104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
# Copyright (c) 2021 Binbin Zhang(binbzha@qq.com)
|
|
#
|
|
# 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 argparse
|
|
|
|
import torch
|
|
import yaml
|
|
|
|
import onnx
|
|
import onnxruntime as ort
|
|
|
|
from wekws.model.kws_model import init_model
|
|
from wekws.utils.checkpoint import load_checkpoint
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser(description='export to onnx model')
|
|
parser.add_argument('--config', required=True, help='config file')
|
|
parser.add_argument('--onnx_model',
|
|
required=True,
|
|
help='output onnx model')
|
|
parser.add_argument('--checkpoint', required=True, help='checkpoint model')
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def main():
|
|
args = get_args()
|
|
with open(args.config, 'r') as fin:
|
|
configs = yaml.load(fin, Loader=yaml.FullLoader)
|
|
feature_dim = configs['model']['input_dim']
|
|
model = init_model(configs['model'])
|
|
is_fsmn = configs['model']['backbone']['type'] == 'fsmn'
|
|
num_layers = configs['model']['backbone']['num_layers']
|
|
if configs['training_config'].get('criterion', 'max_pooling') == 'ctc':
|
|
# if we use ctc_loss, the logits need to be convert into probs
|
|
model.forward = model.forward_softmax
|
|
print(model)
|
|
|
|
load_checkpoint(model, args.checkpoint)
|
|
model.eval()
|
|
# dummy_input: (batch, time, feature_dim)
|
|
dummy_input = torch.randn(1, 100, feature_dim, dtype=torch.float)
|
|
cache = torch.zeros(1,
|
|
model.hdim,
|
|
model.backbone.padding,
|
|
dtype=torch.float)
|
|
if is_fsmn:
|
|
cache = cache.unsqueeze(-1).expand(-1, -1, -1, num_layers)
|
|
torch.onnx.export(model, (dummy_input, cache),
|
|
args.onnx_model,
|
|
input_names=['input', 'cache'],
|
|
output_names=['output', 'r_cache'],
|
|
dynamic_axes={
|
|
'input': {
|
|
1: 'T'
|
|
},
|
|
'output': {
|
|
1: 'T'
|
|
}},
|
|
opset_version=13,
|
|
verbose=False,
|
|
do_constant_folding=True)
|
|
|
|
# Add hidden dim and cache size
|
|
onnx_model = onnx.load(args.onnx_model)
|
|
meta = onnx_model.metadata_props.add()
|
|
meta.key, meta.value = 'cache_dim', str(model.hdim)
|
|
meta = onnx_model.metadata_props.add()
|
|
meta.key, meta.value = 'cache_len', str(model.backbone.padding)
|
|
onnx.save(onnx_model, args.onnx_model)
|
|
|
|
# Verify onnx precision
|
|
torch_output = model(dummy_input, cache)
|
|
ort_sess = ort.InferenceSession(args.onnx_model)
|
|
onnx_output = ort_sess.run(None, {
|
|
'input': dummy_input.numpy(),
|
|
'cache': cache.numpy()
|
|
})
|
|
|
|
if torch.allclose(torch_output[0],
|
|
torch.tensor(onnx_output[0]), atol=1e-6) and \
|
|
torch.allclose(torch_output[1],
|
|
torch.tensor(onnx_output[1]), atol=1e-6):
|
|
print('Export to onnx succeed!')
|
|
else:
|
|
print('''Export to onnx succeed, but pytorch/onnx have different
|
|
outputs when given the same input, please check!!!''')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|