add optimizer

x2paddle/convert.py

@@ -174,6 +174,36 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
     print("Paddle model and code generated.")
 
 
+def pytorch2paddle(model_path, save_dir):
+    # check pytorch installation and version
+    try:
+        import torch
+        version = torch.__version__
+        ver_part = version.split('.')
+        print(ver_part)
+        if int(ver_part[1]) < 5:
+            print("[ERROR] pytorch>=1.5.0 is required")
+            return
+    except:
+        print(
+            "[ERROR] Pytorch is not installed, use \"pip install torch==1.5.0 torchvision\"."
+        )
+        return
+    print("Now translating model from pytorch to paddle.")
+
+    from x2paddle.decoder.pytorch_decoder import PyTorchDecoder
+    from x2paddle.op_mapper.pytorch2paddle import pytorch_op_mapper
+    model = PyTorchDecoder(model_path)
+    mapper = pytorch_op_mapper.PyTorchOpMapper(model)
+    mapper.graph.build()
+    print("Model optimizing ...")
+    from x2paddle.optimizer.optimizer import GraphOptimizer
+    graph_opt = GraphOptimizer()
+    graph_opt.optimize(mapper.graph)
+    print("Model optimized.")
+    mapper.graph.gen_model(save_dir)
+
+
 def paddle2onnx(model_path, save_dir, opset_version=10):
     from x2paddle.decoder.paddle_decoder import PaddleDecoder
     from x2paddle.op_mapper.paddle2onnx.paddle_op_mapper import PaddleOpMapper
@@ -243,6 +273,9 @@ def main():
         if args.params_merge:
             params_merge = True
         onnx2paddle(args.model, args.save_dir, params_merge)
+    elif args.framework == "pytorch":
+        assert args.model is not None, "--model should be defined while translating pytorch model"
+        pytorch2paddle(args.model, args.save_dir)
 
     elif args.framework == "paddle2onnx":
         assert args.model is not None, "--model should be defined while translating paddle model to onnx"

x2paddle/core/program.py

@@ -132,7 +132,8 @@ class PaddleGraph(object):
 
         if self.graph_type == "dygraph":
             self.get_dygraph_inputs()
-            self.get_dygraph_outputs()
+            if len(self.outputs) == 0:
+                self.get_dygraph_outputs()
 
     def get_global_layers(self):
         # 该全局layers的信息是按照拓扑排序组成的
@@ -164,8 +165,8 @@ class PaddleGraph(object):
             f, [
                 "from paddle.fluid.initializer import Constant",
                 "from paddle.fluid.param_attr import ParamAttr",
-                "import paddle.fluid as fluid"
-                "", "def x2paddle_net():"
+                "import paddle.fluid as fluid", "import math", "",
+                "def x2paddle_net():"
             ],
             indent=0)
         for layer_id, layer in self.layers.items():
@@ -204,6 +205,8 @@ class PaddleGraph(object):
         f.close()
 
     def gen_model(self, save_dir):
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
         if self.graph_type == "static":
             code_dir = os.path.join(save_dir, 'model_with_code')
             infer_dir = os.path.join(save_dir, 'inference_model')

x2paddle/op_mapper/pytorch2paddle/aten.py

@@ -451,6 +451,35 @@ def aten_chunk(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten___contains__(mapper, graph, node):
+    """ 构造in的PaddleLayer。
+
+    TorchScript示例:
+        %51 : bool = aten::__contains__(%50, %name.1)
+        参数含义:
+        %51 (bool): 输出，第一个元素是否包含第二个元素。
+        %50 (-): 需对比的输入1。
+        %name.1 (-): 需对比的输入2。
+    """
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    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%50
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    layer_inputs["input"] = inputs_name[0]
+    # 处理输入1，即%name.1
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
+    layer_inputs["element"] = inputs_name[1]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.contain", inputs=layer_inputs, outputs=layer_outputs)
+    return current_inputs, current_outputs
+
+
 def aten_contiguous(mapper, graph, node):
     """ 构造在内存中连续存储的PaddleLayer。
 
