update caffe2paddle

x2paddle/caffe_convert.py

+from x2paddle.decoder.caffe_decoder import CaffeDecoder
+Decoder = CaffeDecoder
+
+from x2paddle.op_mapper.dygraph.caffe2paddle.caffe_op_mapper import CaffeOpMapper
+DygraphOpMapper = CaffeOpMapper
+
+from x2paddle.op_mapper.static.caffe2paddle.caffe_op_mapper import CaffeOpMapper
+StaticOpMapper = CaffeOpMapper
+
+from x2paddle.optimizer.caffe_optimizer import CaffeOptimizer
+StaticOptimizer = CaffeOptimizer
\ No newline at end of file

x2paddle/convert.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from six import text_type as _text_type
+from x2paddle import program
 import argparse
 import sys
 
@@ -66,8 +67,8 @@ def arg_parser():
     parser.add_argument(
         "--without_data_format_optimization",
         "-wo",
-        type=_text_type,
-        default="True",
+        action="store_true",
+        default=False,
         help="tf model conversion without data format optimization")
     parser.add_argument(
         "--define_input_shape",
@@ -88,11 +89,25 @@ def arg_parser():
         default=False,
         help="define whether merge the params")
     parser.add_argument(
-        "--input_shapes",
-        "-is",
+        "--jit_type",
+        "-jt",
+        type=_text_type,
+        default="script",
+        help="define the jit type of pytorch Module.")
+    parser.add_argument(
+        "--input_files",
+        "-if",
         action='append',
         default=None,
-        help="define the inputs' shape")
+        help="define the inputs' file path")
+    parser.add_argument(
+        "--paddle_type",
+        "-pt",
+        type=_text_type,
+        default="dygraph",
+        help="define the paddle model type after converting(dygraph/static)"
+    )
+    
     return parser
 
 
@@ -117,30 +132,27 @@ def tf2paddle(model_path,
             "[ERROR] Tensorflow is not installed, use \"pip install tensorflow\"."
         )
         return
-    from x2paddle import program
+
     from x2paddle.decoder.tf_decoder import TFDecoder
     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.op_mapper.tf_op_mapper_nhwc import TFOpMapperNHWC
+    from x2paddle.optimizer.tf_optimizer import TFOptimizer
 
     print("Now translating model from tensorflow to paddle.")
     model = TFDecoder(model_path, define_input_shape=define_input_shape)
-    mapper = TFOpMapper(model)
+
+    mapper = TFOpMapperNHWC(model)
     program.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)
 
 
-def caffe2paddle(proto, weight, save_dir, caffe_proto, params_merge=False):
-    from x2paddle.decoder.caffe_decoder import CaffeDecoder
-    from x2paddle.op_mapper.caffe_op_mapper import CaffeOpMapper
-    from x2paddle.optimizer.caffe_optimizer import CaffeOptimizer
+def caffe2paddle(proto, weight, save_dir, caffe_proto, 
+                 paddle_type, params_merge=False):
+    from x2paddle.caffe_convert import Decoder
+    if paddle_type == "dygraph":
+        from x2paddle.caffe_convert import DygraphOpMapper as OpMapper
+    else:
+        from x2paddle.caffe_convert import StaticOpMapper as OpMapper
     import google.protobuf as gpb
     ver_part = gpb.__version__.split('.')
     version_satisfy = False
@@ -149,12 +161,10 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto, params_merge=False):
         version_satisfy = True
     assert version_satisfy, '[ERROR] google.protobuf >= 3.6.0 is required'
     print("Now translating model from caffe to paddle.")
-    model = CaffeDecoder(proto, weight, caffe_proto)
-    mapper = CaffeOpMapper(model)
-    optimizer = CaffeOptimizer(mapper)
-    optimizer.merge_bn_scale()
-    optimizer.merge_op_activation()
-    mapper.save_inference_model(save_dir, params_merge)
+    model = Decoder(proto, weight, caffe_proto)
+    mapper = OpMapper(model)
+    mapper.pd_graph.build()
+    mapper.pd_graph.gen_model(save_dir)
 
 
 def onnx2paddle(model_path, save_dir, params_merge=False):
@@ -162,8 +172,8 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
     try:
         import onnx
         version = onnx.version.version
-        if version < '1.6.0':
-            print("[ERROR] onnx>=1.6.0 is required")
+        if version != '1.6.0':
+            print("[ERROR] onnx==1.6.0 is required")
             return
     except:
         print("[ERROR] onnx is not installed, use \"pip install onnx==1.6.0\".")
@@ -185,7 +195,7 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
     print("Paddle model and code generated.")
 
 
-def pytorch2paddle(model_path, save_dir, input_shapes):
+def pytorch2paddle(model_path, save_dir, jit_type, input_files):
     # check pytorch installation and version
     try:
         import torch
@@ -202,9 +212,12 @@ def pytorch2paddle(model_path, save_dir, input_shapes):
         return
     print("Now translating model from pytorch to paddle.")
 
-    from x2paddle.decoder.pytorch_decoder import PyTorchDecoder
+    from x2paddle.decoder.pytorch_decoder import ScriptDecoder, TraceDecoder
     from x2paddle.op_mapper.pytorch2paddle import pytorch_op_mapper
-    model = PyTorchDecoder(model_path)
+    if jit_type == "trace":
+        model = TraceDecoder(model_path, input_files)
+    else:
+        model = ScriptDecoder(model_path)
     mapper = pytorch_op_mapper.PyTorchOpMapper(model)
     mapper.graph.build()
     print("Model optimizing ...")
@@ -212,34 +225,15 @@ def pytorch2paddle(model_path, save_dir, input_shapes):
     graph_opt = GraphOptimizer()
     graph_opt.optimize(mapper.graph)
     print("Model optimized.")
-    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.graph.gen_model(save_dir, jit_type, input_files)
 
 
 def paddle2onnx(model_path, save_dir, opset_version=10):
-    import paddle.fluid as fluid
-    try:
-        import paddle2onnx
-    except:
-        print(
-            "[ERROR] paddle2onnx not installed, use \"pip install paddle2onnx\"")
-
-    import paddle2onnx as p2o
-    model = p2o.PaddleDecoder(model_path, '__model__', '__params__')
-    mapper = p2o.PaddleOpMapper()
-    mapper.convert(
-        model.program,
-        save_dir,
-        scope=fluid.global_scope(),
-        opset_version=opset_version)
+    from x2paddle.decoder.paddle_decoder import PaddleDecoder
+    from x2paddle.op_mapper.paddle2onnx.paddle_op_mapper import PaddleOpMapper
+    model = PaddleDecoder(model_path, '__model__', '__params__')
+    mapper = PaddleOpMapper()
+    mapper.convert(model.program, save_dir, opset_number=opset_version)
 
 
 def main():
@@ -260,6 +254,7 @@ def main():
 
     assert args.framework is not None, "--framework is not defined(support tensorflow/caffe/onnx)"
     assert args.save_dir is not None, "--save_dir is not defined"
+    assert args.paddle_type in ["dygraph", "static"], "--paddle_type must be 'dygraph' or 'static'"
 
     try:
         import paddle
@@ -267,8 +262,8 @@ def main():
         print("paddle.__version__ = {}".format(paddle.__version__))
         if v0 == '0' and v1 == '0' and v2 == '0':
             print("[WARNING] You are use develop version of paddlepaddle")
-        elif int(v0) != 1 or int(v1) < 6:
-            print("[ERROR] paddlepaddle>=1.6.0 is required")
+        elif int(v0) != 2 or int(v1) < 0:
+            print("[ERROR] paddlepaddle>=2.0.0 is required")
             return
     except:
         print(
@@ -277,12 +272,11 @@ def main():
 
     if args.framework == "tensorflow":
         assert args.model is not None, "--model should be defined while translating tensorflow model"
-        assert args.without_data_format_optimization in [
-            "True", "False"
-        ], "--the param without_data_format_optimization should be defined True or False"
+        without_data_format_optimization = False
         define_input_shape = False
         params_merge = False
-        without_data_format_optimization = True if args.without_data_format_optimization == "True" else False
+        if args.without_data_format_optimization:
+            without_data_format_optimization = True
         if args.define_input_shape:
             define_input_shape = True
         if args.params_merge:
@@ -296,7 +290,7 @@ def main():
         if args.params_merge:
             params_merge = True
         caffe2paddle(args.prototxt, args.weight, args.save_dir,
-                     args.caffe_proto, params_merge)
+                     args.caffe_proto, args.paddle_type, params_merge)
     elif args.framework == "onnx":
         assert args.model is not None, "--model should be defined while translating onnx model"
         params_merge = False
@@ -304,10 +298,13 @@ 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, args.jit_type, args.input_files)
 
     elif args.framework == "paddle2onnx":
         assert args.model is not None, "--model should be defined while translating paddle model to onnx"
-        paddle2onnx(args.model, args.save_dir, opset_version=args.onnx_opset)
+        paddle2onnx(args.model, args.save_dir, args.onnx_opset)
 
     else:
         raise Exception(

x2paddle/core/program.py

-# -*- coding:UTF-8 -*-
 #   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"
@@ -21,15 +20,15 @@ import paddle
 from paddle.fluid.proto import framework_pb2
 from collections import OrderedDict
 import numpy
-import collections
 import sys
 import os
 import six
 import pickle
+import numpy as np
 
 
 class PaddleLayer(object):
-    def __init__(self, id, kernel, inputs, outputs, **kwargs):
+    def __init__(self, id, kernel, inputs, outputs, scope_name="", **kwargs):
         assert isinstance(
             inputs,
             dict), "parameter 'inputs' for PaddleLayer should be type of dict"
@@ -53,10 +52,12 @@ class PaddleLayer(object):
         self.kernel = kernel
         self.inputs = inputs
         self.outputs = outputs
+        self.scope_name = scope_name
         self.attrs = kwargs
         self.id = id
         self.blocks = list()
         
+
     def add_block(self, block):
         self.blocks.append(block)
 
@@ -71,13 +72,24 @@ class PaddleGraph(object):
         self.parameters = dict()
         self.parent_layer = parent_layer
         self.graph_type = graph_type
+        self.custom_func = None
+        self.inputs_info = None
 
     def set_name(self, name):
-        self.name = name
+        self.name = name.replace("-", "_").replace("/", "_")
 
     def set_parameters(self, parameters):
         self.parameters = parameters
         
+    def set_custom_func(self, custom_func):
+        self.custom_func = custom_func
+        
+    def set_inputs_info(self, inputs_info):
+        self.inputs_info = inputs_info
+        
+    def set_script(self, script):
+        self.script = script
+
     def clear(self):
         self.layers = OrderedDict()
         self.edges_out = dict()
@@ -90,13 +102,13 @@ class PaddleGraph(object):
         self.edges_out = dict()
         self.edges_in = dict()
 
-    def add_layer(self, kernel, inputs, outputs, **kwargs):
+    def add_layer(self, kernel, inputs, outputs, scope_name="", **kwargs):
         layer_id = str(len(self.layers))
         if self.parent_layer is not None:
             layer_id = "{}.{}.{}".format(self.parent_layer.id,
                                          len(self.parent_layer.blocks),
                                          layer_id)
-        layer = PaddleLayer(layer_id, kernel, inputs, outputs, **kwargs)
+        layer = PaddleLayer(layer_id, kernel, inputs, outputs, scope_name=scope_name, **kwargs)
         self.layers[layer_id] = layer
         return layer_id
 
@@ -156,6 +168,10 @@ class PaddleGraph(object):
                 if not isinstance(vs, list):
                     vs = [vs]
                 for v in vs:
+                    if "[" in v:
+                        remove_index = v.index("[")
+                        v_part = v[remove_index:]
+                        v = v.replace(v_part, "")
                     assert v in outputs_from_nodes or (
                         inputs is not None and v in list(inputs.values())
                     ) or (
@@ -215,9 +231,60 @@ class PaddleGraph(object):
                     block_global_layers = update(block.layers)
                     global_layers.update(block_global_layers)
             return global_layers
-
         return update(self.layers)
     
+    def gen_model(self, save_dir, jit_type=None):
+        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')
+            self.gen_code(code_dir)
+            sys.path.append(code_dir)
+            import x2paddle_model
+            paddle.enable_static()
+            scope = paddle.static.Scope()
+            startup_program = paddle.static.Program()
+            main_program = paddle.static.Program()
+            with paddle.static.scope_guard(scope):
+                with paddle.static.program_guard(main_program, startup_program):
+                    inputs, outputs = x2paddle_model.x2paddle_net()
+                    exe = fluid.Executor(fluid.CPUPlace())
+                    exe.run(startup_program)
+                    param_dir = os.path.join(code_dir, 'weights')
+                    for k, v in self.parameters.items():
+                        if scope.find_var(k):
+                            self.dump_parameter(k, v, param_dir)
+                    def if_exist(var):
+                        b = os.path.exists(
+                            os.path.join(os.path.join(param_dir, var.name)))
+                        return b
+                    fluid.io.load_vars(
+                        exe, param_dir, main_program, predicate=if_exist)
+                    fluid.io.save_inference_model(
+                        dirname=infer_dir,
+                        feeded_var_names=[i.name for i in inputs],
+                        target_vars=outputs,
+                        executor=exe)
+        else:
+            if jit_type == "trace":
+                from x2paddle.optimizer.code_optimizer import HierarchicalTree
+                hierarchical_tree = HierarchicalTree(self)
+                for layer_id, layer in self.layers.items():
+                    hierarchical_tree.insert(layer)
+                hierarchical_tree.save_source_files(save_dir)
+                self.dump_dygraph_parameter(save_dir)
+            else:
+                self.gen_dygraph_code(save_dir)
+                self.dump_dygraph_parameter(save_dir)
+            input_shapes = list()
+            input_types = list()
+            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)
+
     def gen_code(self, code_dir):
         def write_code(f, code_list, indent=0):
             indent_blank = "    " * indent
@@ -235,10 +302,24 @@ class PaddleGraph(object):
             f, [
                 "from paddle.fluid.initializer import Constant",
                 "from paddle.fluid.param_attr import ParamAttr",
-                "import paddle.fluid as fluid", "import math", "",
-                "def x2paddle_net():"
+                "import paddle.fluid as fluid", 
+                "import paddle", "import math", "",
+                
             ],
             indent=0)
+        if self.custom_func is not None:
+            write_code(
+                f, 
+                list(self.custom_func.values()),
+                indent=0)
+        write_code(f, 
+            ["", "def x2paddle_net():"],
+            indent=0)
+        write_code(
+            f, [
+                "paddle.enable_static()"
+            ],
+            indent=1)
         for layer_id, layer in self.layers.items():
             edges_in = self.edges_in.get(layer_id, [])
             edges_out = self.edges_out.get(layer_id, [])
@@ -253,7 +334,9 @@ class PaddleGraph(object):
                 for output in layer.outputs:
                     line += "{}, ".format(output)
                 line = line.strip(", ")
-
+            if layer.kernel.startswith("combination_layer"):
+                line += " = {}(".format(layer.kernel.split(":")[-1].lower() + "_layer")
+            else:
                 line += " = {}(".format(layer.kernel)
             for k, v in layer.inputs.items():
                 if isinstance(v, list):
@@ -274,47 +357,6 @@ class PaddleGraph(object):
             indent=1)
         f.close()
 
-    def gen_model(self, save_dir, input_shapes=None):
-        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')
-            self.gen_code(code_dir)
-            sys.path.append(code_dir)
-            import x2paddle_model
-            scope = fluid.Scope()
-            startup_program = fluid.Program()
-            main_program = fluid.Program()
-            with fluid.scope_guard(scope):
-                with fluid.program_guard(main_program, startup_program):
-                    inputs, outputs = x2paddle_model.x2paddle_net()
-                    exe = fluid.Executor(fluid.CPUPlace())
-                    exe.run(startup_program)
-
-                    param_dir = os.path.join(code_dir, 'weights')
-                    for k, v in self.parameters.items():
-                        if scope.find_var(k):
-                            self.dump_parameter(k, v, param_dir)
-
-                    def if_exist(var):
-                        b = os.path.exists(
-                            os.path.join(os.path.join(param_dir, var.name)))
-                        return b
-
-                    fluid.io.load_vars(
-                        exe, param_dir, main_program, predicate=if_exist)
-                    fluid.io.save_inference_model(
-                        dirname=infer_dir,
-                        feeded_var_names=[i.name for i in inputs],
-                        target_vars=outputs,
-                        executor=exe)
-        else:
-            self.gen_dygraph_code(save_dir)
-            self.dump_dygraph_parameter(save_dir)
-            if input_shapes is not None:
-                # 如果input_shapes非空，则导出推理模型；其值类似[[None, 3, 224, 224]]
-                self.dygraph2static(save_dir, input_shapes)
 
     def dump_parameter(self, param_name, param, save_dir):
         if not os.path.exists(save_dir):
@@ -356,10 +398,10 @@ class PaddleGraph(object):
                 if self.edges_in.get(layer_id, 0) == 0 and self.edges_out.get(
                         layer_id, 0) == 0:
                     continue
-                if layer.kernel == "fluid.dygraph.base.to_variable":
-                    value = layer.attrs["value"]
-                    if not value.startswith("params["):
-                        self.inputs.append(value)
+                if layer.kernel == "paddle.to_tensor":
+                    data = layer.attrs["data"]
+                    if not data.startswith("params["):
+                        self.inputs.append(data)
                 if len(layer.blocks) > 0:
                     for block in layer.blocks:
                         block.get_dygraph_inputs()
@@ -376,11 +418,15 @@ class PaddleGraph(object):
                     layer_id, 0) == 0:
                 continue
             if self.edges_out.get(layer_id, 0) == 0:
-                for output_name in layer.outputs:
-                    if not output_name.startswith("x"):
+                
+                for i, output_name in enumerate(layer.outputs):
+                    if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
+                            (layer.kernel == "paddle.to_tensor" and layer.attrs["data"].startswith("params["))or \
+                            "paddle.fluid.dygraph" in layer.kernel:
+                        if i == 0:
                             continue
+                    if output_name not in self.outputs:
                         self.outputs.append(output_name)
-        self.outputs = list(set(self.outputs))
 
     def gen_dygraph_code(self, code_dir=None, indent=2):
         def gen_codes(code_list, indent=0):
@@ -416,6 +462,23 @@ class PaddleGraph(object):
                     ["def forward(self, {}):".format(input_data_name)],
                     indent=1))
             
