# 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.





import sys

import torch



sys.path.append('RawNet/python/RawNet2/Pre-trained_model')

from RawNet.python.RawNet2.parser import get_args

from model_RawNet2_original_code import RawNet



ptfile = "rawnet2_best_weights.pt"

args = get_args()

args.model['nb_classes'] = 6112

model = RawNet(args.model, device="cpu")

model.load_state_dict(torch.load(ptfile, map_location=torch.device('cpu')))

input_names = ["wav"]

output_names = ["class"]

dynamic_axes = {'wav': {0: '-1'}, 'class': {0: '-1'}}

dummy_input = torch.randn(1, 59049)

export_onnx_file = "RawNet2.onnx"

torch.onnx.export(model, dummy_input, export_onnx_file, input_names=input_names, dynamic_axes=dynamic_axes,

                  output_names=output_names, opset_version=11, verbose=True)