# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function
import argparse
import torch
#from utils.nms.py_cpu_nms import py_cpu_nms
from models.faceboxes import FaceBoxes

parser = argparse.ArgumentParser(description='FaceBoxes')

parser.add_argument('-m', '--trained_model', default='weights/FaceBoxesProd.pth',
                    type=str, help='Trained state_dict file path to open')
parser.add_argument('--save_folder', default='onnx/FaceBoxes.onnx', type=str, help='Dir to save results')
parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference')
parser.add_argument('--dataset', default='PASCAL', type=str, choices=['AFW', 'PASCAL', 'FDDB'], help='dataset')
parser.add_argument('--confidence_threshold', default=0.05, type=float, help='confidence_threshold')
parser.add_argument('--top_k', default=5000, type=int, help='top_k')
parser.add_argument('--nms_threshold', default=0.3, type=float, help='nms_threshold')
parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
parser.add_argument('-s', '--show_image', action="store_true", default=False, help='show detection results')
parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold')
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)
    # net and model
    net = FaceBoxes(phase='test', size=None, num_classes=2)    # initialize detector
    net = load_model(net, args.trained_model, args.cpu)
    net.eval()
    input_names = ["image"]
    output_names = ["class","loc"]
    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}, 'loc': {0: '-1'}}
    dummy_input = torch.randn(1, 3, 1024, 1024)
    torch.onnx.export(net, dummy_input, args.save_folder, input_names = input_names, dynamic_axes = dynamic_axes, output_names = output_names, opset_version=11, verbose=True)