+        def gen_run_net_code(code_dir):
+            input_data_name = ', '.join(self.inputs)
+            self.run_func = gen_codes(
+                [
+                    "",
+                    "def run_net({}):".format(input_data_name),
+                ],
+                indent=0)
+            self.run_func.extend(
+                gen_codes(["paddle.disable_static()",
+                           "params, _ = fluid.load_dygraph('{}/model')".format(code_dir),
+                           "model = {}(params)".format(self.name),
+                           "model.set_dict(params)",
+                           "model.eval()",
+                           "out = model({})".format(input_data_name),
+                           "return out"], indent=1))
+
         def write_code(code_dir):
             f = open(os.path.join(code_dir, 'x2paddle_code.py'), 'w')
             for code_line in self.head:
@@ -431,6 +494,8 @@ class PaddleGraph(object):
             self.forward_func.extend(gen_codes([return_code], indent=2))
             for code_line in self.forward_func:
                 f.write(code_line)
+            for code_line in self.run_func:
+                f.write(code_line)
             f.close()
 
         self.init_func = []
@@ -440,12 +505,12 @@ class PaddleGraph(object):
 
         for layer_id, layer in self.layers.items():
             if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel
-                ) or layer.kernel == "fluid.dygraph.base.to_variable" or \
+                ) or layer.kernel == "paddle.to_tensor" or \
                "paddle.fluid.dygraph" in layer.kernel:
                 line = "{}".format(
                     layer.outputs[0]
-                ) if layer.kernel == "fluid.dygraph.base.to_variable" and not layer.attrs[
-                    "value"].startswith("params[") else "self.{}".format(
+                ) if layer.kernel == "paddle.to_tensor" and not layer.attrs[
+                    "data"].startswith("params[") else "self.{}".format(
                         layer.outputs[0])
                 line += " = {}(".format(layer.kernel)
                 for k, v in layer.attrs.items():
@@ -453,8 +518,8 @@ class PaddleGraph(object):
                 line = line.strip(", ")
                 line += ")"
 
-                if layer.kernel == "fluid.dygraph.base.to_variable" and not layer.attrs[
-                        "value"].startswith("params["):
+                if layer.kernel == "paddle.to_tensor" and not layer.attrs[
+                        "data"].startswith("params["):
                     self.forward_func.extend(gen_codes([line], indent=indent))
                     continue
                 else:
@@ -466,8 +531,8 @@ class PaddleGraph(object):
                     line = layer.outputs[1]
                 else:
                     line = ','.join(layer.outputs[1:])
-                if layer.kernel == "fluid.dygraph.base.to_variable" and layer.attrs[
-                        "value"].startswith("params["):
+                if layer.kernel == "paddle.to_tensor" and layer.attrs[
+                        "data"].startswith("params["):
                     line += " = self.{}".format(layer.outputs[0])
                 else:
                     line += " = self.{}(".format(layer.outputs[0])
@@ -478,7 +543,7 @@ class PaddleGraph(object):
                 self.forward_func.extend(gen_codes([line], indent=indent))
             elif "prim" in layer.kernel:
                 func_name = layer.kernel.replace(".", "_")
-                from x2paddle.op_mapper.pytorch2paddle import prim2code
+                from x2paddle.op_mapper.dygraph import prim2code
                 if hasattr(prim2code, func_name):
                     func = getattr(prim2code, func_name)
                     func(
@@ -504,6 +569,7 @@ class PaddleGraph(object):
                 line += ")"
                 self.forward_func.extend(gen_codes([line], indent=indent))
         if indent == 2:
+            gen_run_net_code(code_dir)
             write_code(code_dir)
         else:
             return self.init_func, self.forward_func
@@ -513,23 +579,22 @@ class PaddleGraph(object):
         pickle.dump(self.parameters, params_output)
         params_output.close()
 
-    def dygraph2static(self, save_dir, input_shapes=[]):
+    def dygraph2static(self, save_dir, input_shapes=[], input_types=[]):
         from paddle.fluid.dygraph.jit import declarative
         sepc_list = list()
         for i, name in enumerate(self.inputs):
+            input_shapes[i][0] = -1
             sepc_list.append(
                 paddle.static.InputSpec(
-                    shape=input_shapes[i], name=name))
+                    shape=input_shapes[i], name=name, dtype=input_types[i]))
         import sys
         path = osp.abspath(save_dir)
         sys.path.insert(0, save_dir)
         import x2paddle_code
-        place = fluid.CPUPlace()
-        with fluid.dygraph.guard(place):
+        paddle.disable_static()
         restore, _ = fluid.load_dygraph(osp.join(save_dir, "model"))
         model = getattr(x2paddle_code, self.name)(restore)
         model.set_dict(restore)
         model.eval()
-            model.forward = declarative(model.forward, sepc_list)
-        fluid.dygraph.jit.save(
-            layer=model, model_path=osp.join(save_dir, "inference"))
+        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/caffe_decoder.py

@@ -18,7 +18,6 @@ from google.protobuf import text_format
 import numpy as np
 from x2paddle.core.graph import GraphNode, Graph
 from x2paddle.core.fluid_code import FluidCode
-from x2paddle.op_mapper import caffe_shape
 
 
 class CaffeResolver(object):
@@ -50,10 +49,10 @@ class CaffeGraphNode(GraphNode):
     def __init__(self, layer, type_str, layer_name=None):
         if layer_name is None:
             super(CaffeGraphNode, self).__init__(
-                layer, layer.name.replace('/', '_').replace('-', '_'))
+                layer, layer.name.replace('/', '_').replace('-', '_').lower())
         else:
             super(CaffeGraphNode, self).__init__(
-                layer, layer_name.replace('/', '_').replace('-', '_'))
+                layer, layer_name.replace('/', '_').replace('-', '_').lower())
         self.layer_type = type_str
         self.fluid_code = FluidCode()
         self.data = None
@@ -66,6 +65,13 @@ class CaffeGraph(Graph):
     def __init__(self, model, params, caffe_pb):
         self.params = params
         self.caffe_pb = caffe_pb
+        if hasattr(model, "name"):
+            if model.name == "":
+                self.graph_name = "CaffeModel"
+            else:
+                self.graph_name = model.name
+        else:
+            self.graph_name = "CaffeModel"
         super(CaffeGraph, self).__init__(model)
 
     def filter_layers(self, layers):

x2paddle/decoder/pytorch_decoder.py

@@ -12,14 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import sys
 import torch
+import numpy as np
 
 
-class PyTorchDecoder(object):
-    def __init__(self, script_path):
-        self.script = torch.jit.load(script_path)
-        self.graph = self._optimize_graph(self.script.inlined_graph)
-
+class Decoder(object):
      def _optimize_graph(self, graph):
         torch._C._jit_pass_constant_propagation(graph)
         torch._C._jit_pass_dce(graph)
@@ -32,3 +31,36 @@ class PyTorchDecoder(object):
         torch._C._jit_pass_lint(graph)
         torch._C._jit_pass_constant_propagation(graph)
         return graph       
+
+
+class ScriptDecoder(Decoder):
+    """ 当script_path非None，直接load ScriptModule;
+        当model_path非None，load PyTorchModule后使用script方式转换为ScriptModule。
+        
+        Args:
+            script_path (str): ScriptModule保存路径。
+            model_path (str): PyTorchModule保存路径。
+    """
+    def __init__(self, script_path=None):
+        self.script = torch.jit.load(script_path)
+        self.graph = self._optimize_graph(self.script.inlined_graph)
+            
+class TraceDecoder(Decoder):
+    """ PyTorchModule后使用trace方式转换为ScriptModule。
+        
+        Args:
+            model_path (str): PyTorchModule保存路径。
+            input_files (list): 输入网络的numpy，每个numpy保存成.npy文件, 
+                                文件路径存储在input_files中。
+    """
+    def __init__(self, model_path, input_files=list()):
+        # TODO(syf): 传入pytorch的Module(即import)，否则出错
+        model = torch.load(model_path)
+        model.eval()
+        input_list = list()
+        for npy_file in input_files:
+            input_list.append(torch.tensor(np.load(npy_file)))
+        self.script = torch.jit.trace(model, input_list, strict=False)
+        self.graph = self._optimize_graph(self.script.inlined_graph)
+#         print(self.graph)
+#         print(getattr(getattr(self.script.decoder.block, "5").layer, "2"))

