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parser.py
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130 lines (122 loc) · 7.68 KB
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# import argparse
#
# def str2bool(v):
# return v.lower() in ('true')
#
# def attrinet_get_parser():
# parser = argparse.ArgumentParser()
#
# # Experiment configuration.
# parser.add_argument('--debug', type=str2bool, default=False,
# help='if true, will automatically set d_iters = 1, set savefrequency=1, easy to run all train step for functional test')
#
# parser.add_argument('--exp_name', type=str, default='attri-net')
# parser.add_argument('--mode', type=str, default='train', choices=['train', 'test'])
#
# # Data configuration.
# parser.add_argument('--dataset', type=str, default='vindr_cxr_withBB', choices=['chexpert', 'nih_chestxray', 'vindr_cxr', 'skmtea', 'contam20','contam50','airogs', 'airogs_color', 'vindr_cxr_withBB'])
# parser.add_argument('--image_size', type=int, default=320, help='image resolution')
# parser.add_argument('--batch_size', type=int, default=4, help='mini-batch size')
#
# # Model configuration.
# # Configurations of latent code generator
# parser.add_argument('--n_fc', type=int, default=8, help='number of fc layer in Intermediate_Generator inside generator')
# parser.add_argument('--n_ones', type=int, default=20, help='number of ones to indicting each task, will affect the latent dim of task vector in generator,default is 20')
# parser.add_argument('--num_out_channels', type=int, default=1, help='number of out channels of generator')
#
# # Configurations of logistic regression classifier
# parser.add_argument('--lgs_downsample_ratio', type=int, default=32,
# help='downsampling ratio of logistic regression classifier, can be 4, 8, 16, 32, 64, 80, 160')
#
# # Configurations of generator
# parser.add_argument('--lambda_critic', type=float, default=1.0, help='weight for critic loss')
# parser.add_argument('--lambda_1', type=float, default=100, help='weight for l1 loss of disease mask')
# parser.add_argument('--lambda_2', type=float, default=200, help='weight for l1 loss of healthy mask')
# parser.add_argument('--lambda_3', type=float, default=100, help='weight for classification loss')
# parser.add_argument('--lambda_centerloss', type=float, default=0.01, help='weight for center loss of disease mask')
# parser.add_argument('--lambda_localizationloss', type=float, default=1, help='weight for center loss of disease mask')
# parser.add_argument('--process_mask', type=str, default='previous', choices=['abs(mx)', 'sum(abs(mx))', 'previous'])
#
# # Training configuration.
# parser.add_argument('--epochs', type=int, default=200, help='number of epochs to train for')
# parser.add_argument('--cls_iteration', type=int, default=5, help='number of classifier iterations per each generator iter, default=5')
# parser.add_argument('--d_iters', type=int, default=5, help='number of discriminator iterations per each generator iter, default=5')
# parser.add_argument('--num_iters', type=int, default=500000, help='number of total iterations for training generator')
# parser.add_argument('--g_lr', type=float, default=0.0001, help='learning rate for G')
# parser.add_argument('--d_lr', type=float, default=0.0001, help='learning rate for D')
# parser.add_argument('--lgs_lr', type=float, default=0.0001, help='learning rate for logistic regression classifier, previous exp use 0.00025')
# parser.add_argument('--weight_decay_lgs', type=float, default=0.00001, help='weight decay for logistic regression classifier')
# parser.add_argument('--beta1', type=float, default=0.0, help='beta1 for adam')
# parser.add_argument('--beta2', type=float, default=0.9, help='beta2 for adam, default 0.9')
# parser.add_argument('--manual_seed', type=int, default=42, help='set seed')
# parser.add_argument('--save_path', type=str, default='/mnt/qb/work/baumgartner/sun22/TMI_exps/attri-net',
# help='path of the exp')
#
# # Step size.
# parser.add_argument('--sample_step', type=int, default=1000,
# help='frequency of saving visualization samples, default = 500')
# parser.add_argument('--model_valid_step', type=int, default=1000, help='frequency of validation')
# parser.add_argument('--lr_update_step', type=int, default=1000, help='frequency of learning rate update')
#
# # Testing configuration.
# parser.add_argument('--test_model_path', type=str, default=None, help='path of the models')
#
# # Miscellaneous.
# parser.add_argument('--use_wandb', type=str2bool, default=True)
# parser.add_argument('--use_gpu', type=str2bool, default=True, help='whether to run on the GPU')
#
# return parser
# def resnet_get_parser():
# parser = argparse.ArgumentParser()
#
# # Experiment configuration.
# parser.add_argument('--exp_name', type=str, default='resnet')
# parser.add_argument('--mode', type=str, default='train', choices=['train', 'test'])
#
# # Data configuration.
# parser.add_argument('--dataset', type=str, default='skmtea', choices=['chexpert', 'nih_chestxray', 'vindr_cxr', 'skmtea', 'airogs', 'airogs_color'])
# parser.add_argument('--image_size', type=int, default=320, help='image resolution')
# parser.add_argument('--batch_size', type=int, default=8, help='mini-batch size')
#
# # Training configuration.
# parser.add_argument('--epochs', type=int, default=200, help='number of epochs to train for')
# parser.add_argument('--lr', type=float, default=0.0001, help='learning rate')
# parser.add_argument('--weight_decay', type=float, default=0.00001, help='weight decay')
# parser.add_argument('--manual_seed', type=int, default=42, help='set seed')
# parser.add_argument('--save_path', type=str, default='/mnt/qb/work/baumgartner/sun22/TMI_exps/resnet', help='path of the exp')
#
# # Testing configuration.
# parser.add_argument('--test_model_path', type=str, default='/mnt/qb/work/baumgartner/sun22/TMI_exps/resnet', help='path of the models')
#
# # Miscellaneous.
# parser.add_argument('--use_wandb', type=str2bool, default=True)
# parser.add_argument('--use_gpu', type=str2bool, default=True, help='whether to run on the GPU')
#
# return parser
# def bcos_resnet_get_parser():
# parser = argparse.ArgumentParser()
#
# # Experiment configuration.
# parser.add_argument('--exp_name', type=str, default='bcos_resnet')
# parser.add_argument('--mode', type=str, default='train', choices=['train', 'test'])
#
# # Data configuration.
# parser.add_argument('--dataset', type=str, default='airogs', choices=['chexpert', 'nih_chestxray', 'vindr_cxr', 'skmtea', 'airogs', 'airogs_color' ,'vindr_cxr_withBB'])
# parser.add_argument('--image_size', type=int, default=320, help='image resolution')
# parser.add_argument('--batch_size', type=int, default=8, help='mini-batch size')
#
# # Training configuration.
# parser.add_argument('--epochs', type=int, default=200, help='number of epochs to train for')
# parser.add_argument('--lr', type=float, default=0.0001, help='learning rate')
# parser.add_argument('--weight_decay', type=float, default=0.00001, help='weight decay')
# parser.add_argument('--manual_seed', type=int, default=42, help='set seed')
# parser.add_argument('--save_path', type=str, default='/mnt/qb/work/baumgartner/sun22/TMI_exps/bcos_resnet', help='path of the exp')
#
# # Testing configuration.
# parser.add_argument('--test_model_path', type=str, default='/mnt/qb/work/baumgartner/sun22/TMI_exps/bcos_resnet', help='path of the models')
#
# # Miscellaneous.
# parser.add_argument('--use_wandb', type=str2bool, default=True)
# parser.add_argument('--use_gpu', type=str2bool, default=True, help='whether to run on the GPU')
#
# return parser