test

x2paddle/convert.py

@@ -140,8 +140,6 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto):
 def onnx2paddle(model_path, save_dir):
     # check onnx installation and version
     try:
-        import torch
-        import paddle.fluid
         import onnx
         version = onnx.version.version
         if version != '1.5.0':

x2paddle/core/op_mapper.py

@@ -46,28 +46,6 @@ def export_paddle_param(param, param_name, dir):
     fp.close()
 
 
-# This func will copy to generate code file
-def run_net(param_dir="./"):
-    import os
-    inputs, outputs = x2paddle_net()
-    for i, out in enumerate(outputs):
-        if isinstance(out, list):
-            for out_part in out:
-                outputs.append(out_part)
-            del outputs[i]
-    exe = fluid.Executor(fluid.CPUPlace())
-    exe.run(fluid.default_startup_program())
-
-    def if_exist(var):
-        b = os.path.exists(os.path.join(param_dir, var.name))
-        return b
-
-    fluid.io.load_vars(exe,
-                       param_dir,
-                       fluid.default_main_program(),
-                       predicate=if_exist)
-
-
 class OpMapper(object):
     def __init__(self):
         self.paddle_codes = ""

x2paddle/core/util.py

@@ -19,3 +19,25 @@ import os
 
 def string(param):
     return "\'{}\'".format(param)
+
+
+# This func will copy to generate code file
+def run_net(param_dir="./"):
+    import os
+    inputs, outputs = x2paddle_net()
+    for i, out in enumerate(outputs):
+        if isinstance(out, list):
+            for out_part in out:
+                outputs.append(out_part)
+            del outputs[i]
+    exe = fluid.Executor(fluid.CPUPlace())
+    exe.run(fluid.default_startup_program())
+
+    def if_exist(var):
+        b = os.path.exists(os.path.join(param_dir, var.name))
+        return b
+
+    fluid.io.load_vars(exe,
+                       param_dir,
+                       fluid.default_main_program(),
+                       predicate=if_exist)

x2paddle/decoder/onnx_decoder.py

@@ -274,6 +274,9 @@ class ONNXGraph(Graph):
         try:
             import torch
             version = torch.__version__
+            if '1.1.0' not in version:
+                print("your model have dynamic graph, torch==1.1.0 is required")
+                return
         except:
             print(
                 "your model have dynamic graph, we use caff2 to inference graph, please use \"pip install torch==1.1.0\"."