@@ -545,6 +574,25 @@ def aten_conv2d(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_dict(mapper, graph, node):
+    """ 构造初始化dict的PaddleLayer。
+
+    TorchScript示例:
+        %features.1 : Dict(str, Tensor) = aten::dict()
+        参数含义:
+        %features.1: 输出，初始化的dict。
+    """
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    current_inputs = {}
+    # 获取当前节点输出的list
+    current_outputs = [output_name]
+
+    graph.add_layer("prim.dict", inputs=layer_inputs, outputs=layer_outputs)
+    return current_inputs, current_outputs
+
+
 def aten_dim(mapper, graph, node):
     """ 构造获取维度的PaddleLayer。
 
@@ -720,6 +768,56 @@ def aten_flatten(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_Float(mapper, graph, node):
+    """ 构造取浮点型的PaddleLayer。
+
+    TorchScript示例:
+        %3992 : float = aten::Float(%3991)
+        参数含义:
+        %3992 (int): 向上取整后的整数。
+        %3991 (float): 需要取整的浮点数。
+    """
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    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%3991
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    layer_inputs["input"] = inputs_name[0]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.float", inputs=layer_inputs, outputs=layer_outputs)
+    return current_inputs, current_outputs
+
+
+def aten_floor(mapper, graph, node):
+    """ 构造向上取整的PaddleLayer。
+
+    TorchScript示例:
+        %3978 : int = aten::floor(%scale.18)
+        参数含义:
+        %3978 (int): 向上取整后的整数。
+        %scale.18 (float): 需要取整的浮点数。
+    """
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    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%scale.18
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    layer_inputs["input"] = inputs_name[0]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.floor", inputs=layer_inputs, outputs=layer_outputs)
+    return current_inputs, current_outputs
+
+
 def aten_floordiv(mapper, graph, node):
     """ 构造向上取整除法的PaddleLayer。
 
@@ -727,7 +825,7 @@ def aten_floordiv(mapper, graph, node):
         %channels_per_group.2 : int = aten::floordiv(%num_channels.2, %3690)
         参数含义:
         %channels_per_group.2 (-): 除后的结果。
-        %%num_channels.2 (-): 被除数。
+        %num_channels.2 (-): 被除数。
         %2 (int): 除数。
     """
     output_name = mapper._get_outputs_name(node)[0]
@@ -854,6 +952,64 @@ def aten_hardtanh_(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten___is__(mapper, graph, node):
+    """ 构造is not的PaddleLayer。
+
+    TorchScript示例:
+        %3949 : bool = aten::__isnot__(%size.122, %3931)
+        参数含义:
+        %3949 (bool): 输出，第一个元素是否不是第二个元素。
+        %size.122 (-): 需对比的输入1。
+        %3931 (-): 需对比的输入2。
+    """
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    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%size.122
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    layer_inputs["x"] = inputs_name[0]
+    # 处理输入1，即%3931
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
+    layer_inputs["y"] = inputs_name[1]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.is", inputs=layer_inputs, outputs=layer_outputs)
+    return current_inputs, current_outputs
+
+
+def aten___isnot__(mapper, graph, node):
+    """ 构造is not的PaddleLayer。
+
+    TorchScript示例:
+        %3949 : bool = aten::__isnot__(%size.122, %3931)
+        参数含义:
+        %3949 (bool): 输出，第一个元素是否不是第二个元素。
+        %size.122 (-): 需对比的输入1。
+        %3931 (-): 需对比的输入2。
+    """
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    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%size.122
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    layer_inputs["x"] = inputs_name[0]
+    # 处理输入1，即%3931
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
+    layer_inputs["y"] = inputs_name[1]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.isnot", inputs=layer_inputs, outputs=layer_outputs)
+    return current_inputs, current_outputs
+
+
 def aten_le(mapper, graph, node):
     """ 构造对比大小的PaddleLayer。
 
@@ -1344,6 +1500,36 @@ def aten_select(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten__set_item(mapper, graph, node):
+    """ 构造对dict加入元素的PaddleLayer。
+
+    TorchScript示例:
+        = aten::_set_item(%features.1, %out_name.1, %x.3)
+        参数含义:
+        %features.1 (list): dict。
+        %out_name.1 (-): dict的key。
+        %x.3 (-): dict的value。
+    """
+    layer_inputs = {}
+    inputs_name, inputs_node = mapper._get_inputs_name(node)
+    # 获取当前节点输出的list
+    current_outputs = []
+    # 处理输入0，即%features.1
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    layer_inputs["dict"] = inputs_name[0]
+    # 处理输入1，即%out_name.1
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
+    layer_inputs["key"] = inputs_name[1]
+    # 处理输入2，即%x.3
+    mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs)
+    layer_inputs["value"] = inputs_name[2]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.set_item", inputs=layer_inputs, outputs=[])
+    return current_inputs, current_outputs
+
+
 def aten_size(mapper, graph, node):
     """ 构造获取shape的PaddleLayer。
 
