# Copyright 2020 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.

import sys
sys.path.append(r"./vnet.pytorch")
import torch
import torch.onnx
from vnet import *

def convert():
    model = VNet(elu=False, nll=True)
    checkpoint = torch.load(input_file, map_location="cpu")
    state_dict = checkpoint['state_dict']
    model.load_state_dict(state_dict)

    model.eval()
    input_names = ["actual_input_1"]
    output_names = ["output1"]
    dynamic_axes = {'actual_input_1': {0: '-1'}, 'output1': {0: '-1'}}

    dummy_input = torch.randn(1, 1, 64, 80, 80)

    torch.onnx.export(model, dummy_input, output_file, input_names = input_names, dynamic_axes = dynamic_axes, output_names = output_names, opset_version=11)

if __name__ == "__main__":
    input_file = sys.argv[1]
    output_file = sys.argv[2]
    convert()