From 43ad2c35634a3ed2a7a46bd7e3afd147934b1c48 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 20 六月 2023 10:02:58 +0800
Subject: [PATCH] export

---
 funasr/export/export_model.py |   47 ++++++++++++++++++++++++-----------------------
 1 files changed, 24 insertions(+), 23 deletions(-)

diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index c02c299..9e13260 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -229,34 +229,35 @@
         # model_script = torch.jit.script(model)
         model_script = model #torch.jit.trace(model)
         model_path = os.path.join(path, f'{model.model_name}.onnx')
-
-        torch.onnx.export(
-            model_script,
-            dummy_input,
-            model_path,
-            verbose=verbose,
-            opset_version=14,
-            input_names=model.get_input_names(),
-            output_names=model.get_output_names(),
-            dynamic_axes=model.get_dynamic_axes()
-        )
+        if not os.path.exists(model_path):
+            torch.onnx.export(
+                model_script,
+                dummy_input,
+                model_path,
+                verbose=verbose,
+                opset_version=14,
+                input_names=model.get_input_names(),
+                output_names=model.get_output_names(),
+                dynamic_axes=model.get_dynamic_axes()
+            )
 
         if self.quant:
             from onnxruntime.quantization import QuantType, quantize_dynamic
             import onnx
             quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
-            onnx_model = onnx.load(model_path)
-            nodes = [n.name for n in onnx_model.graph.node]
-            nodes_to_exclude = [m for m in nodes if 'output' in m]
-            quantize_dynamic(
-                model_input=model_path,
-                model_output=quant_model_path,
-                op_types_to_quantize=['MatMul'],
-                per_channel=True,
-                reduce_range=False,
-                weight_type=QuantType.QUInt8,
-                nodes_to_exclude=nodes_to_exclude,
-            )
+            if not os.path.exists(quant_model_path):
+                onnx_model = onnx.load(model_path)
+                nodes = [n.name for n in onnx_model.graph.node]
+                nodes_to_exclude = [m for m in nodes if 'output' in m]
+                quantize_dynamic(
+                    model_input=model_path,
+                    model_output=quant_model_path,
+                    op_types_to_quantize=['MatMul'],
+                    per_channel=True,
+                    reduce_range=False,
+                    weight_type=QuantType.QUInt8,
+                    nodes_to_exclude=nodes_to_exclude,
+                )
 
 
 if __name__ == '__main__':

--
Gitblit v1.9.1