fix the conflic

x2paddle/convert.py

-# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+## Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"
 # you may not use this file except in compliance with the License.
@@ -117,27 +117,6 @@ def tf2paddle(model_path,
         return
     
     from x2paddle.decoder.tf_decoder import TFDecoder
-<<<<<<< HEAD
-    from x2paddle.op_mapper.tf_op_mapper import TFOpMapper
-    from x2paddle.optimizer.tensorflow.bias import BiasOpt
-    from x2paddle.optimizer.tensorflow.transpose import TransposeOpt
-    from x2paddle.optimizer.tensorflow.batch_norm import BatchNormOpt
-    from x2paddle.optimizer.tensorflow.prelu import PReLUOpt
-
-    print("Now translating model from tensorflow to paddle.")
-    model = TFDecoder(model_path, define_input_shape=define_input_shape)
-    mapper = TFOpMapper(model)
-    program.build()
-    bias_opt = BiasOpt()
-    transpose_opt = TransposeOpt()
-    batch_norm_opt = BatchNormOpt()
-    prelu_opt = PReLUOpt()
-    bias_opt.run(program)
-    batch_norm_opt.run(program)
-    prelu_opt.run(program)
-    transpose_opt.run(program)
-    program.gen_model(save_dir)
-=======
     if paddle_type == "dygraph":
         from x2paddle.op_mapper.dygraph.tf2paddle.tf_op_mapper import TFOpMapper
     else:
@@ -167,7 +146,6 @@ def tf2paddle(model_path,
         transpose_opt.run(mapper.paddle_graph)
     mapper.paddle_graph.gen_model(save_dir)
         
->>>>>>> paddle-2.0
 
 
 def caffe2paddle(proto, weight, save_dir, caffe_proto, 
@@ -259,20 +237,7 @@ def pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None):
     graph_opt = GraphOptimizer(source_frame="pytorch", paddle_type="dygraph", jit_type=jit_type)
     graph_opt.optimize(mapper.paddle_graph)
     print("Model optimized.")
-<<<<<<< HEAD
-    if input_shapes is not None:
-        real_input_shapes = list()
-        for shape in input_shapes:
-            sp = shape[1:-1].split(",")
-            for i, s in enumerate(sp):
-                sp[i] = int(s)
-            real_input_shapes.append(sp)
-    else:
-        real_input_shapes = None
-    mapper.graph.gen_model(save_dir, real_input_shapes)
-=======
     mapper.paddle_graph.gen_model(save_dir, jit_type=jit_type)
->>>>>>> paddle-2.0
 
 
 def main():
@@ -343,12 +308,8 @@ def main():
 
     else:
         raise Exception(
-<<<<<<< HEAD
-            "--framework only support tensorflow/caffe/onnx/ now")
-=======
             "--framework only support tensorflow/caffe/onnx now")
->>>>>>> paddle-2.0
 
 
 if __name__ == "__main__":
-    main()
+    main()
\ No newline at end of file

x2paddle/core/program.py

@@ -293,7 +293,7 @@ class PaddleGraph(object):
             try:
                 self.dygraph2static(save_dir, input_shapes, input_types)
             except Exception as e:
-                print("Fail to generate inference model! Problem happend while export inference model from python code '{}';\n".format(coda_path))
+                print("Fail to generate inference model! Problem happend while export inference model from python code '{}';\n".format(code_path))
                 print("===================Error Information===============")
                 raise e
 

x2paddle/decoder/onnx_decoder.py

@@ -154,11 +154,7 @@ class ONNXGraph(Graph):
         if self.graph is None:
             print('[WARNING] Shape inference by ONNX offical interface.')
             onnx_model = shape_inference.infer_shapes(onnx_model)
-<<<<<<< HEAD
-            self.graph = onnx_model.graph 
-=======
             self.graph = onnx_model.graph
->>>>>>> paddle-2.0
         print("shape inferenced.")
         self.build()
         self.collect_value_infos()
@@ -554,4 +550,4 @@ class ONNXDecoder(object):
                     node.input[i] = self.make_variable_name(node.input[i])
             for i in range(len(node.output)):
                 node.output[i] = self.make_variable_name(node.output[i])
-        return model
+        return model
\ No newline at end of file

x2paddle/decoder/onnx_shape_inference.py

@@ -1601,15 +1601,11 @@ class SymbolicShapeInference:
                     in_mp)
             symbolic_shape_inference._update_output_from_vi()
             if not all_shapes_inferred:
+                print('!' * 10)
                 symbolic_shape_inference.out_mp_ = shape_inference.infer_shapes(
                     symbolic_shape_inference.out_mp_)
-            print('[INFO] Complete symbolic shape inference.')
         except:
-<<<<<<< HEAD
-            print('[WARNING] Incomplete symbolic shape inference.')
-=======
             print('[WARNING] Incomplete symbolic shape inference')
->>>>>>> paddle-2.0
             symbolic_shape_inference.out_mp_ = shape_inference.infer_shapes(
                 symbolic_shape_inference.out_mp_)
-        return symbolic_shape_inference.out_mp_.graph
+        return symbolic_shape_inference.out_mp_.graph
\ No newline at end of file

x2paddle/optimizer/tensorflow/prelu.py

@@ -118,12 +118,6 @@ class PReLUOpt:
                 graph.layers[transpose0.id] = transpose0
                 graph.layers[prelu.id] = prelu
                 graph.layers[transpose1.id] = transpose1
-<<<<<<< HEAD
-                graph.parameters[alpha.outputs[0]] = np.expand_dims(graph.parameters[alpha.outputs[0]], 0)
-        graph.build()
-        
-=======
                 first_axis = graph.parameters[alpha.outputs[0]].shape[0]
                 graph.parameters[alpha.outputs[0]] = np.reshape(graph.parameters[alpha.outputs[0]], (1, first_axis, 1, 1))
-        graph.build()
->>>>>>> paddle-2.0
+        graph.build()
\ No newline at end of file