update export

examples/industrial_data_pretraining/paraformer/export.py

@@ -13,16 +13,16 @@ model = AutoModel(
     model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
 )
 
-res = model.export(type="onnx", quantize=False)
+res = model.export(type="torchscript", quantize=False)
 print(res)
 
 
-# method2, inference from local path
+# # method2, inference from local path
-from funasr import AutoModel
+# from funasr import AutoModel
 
-model = AutoModel(
+# model = AutoModel(
-    model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-)
+# )
 
-res = model.export(type="onnx", quantize=False)
+# res = model.export(type="onnx", quantize=False)
-print(res)
+# print(res)

funasr/auto/auto_model.py

@@ -580,12 +580,6 @@ class AutoModel:
         )
 
         with torch.no_grad():
-
+            export_dir = export_utils.export(model=model, data_in=data_list,  **kwargs)
-            if type == "onnx":
-                export_dir = export_utils.export_onnx(model=model, data_in=data_list, **kwargs)
-            else:
-                export_dir = export_utils.export_torchscripts(
-                    model=model, data_in=data_list, **kwargs
-                )
 
         return export_dir

funasr/models/paraformer/export_meta.py

@@ -31,6 +31,7 @@ def export_rebuild_model(model, **kwargs):
     model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
     model.export_name = types.MethodType(export_name, model)
 
+    model.export_name = 'model'
     return model
 
 

funasr/models/seaco_paraformer/export_meta.py

@@ -109,7 +109,9 @@ def export_rebuild_model(model, **kwargs):
     backbone_model.export_dynamic_axes = types.MethodType(
         export_backbone_dynamic_axes, backbone_model
     )
-    backbone_model.export_name = types.MethodType(export_backbone_name, backbone_model)
+    
+    embedder_model.export_name = "model_eb"
+    backbone_model.export_name = "model_bb"
 
     return backbone_model, embedder_model
 
@@ -192,6 +194,3 @@ def export_backbone_dynamic_axes(self):
         "pre_acoustic_embeds": {1: "feats_length1"},
     }
 
-
-def export_backbone_name(self):
-    return "model.onnx"

funasr/utils/export_utils.py

@@ -2,7 +2,7 @@ import os
 import torch
 
 
-def export_onnx(model, data_in=None, quantize: bool = False, opset_version: int = 14, **kwargs):
+def export(model, data_in=None, quantize: bool = False, opset_version: int = 14, type='onnx', **kwargs):
     model_scripts = model.export(**kwargs)
     export_dir = kwargs.get("output_dir", os.path.dirname(kwargs.get("init_param")))
     os.makedirs(export_dir, exist_ok=True)
@@ -11,14 +11,20 @@ def export_onnx(model, data_in=None, quantize: bool = False, opset_version: int
         model_scripts = (model_scripts,)
     for m in model_scripts:
         m.eval()
-        _onnx(
+        if type == 'onnx':
-            m,
+            _onnx(
-            data_in=data_in,
+                m,
-            quantize=quantize,
+                data_in=data_in,
-            opset_version=opset_version,
+                quantize=quantize,
-            export_dir=export_dir,
+                opset_version=opset_version,
-            **kwargs
+                export_dir=export_dir,
-        )
+                **kwargs
+            )
+        elif type == 'torchscript':
+            _torchscripts(
+                m,
+                path=export_dir,
+            )
         print("output dir: {}".format(export_dir))
 
     return export_dir
@@ -37,7 +43,7 @@ def _onnx(
 
     verbose = kwargs.get("verbose", False)
 
-    export_name = model.export_name() if hasattr(model, "export_name") else "model.onnx"
+    export_name = model.export_name + '.onnx'
     model_path = os.path.join(export_dir, export_name)
     torch.onnx.export(
         model,
@@ -70,3 +76,15 @@ def _onnx(
                 weight_type=QuantType.QUInt8,
                 nodes_to_exclude=nodes_to_exclude,
             )
+
+
+def _torchscripts(model, path, device='cpu'):
+    dummy_input = model.export_dummy_inputs()
+
+    if device == 'cuda':
+        model = model.cuda()
+        dummy_input = tuple([i.cuda() for i in dummy_input])
+
+    # model_script = torch.jit.script(model)
+    model_script = torch.jit.trace(model, dummy_input)
+    model_script.save(os.path.join(path, f'{model.export_name}.torchscripts'))