Merge branch 'paddle-2.0' of https://github.com/PaddlePaddle/X2Paddle into paddle-2.0

x2paddle/convert.py

@@ -177,7 +177,7 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto,
     mapper.paddle_graph.gen_model(save_dir)
 
 
-def onnx2paddle(model_path, save_dir, params_merge=False):
+def onnx2paddle(model_path, save_dir, paddle_type, params_merge=False):
     # check onnx installation and version
     try:
         import onnx
@@ -190,19 +190,23 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
         return
     print("Now translating model from onnx to paddle.")
 
-    from x2paddle.op_mapper.onnx2paddle.onnx_op_mapper import ONNXOpMapper
     from x2paddle.decoder.onnx_decoder import ONNXDecoder
-    from x2paddle.optimizer.onnx_optimizer import ONNXOptimizer
+    if paddle_type == "dygraph":
+        from x2paddle.op_mapper.dygraph.onnx2paddle.onnx_op_mapper import ONNXOpMapper
+    else:
+        from x2paddle.op_mapper.static.onnx2paddle.onnx_op_mapper import ONNXOpMapper
     model = ONNXDecoder(model_path)
     mapper = ONNXOpMapper(model)
-    print("Model optimizing ...")
-    optimizer = ONNXOptimizer(mapper)
-    optimizer.delete_redundance_code()
-    print("Model optimized.")
-
-    print("Paddle model and code generating ...")
-    mapper.save_inference_model(save_dir, params_merge)
-    print("Paddle model and code generated.")
+    if paddle_type == "dygraph":
+        mapper.paddle_graph.build()
+        mapper.paddle_graph.gen_model(save_dir)
+    else:
+        from x2paddle.optimizer.onnx_optimizer import ONNXOptimizer
+        print("Model optimizing ...")
+        optimizer = ONNXOptimizer(mapper)
+        optimizer.delete_redundance_code()
+        print("Model optimized.")
+        mapper.save_inference_model(save_dir, params_merge)
 
 
 def pytorch2paddle(model_path, save_dir, jit_type, input_files):
@@ -318,7 +322,7 @@ def main():
 
         if args.params_merge:
             params_merge = True
-        onnx2paddle(args.model, args.save_dir, params_merge)
+        onnx2paddle(args.model, args.save_dir, args.paddle_type, 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)

x2paddle/core/fluid_code.py

@@ -41,6 +41,8 @@ class Layer(object):
             layer_code = layer_code
         elif self.use_fluid:
             layer_code = layer_code + "fluid." + self.op + "("
+        elif self.op == "full_like":
+            layer_code = layer_code + "paddle." + self.op + "("
         else:
             layer_code = layer_code + "fluid.layers." + self.op + "("
 

x2paddle/core/op_mapper.py

@@ -128,6 +128,7 @@ class OpMapper(object):
         self.add_codes("from paddle.fluid.initializer import Constant")
         self.add_codes("from paddle.fluid.param_attr import ParamAttr")
         self.add_codes("import paddle.fluid as fluid")
+        self.add_codes("import paddle")
         self.add_codes("")
 
     def save_inference_model(self, save_dir, params_merge):
@@ -214,6 +215,7 @@ class OpMapper(object):
                 self.add_codes("", 0)
 
         self.add_codes("\ndef x2paddle_net():", 0)
+        self.add_codes("paddle.enable_static()", 1)
         for i in range(len(self.graph.topo_sort)):
             node_name = self.graph.topo_sort[i]
             node = self.graph.get_node(node_name)

x2paddle/core/program.py

@@ -488,11 +488,12 @@ class PaddleGraph(object):
                 gen_codes(
                     comment_list,
                     indent=1))
+            use_structured_name = False if self.source_type in ["tf", "onnx"] else True
             self.run_func.extend(
                 gen_codes(["paddle.disable_static()",
                            "params, _ = fluid.load_dygraph('{}/model')".format(code_dir),
                            "model = {}()".format(self.name),
-                           "model.set_dict(params)",
+                           "model.set_dict(params, use_structured_name={})".format(use_structured_name),
                            "model.eval()",
                            "out = model({})".format(input_data_name),
                            "return out"], indent=1))
@@ -624,7 +625,7 @@ class PaddleGraph(object):
         paddle.disable_static()
         restore, _ = fluid.load_dygraph(osp.join(save_dir, "model"))
         model = getattr(x2paddle_code, self.name)()
-        if self.source_type == "tf":
+        if self.source_type in ["tf", "onnx"]:
             model.set_dict(restore, use_structured_name=False)
         else:
             model.set_dict(restore)

x2paddle/decoder/onnx_decoder.py

@@ -145,6 +145,7 @@ class ONNXGraph(Graph):
         self.build()
         self.collect_value_infos()
         self.allocate_shapes()
+        self.graph_name = "ONNXModel"
 
     def get_inner_nodes(self):
         """