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_op_mapper.py

+#   Copyright (c) 2019  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.
+
+import numbers
+import numpy as np
+from x2paddle.core.op_mapper import OpMapper
+from x2paddle.core.util import *
+from x2paddle.op_mapper.dygraph.caffe2paddle import caffe_shape
+from x2paddle.core.program import PaddleGraph 
+
+
+class CaffeOpMapper(OpMapper):
+    directly_map_ops = {
+        'Sigmoid': 'paddle.nn.layer.Sigmoid',
+        'TanH': 'paddle.nn.Tanh',
+    }
+
+    def __init__(self, decoder):
+        super(CaffeOpMapper, self).__init__()
+        self.graph = decoder.caffe_graph
+        self.params = dict()
+        self.pd_graph = PaddleGraph(parent_layer=None, graph_type="dygraph")
+        self.pd_graph.outputs = self.graph.output_nodes
+        self.input_index = 0 
+        self.inputs_info = {}
+        self.nn_name2id = {}
+        print("Total nodes: {}".format(len(self.graph.topo_sort)))
+        for node_name in self.graph.topo_sort:
+            node = self.graph.get_node(node_name)
+            if node.layer_type == 'DepthwiseConvolution':
+                node.layer_type = 'ConvolutionDepthwise'
+            op = node.layer_type
+            if hasattr(self, op):
+                self.set_node_shape(node)
+                func = getattr(self, op)
+                func(node)
+            elif op in self.directly_map_ops:
+                self.set_node_shape(node)
+                self.directly_map(node)
+            else:
+                raise Exception(
+                    "The op {} in model is not supported yet.".format(op))
+        self.pd_graph.set_name(self.graph.graph_name)
+        self.pd_graph.set_parameters(self.params)
+        self.pd_graph.set_inputs_info(self.inputs_info)
+                
+    def op_checker(self):
+        unsupported_ops = set()
+        for node_name in self.graph.topo_sort:
+            node = self.graph.get_node(node_name)
+            op = node.layer_type
+            if not hasattr(self, op) and op not in custom_layers:
+                unsupported_ops.add(op)
+        if len(unsupported_ops) == 0:
+            return True
+        else:
+            print("There are {} ops not supported yet, list as below".format(
+                len(unsupported_ops)))
+            for op in unsupported_ops:
+                print(op)
+            return False
+
+    def set_node_shape(self, node):
+        inputs = node.inputs
+        input_shape = []
+        for i, nm in enumerate(inputs):
+            last_node = self.graph.get_node(nm)
+            tmp = node.layer.bottom[i]
+            idx = list(last_node.layer.top).index(tmp)
+            input_shape.append(last_node.output_shape[idx])
+
+        node.input_shape = input_shape
+
+        func_name = 'shape_' + node.layer_type.lower()
+        if node.layer_type.lower() == "permute":
+            node.output_shape = getattr(caffe_shape, func_name)(node.layer,
+                                                                input_shape,
+                                                                node.layer.permute_param.order)
+        elif node.layer_type.lower() == "priorbox":
+            node.output_shape = getattr(caffe_shape, func_name)(node.layer,
+                                                                input_shape,
+                                                                node.layer.prior_box_param.max_size,
+                                                                node.layer.prior_box_param.aspect_ratio)
+        elif node.layer_type.lower() =="roipooling":
+            node.output_shape = getattr(caffe_shape, func_name)(node.layer,
+                                                                input_shape,
+                                                                node.layer.roi_pooling_param.pooled_w,
+                                                                node.layer.roi_pooling_param.pooled_h)
+        elif node.layer_type.lower() =="upsample":
+            node.output_shape = getattr(caffe_shape, func_name)(node.layer,
+                                                                input_shape,
+                                                                node.layer.upsample_param.scale)
+        elif node.layer_type.lower() =="select":
+            node.output_shape = getattr(caffe_shape, func_name)(node.layer,
+                                                                input_shape,
+                                                                node.layer.select_param.slice_point,
+                                                                node.layer.select_param.axis)
+        else:
+            node.output_shape = getattr(caffe_shape, func_name)(node.layer,
+                                                                input_shape)
+
+    def adjust_parameters(self, node):
+        data = node.data
+        # When using the protobuf-backend, each parameter initially has four dimensions.
+        # In certain cases (like FC layers), we want to eliminate the singleton dimensions.
+        # This implementation takes care of the common cases. However, it does leave the
+        # potential for future issues.
+        # The Caffe-backend does not suffer from this problem.
+        data = list(data)
+
+        squeeze_indices = [1]  # Squeeze biases.
+        if node.layer_type == 'InnerProduct':
+            squeeze_indices.append(0)  # Squeeze FC.
+
+        for idx in squeeze_indices:
+            if idx >= len(data):
+                continue
+
+            d = data[idx]
+            assert len(
+                d.shape
+            ) == 4, 'invalid shape[%s] from caffe when adjust_parameters' % (
+                str(d.shape))
+
+            shape_old = d.shape
+            sq_axis = None
+            if idx == 0:
+                sq_axis = (0, 1)
+            elif idx == 1:
+                sq_axis = (0, 1, 2)
+            else:
+                continue
+
+            data[idx] = np.squeeze(d, axis=sq_axis)
+            shape_new = data[idx].shape
+        return data
+
+    def get_kernel_parameters(self, kind, params):
+        assert kind in ["Convolution", "Pooling", "Deconvolution", "ConvolutionDepthwise"]
+        [k_h, k_w] = [1, 1]
+        if isinstance(params.kernel_size, numbers.Number):
+            [k_h, k_w] = [params.kernel_size] * 2
+        elif len(params.kernel_size) > 0:
+            k_h = params.kernel_h if params.kernel_h > 0 else params.kernel_size[
+                0]
+            k_w = params.kernel_w if params.kernel_w > 0 else params.kernel_size[
+                len(params.kernel_size) - 1]
+        elif params.kernel_h > 0 or params.kernel_w > 0:
+            k_h = params.kernel_h
+            k_w = params.kernel_w
+        [s_h, s_w] = [1, 1]
+        if isinstance(params.stride, numbers.Number):
+            [s_h, s_w] = [params.stride] * 2
+        elif len(params.stride) > 0:
+            s_h = params.stride_h if params.stride_h > 0 else params.stride[0]
+            s_w = params.stride_w if params.stride_w > 0 else params.stride[len(
+                params.stride) - 1]
+        elif params.stride_h > 0 or params.stride_w > 0:
+            s_h = params.stride_h
+            s_w = params.stride_w
+        [p_h, p_w] = [0, 0]
+        if isinstance(params.pad, numbers.Number):
+            [p_h, p_w] = [params.pad] * 2
+        elif len(params.pad) > 0:
+            p_h = params.pad_h if params.pad_h > 0 else params.pad[0]
+            p_w = params.pad_w if params.pad_w > 0 else params.pad[len(
+                params.pad) - 1]
+        elif params.pad_h > 0 or params.pad_w > 0:
+            p_h = params.pad_h
+            p_w = params.pad_w
+        dila_h = dila_w = 1
+        group = 1
+        c_o = 1
+        if kind in ["Convolution", "Deconvolution", "ConvolutionDepthwise"]:
+            if kind in ["Convolution", "Deconvolution"]:
+                c_o = params.num_output
+            dila_len = len(params.dilation)
+            if dila_len == 2:
+                dila_h = params.dilation[0]
+                dila_w = params.dilation[1]
+            elif dila_len == 1:
+                dila_h = dila_w = params.dilation[0]
+            else:
+                assert dila_len == 0, "invalid length[%s] of dilation in convolution" % (
+                    dila_len)
+        if kind in ['Convolution', 'Deconvolution']:
+            group = params.group
+        kernel = [k_h, k_w]
+        stride = [s_h, s_w]
+        pad = [p_h, p_w]
+        dilation = [dila_h, dila_w]
+        return c_o, kernel, stride, pad, dilation, group
+
+    def get_input_name(self, node):
+        if hasattr(node, "index"):
+            return node.layer_name + "[{}]".format(node.index)
+        else:
+            return node.layer_name
+
+    def Input(self, node):
+        self.pd_graph.add_layer(
+            "paddle.to_tensor",
+            inputs={},
+            outputs=[node.layer_name],
+            data="x{}".format(self.input_index))
+        shape = list(node.layer.input_param.shape[0].dim)[1:]
+        self.inputs_info["x{}".format(self.input_index)] = [[-1] + shape, "float32"]
+        self.input_index += 1
+
+    def Convolution(self, node):
+        if "conv" in self.nn_name2id:
+            self.nn_name2id["conv"] += 1
+        else:
+            self.nn_name2id["conv"] = 0
+        conv2d_name = "conv" + str(self.nn_name2id["conv"])
+        output_name = node.layer_name
+        layer_outputs = [conv2d_name, output_name]
+        data = node.data
+        params = node.layer.convolution_param
+        out_channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        if data is None:
+            data = []
+            print(
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0"
+                .format(node.layer_name, node.layer_type))
+            data.append(
+                np.zeros([out_channel, node.input_shape[0][1], kernel[0], kernel[1]]).astype(
+                    'float32'))
+            data.append(np.zeros([out_channel, ]).astype('float32'))
+        else:
+            data = self.adjust_parameters(node)
+        self.params[conv2d_name + ".weight"] = data[0]
+        if len(data) == 2:
+            self.params[conv2d_name + ".bias"] = data[1]
+        assert len(node.inputs
+                   ) == 1, "The count of Convolution node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        layer_attrs = {
+            "in_channels": node.input_shape[0][1],
+            "out_channels": out_channel,
+            "kernel_size": kernel,
+            "stride": stride,
+            "padding": pad,
+            "dilation": dilation,
+            "groups": group
+        }
+        if len(data) == 1:
+            layer_attrs["bias_attr"] = False
+        self.pd_graph.add_layer(
+            "paddle.nn.Conv2D",
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs,
+            **layer_attrs)
+
+    def Deconvolution(self, node):
+        if "conv" in self.nn_name2id:
+            self.nn_name2id["conv"] += 1
+        else:
+            self.nn_name2id["conv"] = 0
+        conv2d_name = "conv" + str(self.nn_name2id["conv"])
+        output_name = node.layer_name
+        layer_outputs = [conv2d_name, output_name]
+        data = node.data
+        params = node.layer.convolution_param
+        out_channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        if data is None:
+            data = []
+            print(
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0"
+                .format(node.layer_name, node.layer_type))
+            data.append(
+                np.zeros([out_channel, node.input_shape[0][1], kernel[0], kernel[1]]).astype(
+                    'float32'))
+            data.append(np.zeros([out_channel, ]).astype('float32'))
+        else:
+            data = self.adjust_parameters(node)
+        self.params[conv2d_name + ".weight"] = data[0]
+        if len(data) == 2:
+            self.params[conv2d_name + ".bias"] = data[1]
+        assert len(node.inputs
+                   ) == 1, "The count of Deconvolution node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        layer_attrs = {
+            "in_channels": node.input_shape[0][1],
+            "out_channels": out_channel,
+            "kernel_size": kernel,
+            "stride": stride,
+            "padding": pad,
+            "dilation": dilation,
+            "groups": group
+        }
+        if len(data) == 1:
+            layer_attrs["bias_attr"] = False
+        self.pd_graph.add_layer(
+            "paddle.nn.Conv2DTranspose",
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs,
+            **layer_attrs)
+        
+    def ConvolutionDepthwise(self, node):
+        if "conv" in self.nn_name2id:
+            self.nn_name2id["conv"] += 1
+        else:
+            self.nn_name2id["conv"] = 0
+        conv2d_name = "conv" + str(self.nn_name2id["conv"])
+        output_name = node.layer_name
+        layer_outputs = [conv2d_name, output_name]
+        data = node.data
+        params = node.layer.convolution_param
+        out_channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        out_channel = params.num_output if params.num_output is not None else node.input_shape[0][1]
+        in_channel = node.input_shape[0][1]
+        group = int(in_channel / (in_channel / out_channel)) if in_channel > out_channel else int(in_channel /
+                                                                (out_channel / in_channel))
+        if data is None:
+            data = []
+            print(
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0"
+                .format(node.layer_name, node.layer_type))
+            data.append(
+                np.zeros([out_channel, node.input_shape[0][1], kernel[0], kernel[1]]).astype(
+                    'float32'))
+            data.append(np.zeros([out_channel, ]).astype('float32'))
+        else:
+            data = self.adjust_parameters(node)
+        self.params[conv2d_name + ".weight"] = data[0]
+        if len(data) == 2:
+            self.params[conv2d_name + ".bias"] = data[1]
+        assert len(node.inputs
+                   ) == 1, "The count of Deconvolution node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        layer_attrs = {
+            "in_channels": in_channel,
+            "out_channels": out_channel,
+            "kernel_size": kernel,
+            "stride": stride,
+            "padding": pad,
+            "dilation": dilation,
+            "groups": group
+        }
+        if len(data) == 1:
+            layer_attrs["bias_attr"] = False
+        self.pd_graph.add_layer(
+            "paddle.nn.Conv2D",
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs,
+            **layer_attrs)
+
+    def Pooling(self, node):
+        if "pool" in self.nn_name2id:
+            self.nn_name2id["pool"] += 1
+        else:
+            self.nn_name2id["pool"] = 0
+        pool2d_name = "pool" + str(self.nn_name2id["pool"])
+        output_name = node.layer_name
+        layer_outputs = [pool2d_name, output_name]
+        params = node.layer.pooling_param
+        ceil_mode = getattr(params, "ceil_mod", True)
+        global_pool = getattr(params, "global_pooling", False)
+        assert not global_pool, "The global_pool must be False!"
+        kernel_default = [1, 1]
+        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        if params.pool == 0:
+            pool_type = "max"
+        else:
+            pool_type = "avg"
+        assert len(
+            node.inputs) == 1, "The count of Pooling node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        layer_attrs = {
+            'kernel_size': kernel,
+            'stride': stride,
+            'padding': pad,
+            'ceil_mode': ceil_mode,
+        }
+        if params.pool == 0:
+            self.pd_graph.add_layer(
+                "paddle.nn.MaxPool2D",
+                inputs={"input": self.get_input_name(input)},
+                outputs=layer_outputs,
+                **layer_attrs)
+        else:
+            layer_attrs["count_include_pad"] = True
+            self.pd_graph.add_layer(
+                "paddle.nn.AvgPool2D",
+                inputs={"input": self.get_input_name(input)},
+                outputs=layer_outputs,
+                **layer_attrs)
+
+    def LRN(self, node):
+        assert len(node.inputs) == 1, "The count of LRN node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.lrn_param
+        assert params.local_size % 2 == 1
+        alpha = params.alpha / float(params.local_size)
+        layer_attrs = {
+            "n": params.local_size,
+            "k": params.k,
+            "alpha": alpha,
+            "beta": params.beta,
+        }
+        self.pd_graph.add_layer(
+            "fluid.layers.lrn", 
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+
+    def InnerProduct(self, node):
+        if "linear" in self.nn_name2id:
+            self.nn_name2id["linear"] += 1
+        else:
+            self.nn_name2id["linear"] = 0
+        linear_name = "linear" + str(self.nn_name2id["linear"])
+        output_name = node.layer_name
+        layer_outputs = [linear_name, output_name]
+        data = node.data
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.inner_product_param
+        if data is None:
+            print(
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0."
+                .format(node.layer_name, node.layer_type))
+            data = []
+            data.append(
+                np.zeros([node.input_shape[0][1], params.num_output]).astype("float32").astype(
+                    "float32"))
+            data.append(
+                np.zeros([params.num_output]).astype("float32").astype("float32"))
+        else:
+            data = self.adjust_parameters(node)
+            # Reshape the parameters to Paddle's ordering
+            transpose_order = (1, 0)
+            w = data[0]
+            fc_shape = w.shape
+            output_channels = fc_shape[0]
+            w = w.reshape((output_channels, -1))
+            w = w.transpose(transpose_order)
+            data[0] = w
+
+        self.params[linear_name + ".weight"] = data[0]
+        if len(data) == 2:
+            self.params[linear_name + ".bias"] = data[1]
+        assert len(node.inputs
+                   ) == 1, "The count of InnerProduct node\'s input is not 1."
+        assert params.axis == 1
+        assert params.bias_term == True
+        layer_attrs = {
+            "in_features": data[0].shape[0],
+            "out_features": params.num_output           
+        }
+        if len(data) == 1:
+            layer_attrs["bias"] = False
+        if node.input_shape[0][-1] != data[0].shape[0]:
+            self.pd_graph.add_layer(
+                "paddle.reshape",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[output_name],
+                shape=[-1, data[0].shape[0]])
+            self.pd_graph.add_layer(
+                "paddle.nn.Linear",
+                inputs={"input": output_name},
+                outputs=layer_outputs,
+                **layer_attrs)
+        else:
+            self.pd_graph.add_layer(
+                "paddle.nn.Linear",
+                inputs={"input": self.get_input_name(input)},
+                outputs=layer_outputs,
+                **layer_attrs)
+        
+    def AbsVal(self, node):
+        assert len(
+            node.inputs
+        ) >= 1, "The count of AbsVal node\'s input is not more than 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        self.pd_graph.add_layer(
+            "paddle.abs",
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name])
+
+    def Softmax(self, node):
+        if "softmax" in self.nn_name2id:
+            self.nn_name2id["softmax"] += 1
+        else:
+            self.nn_name2id["softmax"] = 0
+        softmax_name = "softmax" + str(self.nn_name2id["softmax"])
+        output_name = node.layer_name
+        layer_outputs = [softmax_name, output_name]
+        assert len(
+            node.inputs) == 1, "The count of Softmax node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.softmax_param
+        axis = params.axis
+        shape = node.input_shape[0]
+        dims = len(shape)
+        axis = axis + dims if axis < 0 else axis
+        layer_attrs = {'axis': axis}
+        self.pd_graph.add_layer(
+            "paddle.nn.Softmax",
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs,
+            **layer_attrs)
+
+    def Slice(self, node):
+        assert len(
+            node.inputs) == 1, "The count of Slice node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        top_len = len(node.layer.top)
+        params = node.layer.slice_param
+        axis = params.axis
+        slice_dim = params.slice_dim
+        if slice_dim != 1 and axis == 1:
+            axis = slice_dim
+        output_shape = node.output_shape
+        sections_list = []
+        for s in output_shape:
+            sections_list.append(s[axis])
+        layer_attrs = {
+            'num_or_sections': sections_list,
+            'dim': axis,
+        }
+        self.pd_graph.add_layer(
+            "paddle.split",
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+
+    def Concat(self, node):
+        assert len(
+            node.inputs
+        ) >= 1, "The count of Concat node\'s input is not more than 1."
+        inputs_dict = dict()
+        for i in range(len(node.inputs)):
+            input = self.graph.get_bottom_node(node, idx=i, copy=True)
+            inputs_dict["input{}".format(i)] = self.get_input_name(input)
+        params = node.layer.concat_param
+        axis = params.axis
+        layer_attrs = {'axis': axis}
+        self.pd_graph.add_layer(
+            "prim.list",
+            inputs=inputs_dict,
+            outputs=[node.layer_name + "_list"])
+        self.pd_graph.add_layer(
+            "paddle.concat",
+            inputs={"x": node.layer_name + "_list"},
+            outputs=[node.layer_name],
+            **layer_attrs)
+
+    def ReLU(self, node):
+        if "relu" in self.nn_name2id:
+            self.nn_name2id["relu"] += 1
+        else:
+            self.nn_name2id["relu"] = 0
+        relu_name = "relu" + str(self.nn_name2id["relu"])
+        output_name = node.layer_name
+        layer_outputs = [relu_name, output_name]
+        assert len(
+            node.inputs) == 1, "The count of RelU node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.relu_param
+        if params.HasField('negative_slope') and params.negative_slope != 0:
+            negative_slope = float(params.negative_slope)
+
+            layer_attrs = {'alpha': negative_slope}
+            self.pd_graph.add_layer(
+                "paddle.nn.LeakyReLU",
+                inputs={"input": self.get_input_name(input)},
+                outputs=layer_outputs,
+                **layer_attrs)
+        else:
+            self.pd_graph.add_layer(
+                "paddle.nn.ReLU",
+                inputs={"input": self.get_input_name(input)},
+                outputs=layer_outputs)
+
+    def PReLU(self, node):
+        if "prelu" in self.nn_name2id:
+            self.nn_name2id["prelu"] += 1
+        else:
+            self.nn_name2id["prelu"] = 0
+        prelu_name = "prelu" + str(self.nn_name2id["prelu"])
+        output_name = node.layer_name
+        layer_outputs = [prelu_name, output_name]
+        assert len(
+            node.inputs) == 1, "The count of PReLU node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.prelu_param
+        mode_bool = params.channel_shared
+        output_shape = node.output_shape[0]
+        if mode_bool:
+            num_parameters = 1
+        else:
+            num_parameters = output_shape[1]
+        data = node.data
+        self.params[prelu_name + '._weight'] = np.squeeze(data[0])
+        assert data is not None, "The parameter of {} (type is {}) is not set. You need to use python package of caffe to set the default value.".format(
+            node.layer_name, node.layer_type)
+        self.pd_graph.add_layer(
+            "paddle.nn.PReLU",
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs,
+            num_parameters=num_parameters)
+
+    def Accuracy(self, node):
+        assert len(
+            node.inputs) == 2, "The count of Accuracy node\'s input is not 2."
+        inputs_dict = dict()
+        for i, shape in enumerate(node.input_shape):
+            if shape[1] == 1:
+                input = self.graph.get_bottom_node(node, idx=i, copy=True)
+                inputs_dict[y] = self.get_input_name(input)
+            else:
+                input = self.graph.get_bottom_node(node, idx=i, copy=True)
+                inputs_dict[x] = self.get_input_name(input)
+        params = node.layer.accuracy_param
+        top_k = params.top_k
+        axis = params.axis
+        ignore_label = params.ignore_label
+        assert axis == 1, "PaddlePaddle can not support the situation when the axis is not 1."
+        assert not ignore_label >= 0, "PaddlePaddle can not support the situation when the model has ignore label."
+        self.pd_graph.add_layer(
+            "prim.accuracy",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            topk=top_k)
+
+    def Eltwise(self, node):
+        assert len(
+            node.inputs) == 2, "The count of Eltwise node\'s input is not 2."
+        params = node.layer.eltwise_param
+        mode = params.operation
+        inputs = []
