Merge pull request #22 from PaddlePaddle/develop

Merge

x2paddle/decoder/tf_decoder.py

@@ -361,6 +361,8 @@ class TFDecoder(object):
                 continue
             graph_node = TFGraphNode(layer)
             dtype = graph_node.layer.attr['dtype'].type
+            if dtype == 10:
+                continue
 
             need_define_shape = 0
             if self.define_input_shape:

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -4537,6 +4537,72 @@ def aten_upsample_bilinear2d(mapper, graph, node):
         **layer_attrs)
     return current_inputs, current_outputs
 
+def aten_upsample_nearest2d(mapper, graph, node):
+    """ 构造使用nearest上采样的PaddleLayer。
+
+    TorchScript示例:
+        %4997 : Tensor = aten::upsample_nearest2d(%x.13, %4963, %5421, %4995)
+        参数含义:
+        %4997 (Tensor): 输出，上采样后的Tensor。
+        %x.13 (Tensor): 需要上采样的Tensor。
+        %4963 (list): 上采样后的大小。
+        %4995 (float): 高度的乘数因子。
+        %4995 (float): 宽度的乘数因子。
+    """
+    scope_name = mapper.normalize_scope_name(node)
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    layer_attrs = {}
+    inputs_name, inputs_node = mapper._get_inputs_name(node)
+    # 获取当前节点输出的list
+    current_outputs = [output_name]
+    # 处理输入0，即%x.13
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
+    layer_inputs["x"] = inputs_name[0]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+    # 处理输入1，即%4963
+    if inputs_name[1] in mapper.attrs:
+        layer_attrs["size"] = mapper.attrs[inputs_name[1]]
+    else:
+        mapper._check_input(graph, inputs_node[1], inputs_name[1],
+                            current_outputs, scope_name)
+        layer_inputs["size"] = inputs_name[1]
+        current_inputs.append(inputs_name[1])
+        graph.add_layer(
+            "prim.isinstance",
+            inputs={"input": inputs_name[1]},
+            outputs=[inputs_name[1] + "_isinstance"],
+            scope_name=scope_name,
+            cls="paddle.fluid.Variable")
+        # TODO(syf): paddle.Variable
+        graph.add_layer(
+            "prim.if", {"input": inputs_name[1] + "_isinstance"},
+            outputs=[inputs_name[0] + "_if1"],
+            scope_name=scope_name)
+        if_layer = graph.layers[list(graph.layers.keys())[-1]]
+        block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
+        block.add_layer(
+            "prim.var2list",
+            inputs={"input": inputs_name[1]},
+            outputs=[inputs_name[1]],
+            scope_name=scope_name)
+        if_layer.add_block(block)
+        block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
+        if_layer.add_block(block)
+        if_layer.inputs["input-0"] = inputs_name[1]
+    layer_inputs["scale_factor"] = inputs_name[3]
+    layer_attrs["align_mode"] = 0
+    layer_attrs["mode"] = string("nearest")
+    graph.add_layer(
+        "paddle.nn.functional.interpolate",
+        inputs=layer_inputs,
+        outputs=layer_outputs,
+        scope_name=scope_name,
+        **layer_attrs)
+    return current_inputs, current_outputs
+
 
 def aten_values(mapper, graph, node):
     """ 构造对比大小的PaddleLayer。

x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py

@@ -676,8 +676,8 @@ class TFOpMapper(OpMapper):
         input = self.graph.get_input_node(node, 0)
         paddings = self.graph.get_input_node(node, 1)
         assert paddings.layer_type == "Const", "Padding should be Const"
-        paddings = np.flip(paddings.value, 0).flatten().tolist()
-        dim = int(len(paddings) / 2)
+        new_paddings = numpy.flip(paddings.value, 0).flatten().tolist()
+        dim = int(len(new_paddings) / 2)
         transpose_name = gen_name("pad", "transpose")
         self.paddle_graph.add_layer(
             kernel="paddle.transpose",
@@ -688,7 +688,7 @@ class TFOpMapper(OpMapper):
             kernel="paddle.nn.Pad{}D".format(dim),
             inputs={"x": transpose_name},
             outputs=layer_outputs,
-            pad=new_padding)
+            pad=new_paddings)
         self.paddle_graph.add_layer(
             kernel="paddle.transpose",
             inputs={"x": node.name},

x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py

@@ -661,7 +661,7 @@ class TFOpMapper(OpMapper):
         input = self.graph.get_input_node(node, 0)
         paddings = self.graph.get_input_node(node, 1)
         assert paddings.layer_type == "Const", "Padding should be Const"
-        paddings = np.flip(paddings.value, 0).flatten().tolist()
+        new_paddings = numpy.flip(paddings.value, 0).flatten().tolist()
         transpose_name = gen_name("pad", "transpose")
         self.paddle_graph.add_layer(
             kernel="paddle.transpose",
@@ -672,7 +672,7 @@ class TFOpMapper(OpMapper):
             kernel="paddle.nn.functional.pad".format(dim),
             inputs={"x": transpose_name},
             outputs=[node.name],
-            pad=new_padding)
+            pad=new_paddings)
         self.paddle_graph.add_layer(
             kernel="paddle.transpose",
             inputs={"x": node.name},

x2paddle/optimizer/pytorch_code_optimizer/hierachical_tree.py

@@ -300,6 +300,7 @@ class HierarchicalTree(Tree):
         """
         depths = sorted(list(self._hierarchical_order.keys()), reverse=True)
         all_name_old2new = dict()
+        current_module_name_list = list()
         for depth in depths[1:]:
             # Module的名字与子图的对应关系
             module_name2sub_layers = dict()
@@ -352,6 +353,9 @@ class HierarchicalTree(Tree):
                         module_name = None
                     else:
                         module_name = name
+                        while module_name in current_module_name_list:
+                            module_name += "__0"
+                    current_module_name_list.append(module_name)
                     self.merge_node(module_name2sub_layers[name], 
                                    sequentials2attrs_table[name],
                                    node_name2sub_layers,