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_op_mapper.py

@@ -578,9 +578,11 @@ class CaffeOpMapper(OpMapper):
         mode_bool = params.channel_shared
         output_shape = node.output_shape[0]
         if mode_bool:
-            num_parameters = 1
+            mode = 'all'
+            channel = None
         else:
-            num_parameters = output_shape[1]
+            mode = 'channel'
+            channel = 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(
@@ -589,7 +591,8 @@ class CaffeOpMapper(OpMapper):
             "paddle.nn.PReLU",
             inputs={"input": self.get_input_name(input)},
             outputs=layer_outputs,
-            num_parameters=num_parameters)
+            channel=channel,
+            mode=string(mode))
 
     def Eltwise(self, node):
         assert len(
@@ -745,12 +748,29 @@ class CaffeOpMapper(OpMapper):
         inputs_dict = {}
         inputs_dict['x'] = node.layer_name + "_mul"
         inputs_dict['y'] = node.layer_name + "_cparam2"
-        self.paddle_graph.add_layer(
-            "fluid.layers.elementwise_add",
-            inputs=inputs_dict,
-            outputs=[node.layer_name],
-            axis=axis)
-
+        output_shape = node.output_shape[0]
+        if axis == -1:
+            self.paddle_graph.add_layer(
+                "paddle.add",
+                inputs=inputs_dict,
+                outputs=[node.layer_name])
+        else:
+            if axis < 0:
+                axis = axis + len(output_shape)
+            param2_shape = self.params[node.layer_name + "_cparam2"].shape
+            param2_shape_len = len(param2_shape)
+            diff_len = len(output_shape) - axis - param2_shape_len
+            new_shape = param2_shape + [1] * diff_len
+            self.paddle_graph.add_layer(
+                "paddle.reshape",
+                inputs={"x": node.layer_name + "_cparam2"},
+                outputs=[node.layer_name + "_cparam2"],
+                shape=new_shape)
+            self.paddle_graph.add_layer(
+                "paddle.add",
+                inputs=inputs_dict,
+                outputs=[node.layer_name])
+            
     def Reshape(self, node):
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
         output_shape = node.output_shape[0]

x2paddle/op_mapper/dygraph/onnx2paddle/onnx_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.
+
+from x2paddle.op_mapper.dygraph.onnx2paddle.opset9 import OpSet9
+from x2paddle.core.op_mapper import OpMapper
+from x2paddle.decoder.onnx_decoder import ONNXGraphNode
+from x2paddle.core.program import PaddleGraph
+
+
+class ONNXOpMapper(OpMapper):
+    def __init__(self, decoder):
+        super(ONNXOpMapper, self).__init__()
+        self.support_op_sets = [9, ]
+        self.default_op_set = 9
+        self.graph = decoder.graph
+        self.paddle_graph = PaddleGraph(parent_layer=None, graph_type="dygraph", source_type="onnx")
+        self.opset = self.create_opset(decoder)
+        if not self.op_checker():
+            raise Exception("Model are not supported yet.")
+        #mapping op
+        print("Total nodes: {}".format(
+            sum([
+                isinstance(node, ONNXGraphNode)
+                for name, node in self.graph.node_map.items()
+            ])))
+
+        print("Nodes converting ...")
+        for node_name in self.graph.topo_sort:
+            node = self.graph.get_node(node_name)
+            op = node.layer_type
+            if hasattr(self.opset, op):
+                func = getattr(self.opset, op)
+                func(node)
+            elif op in self.opset.default_op_mapping:
+                self.opset.directly_map(node)
+            elif op in self.opset.elementwise_ops:
+                self.opset.elementwise_map(node)
+        print("Nodes converted.")
+        self.weights = self.opset.weights
+        self.inputs_info = self.opset.inputs_info
+        self.paddle_graph.set_name(self.graph.graph_name)
+        self.paddle_graph.set_parameters(self.weights)
+        self.paddle_graph.set_inputs_info(self.inputs_info)
+        self.paddle_graph.outputs = self.graph.output_nodes
+
+    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.opset, op) and \
+                op not in self.opset.default_op_mapping and \
+                op not in self.opset.elementwise_ops:
+                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 create_opset(self, decoder):
+        run_op_set = self.default_op_set
+        opset = ''
+        if decoder.op_set in self.support_op_sets:
+            opset = 'OpSet' + str(decoder.op_set)
+        elif decoder.op_set < self.default_op_set:
+            opset = 'OpSet' + str(self.default_op_set)
+        else:
+            for op_set in self.support_op_sets:
+                if decoder.op_set > op_set:
+                    run_op_set = op_set
+                else:
+                    break
+            opset = 'OpSet' + str(run_op_set)
+        print(
+            'Now, onnx2paddle support convert onnx model opset_verison {},'
+            'opset_verison of your onnx model is {}, automatically treated as op_set: {}.'
+            .format(self.support_op_sets, decoder.op_set, run_op_set))
+        return eval(opset)(decoder, self.paddle_graph)