+        input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
+        input0_name = self.get_input_name(input0)
+        input1_name = self.get_input_name(input1)
+        if mode == 0:
+            inputs_dict = {}
+            inputs_dict['x'] = input0_name
+            inputs_dict['y'] = input1_name
+            self.pd_graph.add_layer(
+                "paddle.multiply",
+                inputs=inputs_dict,
+                outputs=[node.layer_name])
+        elif mode == 1:
+            if hasattr(params, 'coeff') and len(params.coeff) == 2:
+                coeff = params.coeff
+                self.pd_graph.add_layer(
+                    "prim.mul",
+                    inputs={"x": input0_name},
+                    outputs=[node.layer_name + '_mul0'],
+                    y=coeff[0])
+                self.pd_graph.add_layer(
+                    "prim.mul",
+                    inputs={"x": input1_name},
+                    outputs=[node.layer_name + '_mul1'],
+                    y=coeff[2])
+                inputs_dict = {}
+                inputs_dict['x'] = node.layer_name + '_mul0'
+                inputs_dict['y'] = node.layer_name + '_mul1'
+                self.pd_graph.add_layer(
+                    "paddle.add",
+                    inputs=inputs_dict,
+                    outputs=[node.layer_name])
+            else:
+                inputs_dict = {}
+                inputs_dict['x'] = input0_name
+                inputs_dict['y'] = input1_name
+                self.pd_graph.add_layer(
+                    "paddle.add",
+                    inputs=inputs_dict,
+                    outputs=[node.layer_name])
+        else:
+            inputs_dict = {}
+            inputs_dict['x'] = input0_name
+            inputs_dict['y'] = input1_name
+            self.pd_graph.add_layer(
+                "paddle.max",
+                inputs=inputs_dict,
+                outputs=[node.layer_name])
+
+    def BatchNorm(self, node):
+        if "batchnorm" in self.nn_name2id:
+            self.nn_name2id["batchnorm"] += 1
+        else:
+            self.nn_name2id["batchnorm"] = 0
+        batchnorm_name = "batchnorm" + str(self.nn_name2id["batchnorm"])
+        output_name = node.layer_name
+        layer_outputs = [batchnorm_name, output_name]
+        assert len(
+            node.inputs) == 1, "The count of BatchNorm node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.batch_norm_param
+        if hasattr(params, "eps"):
+            eps = params.eps
+        else:
+            eps = 1e-5
+        if node.data is None or len(node.data) != 3:
+            print(
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0"
+                .format(node.layer_name, node.layer_type))
+            mean = np.zeros([node.input_shape[0][1], ]).astype("float32")
+            variance = np.zeros([node.input_shape[0][1], ]).astype("float32")
+            scale = 0
+        else:
+
+            node.data = [np.squeeze(i).astype("float32") for i in node.data]
+            mean, variance, scale = node.data
+        # Prescale the stats
+        scaling_factor = 1.0 / scale if scale != 0 else 0
+        mean *= scaling_factor
+        variance *= scaling_factor
+        self.params[batchnorm_name + "._mean"] = mean
+        self.params[batchnorm_name + '._variance'] = variance
+        layer_attrs = {
+            "num_features": node.input_shape[0][1],
+            "epsilon": eps,
+            "weight_attr": False,
+            "bias_attr": False,
+        }
+        self.pd_graph.add_layer(
+            "paddle.nn.BatchNorm2D",
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs,
+            **layer_attrs)
+   
+    def Scale(self, node):
+        if node.data is None:
+            print(
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0"
+                .format(node.layer_name, node.layer_type))
+            self.params[node.layer_name + ".weight"] = np.zeros([
+                node.input_shape[0][1],
+            ]).astype("float32")
+            self.params[node.layer_name + ".bias"] = np.zeros([
+                node.input_shape[0][1],
+            ]).astype("float32")
+        else:
+            self.params[node.layer_name + ".weight"] = np.squeeze(node.data[
+                0]).astype("float32")
+            self.params[node.layer_name + ".bias"] = np.squeeze(node.data[
+                1]).astype("float32")
+        params = node.layer.scale_param
+        axis = params.axis
+        inputs = []
+        if len(node.inputs) == 2:
+            input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+            input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
+            input0_name = self.get_input_name(input0)
+            input1_name = self.get_input_name(input1)
+            inputs_dict = {}
+            inputs_dict['x'] = input0_name
+            inputs_dict['y'] = input1_name
+            self.pd_graph.add_layer(
+                "paddle.multiply",
+                inputs=inputs_dict,
+                outputs=[node.layer_name + "_mul"],
+                axis=1)
+        else:
+            self.pd_graph.add_layer(
+                "paddle.to_tensor",
+                inputs={},
+                outputs=[node.layer_name + "_cparam1"],
+                data="params[{}]".format(string(node.layer_name + ".weight")))
+            input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+            input0_name = self.get_input_name(input0)
+            inputs_dict = {}
+            inputs_dict['x'] = input0_name
+            inputs_dict['y'] = node.layer_name + "_cparam1"
+            self.pd_graph.add_layer(
+                "paddle.multiply",
+                inputs=inputs_dict,
+                outputs=[node.layer_name + "_mul"],
+                axis=axis)
+        self.pd_graph.add_layer(
+                "paddle.to_tensor",
+                inputs={},
+                outputs=[node.layer_name + "_cparam2"],
+                data="params[{}]".format(string(node.layer_name + ".bias")))
+        inputs_dict = {}
+        inputs_dict['x'] = node.layer_name + "_mul"
+        inputs_dict['y'] = node.layer_name + "_cparam2"
+        self.pd_graph.add_layer(
+            "paddle.add",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            axis=axis)
+
+    def Reshape(self, node):
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        top_count = len(input.layer.top)
+        is_inplace = False if top_count == 1 else True
+        output_shape = node.output_shape[0]
+        print(output_shape)
+        layer_attrs = {
+            'shape': output_shape,
+            'inplace': is_inplace,
+        }
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+
+
+    def ArgMax(self, node):
+        assert len(node.inputs) == 1 and len(
+            node.outputs
+        ) == 1, "The count of ArgMax node\'s input and output is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input_shape = node.input_shape[0]
+        params = node.layer.argmax_param
+        out_max_val = params.out_max_val if hasattr(params,
+                                                    out_max_val) else False
+        top_k = params.top_k if hasattr(params, top_k) else 1
+        axis = parmas.axis if hasattr(params, axis) else -1
+        if axis < 0:
+            axis += len(input_shape)
+        if out_max_val is True:
+            self.pd_graph.add_layer(
+                "paddle.topk",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name + "_topk_var", node.layer_name + "_index_var"],
+                k=top_k)
+            self.pd_graph.add_layer(
+                "paddle.cast",
+                inputs={"x": node.layer_name + "_index_var"},
+                outputs=[node.layer_name + "_index_var"],
+                dtype="{}_topk_var.dtype".format(node.layer_name))
+            self.pd_graph.add_layer(
+                "prim.list",
+                inputs={"input0": node.layer_name + "_topk_var",
+                        "input1": node.layer_name + "_index_var"},
+                outputs=[node.layer_name + "_list"])
+            self.pd_graph.add_layer(
+                "paddle.concat",
+                inputs={"x": node.layer_name + "_list"},
+                outputs=[node.layer_name],
+                axis=axis)
+        else:
+            self.pd_graph.add_layer(
+                "paddle.topk",
+                inputs={"x": self.get_input_name(input)},
+                outputs=["_", node.layer_name],
+                k=top_k)
+            
+    def Axpy(self, node):
+        assert len(node.inputs) == 1 and len(
+            node.outputs
+        ) == 1, "The count of Axpy node\'s input and output is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.axpy_param
+        input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
+        input2 = self.graph.get_bottom_node(node, idx=2, copy=True)
+        input0_name = self.get_input_name(input0)
+        input1_name = self.get_input_name(input1)
+        input2_name = self.get_input_name(input2)
+        inputs_dict = {}
+        inputs_dict['x'] = input1_name
+        inputs_dict['y'] = input0_name
+        self.pd_graph.add_layer(
+            "paddle.multiply",
+            inputs=inputs_dict,
+            outputs=[node.layer_name + "_mul"],
+            axis=0)
+        inputs_dict = {}
+        inputs_dict['x'] = node.layer_name + "_mul"
+        inputs_dict['y'] = input2_name
+        self.pd_graph.add_layer(
+            "paddle.add",
+            inputs=inputs_dict,
+            outputs=[node.layer_name + "_mul"])
+        
+
+    def Crop(self, node):
+        assert len(
+            node.inputs) == 2, "The count of Crop node\'s input is not 2."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        example = self.graph.get_bottom_node(node, idx=1, copy=True)
+        params = node.layer.crop_param
+        axis = params.axis
+        input_shape = node.input_shape[0]
+        if axis < 0:
+            axis += len(input_shape)
+        offset_real = [0] * len(input_shape)
+        if hasattr(params, "offset") and len(params.offset) > 0:
+            offset = list(params.offset)
+            assert (len(input_shape) - axis
+                    ) == len(offset), "invalid offset[%s] in crop layer" % (
+                        str(offset))
+            offset_real = [0] * axis + offset
+        self.pd_graph.add_layer(
+                "paddle.crop",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                shape=node.input_shape[1],
+                offsets=list(offset_real))
+
+    def Flatten(self, node):
+        assert len(
+            node.
+            inputs) == 1, "The count of DetectionOutput node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            shape=node.output_shape[0])
+
+    def Power(self, node):
+        assert len(
+            node.inputs) == 1, "The count of Permute node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.power_param
+        layer_attrs = {
+            'scale': params.scale,
+            'bias': params.shift,
+            'bias_after_scale': True
+        }
+        self.pd_graph.add_layer(
+            "paddle.scale",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+        self.pd_graph.add_layer(
+            "paddle.pow",
+            inputs={"x": node.layer_name},
+            outputs=[node.layer_name],
+            exponent=params.power)
+
+    def Reduction(self, node):
+        assert len(
+            node.inputs) == 1, "The count of Reduction node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.reduction_param
+        operation = params.operation
+        axis = params.axis
+        coeff = params.coeff
+        assert operation >= 1 and operation <= 4, "reduction reduction [%s] error" % (
+            operation)
+        input_len = len(node.input_shape[0])
+        if axis < 0:
+            axis += input_len + 1
+        dim = list(range(input_len))
+        # operation = SUM
+        if operation == 1:  
+            layer_attrs = {
+                "dim": dim[axis:],
+                "keep_dim": False,
+            }
+            self.pd_graph.add_layer(
+                "paddle.sum",
+                inputs={"input": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                **layer_attrs)
+        # operation = ASUM
+        elif operation == 2:  
+            self.pd_graph.add_layer(
+                "paddle.abs",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name])
+            layer_attrs = {
+                "dim": dim[axis:],
+                "keep_dim": False,
+            }
+            self.pd_graph.add_layer(
+                "paddle.sum",
+                inputs={"input": node.layer_name},
+                outputs=[node.layer_name],
+                **layer_attrs)
+        # operation = SUMSQ
+        elif operation == 3: 
+            self.pd_graph.add_layer(
+                "paddle.pow",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                exponent=2.0)
+            layer_attrs = {
+                "dim": dim[axis:],
+                "keep_dim": False,
+            }
+            self.pd_graph.add_layer(
+                "paddle.sum",
+                inputs={"input": node.layer_name},
+                outputs=[node.layer_name],
+                **layer_attrs)
+        # operation = MEAN
+        else: 
+            layer_attrs = {
+                "dim": dim[axis:],
+                "keep_dim": False,
+            }
+            self.pd_graph.add_layer(
+                "paddle.mean",
+                inputs={"input": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                **layer_attrs)
+        self.pd_graph.add_layer(
+            "paddle.scale",
+            inputs={"x": node.layer_name},
+            outputs=[node.layer_name],
+            scale=coeff)
+        
+    def DetectionOutput(self, node):
+        assert len(
+            node.inputs) == 3, "The count of DetectionOutput node\'s input is not 3."
+        inputs_list = list()
+        for i in range(len(node.inputs)):
+            input = self.graph.get_bottom_node(node, idx=i, copy=True)
+            if i == 1:
+                input = self.graph.get_bottom_node(node, idx=i, copy=True)
+                while input is not None \
+                      and input.layer_type != 'Softmax' \
+                      and input.layer_type != 'Sigmoid':
+                    input = self.graph.get_bottom_node(input, idx=0, copy=True)
+                assert input is not None, 'This kind of DetectionOutput is not supported!'
+                input = self.graph.get_bottom_node(input, idx=0, copy=True)
+            inputs_list.append(self.get_input_name(input))
+        params = node.layer.detection_output_param
+        nms_param = params.nms_param
+        nms_param_dict = dict()
+        nms_param_dict["nms_threshold"] = nms_param.nms_threshold
+        nms_param_dict["top_k"] = nms_param.top_k
+        nms_param_dict["eta"] = nms_param.eta
+        if nms_param is None:
+            nms_param_dict = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}
+        self.pd_graph.add_layer(
+            "paddle.split",
+            inputs={"input": inputs_list[2]},
+            outputs=[node.layer_name + "_priorbox_list"],
+            num_or_sections=2,
+            dim=1)
+        self.pd_graph.add_layer(
+            "prim.getitem",
+            inputs={"list": node.layer_name + "_priorbox_list"},
+            outputs=[node.layer_name + "_pb"],
+            index=0)
+        self.pd_graph.add_layer(
+            "prim.getitem",
+            inputs={"list": node.layer_name + "_priorbox_list"},
+            outputs=[node.layer_name + "_pbv"],
+            index=1)
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": node.layer_name + "_pb"},
+            outputs=[node.layer_name + "_pb"],
+            shape=[-1, 4])
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": node.layer_name + "_pbv"},
+            outputs=[node.layer_name + "_pbv"],
+            shape=[-1, 4])
+        self.pd_graph.add_layer(
+            "prim.shape_dim",
+            inputs={"input": node.layer_name + "_pb"},
+            outputs=[node.layer_name + "_pb_dim"],
+            dim=0)
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": inputs_list[0]},
+            outputs=[node.layer_name + "_loc"],
+            shape="[-1, {}_pb_dim, 4]".format(node.layer_name))
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": inputs_list[1]},
+            outputs=[node.layer_name + "_conf_flatten"],
+            shape="[0, {}_pb_dim, -1]".format(node.layer_name))
+        default = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}
+        fields = ["eta", "top_k", "nms_threshold"]
+        for f in default.keys():
+            if f not in nms_param_dict:
+                nms_param_dict[f] = default[f]
+        inputs_dict = {}
+        inputs_dict["loc"] = node.layer_name + "_loc"
+        inputs_dict["scores"] = node.layer_name + "_conf_flatten"
+        inputs_dict["prior_box"] = node.layer_name + "_pb"
+        inputs_dict["prior_box_var"] = node.layer_name + "_pbv"
+        layer_attrs = {
+            "background_label": params.background_label_id,
+            "nms_threshold": nms_param_dict["nms_threshold"],
+            "nms_top_k": nms_param_dict["top_k"],
+            "keep_top_k": params.keep_top_k,
+            "score_threshold": params.confidence_threshold,
+            "nms_eta": nms_param_dict["eta"]}
+        self.pd_graph.add_layer(
+            "fluid.layers.detection_output",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            **layer_attrs)
+                    
+    def Normalize(self, node):
+        assert len(
+            node.inputs) == 1, "The count of Normalize node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.norm_param
+        if node.data is None or len(node.data) != 1:
+            print(
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0"
+                .format(node.layer_name, node.layer_type))
+            self.parmas[node.layer_name + ".scale"] = \
+                np.zeros([1] if params.channel_shared else [1, 1, 1, node.input_shape[0][1]]).astype("float32")
+        else:
+            self.parmas[node.layer_name + ".scale"] = self.adjust_parameters(node)[0]
+        self.pd_graph.add_layer(
+            "paddle.nn.functional.normalize",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name + "_l2"],
+            p=2,
+            axis=1)
+        graph.add_layer(
+            "paddle.to_tensor",
+            inputs={},
+            outputs=[node.layer_name + "_param"],
+            data="params[{}]".format(string(node.layer_name + ".scale")))
+        inputs_dict = {}
+        inputs_dict["x"] = node.layer_name + "_l2"
+        inputs_dict["y"] = node.layer_name + "_param"
+        self.pd_graph.add_layer(
+            "paddle.multiply",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            axis=-1 if params.channel_shared else 1)
+        
+    def Permute(self, node):
+        assert len(
+            node.inputs) == 1, "The count of Permute node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.permute_param
+        order = list(params.order)    
+        self.pd_graph.add_layer(
+            "paddle.transpose",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            perm=order)
+        
+    def PriorBox(self, node):
+        assert len(
+            node.inputs) == 2, "The count of PriorBox node\'s input is not 2."
+        input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
+        inputs_dict = {}
+        inputs_dict["input"] = self.get_input_name(input0)
+        inputs_dict["image"] = self.get_input_name(input1)
+        params = node.layer.prior_box_param
+        steps = tuple(params.step) if type(params.step) \
+                is list or type(params.step) is tuple \
+                else (params.step, params.step)
+        layer_attrs = {
+            "min_sizes": params.min_size,
+            "max_sizes": params.max_size,
+            "aspect_ratios": params.aspect_ratio,
+            "variance": params.variance,
+            "flip": params.flip,
+            "clip": params.clip,
+            "steps": steps,
+            "offset": params.offset,
+            "min_max_aspect_ratios_order": True}
+        self.pd_graph.add_layer(
+            "fluid.layers.prior_box",
+            inputs=inputs_dict,
+            outputs=[node.layer_name + "_box", node.layer_name + "_var"],
+            **layer_attrs)
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": node.layer_name + "_box"},
+            outputs=[node.layer_name + "_box"],
+            shape=[1, 1, -1])
+        self.pd_graph.add_layer(
+            "paddle.reshape",
+            inputs={"x": node.layer_name + "_var"},
+            outputs=[node.layer_name + "_var"],
+            shape=[1, 1, -1])
+        self.pd_graph.add_layer(
+            "prim.list",
+            inputs={"input0": node.layer_name + "_box",
+                    "input1": node.layer_name + "_var"},
+            outputs=[node.layer_name + "_list"])
+        self.pd_graph.add_layer(
+            "paddle.concat",
+            inputs={"x": node.layer_name + "_list"},
+            outputs=[node.layer_name],
+            axis=1)
+
+    def ReLU6(self, node):
+        if "relu6" in self.nn_name2id:
+            self.nn_name2id["relu6"] += 1
+        else:
+            self.nn_name2id["relu6"] = 0
+        relu6_name = "relu6" + str(self.nn_name2id["relu6"])
+        output_name = node.layer_name
+        layer_outputs = [relu6_name, output_name]
+        assert len(
+            node.inputs) == 1, "The count of RelU6 node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        self.pd_graph.add_layer(
+            "paddle.nn.ReLU6",
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs)
+        
+    def ROIPooling(self, node):
+        assert len(
+            node.inputs) == 2, "The count of ROIPooling node\'s input is not 2."
+        input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
+        inputs_dict = {}
+        inputs_dict["input"] = self.get_input_name(input0)
+        inputs_dict["roi"] = self.get_input_name(input1)
+        params = node.layer.roi_pooling_param
+        self.pd_graph.add_layer(
+            "paddle.slice",
+            inputs={"input": self.get_input_name(input1)},
+            outputs=[self.get_input_name(input1)],
+            axes=[1], 
+            starts=[1], 
+            ends=[5])
+        layer_attrs = {
+            "pooled_height": params.pooled_h,
+            "pooled_width": params.pooled_w,
+            "spatial_scale": params.spatial_scale}
+        self.pd_graph.add_layer(
+            "fluid.layers.roi_pool",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            **layer_attrs)
+        
+    def ShuffleChannel(self, node):
+        assert len(
+            node.inputs) == 1, "The count of ShuffleChannel node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.shuffle_channel_param
+        self.pd_graph.add_layer(
+            "fluid.layers.shuffle_channel",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            group=params.group)
+        
+    def Upsample(self, node):
+        assert len(
+            node.inputs) == 1, "The count of Upsample node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        params = node.layer.upsample_param
+        layer_attrs = {
+            "align_corners": False,
+            "scale_factor": params.scale,
+            "mode": "nearest"}
+        self.pd_graph.add_layer(
+            "paddle.nn.functioanl.interpolate",
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+    
+    def Select(self, node):
+        assert len(
+            node.inputs) == 1, "The count of Select node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        input_shape = node.input_shape[0]
+        params = node.layer.select_param
+        layer_attrs = {
+            "input_shape": input_shape,
+            "point": params.slice_point,
+            "axis": params.axis}
+        self.pd_graph.add_layer(
+            "prim.update_end",
+            inputs={},
+            outputs=[node.layer_name + "_end"],
+            **layer_attrs)
+        layer_attrs = {
+            "axes": [params.axis],
+            "starts": [params.slice_point[0]]}
+        self.pd_graph.add_layer(
+            "paddle.split",
+            inputs={"input": self.get_input_name(input),
+                    "end": node.layer_name + "_end"},
+            outputs=[node.layer_name],
+            **layer_attrs)
+        
+
+    def directly_map(self, node):
+        assert node.layer_type in self.directly_map_ops
+        op_info = self.directly_map_ops[node.layer_type]
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        prefix_name = node.layer_type.lower()
+        if prefix_name in self.nn_name2id:
+            self.nn_name2id[prefix_name] += 1
+        else:
+            self.nn_name2id[prefix_name] = 0
+        first_output_name = prefix_name + str(self.nn_name2id[prefix_name])
+        output_name = node.layer_name
+        layer_outputs = [relu_name, output_name]
+        assert len(
+            node.inputs) == 1, "The count of Activate node\'s input is not 1."
+        input = self.graph.get_bottom_node(node, idx=0, copy=True)
+        self.pd_graph.add_layer(
+            op_info,
+            inputs={"input": self.get_input_name(input)},
+            outputs=layer_outputs)
+

