fix

x2paddle/decoder/tf_decoder.py

@@ -191,7 +191,7 @@ class TFGraph(Graph):
         return node
     
     def get_input_node(self, node, idx=0, copy=False):
-        input_node_name = node.inputs[idx]
+        input_node_name = node.layer.input[idx]
         return self.get_node(input_node_name, copy)
 
     def remove_node(self, node_name):
@@ -488,3 +488,96 @@ class TFDecoder(object):
             return results[0].tolist()
         else:
             raise Exception("Couldn't infer a stable shape shape tensor value")
+            
+#     def infer_tensor(self, graph_node):
+#         if hasattr(graph_node, "index"):
+#             tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
+#         else:
+#             tensor_name = graph_node.layer.name + ":0"
+#         feed = dict()
+#         for input_name, info in self.inputs_info.items():
+#             (shape, dtype) = cp.deepcopy(info)
+#             input_tensor = self.sess.graph.get_tensor_by_name(input_name + ":0")
+#             if shape.count(-1) > 0:
+#                 shape[shape.index(-1)] = 2
+#             feed[input_tensor] = numpy.random.random_sample(shape)
+#         output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
+#         return self.sess.run([output_tensor], feed)[0]
+
+#     def infer_shape_tensor(self, graph_node, out_shape=None):
+#         if hasattr(graph_node, "index"):
+#             tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
+#         else:
+#             tensor_name = graph_node.layer.name + ":0"
+#         feed = dict()
+#         batch_size = [2, 3, 5]
+#         results = list()
+#         for b in batch_size:
+#             for input_name, info in self.inputs_info.items():
+#                 (shape, dtype) = cp.deepcopy(info)
+#                 input_tensor = self.sess.graph.get_tensor_by_name(input_name +
+#                                                                   ":0")
+#                 if shape.count(-1) > 0:
+#                     shape[shape.index(-1)] = b
+#                 feed[input_tensor] = numpy.random.random_sample(shape)
+#             output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
+#             results.append(self.sess.run([output_tensor], feed)[0].flatten())
+
+#         compare01 = (results[0] == results[1])
+#         compare12 = (results[1] == results[2])
+
+#         if compare01.all() and compare12.all():
+#             return results[0].tolist()
+
+#         if (compare01 == compare12).all():
+#             index = numpy.argwhere(compare01 == False).flatten()
+#             if index.shape[0] != 1:
+#                 raise Exception("There's not only one unstable dimension")
+#             results[0][index[0]] = -1
+
+#             index = numpy.argwhere(results[0] < 0).flatten()
+#             if index.shape[0] > 2:
+#                 print("Warning: More than two dimension less than zero")
+#             if index.shape[0] == 2 and out_shape is not None:
+#                 if out_shape[index[1]] > 0:
+#                     results[0][index[1]] = out_shape[index[1]]
+#                 else:
+#                     results[0][index[0]] = out_shape[index[0]]
+#             return results[0].tolist()
+#         else:
+#             raise Exception("Couldn't infer a stable shape shape tensor value")
+
+#     def infer_tensor_shape(self, graph_node):
+#         if hasattr(graph_node, "index"):
+#             tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
+#         else:
+#             tensor_name = graph_node.layer.name + ":0"
+#         feed = dict()
+#         batch_size = [2, 3, 5]
+#         shapes = list()
+#         for b in batch_size:
+#             for input_name, info in self.inputs_info.items():
+#                 (shape, dtype) = cp.deepcopy(info)
+#                 input_tensor = self.sess.graph.get_tensor_by_name(input_name +
+#                                                                   ":0")
+#                 if shape.count(-1) > 0:
+#                     shape[shape.index(-1)] = b
+#                 feed[input_tensor] = numpy.random.random_sample(shape)
+#             output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
+#             shape = self.sess.run([output_tensor], feed)[0].shape
+#             shapes.append(numpy.array(shape))
+
+#         compare01 = (shapes[0] == shapes[1])
+#         compare12 = (shapes[1] == shapes[2])
+
+#         if compare01.all() and compare12.all():
+#             return shape[0].tolist()
+
+#         if (compare01 == compare12).all():
+#             index = numpy.argwhere(compare01 == False).flatten()
+#             if index.shape[0] != 1:
+#                 raise Exception("There's not only one unstable dimension")
+#             if index[0] != 0:
+#                 raise Exception("Batch size not in the first dimension")
+#             shapes[0][0] = -1
+#             return shapes[0].tolist()