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

+from .opset import OpSet9

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.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.
+
+from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
+from x2paddle.core.graph import GraphNode
+from x2paddle.core.fluid_code import Layer
+from x2paddle.core.fluid_code import FluidCode
+from x2paddle.core.util import *
+from functools import reduce
+import numpy as np
+import onnx
+import onnx.numpy_helper as numpy_helper
+from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
+import logging as _logging
+from collections import OrderedDict
+import math
+import os
+import copy
+import sys
+import shutil
+
+_logger = _logging.getLogger(__name__)
+
+
+def _const_weight_or_none(node, necessary=False):
+    if 'Constant' in node.layer_type:
+        return node.value
+    if isinstance(node, ONNXGraphDataNode):
+        return node.weight
+    if necessary:
+        assert '{} should be an initializer or Constant operator.'.format(
+            node.layer_name)
+    return None
+
+
+def _is_static_shape(shape):
+    negtive_dims = 0
+    error_dims = 0
+    for dim in shape:
+        if dim < 0:
+            negtive_dims += 1
+        if dim < -1:
+            error_dims += 1
+    if negtive_dims > 1:
+        return False
+    if error_dims > 0:
+        return False
+    return True
+
+
+def _get_same_padding(in_size, kernel_size, stride):
+    new_size = int(math.ceil(in_size * 1.0 / stride))
+    pad_size = (new_size - 1) * stride + kernel_size - in_size
+    pad0 = int(pad_size / 2)
+    pad1 = pad_size - pad0
+    return [pad0, pad1]
+
+
+def print_mapping_info(func):
+    def run_mapping(*args, **kwargs):
+        node = args[1]
+        try:
+            res = func(*args, **kwargs)
+        except:
+            print("convert failed node:{}, op_type is {}".format(
+                node.layer_name[9:], node.layer_type))
+            raise
+        else:
+            return res
+
+    return run_mapping
+
+
+class OpSet9():
+    elementwise_ops = {
+        'Add': 'paddle.add',
+        'Div': 'paddle.divide',
+        'Sub': 'fluid.layers.elementwise_sub',
+        'Mul': 'paddle.multiply',
+        'Pow': 'paddle.pow',
+    }
+
+    default_op_mapping_field_values = OrderedDict()
+    default_op_mapping_field_values['PADDLE_OP'] = ''
+    default_op_mapping_field_values['PADDLE_INPUT_ARGS'] = None
+    default_op_mapping_field_values['ATTR_MAPPING'] = dict()
+    default_op_mapping_field_values['DEFAULTS'] = dict()
+
+    default_op_mapping = {
+        'Shape': ['paddle.shape', ['input']],
+        'Ceil': ['paddle.ceil', ['x']],
+        'ReduceMean': [
+            'paddle.mean', ['x'], dict(
+                axes='axis', keepdims='keepdim'), dict(keepdim=1)
+        ],
+        'ReduceSum': [
+            'paddle.sum', ['x'], dict(
+                axes='axis', keepdims='keepdim'), dict(keepdim=1)
+        ],
+        'ReduceMin': [
+            'paddle.min', ['x'], dict(
+                axes='axis', keepdims='keepdim'), dict(keepdim=1)
+        ],
+        'ReduceMax': [
+            'paddle.max', ['x'], dict(
+                axes='axis', keepdims='keepdim'), dict(keepdim=1)
+        ],
+        #active function
+        'Relu': ['paddle.nn.ReLU', ['x']],
+        'LeakyRelu': ['paddle.nn.LeakyReLU', ['x'], dict(alpha='negative_slope'), 
+                      dict(negative_slope=.01)],
+        'Elu': ['paddle.nn.functional.elu', ['x'], dict(), dict(alpha=1.)],
+        'ThresholdedRelu': [
+            'paddle.nn.functional.thresholded_relu', ['x'], dict(alpha='threshold'),
+            dict(alpha=1.)
+        ],
+        'Tanh': ['paddle.nn.Tanh', ['x']],
+        'Sigmoid': ['paddle.nn.Sigmoid', ['x']],
+        'Softsign': ['paddle.nn.Softsign', ['x']],
+        'Softplus': ['paddle.nn.Softplus', ['x'], dict(), dict(threshold=float(sys.maxsize))],
+        'Exp': ['paddle.exp', ['x']],
+        'Softmax': ['paddle.nn.Softmax', ['x'], dict(), dict(axis=1)],
+        'Sqrt': ['paddle.sqrt', ['x']],
