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from __future__ import print_function
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import os
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import argparse
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import torch
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import torch.backends.cudnn as cudnn
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import numpy as np
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from data import cfg_mnet, cfg_re50
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from layers.functions.prior_box import PriorBox
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from utils.nms.py_cpu_nms import py_cpu_nms
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import cv2
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from models.retinaface import RetinaFace
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from utils.box_utils import decode, decode_landm
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from utils.timer import Timer
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parser = argparse.ArgumentParser(description='Test')
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parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth',
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type=str, help='Trained state_dict file path to open')
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parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
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parser.add_argument('--long_side', default=640, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)')
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parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference')
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args = parser.parse_args()
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def check_keys(model, pretrained_state_dict):
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ckpt_keys = set(pretrained_state_dict.keys())
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model_keys = set(model.state_dict().keys())
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used_pretrained_keys = model_keys & ckpt_keys
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unused_pretrained_keys = ckpt_keys - model_keys
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missing_keys = model_keys - ckpt_keys
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print('Missing keys:{}'.format(len(missing_keys)))
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print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
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print('Used keys:{}'.format(len(used_pretrained_keys)))
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assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
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return True
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def remove_prefix(state_dict, prefix):
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''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
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print('remove prefix \'{}\''.format(prefix))
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f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
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return {f(key): value for key, value in state_dict.items()}
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def load_model(model, pretrained_path, load_to_cpu):
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print('Loading pretrained model from {}'.format(pretrained_path))
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if load_to_cpu:
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pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
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else:
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device = torch.cuda.current_device()
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pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
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if "state_dict" in pretrained_dict.keys():
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pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
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else:
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pretrained_dict = remove_prefix(pretrained_dict, 'module.')
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check_keys(model, pretrained_dict)
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model.load_state_dict(pretrained_dict, strict=False)
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return model
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if __name__ == '__main__':
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torch.set_grad_enabled(False)
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cfg = None
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if args.network == "mobile0.25":
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cfg = cfg_mnet
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elif args.network == "resnet50":
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cfg = cfg_re50
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net = RetinaFace(cfg=cfg, phase = 'test')
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net = load_model(net, args.trained_model, args.cpu)
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net.eval()
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print('Finished loading model!')
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print(net)
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device = torch.device("cpu" if args.cpu else "cuda")
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net = net.to(device)
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output_onnx = 'FaceDetector.onnx'
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print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
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input_names = ["input0"]
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output_names = ["output0"]
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inputs = torch.randn(1, 3, args.long_side, args.long_side).to(device)
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torch_out = torch.onnx._export(net, inputs, output_onnx, export_params=True, verbose=False,
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input_names=input_names, output_names=output_names)
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