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_shape.py

+#   Copyright (c) 2019  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.
+
+import math
+import numbers
+from functools import reduce
+
+
+def get_kernel_parameters(params):
+    [k_h, k_w] = [1, 1]
+    if isinstance(params.kernel_size, numbers.Number):
+        [k_h, k_w] = [params.kernel_size] * 2
+    elif len(params.kernel_size) > 0:
+        k_h = params.kernel_h if params.kernel_h > 0 else params.kernel_size[0]
+        k_w = params.kernel_w if params.kernel_w > 0 else params.kernel_size[
+            len(params.kernel_size) - 1]
+    elif params.kernel_h > 0 or params.kernel_w > 0:
+        k_h = params.kernel_h
+        k_w = params.kernel_w
+    [s_h, s_w] = [1, 1]
+    if isinstance(params.stride, numbers.Number):
+        [s_h, s_w] = [params.stride] * 2
+    elif len(params.stride) > 0:
+        s_h = params.stride_h if params.stride_h > 0 else params.stride[0]
+        s_w = params.stride_w if params.stride_w > 0 else params.stride[len(
+            params.stride) - 1]
+    elif params.stride_h > 0 or params.stride_w > 0:
+        s_h = params.stride_h
+        s_w = params.stride_w
+    [p_h, p_w] = [0, 0]
+    if isinstance(params.pad, numbers.Number):
+        [p_h, p_w] = [params.pad] * 2
+    elif len(params.pad) > 0:
+        p_h = params.pad_h if params.pad_h > 0 else params.pad[0]
+        p_w = params.pad_w if params.pad_w > 0 else params.pad[len(params.pad) -
+                                                               1]
+    elif params.pad_h > 0 or params.pad_w > 0:
+        p_h = params.pad_h
+        p_w = params.pad_w
+    dila_h = dila_w = 1
+    if hasattr(params, 'dilation'):
+        dila_len = len(params.dilation)
+        if dila_len == 2:
+            dila_h = params.dilation[0]
+            dila_w = params.dilation[1]
+        elif dila_len == 1:
+            dila_h = dila_w = params.dilation[0]
+        else:
+            assert dila_len == 0, "invalid length[%s] of dilation in convolution" % (
+                dila_len)
+    return dila_h, dila_w, p_h, p_w, k_h, k_w, s_h, s_w
+
+
+def get_strided_kernel_output_shape(params, input_shape, round_func):
+    i_h = input_shape[2]
+    i_w = input_shape[3]
+    dila_h, dila_w, pad_h, pad_w, kernel_h, kernel_w, stride_h, stride_w = get_kernel_parameters(
+        params)
+    o_h = (i_h + 2 * pad_h - (dila_h *
+                              (kernel_h - 1) + 1)) / float(stride_h) + 1
+    o_w = (i_w + 2 * pad_w - (dila_w *
+                              (kernel_w - 1) + 1)) / float(stride_w) + 1
+    o_h = int(round_func(o_h))
+    o_w = int(round_func(o_w))
+    has_c_o = hasattr(params, 'num_output')
+    c = params.num_output if has_c_o else input_shape[1]
+    return [[input_shape[0], c, o_h, o_w]]
+
+
+def shape_convolution(layer, input_shape):
+    params = layer.convolution_param
+    return get_strided_kernel_output_shape(params, input_shape[0], math.floor)
+
+
+def shape_deconvolution(layer, input_shape):
+
+    h_i = input_shape[0][2]
+    w_i = input_shape[0][3]
+
+    params = layer.convolution_param
+    dila_h, dila_w, pad_h, pad_w, kernel_h, kernel_w, stride_h, stride_w = get_kernel_parameters(
+        params)
+
+    h_o = (h_i - 1) * stride_h - 2 * pad_h + dila_h * (kernel_h - 1) + 1
+    w_o = (w_i - 1) * stride_w - 2 * pad_w + dila_w * (kernel_w - 1) + 1
+
+    has_c_o = hasattr(params, 'num_output')
+    c = params.num_output if has_c_o else input_shape.channels
+    return [[input_shape[0][0], c, h_o, w_o]]
+
+
+def shape_pooling(layer, input_shape):
+    params = layer.pooling_param
+    global_pool = getattr(params, 'global_pooling', False)
+    if global_pool:
+        return [[input_shape[0][0], input_shape[0][1], 1, 1]]
+
+    ceil_mode = getattr(params, 'ceil_mode', True)
+    if ceil_mode is True:
+        method = math.ceil
+    else:
+        method = math.floor
+    return get_strided_kernel_output_shape(params, input_shape[0], method)
+
+
+def shape_convolutiondepthwise(layer, input_shape):
+    params = layer.convolution_param
+    return get_strided_kernel_output_shape(params, input_shape[0], math.floor)
+
+
+def shape_innerproduct(layer, input_shape):
+    params = layer.inner_product_param
+    return [[input_shape[0][0], params.num_output]]
+
+
+def shape_lrn(layer, input_shape):
+    return input_shape
+
+
+def shape_relu(layer, input_shape):
+    return input_shape
+
+
+def shape_softmax(layer, input_shape):
+    return input_shape
+
+
+def shape_input(layer, input_shape):
+    return [list(layer.input_param.shape[0].dim)]
+
+
+def shape_memorydata(layer, input_shape):
+    params = layer.memory_data_param
+    shape = []
+    shape.append(int(params.batch_size))
+    shape.append(int(params.channels))
+    shape.append(int(params.height))
+    shape.append(int(params.width))
+    return [shape]
+
+
+def shape_concat(layer, input_shape):
+    params = layer.concat_param
+    axis = params.axis
+    output_shape = None
+    for shape in input_shape:
+        if output_shape is None:
+            output_shape = []
+            for i in range(len(shape)):
+                output_shape.append(shape[i])
+        else:
+            output_shape[axis] += shape[axis]
+    return [output_shape]
+
+
+def shape_slice(layer, input_shape):
+    inshape = input_shape[0]
+
+    top_len = len(layer.top)
+    params = layer.slice_param
+    axis = params.axis
+    slice_dim = params.slice_dim
+    if slice_dim != 1 and axis == 1:
+        axis = slice_dim
+    points = list(params.slice_point)
+    count = inshape[axis]
+    if len(points) == 0:
+        assert count % top_len == 0, "the parameter of Slice is wrong"
+        part = count / top_len
+        t = part
+        while t < count:
+            points.append(int(t))
+            t += part
+    points = [0] + points + [count]
+    output_shape = []
+    for i in range(len(points)):
+        shape = []
+        for ii in range(len(inshape)):
+            shape.append(inshape[ii])
+        size = points[i + 1] - points[i]
+        shape[axis] = size
+        output_shape.append(shape)
+        if i == len(points) - 2:
+            break
+    return output_shape
+
+
+def shape_prelu(layer, input_shape):
+    return input_shape
+
+
+def shape_sigmoid(layer, input_shape):
+    return input_shape
+
+
+def shape_absval(layer, input_shape):
+    return input_shape
+
+
+def shape_accuracy(layer, input_shape):
+    return [[1]]
+
+
+def shape_tanh(layer, input_shape):
+    return input_shape
+
+
+def shape_eltwise(layer, input_shape):
+    return [input_shape[0]]
+
+
+def shape_batchnorm(layer, input_shape):
+    return input_shape
+
+
+def shape_scale(layer, input_shape):
+    return input_shape
+
+
+def shape_reshape(layer, input_shape):
+    def count(num_list):
+        return reduce(lambda a, b: a * b, num_list)
+
+    inshape = input_shape[0]
+    params = layer.reshape_param
+    axis = params.axis if hasattr(params, 'axis') else 0
+    num_axes = params.num_axes if hasattr(params, 'num_axes') else -1
+    if inshape[0] == -1:
+        inshape[0] = 1
+    input_count = count(inshape)
+
+    input_num_axes = len(inshape)
+
+    input_start_axis = axis
+    start_axis = input_start_axis if input_start_axis >= 0 \
+            else input_num_axes + input_start_axis + 1
+
+    assert start_axis >= 0, "[Reshape]axis %d out of range" % (input_start_axis)
+    assert start_axis <= input_num_axes, "[Reshape]axis %d out of range for %d-D input data"\
+            % (input_start_axis, input_num_axes)
+
+    assert num_axes >= -1, "[Reshape]num_axes must be >= 0, or -1 for all"
+
+    end_axis = input_num_axes if num_axes == -1 else start_axis + num_axes
+    assert end_axis <= input_num_axes, "end_axis[%d] = axis[%d] + num_axes[%d] is out of range"\
+            % (end_axis, start_axis, num_axes)
+
+    num_axes_replaced = end_axis - start_axis
+    num_axes_retained = input_num_axes - num_axes_replaced
+    num_new_axes = len(list(params.shape.dim))
+    output_shape = []
+
+    for i in range(start_axis):
+        output_shape.append(inshape[i])
+
+    for i in range(num_new_axes):
+        output_shape.append(params.shape.dim[i])
+
+    for i in range(end_axis, input_num_axes):
+        output_shape.append(inshape[i])
+
+    assert len(output_shape) == num_axes_retained + num_new_axes,\
+            "[Reshape]invalid dims of output shape[%s]" % (str(output_shape))
+
+    inferred_axis = -1
+    copy_axes = []
+    constant_count = 1
+    for i in range(num_new_axes):
+        top_dim = params.shape.dim[i]
+        if top_dim == 0:
+            copy_axes.append(i)
+            copy_axis_index = start_axis + i
+            output_shape[copy_axis_index] = inshape[copy_axis_index]
+        elif top_dim == -1:
+            assert inferred_axis == -1, "[Reshape]new shape contains multiple -1 dims"
+            inferred_axis = i
+        else:
+            constant_count *= top_dim
+
+    if inferred_axis >= 0:
+        explicit_count = constant_count
+        l = inshape[0:start_axis]
+        if len(l) > 0:
+            explicit_count *= count(l)
+        l = inshape[end_axis:]
+        if len(l) > 0:
+            explicit_count *= count(l)
+        for i in range(len(copy_axes)):
+            explicit_count *= output_shape[start_axis + copy_axes[i]]
+        assert input_count % explicit_count == 0, "[Reshape]botom count[%d] "\
+                "must be divisible by product of the specified dimensions[%d] "\
+                % (input_count, explicit_count)
+        output_shape[start_axis + inferred_axis] = int(input_count / explicit_count)
+
+    output_count = count(output_shape)
+    assert output_count == input_count, "[Reshape]output count[%d] must match input count[%d]" % (
+        output_count, input_count)
+    output_shape[0] = -1
+    return [output_shape]
+
+
+def shape_argmax(layer, input_shape):
+    inshape = input_shape[0]
+    params = layer.argmax_param
+    out_max_val = params.out_max_val if hasattr(params, out_max_val) else False
+    top_k = params.top_k if hasattr(params, top_k) else 1
+    axis = parmas.axis if hasattr(params, axis) else -1
+    if axis < 0:
+        axis += len(inshape)
+    assert (axis + 1 == len(inshape)
+            ), 'only can be applied on the last dimension[axis:%d, %s] now,'\
+                    'make sure you have set axis param in xxx.prototxt file' \
+                    % (axis, str(inshape))
+
+    output_shape = inshape
+    output_shape[-1] = top_k
+    if out_max_val is True:
+        output_shape[-1] *= 2
+    return [output_shape]
+
+
+def shape_crop(layer, input_shape):
+    assert len(input_shape) == 2, "the number of crop's inputs must be 2"
+    return [input_shape[1]]
+
+
+def shape_flatten(layer, input_shape):
+    assert len(input_shape) == 1, "the number of flatten's inputs must be 1"
+    inshape = input_shape[0]
+    params = layer.flatten_param
+    start_axis = params.axis
+    end_axis = params.end_axis
+    if start_axis < 0:
+        start_axis += len(inshape)
+    if end_axis < 0:
+        end_axis += len(inshape) + 1
+    assert start_axis <= end_axis, 'invalid axis[%d] or end_axis[%d] params'\
+            % (start_axis, end_axis)
+    output_shape = inshape[0:start_axis]
+    if len(inshape[start_axis:end_axis]) != 0:
+        flat_sz = reduce(lambda a, b: a * b, inshape[start_axis:end_axis])
+        output_shape += [flat_sz]
+    output_shape += inshape[end_axis:len(inshape)]
+    output_shape[0] = -1
+    return [output_shape]
+
+
+def shape_power(layer, input_shape):
+    return input_shape
+
+
+def shape_reduction(layer, input_shape):
+    params = layer.reduction_param
+    axis = params.axis
+    if axis < 0:
+        axis += len(input_shape[0]) + 1
+    assert axis <= len(input_shape[0]), 'invalid axis[%d] error' % (axis)
+    return [input_shape[0:axis]]
+
+def shape_axpy(layer, input_shape):
+    assert len(input_shapes) == 3, "not valid input shape for axpy layer"
+    assert len(input_shapes[0]) == len(input_shapes[1]), 'should have same dims'
+    output_shape = input_shapes[1]
+    assert (input_shapes[2] == output_shape),\
+            "shape not consistent for axpy[%s <--> %s]" \
+            % (str(output_shape), str(input_shapes[2]))
+    return [output_shape]
+
+def shape_detectionoutput(layer, input_shape):
+    return [[-1, 6]]
+
+def shape_normalize(layer, input_shape):
+    return input_shape
+
+def shape_permute(layer, input_shape, order=None):
+    inshape = input_shape[0]
+    output_shape = []
+    order = list(order)
+    for ii in order:
+        assert ii < len(inshape), "invalid order for permute[%s]" % (name)
+        output_shape.append(inshape[ii])
+    return [output_shape]
+
+def shape_priorbox(layer, input_shape, max_size=None, aspect_ratio=None):
+    fc_shape = input_shape[0]
+    N = 1
+    if not max_size == None:
+        N += 1
+    if not aspect_ratio == None:
+        N += 2 * len(aspect_ratio)
+    N_bbx = fc_shape[2] * fc_shape[3] * N
+    output_shape = [1, 2, 4 * N_bbx]
+    return [output_shape]
+
+def shape_relu6(layer, input_shape):
+    return input_shape
+
+def shape_roipooling(layer, input_shape, pooled_w=None, pooled_h=None):
+    base_fea_shape = input_shapes[0]
+    rois_shape = input_shapes[1]
+    output_shape = base_fea_shape
+    output_shape[0] = rois_shape[0]
+    output_shape[2] = pooled_h
+    output_shape[3] = pooled_w
+    return [output_shape]
+
+def shape_shufflechannel(layer, input_shape):
+    return input_shape
+
+def shape_upsample(layer, input_shape, scale):
+    assert len(input_shapes) == 1, "not valid input shape for upsample layer"
+    assert type(scale) is int
+    input_shape = input_shapes[0]
+    new_h = scale * input_shape[2]
+    new_w = scale * input_shape[3]
+
+    output_shape = [input_shape[0], input_shape[1], new_h, new_w]
+    return [output_shape]
+
+def shape_select(layer, input_shape, slice_point, axis):
+    input_shape = input_shapes[0]
+    start = slice_point[0]
+    if len(slice_point) == 2:
+        end = slice_point[1]
+    else:
+        end = input_shape[axis]
+    assert end > start, "invalid slice_point with [start:%d, end:%d]"\
+             % (start, end)
+    output_shape = input_shape
+    output_shape[axis] = end - start
+    return [output_shape]
+

