[kws] support onnx export (#53)

examples/hi_xiaowen/s0/run.sh

@@ -144,3 +144,17 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
   done
 fi
 
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+  jit_model=$(basename $score_checkpoint | sed -e 's:.pt$:.zip:g')
+  onnx_model=$(basename $score_checkpoint | sed -e 's:.pt$:.onnx:g')
+  python kws/bin/export_jit.py \
+    --config $dir/config.yaml \
+    --checkpoint $score_checkpoint \
+    --jit_model $dir/$jit_model
+  python kws/bin/export_onnx.py \
+    --config $dir/config.yaml \
+    --jit_model $dir/$jit_model  \
+    --onnx_model $dir/$onnx_model
+fi
+

kws/bin/export_jit.py

@@ -28,10 +28,7 @@ def get_args():
     parser = argparse.ArgumentParser(description='export your script model')
     parser.add_argument('--config', required=True, help='config file')
     parser.add_argument('--checkpoint', required=True, help='checkpoint model')
-    parser.add_argument('--output_file', required=True, help='output file')
-    parser.add_argument('--output_quant_file',
-                        default=None,
-                        help='output quantized model file')
+    parser.add_argument('--jit_model', required=True, help='output jit model')
     args = parser.parse_args()
     return args
 
@@ -50,18 +47,8 @@ def main():
     # Export jit torch script model
 
     script_model = torch.jit.script(model)
-    script_model.save(args.output_file)
-    print('Export model successfully, see {}'.format(args.output_file))
-
-    # Export quantized jit torch script model
-    if args.output_quant_file:
-        quantized_model = torch.quantization.quantize_dynamic(
-            model, {torch.nn.Linear}, dtype=torch.qint8)
-        print(quantized_model)
-        script_quant_model = torch.jit.script(quantized_model)
-        script_quant_model.save(args.output_quant_file)
-        print('Export quantized model successfully, '
-              'see {}'.format(args.output_quant_file))
+    script_model.save(args.jit_model)
+    print('Export model successfully, see {}'.format(args.jit_model))
 
 
 if __name__ == '__main__':

kws/bin/export_onnx.py

@@ -0,0 +1,65 @@
+# 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 onnxruntime as ort
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='export to onnx model')
+    parser.add_argument('--config', required=True, help='config file')
+    parser.add_argument('--jit_model',
+                        required=True,
+                        help='pytorch jit script model')
+    parser.add_argument('--onnx_model',
+                        required=True,
+                        help='output onnx 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 = torch.jit.load(args.jit_model)
+    print(model)
+    # dummy_input: (batch, time, feature_dim)
+    dummy_input = torch.randn(1, 100, feature_dim, dtype=torch.float)
+    torch.onnx.export(model,
+                      dummy_input,
+                      args.onnx_model,
+                      input_names=['input'],
+                      output_names=['output'],
+                      dynamic_axes={'input': {
+                          1: 'T'
+                      }})
+
+    torch_output = model(dummy_input)
+    ort_sess = ort.InferenceSession(args.onnx_model)
+    onnx_input = dummy_input.numpy()
+    onnx_output = ort_sess.run(None, {'input': onnx_input})
+    if torch.allclose(torch_output, torch.tensor(onnx_output[0])):
+        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()

requirements.txt

@@ -2,4 +2,5 @@ flake8==3.8.2
 pyyaml>=5.1
 tensorboard
 tensorboardX
-matplotlib
+matplotlib
+onnxruntime