+        'Floor': ['paddle.floor', ['x']],
+        'Abs': ['paddle.abs', ['x']],
+        'Erf': ['paddle.erf', ['x']],
+    }
+
+    def __init__(self, decoder, paddle_graph):
+        super(OpSet9, self).__init__()
+        self.graph = decoder.graph
+        self.paddle_graph = paddle_graph
+        self.input_index = 0
+        self.inputs_info = dict()
+        self.weights = dict()
+        self.nn_name2id = dict()
+        
+    def get_node_name(self, node):
+        if hasattr(node, "index"):
+            return "{}_{}".format(node.layer_name, node.index)
+        else:
+            return node.layer_name
+
+    @print_mapping_info
+    def directly_map(self, node, *args, **kwargs):
+        inputs = node.layer.input
+        op_type = node.layer_type
+        attrs = node.attr_map
+        info = self.default_op_mapping[op_type]
+        info.extend(
+            list(self.default_op_mapping_field_values.values())[len(info):])
+        (paddle_op,
+        paddle_input_args,
+        attr_mapping,
+        default_attrs) = info
+        mapped_attrs = {
+            attr_mapping.get(key, key): value
+            for key, value in attrs.items()
+        }
+        if '' in mapped_attrs:
+            mapped_attrs.pop('')
+        if '_' in mapped_attrs:
+            mapped_attrs.pop('_')
+        layer_attrs = default_attrs.copy()
+        layer_attrs.update(mapped_attrs)
+        assert len(inputs) == 1, 'directly_map error with multi inputs'
+        input = self.graph.get_input_node(node, idx=0, copy=True)
+        if paddle_op.startswith("paddle.nn"):
+            op_name = paddle_op[10:].lower()
+            op_name = name_generator(op_name, self.nn_name2id)
+            output_name = node.layer_name
+            layer_outputs = [op_name, output_name]
+            self.paddle_graph.add_layer(
+                kernel=paddle_op,
+                inputs={paddle_input_args[0]: self.get_node_name(input)},
+                outputs=layer_outputs,
+                **layer_attrs)
+        else:
+            self.paddle_graph.add_layer(
+                kernel=paddle_op,
+                inputs={paddle_input_args[0]: self.get_node_name(input)},
+                outputs=[node.layer_name],
+                **layer_attrs)        
+        if paddle_op == 'paddle.shape':
+            self.paddle_graph.add_layer(
+                'paddle.cast',
+                inputs={"x": node.layer_name},
+                outputs=[node.layer_name],
+                dtype=string('int64'))
+            
+    @print_mapping_info
+    def elementwise_map(self, node):
+        assert node.layer_type in self.elementwise_ops
+        op_type = self.elementwise_ops[node.layer_type]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        inputs_dict = {'x': self.get_node_name(val_x), 
+                       'y': self.get_node_name(val_y)}
+        self.paddle_graph.add_layer(
+            op_type, 
+            inputs=inputs_dict, 
+            outputs=[node.layer_name])
+
+    @print_mapping_info
+    def place_holder(self, node):
+        shape = node.out_shapes[0]
+        for i, dim_shape in enumerate(shape):
+            if dim_shape == 0 and i == 0:
+                shape[i] = 1
+            if dim_shape == 0 and i != 0:
+                assert 'shape of input is not assigned'
+        self.paddle_graph.add_layer(
+            kernel="paddle.to_tensor",
+            inputs={},
+            outputs=[node.layer_name],
+            data="x{}".format(self.input_index))
+        self.inputs_info["x{}".format(self.input_index)] = [shape, node.dtype]
+        self.input_index += 1
+
+    @print_mapping_info
+    def create_parameter(self, node, parameter=None):
+        if parameter is not None:
+            node = parameter
+        dtype = node.dtype
+        shape = node.out_shapes[0]
+        if len(node.weight.shape) == 0:
+            self.paddle_graph.add_layer(
+                "paddle.full", 
+                inputs={}, 
+                outputs=[node.layer_name],
+                dtype=string(dtype),
+                shape=[1],
