fix bug for (import torch)

x2paddle/convert.py

@@ -139,17 +139,6 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto):
 
 def onnx2paddle(model_path, save_dir):
     # check onnx installation and version
-    try:
-        import torch
-        version = torch.__version__
-        if '1.2.0' not in  version:
-            print("torch==1.2.0 is required")
-            return
-    except:
-        print(
-                "we use caffe2 to inference graph, please use \"pip install torch==1.2.0\"."
-            )
-        return
     try:
         import onnx
         version = onnx.version.version
@@ -193,6 +182,17 @@ def main():
     assert args.framework is not None, "--framework is not defined(support tensorflow/caffe/onnx)"
     assert args.save_dir is not None, "--save_dir is not defined"
 
+    try:
+        import paddle
+        v0, v1, v2 = paddle.__version__.split('.')
+        if int(v0) != 1 or int(v1) < 5:
+            print("paddlepaddle>=1.5.0 is required")
+            return
+    except:
+        print("paddlepaddle not installed, use \"pip install paddlepaddle\"")
+    assert args.framework is not None, "--framework is not defined(support tensorflow/caffe/onnx)"
+    assert args.save_dir is not None, "--save_dir is not defined"
+
     if args.framework == "tensorflow":
         assert args.model is not None, "--model should be defined while translating tensorflow model"
         without_data_format_optimization = False

x2paddle/core/op_mapper.py

@@ -11,6 +11,7 @@
 # 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 paddle.fluid as fluid
 from paddle.fluid.proto import framework_pb2
 from x2paddle.core.util import *
 import inspect
@@ -46,6 +47,28 @@ 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

@@ -11,8 +11,6 @@
 # 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 paddle.fluid as fluid
 import numpy
 import math
 import os
@@ -20,25 +18,3 @@ 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

@@ -271,6 +271,17 @@ class ONNXGraph(Graph):
         return value_info
 
     def get_results_of_inference(self, model, shape):
+        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\"."
+            )
+            return
         from x2paddle.decoder.onnx_backend import prepare
 
         np_images = np.random.rand(shape[0], shape[1], shape[2],