@@ -1569,6 +1755,70 @@ def aten_unsqueeze(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_upsample_bilinear2d(mapper, graph, node):
+    """ 构造使用bilinear上采样的PaddleLayer。
+
+    TorchScript示例:
+        %4997 : Tensor = aten::upsample_bilinear2d(%x.13, %4963, %5421, %4995, %4996)
+        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。
+        %5421 (bool): 若为True，则将输入和输出张量的4个角落像素的中心对齐，并保留角点像素的值。
+        %4995 (float): 高度的乘数因子。
+        %4995 (float): 宽度的乘数因子。
+    """
+    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)
+    layer_inputs["input"] = 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["out_shape"] = mapper.attrs[inputs_name[1]]
+    else:
+        mapper._check_input(graph, inputs_node[1], inputs_name[1],
+                            current_outputs)
+        layer_inputs["out_shape"] = inputs_name[1]
+        current_inputs.append(inputs_name[1])
+    # 处理输入2，即%5421
+    if inputs_name[2] in mapper.attrs:
+        layer_attrs["align_corners"] = mapper.attrs[inputs_name[2]]
+    else:
+        mapper._check_input(graph, inputs_node[2], inputs_name[2],
+                            current_outputs)
+        layer_inputs["align_corners"] = inputs_name[2]
+        current_inputs.append(inputs_name[2])
+    # 处理输入3和4，构造assert
+    list_layer_inputs = {}
+    mapper._check_input(graph, inputs_node[3], inputs_name[3], current_outputs)
+    list_layer_inputs["key"] = inputs_name[3]
+    current_inputs.append(inputs_name[3])
+    mapper._check_input(graph, inputs_node[4], inputs_name[4], current_outputs)
+    list_layer_inputs["value"] = inputs_name[4]
+    current_inputs.append(inputs_name[4])
+    graph.add_layer(
+        "prim.assert",
+        inputs=list_layer_inputs,
+        outputs=[output_name + "_assert"],
+        type="eq")
+    layer_inputs["scale"] = inputs_name[3]
+    layer_attrs["align_mode"] = 0
+    graph.add_layer(
+        "fluid.layers.interpolate",
+        inputs=layer_inputs,
+        outputs=layer_outputs,
+        **layer_attrs)
+    return current_inputs, current_outputs
+
+
 def aten_view(mapper, graph, node):
     """ 构造调整大小的PaddleLayer。
 

x2paddle/op_mapper/pytorch2paddle/prim.py

@@ -111,13 +111,14 @@ def prim_If(mapper, graph, node):
         %107 (bool): if判断条件。
         %input.5 (Tensor): if控制流的输出，与%output.4对应。
     """
-    output_name = mapper._get_outputs_name(node)[0]
-    node_outputs = [output_name]
+    outputs_name = mapper._get_outputs_name(node)
+    node_outputs = outputs_name.copy()
+    current_outputs = outputs_name.copy()
     input_node = list(node.inputs())[0].node()
     script_input_unique_id = list(node.inputs())[0].unique()
     input_node_name = mapper.outputs_info[script_input_unique_id]
-    mapper._check_input(graph, input_node, input_node_name, node_outputs)
-    graph.add_layer("prim.if", {'input': input_node_name}, [output_name])
+    mapper._check_input(graph, input_node, input_node_name, current_outputs)
+    graph.add_layer("prim.if", {'input': input_node_name}, node_outputs)
     current_layer = list(graph.layers.values())[-1]
     block0 = list(node.blocks())[0]
     block0_graph, graph_inputs0 = mapper.traverse(block0, current_layer)
@@ -131,7 +132,7 @@ def prim_If(mapper, graph, node):
     for i, input_name in enumerate(graph_inputs1):
         current_layer.inputs['input-{}'.format(len0 + 1 + i)] = input_name
     current_layer.add_block(block1_graph)
-    return list(current_layer.inputs.values()), node_outputs
+    return list(current_layer.inputs.values()), current_outputs
 
