randomly choosing
self.n_tab = 7 # number of tabular features used
# select which models to train
self.train_models = ['resnet18' , 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50', 'resnext101', 'efnb0', 'efnb1', 'efnb2', 'efnb3', 'efnb4', 'efnb5', 'efnb6', 'efnb7']
self.gpu_index = 0
self.results_dir = "results_qreg"
self.nfold = 5
self.n_epochs = 40
self.batch_size = 8
self.final_lr = 0.0002
self.loss_weight = 0.7
self.dummy_training = False
self.dummy_train_rows = 400
elif model_type == "attn_train":
# ablation study
self.seed = 1997
self.data_folder = '..' # .. one level up
self.ct_tab_feature_csv = 'train_data_ct_tab.csv' # some extra features
self.strip_ct = .15 # strip this amount of ct slices before randomly choosing
self.n_tab = 5 # number of tabular features used
# self.cnn_dim = 32 # compressed cnn feature dim
self.fc_dim = [16, 32]
# select which models to train
self.train_models = ['efnb2_attn']
self.gpu_index = 0
self.num_workers = 0 # 0 for bug fix/docker
self.results_dir = "results_attn"
self.nfold = 5
self.n_epochs = 40
self.batch_size = 10
self.final_lr = 0.0002
self.attn_filters = [32, 64, 128] # attn_filters and cnn_dim should be same
self.n_attn_layers = [1, 2, 3]
elif model_type == "attn_train_best_config":
# ablation study
self.seed = 1997
self.data_folder = '..' # .. one level up
self.ct_tab_feature_csv = 'train_data_ct_tab.csv' # some extra features
self.strip_ct = .15 # strip this amount of ct slices before randomly choosing
self.n_tab = 5 # number of tabular features used
# self.cnn_dim = 32 # compressed cnn feature dim
self.fc_dim = [32]
# select which models to train
self.train_models = [ 'resnet18' , 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50', 'resnext101', 'efnb0', 'efnb1', 'efnb2', 'efnb3', 'efnb4' ]
# train 1 : 'resnet18' , 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50'
# train 2 : 'resnext101', 'efnb0', 'efnb1', 'efnb2', 'efnb3', 'efnb4'
self.gpu_index = 0
self.num_workers = 0 # 0 for bug fix/docker
self.results_dir = "results_attn_bc"
self.nfold = 5
self.n_epochs = 40
self.batch_size = 10
self.final_lr = 0.0002
self.attn_filters = [32] # [32, 128] # attn_filters and cnn_dim should be same
self.n_attn_layers = [3]
elif model_type == "singlemodal_ct":
# ablation study
self.seed = 1997
self.data_folder = '..' # .. one level up
self.ct_tab_feature_csv = 'train_data_ct_tab.csv' # some extra features
self.strip_ct = .15 # strip this amount of ct slices before randomly choosing
self.n_tab = 5 # number of tabular features used
# self.cnn_dim = 32 # compressed cnn feature dim
self.fc_dim = [32]
# select which models to train
self.train_models = [ 'efnb0', 'efnb1', 'efnb2' ]
# train 1 : 'resnet18' , 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50'
# train 2 : 'resnext101', 'efnb0', 'efnb1', 'efnb2', 'efnb3', 'efnb4'
self.gpu_index = 0
self.num_workers = 0 # 0 for bug fix/docker
self.results_dir = "results_sm"
self.nfold = 5
self.n_epochs = 40
self.batch_size = 10
self.final_lr = 0.0002
self.attn_filters = [32] # [32, 128] # attn_filters and cnn_dim should be same
self.n_attn_layers = [3]
elif model_type == "singlemodal_clinical":
# ablation study
self.seed = 1997
self.data_folder = '..' # .. one level up
self.ct_tab_feature_csv = 'train_data_ct_tab.csv' # some extra features
self.strip_ct = .15 # strip this amount of ct slices before randomly choosing
self.n_tab = 5 # number of tabular features used
# self.cnn_dim = 32 # compressed cnn feature dim
self.fc_dim = [32]
# select which models to train
self.train_models = [ 'mlp' ]
# train 1 : 'resnet18' , 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50'
# train 2 : 'resnext101', 'efnb0', 'efnb1', 'efnb2', 'efnb3', 'efnb4'
self.gpu_index = 0
self.num_workers = 0 # 0 for bug fix/docker
self.results_dir = "results_sm"
self.nfold = 5
self.n_epochs = 40
self.batch_size = 10
self.final_lr = 0.0002
self.attn_filters = [32] # [32, 128] # attn_filters and cnn_dim should be same
self.n_attn_layers = [3]