x2paddle/op_mapper/dygraph/prim2code.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.
+
+NO_OUTPUT_COUNT = 0
+ 
+def gen_codes(code_list, indent=0):
+    indent_blank = "    " * indent
+    codes = []
+    for code_line in code_list:
+        if code_line.strip() == "":
+            codes.append('\n')
+        else:
+            codes.append(indent_blank + code_line + '\n')
+    return codes
+
+
+def get_value(layer, key, layer_id=None, different_attrs=None):
+    """ 进行optimizer后可能把inputs的value直接用数值代替（ConstantFuser），
+        会把input换成attr，所以需要此处的操作。
+    """
+    if key in layer.inputs:
+        return layer.inputs[key]
+    else:
+        if different_attrs is None:
+            return str(layer.attrs[key])
+        else:
+            key_name = "{}_{}".format(layer.outputs[0], key)
+            if key_name in different_attrs:
+                return key_name
+            else:
+                if layer_id is None:
+                    return str(layer.attrs[key])
+                key_name = "{}_{}".format("layer_id/{}".format(layer_id), key)
+                if key_name in different_attrs:
+                    new_key_name = "attr_{}".format(NO_OUTPUT_COUNT)
+                    NO_OUTPUT_COUNT += 1
+                    diff_index = different_attrs.index(key_name)
+                    different_attrs[diff_index] = new_key_name
+                    return new_key_name
+                else:
+                    return str(layer.attrs[key])
+
+
+def prim_add(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} + {}".format(layer.outputs[0],
+                                 get_value(layer, "x", different_attrs), get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_add_(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} + {} * {}".format(layer.outputs[0],
+                                      get_value(layer, "x", different_attrs),
+                                      layer.attrs["alpha"],
+                                      get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_and(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} and {}".format(layer.outputs[0],
+                                   get_value(layer, "x", different_attrs), get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_append(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{}.append({})".format(
+        get_value(layer, "list", layer_id, different_attrs), 
+        get_value(layer, "element", layer_id, different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_assert(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    if layer.attrs["type"] == "eq":
+        values = get_value(layer, "key")
+        if "value" in layer.attrs:
+            values = layer.attrs["value"]
+        if isinstance(values, list):
+            s = ""
+            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, get_value(layer, "key"), get_value(layer, "value"))
+        else:
+            line = "assert {} == {}, \'The {} must be {}!\'".format(
+                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))
+
+
+def prim_check_dim(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    lines = []
+    dim = get_value(layer, "dim", different_attrs)
+    lines.append("if {} < 0:".format(dim))
+    lines.append("    {} = {} + {}".format(layer.outputs[
+        0], dim, get_value(layer, "len", different_attrs)))
+    lines.append("else:")
+    lines.append("    {} = {}".format(layer.outputs[0], dim))
+    forward_func.extend(gen_codes(lines, indent=indent))
+
+
+def prim_constant(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}".format(layer.outputs[0], layer.attrs["value"])
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_contain(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} in {}".format(layer.outputs[0],
+                                  get_value(layer, "element", different_attrs),
+                                  get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_dict(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = dict()".format(layer.outputs[0])
+    forward_func.extend(gen_codes([line], indent=indent))
+    
+    
+def prim_dict_construct(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    lines = list()
+    line = "{} = dict()".format(layer.outputs[0])
+    lines.append(line)
+    for i in range(len(layer.inputs)):
+        line = "{}[{}] = {}".format(layer.outputs[0],
+                                    get_value(layer, "key{}".format(i), different_attrs),
+                                    get_value(layer, "value{}".format(i), different_attrs))
+        lines.append(line)
+    forward_func.extend(gen_codes(lines, indent=indent))
+
+
+def prim_div(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} / {}".format(layer.outputs[0],
+                                 get_value(layer, "x", different_attrs), 
+                                 get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_eq(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} == {}".format(layer.outputs[0],
+                                  get_value(layer, "x", different_attrs), 
+                                  get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_equal(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_exception(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "raise RaiseException({})".format(get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_float(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = float({})".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_floor(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = math.floor({})".format(layer.outputs[0],
+                                        get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_floordiv(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} // {}".format(layer.outputs[0],
+                                  get_value(layer, "x", different_attrs), 
+                                  get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_getitem(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}[{}]".format(layer.outputs[0],
+                                get_value(layer, "list", different_attrs),
+                                get_value(layer, "index", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_gt(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} > {}".format(layer.outputs[0],
+                                 get_value(layer, "x", different_attrs), 
+                                 get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_if(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "if {} :".format(get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+    block = layer.blocks[0]
+    b_init_lines, b_forward_lines = block.gen_dygraph_code(indent=indent + 1)
+    init_func.extend(b_init_lines)
+    forward_func.extend(b_forward_lines)
+    block = layer.blocks[1]
+    if len(block.layers) > 0:
+        b_init_lines, b_forward_lines = block.gen_dygraph_code(
+            indent=indent + 1)
+        if len(b_forward_lines) != 0:
+            line = "else:"
+            forward_func.extend(gen_codes([line], indent=indent))
+        init_func.extend(b_init_lines)
+        forward_func.extend(b_forward_lines)
+
+
+def prim_int(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = int({})".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_is(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} is {}".format(layer.outputs[0],
+                                  get_value(layer, "x", different_attrs), 
+                                  get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_isinstance(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = isinstance({}, {})".format(layer.outputs[0],
+                                            get_value(layer, "input", different_attrs),
+                                            layer.attrs["cls"])
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_isnot(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} is not {}".format(layer.outputs[0],
+                                      get_value(layer, "x", different_attrs),
+                                      get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_le(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} <= {}".format(layer.outputs[0],
+                                  get_value(layer, "x", different_attrs), 
+                                  get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_len(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = len({})".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_len2list(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    lines = []
+    lines.append("{} = []".format(layer.outputs[0]))
+    lines.append("for i in range({}):".format(get_value(layer, "len", different_attrs)))
+    lines.append("    {}.append(i)".format(layer.outputs[0]))
+    forward_func.extend(gen_codes(lines, indent=indent))
+
+
+def prim_lt(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} < {}".format(layer.outputs[0],
+                                 get_value(layer, "x", different_attrs), 
+                                 get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_list(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    input_len = len(layer.inputs) + len(layer.attrs)
+    inputs_list = list()
+    for i in range(input_len):
+        inputs_list.append(get_value(layer, "input{}".format(i), different_attrs))
+    inputs_str = ', '.join(inputs_list)
+    line = "{} = [{}]".format(layer.outputs[0], inputs_str)
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+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))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_loop(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    loop_range = get_value(layer, "input", different_attrs)
+    line = "for {} in range({}):".format(layer.outputs[1], loop_range)
+    forward_func.extend(gen_codes([line], indent=indent))
+    block = layer.blocks[0]
+    b_init_lines, b_forward_lines = block.gen_dygraph_code(indent=indent + 1)
+    init_func.extend(b_init_lines)
+    forward_func.extend(b_forward_lines)
+
+
+def prim_min(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = min({})".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_mul(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} * {}".format(layer.outputs[0],
+                                 get_value(layer, "x", different_attrs), 
+                                 get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_ne(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} != {}".format(layer.outputs[0],
+                                  get_value(layer, "x", different_attrs), 
+                                  get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_neg(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = -{}".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_not(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = not {}".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_or(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} or {}".format(layer.outputs[0],
+                                  get_value(layer, "x", different_attrs), 
+                                  get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_replaceitem(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{}[{}] = {}".format(
+        get_value(layer, "list", layer_id, different_attrs),
+        get_value(layer, "index", layer_id, different_attrs), 
+        get_value(layer, "item", layer_id, different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_requires_grad(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = not {}.stop_gradient".format(layer.outputs[0],
+                                              get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+                        
+
+def prim_rsub(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} - {} * {}".format(layer.outputs[0],
+                                      get_value(layer, "y", different_attrs),
+                                      get_value(layer, "x", different_attrs),
+                                      get_value(layer, "alpha", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_select(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}[".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    for dim in range(layer.attrs["dim"]):
+        line += ":, "
+    line += (get_value(layer, "index", different_attrs) + "]")
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_set_attr(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_set_item(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{}[{}] = {}".format(
+        get_value(layer, "dict", different_attrs),
+        get_value(layer, "key", different_attrs), get_value(layer, "value", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_shape_dim(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = fluid.layers.shape({})[{}]".format(layer.outputs[0],
+                                                    get_value(layer, "input", different_attrs),
+                                                    get_value(layer, "dim", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_slice(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}[{}: {}: {}]".format(layer.outputs[0],
+                                        get_value(layer, "input", different_attrs),
+                                        get_value(layer, "start", different_attrs),
+                                        get_value(layer, "end", different_attrs),
+                                        get_value(layer, "step", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_str(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = str({})".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_sub(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {} - {}".format(layer.outputs[0],
+                                 get_value(layer, "x", different_attrs), 
+                                 get_value(layer, "y", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_tuple(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    input_len = len(layer.inputs) + len(layer.attrs)
+    inputs_list = list()
+    for i in range(input_len):
+        inputs_list.append(get_value(layer, "input{}".format(i), different_attrs))
+    inputs_str = ', '.join(inputs_list)
+    line = "{} = ({})".format(layer.outputs[0], inputs_str)
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_tuple_unpack(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    outputs_str = ', '.join(layer.outputs)
+    line = "{} = {}".format(outputs_str, get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_type(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}.dtype".format(layer.outputs[0], get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+                        
+                        
+def prim_update_end(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    lines = []
+    input_shape = get_value(layer, "input_shape", different_attrs)
+    point = get_value(layer, "point", different_attrs)
+    axis = get_value(layer, "axis", different_attrs)
+    lines.append("if len{} == 2:".format(point))
+    lines.append("    {} = {}[1]".format(layer.outputs[0], point))
+    lines.append("else:")
+    lines.append("    {} = {}[]".format(layer.outputs[0], dim))
+    forward_func.extend(gen_codes(lines, indent=indent))
+
+
+def prim_var2list(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}.numpy().tolist()".format(layer.outputs[0],
+                                             get_value(layer, "input", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
+
+
+def prim_warnings(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    lines = ["import warnings"]
+    line = "warnings.warn({}, stacklevel={})".format(
+        get_value(layer, "input", different_attrs), layer.attrs["stacklevel"])
+    lines.append(line)
+    forward_func.extend(gen_codes(lines, indent=indent))

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/axpy.py

@@ -16,8 +16,8 @@ def axpy_layer(inputs, input_shape=None, name=None):
     alpha = inputs[0]
     x = inputs[1]
     y = inputs[2]
-    out = fluid.layers.elementwise_mul(x, alpha, axis=0)
-    out = fluid.layers.elementwise_add(out, y, name=name)
+    out = paddle.multiply(x, alpha, axis=0)
+    out = paddle.add(out, y, name=name)
     return out
 
 

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/convolutiondepthwise.py

@@ -15,15 +15,6 @@ def convolutiondepthwise_shape(input_shape,
                                kernel_w=None,
                                stride_h=None,
                                stride_w=None):
-    [k_h, k_w] = [1, 1]
-    if isinstance(kernel_size, numbers.Number):
-        [k_h, k_w] = [kernel_size] * 2
-    elif len(kernel_size) > 0:
-        k_h = kernel_h if kernel_h > 0 else kernel_size[0]
-        k_w = kernel_w if kernel_w > 0 else kernel_size[len(kernel_size) - 1]
-    elif kernel_h > 0 or kernel_w > 0:
-        k_h = kernel_h
-        k_w = kernel_w
     [s_h, s_w] = [1, 1]
     if isinstance(stride, numbers.Number):
         [s_h, s_w] = [stride] * 2
@@ -79,15 +70,6 @@ def convolutiondepthwise_layer(inputs,
                                input_shape=None,
                                name=None):
     import numbers
-    [k_h, k_w] = [1, 1]
-    if isinstance(kernel_size, numbers.Number):
-        [k_h, k_w] = [kernel_size] * 2
-    elif len(kernel_size) > 0:
-        k_h = kernel_h if kernel_h > 0 else kernel_size[0]
-        k_w = kernel_w if kernel_w > 0 else kernel_size[len(kernel_size) - 1]
-    elif kernel_h > 0 or kernel_w > 0:
-        k_h = kernel_h
-        k_w = kernel_w
     [s_h, s_w] = [1, 1]
     if isinstance(stride, numbers.Number):
         [s_h, s_w] = [stride] * 2
@@ -122,16 +104,14 @@ def convolutiondepthwise_layer(inputs,
     c_out = num_output if num_output is not None else input_shape[0][1]
     group = int(c_in / (c_in / c_out)) if c_in > c_out else int(c_in /
                                                                 (c_out / c_in))
-    out = fluid.layers.conv2d(
+    out = paddle.nn.functional.conv2d(
         input,
         dilation=[dila_h, dila_w],
-        filter_size=[k_h, k_w],
         stride=[s_h, s_w],
         padding=[p_h, p_w],
         groups=group,
-        num_filters=c_out,
-        param_attr=name + '_weights',
-        bias_attr=name + '_bias',
+        weight=name + '_weights',
+        bias=name + '_bias',
         name=name)
     return out
 

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/detectionoutput.py

@@ -14,30 +14,18 @@ def detectionoutput_layer(inputs,
                           confidence_threshold=0.1,
                           input_shape=None,
                           name=None):
-    nms_param_str = nms_param
-    nms_param = {}
-    part = nms_param_str.split(',')
-    for s in part:
-        if s == '':
-            break
-        else:
-            name, obj = s.split(': ')
-            if name == 'top_k':
-                nms_param[name] = int(obj)
-            else:
-                nms_param[name] = float(obj)
     if nms_param is None:
         nms_param = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}
     mbox_conf_flatten = inputs[1]
     mbox_priorbox = inputs[2]
-    mbox_priorbox_list = fluid.layers.split(mbox_priorbox, 2, dim=1)
+    mbox_priorbox_list = paddle.split(mbox_priorbox, 2, dim=1)
     pb = mbox_priorbox_list[0]
     pbv = mbox_priorbox_list[1]
-    pb = fluid.layers.reshape(x=pb, shape=[-1, 4])
-    pbv = fluid.layers.reshape(x=pbv, shape=[-1, 4])
+    pb = paddle.reshape(x=pb, shape=[-1, 4])
+    pbv = paddle.reshape(x=pbv, shape=[-1, 4])
     mbox_loc = inputs[0]
-    mbox_loc = fluid.layers.reshape(x=mbox_loc, shape=[-1, pb.shape[0], 4])
-    mbox_conf_flatten = fluid.layers.reshape(
+    mbox_loc = paddle.reshape(x=mbox_loc, shape=[-1, pb.shape[0], 4])
+    mbox_conf_flatten = paddle.reshape(
         x=mbox_conf_flatten, shape=[0, pb.shape[0], -1])
 
     default = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/normalize.py

@@ -13,14 +13,14 @@ def normalize_layer(inputs,
                     name=None):
     assert across_spatial == False, "Only support across_spatial == False for Normalize"
     input = inputs[0]
-    l2_norm = fluid.layers.l2_normalize(input, axis=1, name=name + '_l2')
-    scale_param = fluid.layers.create_parameter(
+    l2_norm = paddle.nn.functional.normalize(input, axis=1, p=2, name=name + '_l2')
+    scale_param = paddle.static.create_parameter(
         shape=[1] if channel_shared else [1, 1, 1, input_shape[0][1]],
         dtype=input.dtype,
-        attr=fluid.ParamAttr(name=name + '_scale'))
-    scale_param = fluid.layers.reshape(x=scale_param, \
+        attr=paddle.ParamAttr(name=name + '_scale'))
+    scale_param = paddle.reshape(x=scale_param, \
                   shape=[1] if channel_shared else [input_shape[0][1]])
-    out = fluid.layers.elementwise_mul(
+    out = paddle.multiply(
         x=l2_norm, y=scale_param, axis=-1 if channel_shared else 1)
     return out
 

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/permute.py

@@ -14,7 +14,7 @@ def permute_shape(input_shape, order=None):
 def permute_layer(inputs, order=None, input_shape=None, name=None):
     input = inputs[0]
     order = list(order)
-    out = fluid.layers.transpose(input, perm=order, name=name)
+    out = paddle.transpose(input, perm=order, name=name)
     return out
 
 

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/relu6.py

@@ -8,7 +8,7 @@ def relu6_shape(input_shape):
 
 def relu6_layer(inputs, input_shape=None, name=None):
     input = inputs[0]
-    out = fluid.layers.relu6(x=input)
+    out = paddle.nn.functional.relu6(x=input)
     return out
 
