# TensorBoard related parameters. max_images=8, # Maximum number of images to save. shrink_scale=1, # Scale to shrink output image size. # Channel Attention. use_ca=False, reduction=8, use_gap=False, use_gmp=False, # Learning rate scheduling. lr_red_epochs=[20, 25], lr_red_rate=0.1, # Variables that change frequently. use_slice_metrics=True, num_epochs=30, gpu=0, # Set to None for CPU mode. num_workers=4, init_lr=2E-4, max_to_keep=1, prev_model_ckpt='', sample_rate_train=1, start_slice_train=0, sample_rate_val=1, start_slice_val=0, ) arguments = create_arg_parser(**settings).parse_args() train_cmg_to_img(arguments)
# TensorBoard related parameters. max_images=8, # Maximum number of images to save. shrink_scale=1, # Scale to shrink output image size. # # Channel Attention. # use_ca=True, # reduction=8, # use_gap=True, # use_gmp=False, # Learning rate scheduling. lr_red_epochs=[20, 40], lr_red_rate=0.25, # Variables that change frequently. use_slice_metrics=True, num_epochs=50, gpu=0, # Set to None for CPU mode. num_workers=2, init_lr=1E-4, max_to_keep=1, # prev_model_ckpt='', sample_rate_train=0.1, start_slice_train=0, sample_rate_val=1, start_slice_val=0, ) options = create_arg_parser(**settings).parse_args() train_xnet(options)
dilation_value=2, pool_stride=1, use_ca=True, use_sa=True) train_model(my_model34, args=args) train_model(my_model50, args=args) if __name__ == '__main__': defaults = dict( batch_size=12, num_workers=1, init_lr=0.001, gamma=0.1, # Factor by which to reduce lr. step_size=20, gpu=0, # Set to None for CPU mode. num_epochs=30, verbose=False, save_best_only=True, max_to_keep=1, data_root='/home/veritas/PycharmProjects/PA1/data', ckpt_root='/home/veritas/PycharmProjects/PA1/checkpoints', log_root='/home/veritas/PycharmProjects/PA1/logs') parser = create_arg_parser(**defaults).parse_args() # models34(parser) # models50(parser) my_model(parser)