import sys
from .model_utils import get_block
# sys.path.append('../../../lungNoduleDensityClassification/nodulecls/utils')
# from RWconfig import LoadJson
import os
# os.environ["CUDA_VISIBLE_DEVICES"]="2"

import torch
# from torchsummary import summary
def build_model(model_name,cfg):
    func = get_block(model_name)
    if "ResNet" in model_name :
        cfg['block'] = get_block(cfg['block'])
    return func(model_params=cfg)



# if __name__ == "__main__":
#     cfg_path = '../files/modelParams/model_parameters.json'
#     model_cfgs = LoadJson(cfg_path)

#     model_name = "NoduleSegDecoder_proxima"
#     cfg = model_cfgs[model_name]
#     if "encoder_model_name" in cfg.keys():
#         encoder_model_name = "XnetEncoder"
#         encoder_cfg = model_cfgs[encoder_model_name]
#         encoder = build_model(encoder_model_name,encoder_cfg)
#         cfg['encoder_model'] = encoder
#         model = build_model(model_name,cfg)
#     else:
#         model = build_model(model_name,cfg)
    
#     print ('cfg is ',cfg)
#     summary(model.cuda(),(1,32,96,96))
#     total_params = sum(p.numel() for p in model.parameters())
#     print(f'{total_params:,} total parameters.')
# #     summary(model,(1,24,24,24),device='cpu')