 

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/roipooling.py

@@ -20,7 +20,7 @@ def roipooling_layer(inputs,
                      name=None):
     input = inputs[0]
     roi = inputs[1]
-    roi = fluid.layers.slice(roi, axes=[1], starts=[1], ends=[5])
+    roi = paddle.slice(roi, axes=[1], starts=[1], ends=[5])
     out = fluid.layers.roi_pool(
         input,
         roi,

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/select.py

@@ -30,7 +30,7 @@ def select_layer(inputs,
     out = []
     for i in range(len(slice_point)):
         out.append(
-            fluid.layers.slice(
+            paddle.slice(
                 input,
                 axes=[axis],
                 starts=[slice_point[i]],

x2paddle/op_mapper/static/caffe2paddle/caffe_custom_layer/upsample.py

@@ -42,8 +42,8 @@ def upsample_layer(inputs, scale, input_shape=None, name=None):
     :return:
     """
     x = inputs[0]
-    out = fluid.layers.resize_nearest(
-        x, align_corners=False, scale=scale, name=name)
+    out = paddle.nn.functioanl.interpolate(
+        x, align_corners=False, scale_factor=scale, name=name)
 
     return out
 

x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py

@@ -13,19 +13,21 @@
 # limitations under the License.
 
 import numbers
+import copy
 import numpy as np
 from x2paddle.decoder.caffe_decoder import CaffeGraph
 from x2paddle.core.op_mapper import OpMapper
 from x2paddle.core.util import *
-from x2paddle.op_mapper import caffe_shape
-from x2paddle.op_mapper.caffe_custom_layer import *
+from x2paddle.op_mapper.static.caffe2paddle import caffe_shape
+from x2paddle.op_mapper.static.caffe2paddle.caffe_custom_layer import *
+from x2paddle.core.program import PaddleGraph 
 
 
 class CaffeOpMapper(OpMapper):
     directly_map_ops = {
-        'AbsVal': 'abs',
-        'Sigmoid': 'sigmoid',
-        'TanH': 'tanh',
+        'AbsVal': 'paddle.abs',
+        'Sigmoid': 'paddle.nn.functional.sigmoid',
+        'TanH': 'paddle.tanh',
     }
 
     def __init__(self, decoder):
@@ -34,6 +36,9 @@ class CaffeOpMapper(OpMapper):
         self.weights = dict()
         resolver = decoder.resolver
         self.used_custom_layers = {}
+        self.pd_graph = PaddleGraph(parent_layer=None, graph_type="static")
+        self.pd_graph.inputs = self.graph.input_nodes
+        self.pd_graph.outputs = self.graph.output_nodes
 
         print("Total nodes: {}".format(len(self.graph.topo_sort)))
         for node_name in self.graph.topo_sort:
@@ -46,7 +51,7 @@ class CaffeOpMapper(OpMapper):
                 func = getattr(self, op)
                 func(node)
             elif op in custom_layers:
-                self.set_node_shape(node, is_fluid_op=False)
+                self.set_node_shape(node, is_custom_op=True)
                 self.deal_custom_layer(node)
             elif op in self.directly_map_ops:
                 self.set_node_shape(node)
@@ -54,6 +59,9 @@ class CaffeOpMapper(OpMapper):
             else:
                 raise Exception(
                     "The op {} in model is not supported yet.".format(op))
+        self.pd_graph.set_parameters(self.weights)
+        self.pd_graph.set_custom_func(self.used_custom_layers)
+
 
     def op_checker(self):
         unsupported_ops = set()
@@ -71,7 +79,7 @@ class CaffeOpMapper(OpMapper):
                 print(op)
             return False
 
-    def set_node_shape(self, node, is_fluid_op=True):
+    def set_node_shape(self, node, is_custom_op=False):
         inputs = node.inputs
         input_shape = []
         for i, nm in enumerate(inputs):
@@ -83,11 +91,11 @@ class CaffeOpMapper(OpMapper):
         node.input_shape = input_shape
 
         func_name = 'shape_' + node.layer_type.lower()
-        if is_fluid_op:
+        if is_custom_op:
+            node.output_shape = compute_output_shape(node)
+        else:
             node.output_shape = getattr(caffe_shape, func_name)(node.layer,
                                                                 input_shape)
-        else:
-            node.output_shape = compute_output_shape(node)
 
     def adjust_parameters(self, node):
         data = node.data
@@ -189,27 +197,16 @@ class CaffeOpMapper(OpMapper):
     def Input(self, node):
         shape = list(node.layer.input_param.shape[0].dim)[1:]
         dtype = 'float32'
-        attr = {
-            'dtype': string(dtype),
-            'shape': shape,
-            'name': string(node.layer_name)
-        }
-        node.fluid_code.add_layer(
-            "data", inputs=None, output=node, param_attr=attr)
-
-    def MemoryData(self, node):
-        # TODO(syf): Paddlepaddle can't fully support
-        shape = node.output_shape[0][1:]
-        dtype = 'float32'
-        attr = {
-            'dtype': string(dtype),
-            'shape': shape,
-            'name': string(node.layer_name)
+        layer_attrs = {
+            "dtype": string(dtype),
+            "shape": [-1] + shape,
+            "name": string(node.layer_name)
         }
-        node.fluid_code.add_layer(
-            "data", inputs=None, output=node.layer_name + '0', param_attr=attr)
-        node.fluid_code.add_note('{} = [{}]'.format(node.layer_name,
-                                                    node.layer_name + '0'))
+        self.pd_graph.add_layer(
+            kernel="paddle.static.data",
+            inputs={},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def Convolution(self, node):
         data = node.data
@@ -219,7 +216,7 @@ class CaffeOpMapper(OpMapper):
         if data is None:
             data = []
             print(
-                'The parameter of {} (type is {}) is not set. So we set the parameters as 0'
+                "The parameter of {} (type is {}) is not set. So we set the parameters as 0"
                 .format(node.layer_name, node.layer_type))
             input_c = node.input_shape[0][1]
             output_c = channel
@@ -229,27 +226,52 @@ class CaffeOpMapper(OpMapper):
             data.append(np.zeros([output_c, ]).astype('float32'))
         else:
             data = self.adjust_parameters(node)
-        self.weights[node.layer_name + '_weights'] = data[0]
+        self.weights[node.layer_name + '_weight_attr'] = data[0]
         if len(data) == 2:
-            self.weights[node.layer_name + '_bias'] = data[1]
+            self.weights[node.layer_name + '_bias_attr'] = data[1]
         assert len(node.inputs
                    ) == 1, 'The count of Convolution node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
-
-        attr = {
-            'filter_size': kernel,
-            'num_filters': channel,
+        layer_attrs = {
+            "dtype": string("float32"),
+            "shape": data[0].shape,
+            "name": string("{}_weight".format(node.layer_name))
+        }
+        self.pd_graph.add_layer(
+            kernel="paddle.static.data",
+            inputs={},
+            outputs=["{}_weight".format(node.layer_name)],
+            **layer_attrs)
+        self.pd_graph.add_layer(
+            kernel="paddle.ParamAttr",
+            inputs={},
+            outputs=["{}_weight_attr".format(node.layer_name)],
+            name = string("{}_weight_attr".format(node.layer_name)))
+        
+        layer_attrs = {
             'stride': stride,
             'padding': pad,
             'dilation': dilation,
             'groups': group,
             'name': string(node.layer_name),
-            'param_attr': string(node.layer_name + '_weights'),
-            'bias_attr': False
-            if len(data) == 1 else string(node.layer_name + '_bias'),
         }
-        node.fluid_code.add_layer(
-            "conv2d", inputs=input, output=node, param_attr=attr)
+        inputs_dict = {"x": self.get_input_name(input),
+                       "weight": "{}_weight".format(node.layer_name),
+                       "weight_attr": "{}_weight_attr".format(node.layer_name)}
+        if len(data) == 1:
+            layer_attrs["bias_attr"] = False
+        else:
+            self.pd_graph.add_layer(
+                kernel="paddle.ParamAttr",
+                inputs={},
+                outputs=["{}_bias_attr".format(node.layer_name)],
+                name = string("{}_bias_attr".format(node.layer_name)))
+            inputs_dict["bias_attr"] = "{}_bias_attr".format(node.layer_name) 
+        self.pd_graph.add_layer(
+            kernel="paddle.nn.functional.conv2d",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            **layer_attrs)        
         
     def Deconvolution(self, node):
         data = node.data
@@ -275,48 +297,52 @@ class CaffeOpMapper(OpMapper):
         assert len(node.inputs
                    ) == 1, 'The count of Deconvolution node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
-        attr = {
+        layer_attrs = {
             'output_size': None,
-            'filter_size': kernel,
-            'num_filters': channel,
             'stride': stride,
             'padding': pad,
             'dilation': dilation,
             'groups': group,
             'name': string(node.layer_name),
-            'param_attr': string(node.layer_name + '_weights'),
-            'bias_attr': False
+            'weight': string(node.layer_name + '_weights'),
+            'bias': False
             if len(data) == 1 else string(node.layer_name + '_bias')
         }
-        node.fluid_code.add_layer(
-            "conv2d_transpose", inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.nn.functional.conv_transpose2d",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)    
 
     def Pooling(self, node):
         params = node.layer.pooling_param
         ceil_mode = getattr(params, 'ceil_mode', True)
         global_pool = getattr(params, 'global_pooling', False)
+        assert not global_pool, "The global_pool must be False!"
         kernel_default = [1, 1]
         channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
             node.layer_type, params)
-        if params.pool == 0:
-            pool_type = 'max'
-        else:
-            pool_type = 'avg'
         assert len(
             node.inputs) == 1, 'The count of Pooling node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
-        attr = {
-            'pool_size': kernel,
-            'pool_stride': stride,
-            'pool_padding': pad,
+        layer_attrs = {
+            'kernel_size': kernel,
+            'stride': stride,
+            'padding': pad,
             'ceil_mode': ceil_mode,
-            'pool_type': string(pool_type),
-            'exclusive': False,
-            'global_pooling': global_pool,
-            'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer(
-            "pool2d", inputs=input, output=node, param_attr=attr)
+        if params.pool == 0:
+            self.pd_graph.add_layer(
+                kernel="paddle.nn.functional.max_pool2d",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                **layer_attrs)    
+        else:
+            self.pd_graph.add_layer(
+                kernel="paddle.nn.functional.avg_pool2d",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                **layer_attrs)    
 
     def LRN(self, node):
         assert len(node.inputs) == 1, 'The count of LRN node\'s input is not 1.'
@@ -329,15 +355,18 @@ class CaffeOpMapper(OpMapper):
         # We'll account for that here.
         alpha = params.alpha / float(params.local_size)
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
-        attr = {
+        layer_attrs = {
             'n': params.local_size,
             'k': params.k,
             'alpha': alpha,
             'beta': params.beta,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer(
-            "lrn", inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="fluid.layers.lrn",
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def InnerProduct(self, node):
         data = node.data
@@ -374,7 +403,7 @@ class CaffeOpMapper(OpMapper):
         assert params.axis == 1
         assert params.bias_term == True
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
-        attr = {
+        layer_attrs = {
             'size': params.num_output,
             'name': string(node.layer_name),
             'act': None,
@@ -382,8 +411,11 @@ class CaffeOpMapper(OpMapper):
             'bias_attr': False
             if len(data) == 1 else string(node.layer_name + '_bias')
         }
-        node.fluid_code.add_layer(
-            "fc", inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.static.nn.fc",
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def Softmax(self, node):
         assert len(
@@ -394,9 +426,12 @@ class CaffeOpMapper(OpMapper):
         shape = node.input_shape[0]
         dims = len(shape)
         axis = axis + dims if axis < 0 else axis
-        attr = {'axis': axis, 'name': string(node.layer_name + '_softmax')}
-        node.fluid_code.add_layer(
-            "softmax", inputs=input, output=node, param_attr=attr)
+        layer_attrs = {'axis': axis, 'name': string(node.layer_name + '_softmax')}
+        self.pd_graph.add_layer(
+            kernel="paddle.nn.functional.softmax",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def Slice(self, node):
         assert len(
@@ -412,27 +447,33 @@ class CaffeOpMapper(OpMapper):
         sections_list = []
         for s in output_shape:
             sections_list.append(s[axis])
-        attr = {
+        layer_attrs = {
             'num_or_sections': sections_list,
             'dim': axis,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer(
-            "split", inputs=input, output=node.layer_name, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.split",
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def Concat(self, node):
         assert len(
             node.inputs
         ) >= 1, 'The count of Concat node\'s input is not more than 1.'
-        inputs = []
+        inputs_list = []
         for i in range(len(node.inputs)):
             input = self.graph.get_bottom_node(node, idx=i, copy=True)
-            inputs.append(input)
+            inputs_list.append(self.get_input_name(input))
         params = node.layer.concat_param
         axis = params.axis
-        attr = {'axis': axis, 'name': string(node.layer_name)}
-        node.fluid_code.add_layer(
-            "concat", inputs=inputs, output=node, param_attr=attr)
+        layer_attrs = {'axis': axis, 'name': string(node.layer_name)}
+        self.pd_graph.add_layer(
+            kernel="paddle.concat",
+            inputs={"x": inputs_list},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def ReLU(self, node):
         """
@@ -447,12 +488,16 @@ class CaffeOpMapper(OpMapper):
         params = node.layer.relu_param
         if params.HasField('negative_slope') and params.negative_slope != 0:
             negative_slope = float(params.negative_slope)
-
-            attr = {'alpha': negative_slope}
-            node.fluid_code.add_layer(
-                'leaky_relu', inputs=input, output=node, param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.nn.functional.leaky_relu",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                negative_slope=negative_slope)
         else:
-            node.fluid_code.add_layer('relu', inputs=input, output=node)
+            self.pd_graph.add_layer(
+                kernel="paddle.nn.functional.relu",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name])
 
     def PReLU(self, node):
         assert len(
@@ -467,44 +512,40 @@ class CaffeOpMapper(OpMapper):
         data = node.data
         assert data is not None, 'The parameter of {} (type is {}) is not set. You need to use python package of caffe to set the default value.'.format(
             node.layer_name, node.layer_type)
-        import paddle
-        pd_version = paddle.__version__
-        if pd_version.startswith("1.8.4") or pd_version.startswith("1.8.3"):
-            self.weights[node.layer_name + '_weights'] = data[0].reshape(1, -1)
-        else:
         self.weights[node.layer_name + '_weights'] = data[0]
-        attr = {
+        layer_attrs = {
             'mode': string(mode),
             'param_attr': string(node.layer_name + '_weights'),
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer(
-            "prelu", inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.nn.functional.prelu",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def Accuracy(self, node):
         assert len(
             node.inputs) == 2, 'The count of Accuracy node\'s input is not 2.'
-        inputs = []
-        inputs[0] = None
-        inputs[1] = None
-        i = 0
-        for shape in node.input_shape:
+        inputs_dict = dict()
+        for i, shape in enumerate(node.input_shape):
             if shape[1] == 1:
                 input = self.graph.get_bottom_node(node, idx=i, copy=True)
-                inputs[1] = input
+                inputs_dict["label"] = self.get_input_name(input)
             else:
                 input = self.graph.get_bottom_node(node, idx=i, copy=True)
-                inputs[0] = input
-            i += 1
+                inputs_dict["input"] = self.get_input_name(input)
         params = node.layer.accuracy_param
         top_k = params.top_k
         axis = params.axis
         ignore_label = params.ignore_label
         assert axis == 1, 'PaddlePaddle can not support the situation when the axis is not 1.'
         assert not ignore_label >= 0, 'PaddlePaddle can not support the situation when the model has ignore label.'
-        attr = {'k': top_k}
-        node.fluid_code.add_layer(
-            "accuracy", inputs=inputs, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.metric.accuracy",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            k=top_k)
 