+                fill_value=node.weight)
+        else:
+            self.weights[node.layer_name] = node.weight
+            self.paddle_graph.add_layer(
+                "self.create_parameter",
+                inputs={},
+                outputs=[node.layer_name],
+                shape=shape,
+                attr=string(node.layer_name),
+                dtype=string(dtype),
+                default_initializer="paddle.nn.initializer.Constant(value=0.0)")
+        
+
+    def _pad_if_asymmetric(self, node, pads, val_name):  # pads: SSEE
+        assert len(pads) & 1 == 0
+        symmetric = True
+        ndims = len(pads) // 2
+        for idx_dim in range(ndims):
+            if pads[idx_dim] != pads[ndims + idx_dim]:
+                symmetric = False
+                break
+        if symmetric:
+            return pads[:ndims], val_name
+        val_padded = self.Pad(node, op_independent=False)
+        return [0] * ndims, val_padded
+
+    def _interpolate(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        inputs = {'x': self.get_node_name(val_x)}
+        if node.layer_type == 'Resize':
+            if len(node.layer.input) == 2:
+                # opset 10
+                val_scales = self.graph.get_input_node(node, idx=1, copy=True)
+                inputs['scale_factor'] = self.get_node_name(val_scales)
+            elif len(node.layer.input) == 3:
+                # opset 11
+                val_scales = self.graph.get_input_node(node, idx=2, copy=True)
+                inputs['scale_factor'] = self.get_node_name(val_scales)
+            elif len(node.layer.input) == 4:
+                # opset 11
+                val_sizes = self.graph.get_input_node(node, idx=3, copy=True)
+                var_nc, var_hw = val_sizes.layer_name + '_nc', val_sizes.layer_name + '_hw'
+                self.paddle_graph.add_layer(
+                    'paddle.split',
+                    inputs={"x": self.get_node_name(val_sizes)},
+                    outputs=[var_nc, var_hw],
+                    num_or_sections=[2, 2],
+                    axis=0)
+                self.paddle_graph.add_layer(
+                    "paddle.cast",
+                    inputs={"x": var_hw},
+                    outputs=[var_hw],
+                    dtype=string('int32'))
+#                 inputs['size'] = var_hw
+                
+                # TODO(syf): all use 
+                inputs['out_shape'] = var_hw
+                ipt = inputs.pop("x")
+                inputs["input"] = ipt
+                mode = node.get_attr('mode', 'nearest')
+                attrs = {"align_corners": False}
+                self.paddle_graph.add_layer(
+                    kernel="fluid.layers.resize_nearest",
+                    inputs=inputs,
+                    outputs=[node.layer_name],
+                    **attrs)
+                return
+        elif node.layer_type == 'Upsample':
+            val_scales = self.graph.get_input_node(node, idx=1, copy=True)
+            inputs['scale'] = val_scales
+
+        mode = node.get_attr('mode', 'nearest')
+        attrs = {"align_corners": False,
+                 "mode": string(mode),
+                 "align_mode": 1}
+        self.paddle_graph.add_layer(
+            kernel="paddle.nn.functional.interpolate",
+            inputs=inputs,
+            outputs=[node.layer_name],
+            **attrs)
+        
+    @print_mapping_info
+    def HardSigmoid(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        alpha = node.get_attr('alpha', 0.2)
+        beta = node.get_attr('beta', 0.5)
+        self.paddle_graph.add_layer(
+            kernel="paddle.scale",
+            inputs={"x": self.get_node_name(val_x)},
+            outputs=[node.layer_name + "_val"],
+            scale=alpha,
+            bias=beta)
+        self.paddle_graph.add_layer(
+            kernel="paddle.clip",
+            inputs={"x": node.layer_name + "_val"},
+            outputs=[node.layer_name],
+            min=0.0,
+            max=1.0)        
+
+    @print_mapping_info