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -953,7 +953,6 @@ class OpSet9():
     @print_mapping_info
     def Split(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
-
         paddle_op = 'split'
         split = node.get_attr('split')
         axis = node.get_attr('axis', 0)
@@ -963,11 +962,10 @@ class OpSet9():
         }
         outputs_list = list()
         if isinstance(split, list) or isinstance(split, tuple):
-            for i, s in enumerate(split):
-                outputs_list.append("{}_p{}".format(node.name, i))
+            for i in range(len(split)):
+                outputs_list.append("{}_p{}".format(node.layer_name, i))
         else:
             outputs_list.append(node.name)
-
         self.paddle_graph.add_layer(
             'paddle.split', 
             inputs={"x": val_x.name}, 

x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py

@@ -153,7 +153,7 @@ class TFOpMapper(OpMapper):
         layer_attrs = dict()
         if len(op_info) > 1:
             attrs_name_map_dict = op_info[1]
-            for tf_attr_name, pd_attr_name in attrs_name_map_dict:
+            for tf_attr_name, pd_attr_name in attrs_name_map_dict.items():
                 layer_attrs[pd_attr_name] = node.get_attr(tf_attr_name)
         if paddle_op.startswith("paddle.nn"):
             op_name = paddle_op[10:].lower()
@@ -767,11 +767,12 @@ class TFOpMapper(OpMapper):
         inputs_list = list()
         for i in range(len(node.inputs) - 1):
             inputs_list.append(self.graph.get_input_node(node, i))
+#         inputs_list = [self.graph.get_node(name) for name in node.layer.input[:-1]]
         axis = self.graph.get_input_node(node, -1)
         assert axis.layer_type == "Const", "axis for ConcatV2 must be type Const"
         axis = axis.value
         if axis < 0:
-            axis += len(inputs[0].out_shapes[0])
+            axis += len(inputs_list[0].out_shapes[0])
 
         input_names = [i.name for i in inputs_list]
         self.paddle_graph.add_layer(
@@ -846,6 +847,13 @@ class TFOpMapper(OpMapper):
                 new_end.append(999999)
             else:
                 new_end.append(end[i])
+            
+        if input.dtype == "bool":
+            self.paddle_graph.add_layer(
+                "paddle.cast",
+                inputs={"x": input.name},
+                outputs=[input.name],
+                dtype=string("int32"))
 
         self.paddle_graph.add_layer(
             kernel="paddle.slice",
@@ -854,6 +862,14 @@ class TFOpMapper(OpMapper):
             axes=[i for i in range(len(new_begin))],
             starts=new_begin,
             ends=new_end)
+        
+        if input.dtype == "bool":
+            self.paddle_graph.add_layer(
+                "paddle.cast",
+                inputs={"x": node.name},
+                outputs=[node.name],
+                dtype=string("bool"))
+
         if len(new_axes) > 0:
             self.paddle_graph.add_layer(
                 kernel="paddle.unsqueeze",
@@ -905,7 +921,7 @@ class TFOpMapper(OpMapper):
             #                 outputs=[reshape_name],
             #                 shape=shape)
             #             inputs['offsets'] = reshape_name
-            begin = self.decoder.infer_tensor(begin).tolist()
+            begin = self.decoder.infer_tensor(begin, use_diff_inputs=False).to_list()
             attrs['offsets'] = begin
         if size.layer_type == "Const":
             size = size.value.tolist()

x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py

@@ -897,7 +897,7 @@ class TFOpMapper(OpMapper):
             #                 outputs=[reshape_name],
             #                 shape=shape)
             #             inputs['offsets'] = reshape_name
-            begin = self.decoder.infer_tensor(begin).tolist()
+            begin = self.decoder.infer_tensor(begin, use_diff_inputs=False).to_list()
             attrs['offsets'] = begin
         if size.layer_type == "Const":
             size = size.value.tolist()