add tf dygraph

x2paddle/convert.py

@@ -103,6 +103,7 @@ def tf2paddle(model_path,
               save_dir,
               without_data_format_optimization=False,
               define_input_shape=False,
+              paddle_type="dygraph",
               params_merge=False):
     # check tensorflow installation and version
     try:
@@ -120,25 +121,28 @@ def tf2paddle(model_path,
             "[ERROR] Tensorflow is not installed, use \"pip install tensorflow\"."
         )
         return
-
+    
     from x2paddle.decoder.tf_decoder import TFDecoder
-    from x2paddle.op_mapper.tf_op_mapper import TFOpMapper
+    if paddle_type == "dygraph":
+        from x2paddle.op_mapper.dygraph.tf2paddle.tf_op_mapper import TFOpMapper
+    else:
+        from x2paddle.op_mapper.static.tf2paddle.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
-
     print("Now translating model from tensorflow to paddle.")
     model = TFDecoder(model_path, define_input_shape=define_input_shape)
-
     mapper = TFOpMapper(model)
-    program.build()
+    mapper.paddle_graph.build()
     bias_opt = BiasOpt()
     transpose_opt = TransposeOpt()
     batch_norm_opt = BatchNormOpt()
     bias_opt.run(program)
     batch_norm_opt.run(program)
     transpose_opt.run(program)
-    program.gen_model(save_dir)
+    mapper.paddle_graph.gen_model(save_dir)
+        
 
 
 def caffe2paddle(proto, weight, save_dir, caffe_proto, 
@@ -293,7 +297,7 @@ def main():
         if args.params_merge:
             params_merge = True
         tf2paddle(args.model, args.save_dir, without_data_format_optimization,
-                  define_input_shape, params_merge)
+                  define_input_shape, args.paddle_type, params_merge)
 
     elif args.framework == "caffe":
         assert args.prototxt is not None and args.weight is not None, "--prototxt and --weight should be defined while translating caffe model"

x2paddle/core/program.py

@@ -76,6 +76,7 @@ class PaddleGraph(object):
         self.custom_code = None
         self.inputs_info = None
 
+
     def set_name(self, name):
         self.name = name.replace("-", "_").replace("/", "_")
 
@@ -285,8 +286,8 @@ class PaddleGraph(object):
         for input_name in self.inputs:
             input_shapes.append(self.inputs_info[input_name][0])
             input_types.append(self.inputs_info[input_name][1])
-            # 如果input_files非空，则导出推理模型；其值类似[[None, 3, 224, 224]]
-            self.dygraph2static(save_dir, input_shapes, input_types)
+        # 如果input_files非空，则导出推理模型；其值类似[[None, 3, 224, 224]]
+        self.dygraph2static(save_dir, input_shapes, input_types)
 
     def gen_static_code(self, code_dir):
         def write_code(f, code_list, indent=0):
@@ -446,6 +447,8 @@ class PaddleGraph(object):
             if self.source_type == "caffe":
                 custom_import = "from x2paddle.op_mapper.dygraph.caffe2paddle " + \
                                  "import caffe_custom_layer as x2paddle_nn"
+            else:
+                custom_import = ""
             self.head = gen_codes(
                 [
                     "from paddle.fluid.initializer import Constant",
@@ -618,7 +621,10 @@ class PaddleGraph(object):
         paddle.disable_static()
         restore, _ = fluid.load_dygraph(osp.join(save_dir, "model"))
         model = getattr(x2paddle_code, self.name)()
-        model.set_dict(restore)
+        if self.source_type == "tf":
+            model.set_dict(restore, use_structured_name=False)
+        else:
+            model.set_dict(restore)
         model.eval()
         static_model = paddle.jit.to_static(model, input_spec=sepc_list)
         paddle.jit.save(static_model, osp.join(save_dir, "inference_model/model"))
\ No newline at end of file

x2paddle/decoder/tf_decoder.py

@@ -132,6 +132,7 @@ class TFGraph(Graph):
         self.identity_map = dict()
         self.multi_out_ops = ['Split', 'SplitV', 'IteratorV2']
         self.tf_data_format = data_format
+        self.graph_name = "TFModel"
 
     def build(self):
         for layer in self.model.node:

x2paddle/op_mapper/dygraph/prim2code.py

@@ -277,7 +277,11 @@ def prim_list(layer, indent=1, init_func=[], forward_func=[], layer_id=None, dif
     inputs_str = ', '.join(inputs_list)
     line = "{} = [{}]".format(layer.outputs[0], inputs_str)
     forward_func.extend(gen_codes([line], indent=indent))
-
+    
+def prim_list_mul(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = [a*b for a,b in zip({}, {})]".format(layer.outputs[0],
+                                                      get_value(layer, "list0", different_attrs),
+                                                      get_value(layer, "list1", different_attrs))
 
 def prim_list_unpack(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
     line = "{} = {}".format(", ".join(layer.outputs), get_value(layer, "input", different_attrs))