     def Eltwise(self, node):
         assert len(
@@ -518,79 +559,68 @@ class CaffeOpMapper(OpMapper):
         inputs.append(input1)
         if mode == 0:
             inputs_dict = {}
-            inputs_dict['x'] = inputs[0]
-            inputs_dict['y'] = inputs[1]
-            attr = {'act': None, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer(
-                "elementwise_mul",
+            inputs_dict['x'] = self.get_input_name(inputs[0])
+            inputs_dict['y'] = self.get_input_name(inputs[1])
+            self.pd_graph.add_layer(
+                kernel="paddle.multiply",
                 inputs=inputs_dict,
-                output=node,
-                param_attr=attr)
+                outputs=[node.layer_name])
         elif mode == 1:
             if hasattr(params, 'coeff') and len(params.coeff) == 2:
                 coeff = params.coeff
                 input1_name = self.get_input_name(inputs[0])
-                attr = {
+                layer_attrs = {
                     'shape': [1],
-                    'value': coeff[0],
+                    'fill_value': coeff[0],
                     'dtype': '{}.dtype'.format(input1_name)
                 }
-                node.fluid_code.add_layer(
-                    "fill_constant",
-                    inputs=None,
-                    output=node.layer_name + '_const1',
-                    param_attr=attr)
-                attr = {'act': None, 'name': string(node.layer_name + '_mul1')}
-                node.fluid_code.add_layer(
-                    "elementwise_mul",
-                    inputs=input1_name + ', ' + node.layer_name + '_const1',
-                    output=node.layer_name + '_mul1',
-                    param_attr=attr)
+                self.pd_graph.add_layer(
+                    kernel="paddle.full",
+                    inputs={},
+                    outputs=["{}_const1".format(node.layer_name)],
+                    **layer_attrs)
+                self.pd_graph.add_layer(
+                    kernel="paddle.multiply",
+                    inputs={"x": input1_name,
+                            "y": "{}_const1".format(node.layer_name)},
+                    outputs=["{}_mul1".format(node.layer_name)])
                 input2_name = self.get_input_name(inputs[1])
-                attr = {
+                layer_attrs = {
                     'shape': [1],
-                    'value': coeff[1],
+                    'fill_value': coeff[1],
                     'dtype': '{}.dtype'.format(input2_name)
                 }
-                node.fluid_code.add_layer(
-                    "fill_constant",
-                    inputs=None,
-                    output=node.layer_name + '_const2',
-                    param_attr=attr)
-                attr = {'act': None, 'name': string(node.layer_name + '_mul2')}
-                node.fluid_code.add_layer(
-                    "elementwise_mul",
-                    inputs=input2_name + ', ' + node.layer_name + '_const2',
-                    output=node.layer_name + '_mul2',
-                    param_attr=attr)
-
-                attr = {'act': None, 'name': string(node.layer_name)}
-                node.fluid_code.add_layer(
-                    "elementwise_add",
-                    inputs='{}_mul1, {}_mul2'.format(node.layer_name,
-                                                     node.layer_name),
-                    output=node,
-                    param_attr=attr)
+                self.pd_graph.add_layer(
+                    kernel="paddle.full",
+                    inputs={},
+                    outputs=["{}_const2".format(node.layer_name)],
+                    **layer_attrs)
+                self.pd_graph.add_layer(
+                    kernel="paddle.multiply",
+                    inputs={"x": input2_name,
+                            "y": "{}_const2".format(node.layer_name)},
+                    outputs=["{}_mul2".format(node.layer_name)])
+                self.pd_graph.add_layer(
+                    kernel="paddle.add",
+                    inputs={"x": "{}_mul1".format(node.layer_name),
+                            "y": "{}_mul2".format(node.layer_name)},
+                    outputs=[node.layer_name])
             else:
                 inputs_dict = {}
-                inputs_dict['x'] = inputs[0]
-                inputs_dict['y'] = inputs[1]
-                attr = {'act': None, 'name': string(node.layer_name)}
-                node.fluid_code.add_layer(
-                    "elementwise_add",
+                inputs_dict['x'] = self.get_input_name(inputs[0])
+                inputs_dict['y'] = self.get_input_name(inputs[1])
+                self.pd_graph.add_layer(
+                    kernel="paddle.add",
                     inputs=inputs_dict,
-                    output=node,
-                    param_attr=attr)
+                    outputs=[node.layer_name])
         else:
             inputs_dict = {}
-            inputs_dict['x'] = inputs[0]
-            inputs_dict['y'] = inputs[1]
-            attr = {'act': None, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer(
-                "elementwise_max",
+            inputs_dict['x'] = self.get_input_name(inputs[0])
+            inputs_dict['y'] = self.get_input_name(inputs[1])
+            self.pd_graph.add_layer(
+                    kernel="paddle.add",
                     inputs=inputs_dict,
-                output=node,
-                param_attr=attr)
+                    outputs=[node.layer_name])
 
     def BatchNorm(self, node):
         assert len(
@@ -619,7 +649,7 @@ class CaffeOpMapper(OpMapper):
         variance *= scaling_factor
         self.weights[node.layer_name + '_mean'] = mean
         self.weights[node.layer_name + '_variance'] = variance
-        attr = {
+        layer_attrs = {
             'is_test': True,
             'param_attr': None,
             'bias_attr': None,
@@ -628,8 +658,11 @@ class CaffeOpMapper(OpMapper):
             'epsilon': eps,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer(
-            "batch_norm", inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.nn.functional.batch_norm",
+            inputs={"input": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def Scale(self, node):
         if node.data is None:
@@ -659,73 +692,87 @@ class CaffeOpMapper(OpMapper):
             input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
             input1 = self.graph.get_bottom_node(node, idx=1, copy=True)
             inputs_dict = {}
-            inputs_dict['x'] = input0
-            inputs_dict['y'] = input1
-            attr = {'axis': axis, 'name': string(node.layer_name + '_mul')}
-            node.fluid_code.add_layer(
-                "elementwise_mul",
+            inputs_dict['x'] = self.get_input_name(input0)
+            inputs_dict['y'] = self.get_input_name(input1)
+            self.pd_graph.add_layer(
+                kernel="paddle.multiply",
                 inputs=inputs_dict,
-                output=node.layer_name + '_mul',
-                param_attr=attr)
+                outputs=["{}_mul".format(node.layer_name)],
+                axis=axis)
         else:
             bias_shape = node.input_shape[0][axis:axis + num_axes]
             input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
             input0_name = self.get_input_name(input0)
-            attr = {
+            self.pd_graph.add_layer(
+                kernel="paddle.ParamAttr",
+                inputs={},
+                outputs=["{}_scale".format(node.layer_name)],
+                name = string("{}_scale".format(node.layer_name)))
+            layer_attrs = {
                 'dtype': '{}.dtype'.format(input0_name),
                 'shape': bias_shape,
                 'name': string(node.layer_name + '_cparam1'),
-                'attr': string(node.layer_name + '_scale'),
                 'is_bias': True,
                 'default_initializer': 'Constant(value=1.0)'
             }
-            node.fluid_code.add_layer(
-                "create_parameter", inputs=None, output=node, param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.static.create_parameter",
+                inputs={"attr": node.layer_name + '_scale',},
+                outputs=["{}_cparam1".format(node.layer_name)],
+                **layer_attrs)
             inputs_dict = {}
-            inputs_dict['x'] = input0
-            inputs_dict['y'] = node
-            attr = {'axis': axis, 'name': string(node.layer_name + '_mul')}
-            node.fluid_code.add_layer(
-                "elementwise_mul",
+            inputs_dict['x'] = self.get_input_name(input0)
+            inputs_dict['y'] = "{}_cparam1".format(node.layer_name)
+            self.pd_graph.add_layer(
+                kernel="paddle.multiply",
                 inputs=inputs_dict,
-                output=node.layer_name + '_mul',
-                param_attr=attr)
+                outputs=["{}_mul".format(node.layer_name)],
+                axis=axis)
         scale_shape = bias_shape
         input0_name = self.get_input_name(input0)
-        attr = {
+        self.pd_graph.add_layer(
+            kernel="paddle.ParamAttr",
+            inputs={},
+            outputs=["{}_offset".format(node.layer_name)],
+            name = string("{}_offset".format(node.layer_name)))
+        layer_attrs = {
             'dtype': '{}.dtype'.format(input0_name),
             'shape': scale_shape,
             'name': string(node.layer_name + '_cparam2'),
-            'attr': string(node.layer_name + '_offset'),
             'is_bias': True,
             'default_initializer': 'Constant(value=1.0)'
         }
-        node.fluid_code.add_layer(
-            "create_parameter",
-            inputs=None,
-            output=node.layer_name + '_offset_param',
-            param_attr=attr)
-        attr = {'axis': axis, 'name': string(node.layer_name + '_add')}
-        node.fluid_code.add_layer(
-            "elementwise_add",
-            inputs='{}_mul, {}_offset_param'.format(node.layer_name,
-                                                    node.layer_name),
-            output=node,
-            param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.static.create_parameter",
+            inputs={"attr": node.layer_name + '_offset'},
+            outputs=["{}_cparam2".format(node.layer_name)],
+            **layer_attrs)
+        inputs_dict = {}
+        inputs_dict['x'] = "{}_mul".format(node.layer_name)
+        inputs_dict['y'] = "{}_cparam2".format(node.layer_name)
+        self.pd_graph.add_layer(
+            kernel="paddle.add",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            axis=axis)
+        
 
     def Reshape(self, node):
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
         top_count = len(input.layer.top)
         is_inplace = False if top_count == 1 else True
         output_shape = node.output_shape[0]
-        attr = {
+        layer_attrs = {
             'shape': output_shape,
             'inplace': is_inplace,
             'act': None,
             'name': string(node.layer_name)
         }
-        node.fluid_code.add_layer(
-            "reshape", inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.reshape",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
 
     def ArgMax(self, node):
         assert len(node.inputs) == 1 and len(
@@ -741,33 +788,29 @@ class CaffeOpMapper(OpMapper):
         if axis < 0:
             axis += len(input_shape)
         if out_max_val is True:
-            attr = {'k': top_k, 'name': string(node.layer_name + '_topk')}
-            node.fluid_code.add_layer(
-                "topk",
-                inputs=input,
-                output='{}_topk_var, {}_index_var'.format(node.layer_name,
-                                                          node.layer_name),
-                param_attr=attr)
-            attr = {'dtype': '{}_topk_var.dtype'.format(node.layer_name)}
-            node.fluid_code.add_layer(
-                "cast",
-                inputs='{}_index_var'.format(node.layer_name),
-                output='{}_index_var'.format(node.layer_name),
-                param_attr=attr)
-            attr = {'axis': axis, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer(
-                "concat",
-                inputs='{}_topk_var, {}_index_var'.format(node.layer_name,
-                                                          node.layer_name),
-                output=node,
-                param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.topk",
+                inputs={"input": self.get_input_name(input)},
+                outputs=["{}_topk_var".format(node.layer_name),
+                         "{}_index_var".format(node.layer_name)],
+                k=top_k)
+            self.pd_graph.add_layer(
+                kernel="paddle.cast",
+                inputs={"x": "{}_topk_var".format(node.layer_name)},
+                outputs=["{}_topk_var".format(node.layer_name)],
+                dtype="{}_topk_var.dtype".format(node.layer_name))
+            self.pd_graph.add_layer(
+                kernel="paddle.concat",
+                inputs={"x": "[{}_topk_var, {}_index_var]".format(node.layer_name,
+                                                                  node.layer_name)},
+                outputs=[node.layer_name],
+                axis=axis)
         else:
-            attr = {'k': top_k, 'name': string(node.layer_name)}
-            node.fluid_code.add_layer(
-                "topk",
-                inputs=input,
-                output='_, {}'.format(node.layer_name),
-                param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.topk",
+                inputs={"input": self.get_input_name(input)},
+                outputs=["_", node.layer_name],
+                k=top_k)
 
     def Crop(self, node):
         assert len(
@@ -786,23 +829,24 @@ class CaffeOpMapper(OpMapper):
                     ) == len(offset), "invalid offset[%s] in crop layer" % (
                         str(offset))
             offset_real = [0] * axis + offset
-        attr = {'offsets': list(offset_real), 'name': string(node.layer_name)}
-        node.fluid_code.add_layer(
-            "crop",
-            inputs={'x': input,
-                    'shape': node.input_shape[1]},
-            output=node,
-            param_attr=attr)
+        layer_attrs = {"offsets": list(offset_real), 
+                       "shape": node.input_shape[1]}
+        self.pd_graph.add_layer(
+            kernel="paddle.crop",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
         
     def Flatten(self, node):
         assert len(
             node.
             inputs) == 1, 'The count of DetectionOutput node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
-        shape = node.output_shape[0]
-        attr = {'shape': shape, 'name': string(node.layer_name)}
-        node.fluid_code.add_layer(
-            "reshape", inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.reshape",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            shape = node.output_shape[0])
         
     def Power(self, node):
         assert len(
@@ -812,17 +856,22 @@ class CaffeOpMapper(OpMapper):
         power = params.power
         scale = params.scale
         shift = params.shift
-        attr = {
+        layer_attrs = {
             'scale': scale,
             'bias': shift,
             'bias_after_scale': True,
             'name': string(node.layer_name + '_scale')
         }
-        node.fluid_code.add_layer(
-            "scale", inputs=input, output=node, param_attr=attr)
-        attr = {'factor': power, 'name': string(node.layer_name)}
-        node.fluid_code.add_layer(
-            "pow", inputs=node, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel="paddle.scale",
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+        self.pd_graph.add_layer(
+            kernel="paddle.pow",
+            inputs={"x": node.layer_name},
+            outputs=[node.layer_name],
+            factor=power)
 
     def Reduction(self, node):
         assert len(
@@ -839,46 +888,63 @@ class CaffeOpMapper(OpMapper):
             axis += input_len + 1
         dim = list(range(input_len))
         if operation == 1:  ## operation = SUM
-            attr = {
+            layer_attrs = {
                 'dim': dim[axis:],
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer(
-                "reduce_sum", inputs=input, output=node, param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.sum",
+                inputs={"input": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                **layer_attrs)
         elif operation == 2:  ## operation = ASUM
-            attr = {'name': string(node.layer_name + '_abs')}
-            node.fluid_code.add_layer(
-                "abs", inputs=input, output=node, param_attr=attr)
-            attr = {
+            self.pd_graph.add_layer(
+                kernel="paddle.abs",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name])
+            layer_attrs = {
                 'dim': dim[axis:],
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer(
-                "reduce_sum", inputs=node, output=node, param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.sum",
+                inputs={"input": node.layer_name},
+                outputs=[node.layer_name],
+                **layer_attrs)
         elif operation == 3:  ## operation = SUMSQ
-            attr = {'factor': 2.0, 'name': string(node.layer_name + '_pow')}
-            node.fluid_code.add_layer(
-                "pow", inputs=input, output=node, param_attr=attr)
-            attr = {
+            self.pd_graph.add_layer(
+                kernel="paddle.pow",
+                inputs={"x": self.get_input_name(input)},
+                outputs=[node.layer_name],
+                factor=2.0)
+            layer_attrs = {
                 'dim': dim[axis:],
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer(
-                "reduce_sum", inputs=node, output=node, param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.sum",
+                inputs={"input": node.layer_name},
+                outputs=[node.layer_name],
+                **layer_attrs)
         else:  ## operation = MEAN
-            attr = {
+            layer_attrs = {
                 'dim': dim[axis:],
                 'keep_dim': False,
                 'name': string(node.layer_name)
             }
-            node.fluid_code.add_layer(
-                "reduce_mean", inputs=node, output=node, param_attr=attr)
-        attr = {'scale': coeff}
-        node.fluid_code.add_layer(
-            "scale", inputs=node, output=node, param_attr=attr)
+            self.pd_graph.add_layer(
+                kernel="paddle.mean",
+                inputs={"input": node.layer_name},
+                outputs=[node.layer_name],
+                **layer_attrs)
+        self.pd_graph.add_layer(
+            kernel="paddle.scale",
+            inputs={"x": node.layer_name},
+            outputs=[node.layer_name],
+            scale=coeff)
 
     def deal_custom_layer(self, node):
         op = node.layer_type
@@ -893,7 +959,7 @@ class CaffeOpMapper(OpMapper):
             weights_name = deal_weights(node)
             for i in range(len(data)):
                 self.weights[weights_name[i]] = data[i]
-        inputs_node = []
+        inputs_list = []
         for i in range(len(node.inputs)):
             input = self.graph.get_bottom_node(node, idx=i, copy=True)
             if i == 1 and op == 'DetectionOutput':
@@ -904,13 +970,19 @@ class CaffeOpMapper(OpMapper):
                     input = self.graph.get_bottom_node(input, idx=0, copy=True)
                 assert input is not None, 'This kind of DetectionOutput is not supported!'
                 input = self.graph.get_bottom_node(input, idx=0, copy=True)
-            inputs_node.append(input)
-        node.fluid_code.add_layer(
-            func.__code__.co_name,
-            inputs=inputs_node,
-            output=node,
-            param_attr=kwargs,
-            is_custom_layer=True)
+            inputs_list.append(self.get_input_name(input))
+        kwargs_tmp = copy.deepcopy(kwargs)
+        for k, v in kwargs_tmp.items():
+            if str(type(v)) == "<class 'caffe_pb2.NonMaximumSuppressionParameter'>":
+                kwargs[k] = dict()
+                kwargs[k]["nms_threshold"] = v.nms_threshold
+                kwargs[k]["top_k"] = v.top_k
+                kwargs[k]["eta"] = v.eta
+        self.pd_graph.add_layer(
+            kernel="combination_layer:{}".format(op),
+            inputs={"inputs": inputs_list},
+            outputs=[node.layer_name],
+            **kwargs)
         if op not in self.used_custom_layers:
             self.used_custom_layers[op] = custom_code
 
@@ -918,6 +990,8 @@ class CaffeOpMapper(OpMapper):
         assert node.layer_type in self.directly_map_ops
         op_info = self.directly_map_ops[node.layer_type]
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
-        attr = {'name': string(node.layer_name)}
-        node.fluid_code.add_layer(
-            op_info, inputs=input, output=node, param_attr=attr)
+        self.pd_graph.add_layer(
+            kernel=op_info,
+            inputs={"x": self.get_input_name(input)},
+            outputs=[node.layer_name])
+        
\ No newline at end of file