clx_model_params_summary.py

from __future__ import print_function
import os
import argparse
import torch
from data import cfg_mnet, cfg_re50
from models.retinaface import RetinaFace

from torchinfo import summary

parser = argparse.ArgumentParser(description='Retinaface')
parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth',
                    type=str, help='Trained state_dict file path to open')
parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
parser.add_argument('--width', default=640, type=int, help='width of input image')
parser.add_argument('--height', default=480, type=int, help='height of input image')
args = parser.parse_args()


def check_keys(model, pretrained_state_dict):
    ckpt_keys = set(pretrained_state_dict.keys())
    model_keys = set(model.state_dict().keys())
    used_pretrained_keys = model_keys & ckpt_keys
    unused_pretrained_keys = ckpt_keys - model_keys
    missing_keys = model_keys - ckpt_keys
    print('Missing keys:{}'.format(len(missing_keys)))
    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
    print('Used keys:{}'.format(len(used_pretrained_keys)))
    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
    return True


def remove_prefix(state_dict, prefix):
    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
    print('remove prefix \'{}\''.format(prefix))
    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
    return {f(key): value for key, value in state_dict.items()}


def load_model(model, pretrained_path, load_to_cpu):
    print('Loading pretrained model from {}'.format(pretrained_path))
    if load_to_cpu:
        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
    else:
        device = torch.cuda.current_device()
        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
    if "state_dict" in pretrained_dict.keys():
        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
    else:
        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
    check_keys(model, pretrained_dict)
    model.load_state_dict(pretrained_dict, strict=False)
    return model

if __name__ == '__main__':
    torch.set_grad_enabled(False)

    cfg = None
    if args.network == "mobile0.25":
        cfg = cfg_mnet
    elif args.network == "resnet50":
        cfg = cfg_re50
    # net and model
    net = RetinaFace(cfg=cfg, phase = 'test')
    net = load_model(net, args.trained_model, args.cpu)
    net.eval()
    # print('Finished loading model!')
    # print(net)

    batch_size = 1
    summary(net, input_size=(batch_size, 3, args.height, args.width))