 
 def prim_ListConstruct(mapper, graph, node):
@@ -436,6 +437,34 @@ def prim_TupleUnpack(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def prim_unchecked_cast(mapper, graph, node):
+    """ 构造确认类型的PaddleLayer。
+
+    TorchScript示例:
+        %size.64 : int[] = prim::unchecked_cast(%size.63)
+        参数含义:
+        %size.64 (-): 输出。
+        %size.63 (-): 输入。
+
+    【注意】Paddle中无此用法，所以此处翻译成赋值。
+    """
+    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%size.63
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    layer_inputs["input"] = inputs_name[0]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.equal", inputs=layer_inputs, outputs=layer_outputs)
+    return current_inputs, current_outputs
+
+
 def prim_Uninitialized(mapper, graph, node):
     """ 构造表示编译器永远不会使用的值的PaddleLayer，该节点转换为None。
 

x2paddle/op_mapper/pytorch2paddle/prim2code.py

@@ -62,18 +62,22 @@ def prim_append(layer, indent=1, init_func=[], forward_func=[]):
 
 def prim_assert(layer, indent=1, init_func=[], forward_func=[]):
     if layer.attrs["type"] == "eq":
-        if isinstance(layer.attrs["value"], list):
+        values = get_value(layer, "key")
+        if "value" in layer.attrs:
+            values = layer.attrs["value"]
+        if isinstance(values, list):
             s = ""
-            for v in layer.attrs["value"]:
-                s += "{} == {} or ".format(layer.attrs["key"], v)
+            for v in values:
+                s += "{} == {} or ".format(get_value(layer, "key"), v)
             if len(s) > 0:
                 s = s[:-4]
             line = "assert {}, \'The {} must be {}!\'".format(
-                s, layer.attrs["key"], layer.attrs["value"])
+                s, get_value(layer, "key"), get_value(layer, "value"))
         else:
             line = "assert {} == {}, \'The {} must be {}!\'".format(
-                layer.attrs["key"], layer.attrs["value"], layer.attrs["key"],
-                layer.attrs["value"])
+                get_value(layer, "key"),
+                get_value(layer, "value"),
+                get_value(layer, "key"), get_value(layer, "value"))
     else:
         raise Exception("Not implement yet!")
     forward_func.extend(gen_codes([line], indent=indent))
@@ -84,6 +88,18 @@ def prim_constant(layer, indent=1, init_func=[], forward_func=[]):
     forward_func.extend(gen_codes([line], indent=indent))
 
 
+def prim_contain(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = {} in {}".format(layer.outputs[0],
+                                  get_value(layer, "element"),
+                                  get_value(layer, "input"))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_dict(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = dict()".format(layer.outputs[0])
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
 def prim_eq(layer, indent=1, init_func=[], forward_func=[]):
     line = "{} = {} == {}".format(layer.outputs[0],
                                   get_value(layer, "x"), get_value(layer, "y"))
@@ -100,12 +116,36 @@ def prim_exception(layer, indent=1, init_func=[], forward_func=[]):
     forward_func.extend(gen_codes([line], indent=indent))
 
 
+def prim_float(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = float({})".format(layer.outputs[0], get_value(layer, "input"))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_floor(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = math.floor({})".format(layer.outputs[0],
+                                        get_value(layer, "input"))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
 def prim_floordiv(layer, indent=1, init_func=[], forward_func=[]):
     line = "{} = {} // {}".format(layer.outputs[0],
                                   get_value(layer, "x"), get_value(layer, "y"))
     forward_func.extend(gen_codes([line], indent=indent))
 
 
+def prim_getitem(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = {}[{}]".format(layer.outputs[0],
+                                get_value(layer, "list"),
+                                get_value(layer, "index"))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_gt(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = {} > {}".format(layer.outputs[0],
+                                 get_value(layer, "x"), get_value(layer, "y"))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
 def prim_if(layer, indent=1, init_func=[], forward_func=[]):
     line = "if {} :".format(get_value(layer, "input"))
     forward_func.extend(gen_codes([line], indent=indent))
@@ -123,16 +163,16 @@ def prim_if(layer, indent=1, init_func=[], forward_func=[]):
         forward_func.extend(b_forward_lines)
 
