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convert_to_onnx.py
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/
convert_to_onnx.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from data import cfg_mnet, cfg_re50
from layers.functions.prior_box import PriorBox
from utils.nms.py_cpu_nms import py_cpu_nms
import cv2
from models.retinaface import RetinaFace
from utils.box_utils import decode, decode_landm
from utils.timer import Timer
parser = argparse.ArgumentParser(description='Test')
parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_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('--long_side', default=640, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)')
parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference')
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)
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
# ------------------------ export -----------------------------
output_onnx = 'FaceDetector.onnx'
print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
input_names = ["input0"]
output_names = ["output0"]
inputs = torch.randn(1, 3, args.long_side, args.long_side).to(device)
torch_out = torch.onnx._export(net, inputs, output_onnx, export_params=True, verbose=False,
input_names=input_names, output_names=output_names)