+    def RoiAlign(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_rois = self.graph.get_input_node(node, idx=1, copy=True)
+
+        pooled_height = node.get_attr('output_height')
+        pooled_width = node.get_attr('output_width')
+        spatial_scale = node.get_attr('spatial_scale')
+        sampling_ratio = node.get_attr('sampling_ratio')
+        layer_attrs = {
+            'pooled_height': pooled_height,
+            'pooled_width': pooled_width,
+            'spatial_scale': spatial_scale,
+            'sampling_ratio': sampling_ratio,
+        }
+        self.paddle_graph.add_layer(
+            'fluid.layers.roi_align',
+            inputs={'input': self.get_node_name(val_x),
+                    'rois': self.get_node_name(val_rois)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+                       
+
+    @print_mapping_info
+    def MaxRoiPool(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_rois = self.graph.get_input_node(node, idx=1, copy=True)
+
+        spatial_scale = node.get_attr('spatial_scale')
+        pooled_height, pooled_width = node.get_attr('pooled_shape')
+        layer_attrs = {
+            'pooled_height': pooled_height,
+            'pooled_width': pooled_width,
+            'spatial_scale': spatial_scale,
+        }
+        self.paddle_graph.add_layer(
+            'fluid.layers.roi_pool',
+            inputs={'input': self.get_node_name(val_x),
+                    'rois': self.get_node_name(val_rois)},
+            outputs=[node.layer_name],
+            **layer_attrs)
+
+    @print_mapping_info
+    def Pad(self, node, op_independent=True):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        pads = node.get_attr('pads')
+        mode = node.get_attr('mode', 'constant')
+        value = node.get_attr('value', 0.)
+        data_shape = val_x.out_shapes[0]
+        output_shape = node.out_shapes[0]
+        assume_pad2d = False
+        layer_attrs = {}
+        layer_attrs['mode'] = string(mode)
+        paddings = []
+        if len(pads) == 4:
+            assume_pad2d |= mode != 'constant'
+            if data_shape:
+                assume_pad2d |= data_shape and len(data_shape) == 4  # NCHW
+            if output_shape:
+                assume_pad2d |= output_shape and len(output_shape) == 4  # NCHW
+        if assume_pad2d:
+            paddle_op = 'paddle.nn.Pad2D'
+            layer_attrs['data_format'] = string('NCHW')
+            layer_attrs['value'] = value
+        else:
+            paddle_op = 'fluid.layers.pad'
+            layer_attrs["pad_value"] = value
+        if len(pads) == 4:
+            paddings = np.array(pads).reshape(
+                (-1, 2)).transpose().flatten().tolist()  # SSEE -> SESE
+        elif len(pads) == 8:
+            paddings = np.array(pads).reshape(
+                (-1, 4)).transpose().flatten().tolist()  # SSEE -> SESE
+            if sum(paddings[:4]) == 0:
+                paddle_op = 'paddle.nn.Pad2D'
+                paddings = paddings[4:]
+                layer_attrs['value'] = value
+                if 'pad_value' in layer_attrs:
+                    layer_attrs.pop('pad_value')
+        tmp_paddings = copy.deepcopy(paddings)
+        paddings[0] = tmp_paddings[2]
+        paddings[1] = tmp_paddings[3]
+        paddings[2] = tmp_paddings[0]
+        paddings[3] = tmp_paddings[1]
+        if paddle_op == 'paddle.nn.Pad2D':
+            layer_attrs['padding'] = paddings
+            nn_op_name = name_generator("pad2d", self.nn_name2id)
+        else:
+            layer_attrs['paddings'] = paddings
+        if op_independent:
+            self.paddle_graph.add_layer(
+                paddle_op, 
+                inputs={'x': self.get_node_name(val_x)}, 
+                outputs=[nn_op_name, node.layer_name] if paddle_op == 'paddle.nn.Pad2D' else [node.layer_name], 
+                **layer_attrs)
+        else:
+            self.paddle_graph.add_layer(
+                paddle_op,
+                inputs={'x': self.get_node_name(val_x)},
+                outputs=[nn_op_name, node.layer_name + '_paded'] if paddle_op == 'paddle.nn.Pad2D' \