 
-def prim_getitem(layer, indent=1, init_func=[], forward_func=[]):
-    line = "{} = {}[{}]".format(layer.outputs[0],
-                                get_value(layer, "list"),
-                                get_value(layer, "index"))
+def prim_is(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = {} is {}".format(layer.outputs[0],
+                                  get_value(layer, "x"), get_value(layer, "y"))
     forward_func.extend(gen_codes([line], indent=indent))
 
 
-def prim_gt(layer, indent=1, init_func=[], forward_func=[]):
-    line = "{} = {} > {}".format(layer.outputs[0],
-                                 get_value(layer, "x"), get_value(layer, "y"))
+def prim_isnot(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{} = {} is not {}".format(layer.outputs[0],
+                                      get_value(layer, "x"),
+                                      get_value(layer, "y"))
     forward_func.extend(gen_codes([line], indent=indent))
 
 
@@ -239,6 +279,13 @@ def prim_set_attr(layer, indent=1, init_func=[], forward_func=[]):
     forward_func.extend(gen_codes([line], indent=indent))
 
 
+def prim_set_item(layer, indent=1, init_func=[], forward_func=[]):
+    line = "{}[{}] = {}".format(
+        get_value(layer, "dict"),
+        get_value(layer, "key"), get_value(layer, "value"))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
 def prim_shape(layer, indent=1, init_func=[], forward_func=[]):
     line = "{} = {}.shape".format(layer.outputs[0], get_value(layer, "input"))
     forward_func.extend(gen_codes([line], indent=indent))

x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py

@@ -108,9 +108,14 @@ class PyTorchOpMapper(OpMapper):
                     parent_layer=parent_layer,
                     index=i)
                 _update_graph_inputs("equal", inputs, outputs)
-        # 设置graph的参数
+
+        # 设置graph的参数和输出节点
         if isinstance(script_graph, torch._C.Graph):
             graph.set_parameters(self.paddle_params)
+            if hasattr(script_graph, 'return_node'):
+                inputs_name, inputs_node = self._get_inputs_name(
+                    script_graph.return_node())
+                graph.outputs = inputs_name
         return graph, graph_inputs
 
     def _get_outputs_name(self, node, attr_name=None):

x2paddle/optimizer/fusion/__init__.py

@@ -12,11 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .fc_fuser import FcFuser
-from .fc_fuse_pass import FcFusePass
 from .adaptive_pool2d_fuser import AdaptivePool2dFuser
 from .adaptive_pool2d_fuse_pass import AdaptivePool2dFusePass
-from .constant_fuser import ConstantFuser
-from .constant_fuse_pass import ConstantFusePass
 from .batchnorm2d_fuser import BatchNorm2dFuser
 from .batchnorm2d_fuse_pass import BatchNorm2dFusePass
+from .constant_fuser import ConstantFuser
+from .constant_fuse_pass import ConstantFusePass
+from .fc_fuser import FcFuser
+from .fc_fuse_pass import FcFusePass
+from .interpolate_bilinear_fuser import InterpolateBilinearFuser
+from .interpolate_bilinear_fuse_pass import InterpolateBilinearFusePass

x2paddle/optimizer/fusion/interpolate_bilinear_fuse_pass.py

+#   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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from x2paddle.optimizer.pass_ import Pass
+from x2paddle.optimizer.fusion import InterpolateBilinearFuser
+from x2paddle.optimizer.pass_manager import pass_register
+
+
+@pass_register
+class InterpolateBilinearFusePass(Pass):
+    name = "interpolate_bilinear_fuse_pass"
+
+    def __init__(self):
+        Pass.__init__(self)
+
+    def apply(self, graph):
+        fuser = InterpolateBilinearFuser()
+        fuser.operate(graph, match_kind="topo")
+
+
+# 用于注册
+interpolate_bilinear_fuse_pass = InterpolateBilinearFusePass()

x2paddle/optimizer/optimizer.py

@@ -19,8 +19,9 @@ from x2paddle.optimizer.pass_manager import PassManager
 class GraphOptimizer(object):
     def __init__(self):
         self.passes = [
-            "fc_fuse_pass", "adaptive_pool2d_fuse_pass",
-            "batchnorm2d_fuse_pass", "constant_fuse_pass"
+            "interpolate_bilinear_fuse_pass", "fc_fuse_pass",
+            "adaptive_pool2d_fuse_pass", "batchnorm2d_fuse_pass",
+            "constant_fuse_pass"
         ]
 
     def optimize(self, graph):