<reponame>lyrgwlr/HRNet-Facial-Landmark-Detection-v1
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Created by <NAME>(<EMAIL>)
# ------------------------------------------------------------------------------

import os
import pprint
import argparse

import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
import lib.models as models
from lib.config import config, update_config
from lib.utils import utils
from lib.datasets import get_dataset
from lib.core import function
from lib.utils import flops_counter

def parse_args():

    parser = argparse.ArgumentParser(description='Train Face Alignment')

    parser.add_argument('--cfg', help='experiment configuration filename',
                        required=True, type=str)
    args = parser.parse_args()
    update_config(config, args)
    return args


def main():

    args = parse_args()
    # import pudb; pudb.set_trace()
    logger, final_output_dir, tb_log_dir = \
        utils.create_logger(config, args.cfg, 'test')

    logger.info(pprint.pformat(args))
    logger.info(pprint.pformat(config))

    cudnn.benchmark = config.CUDNN.BENCHMARK
    cudnn.determinstic = config.CUDNN.DETERMINISTIC
    cudnn.enabled = config.CUDNN.ENABLED

    config.defrost()
    config.MODEL.INIT_WEIGHTS = False
    config.freeze()
    model = models.get_face_alignment_net(config)

    
    flops, params = flops_counter.get_model_complexity_info(model, (256,256), as_strings=True, print_per_layer_stat=True, channel=3)
    print('flops:{}, parmas:{}'.format(flops, params))
    

if __name__ == '__main__':
    main()