+                    else [node.layer_name + '_paded'],
+                **layer_attrs)
+            return node.layer_name + '_paded'
+
+    @print_mapping_info
+    def Unsqueeze(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        axes = node.get_attr('axes')
+        layer_attrs = {'axis': axes}
+        if len(val_x.out_shapes[0]) == 0:
+            if node.layer_name:
+                self.paddle_graph.add_layer(
+                    'paddle.reshape',
+                    inputs={"x": self.get_node_name(val_x)},
+                    outputs=[node.layer_name],
+                    shape=[1])
+        else:
+            self.paddle_graph.add_layer(
+                'paddle.unsqueeze', 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[node.layer_name],
+                **layer_attrs)
+
+    @print_mapping_info
+    def Shrink(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        bias = node.get_attr('bias')
+        lambd = node.get_attr('lambd')
+        assert bias == 0.0, 'not support bias!=0'
+        self.paddle_graph.add_layer(
+            'paddle.nn.functional.hardshrink', 
+            inputs={"x": self.get_node_name(val_x)}, 
+            outputs=[node.layer_name], 
+            threshold=lambd)
+
+    @print_mapping_info
+    def Constant(self, node):
+        val_output = self.graph.get_node(node.layer.output[0], copy=True)
+
+        value = node.get_attr('value')
+        dtype = np.dtype(value.dtype)
+        output_dtype = val_output.dtype
+        if output_dtype:
+            assert dtype == output_dtype, 'tensor dtype unmatches storage dtype'
+
+        shape = node.get_attr('shape', None)
+
+        if shape is None:
+            shape = val_output.out_shapes[0]
+        if shape is None:
+            shape = list(value.shape)
+            _logger.warning('in (Constant -> %s): '
+                            'attribute "shape" of %s not inferred, '
+                            'using value as 1-D tensor may lead to fails',
+                            val_output.layer_name, val_output.layer_name)
+        if len(value) == 1:
+            value = value.tolist()
+            value = value[0]
+            self.paddle_graph.add_layer(
+                "paddle.full", 
+                inputs={}, 
+                outputs=[node.layer_name],
+                dtype=string(dtype),
+                shape=[1],
+                fill_value=value)
+        else:
+            value = np.reshape(value, shape)
+            self.weights[node.layer_name] = value
+            self.paddle_graph.add_layer(
+                "self.create_parameter",
+                inputs={},
+                outputs=[node.layer_name],
+                shape=shape,
+                attr=string(node.layer_name),
+                dtype=string(dtype),
+                default_initializer="paddle.nn.initializer.Constant(value=0.0)")
+
+    @print_mapping_info
+    def Resize(self, node):
+        self._interpolate(node)
+
+    @print_mapping_info
+    def Upsample(self, node):
+        self._interpolate(node)
+
+    @print_mapping_info
+    def InstanceNormalization(self, node):
+        op_name = name_generator("instanse_norm", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_scale = self.graph.get_input_node(node, idx=1, copy=True)
+        val_b = self.graph.get_input_node(node, idx=2, copy=True)
+        epsilon = node.get_attr('epsilon', 1e-5)
+        layer_attrs = {
+            'num_features': node.out_shapes[0][1],
+            'epsilon': epsilon,
+            'weight_attr': string(self.get_node_name(val_scale)),
+            'bias_attr': string(self.get_node_name(val_b))
+        }
+        dim = len(val_x.out_shapes[0])
+        if dim == 2 or dim == 3:
+            paddle_op = "paddle.nn.InstanceNorm1D"
+        elif dim == 4:
+            paddle_op = "paddle.nn.InstanceNorm2D"
+        elif dim == 5:
+            paddle_op = "paddle.nn.InstanceNorm3D"
+        else:
+            raise Exception("The paddle only support 2D, 3D, 4D or 5D input in InstanceNormalization.")
+        self.paddle_graph.add_layer(
+            paddle_op, 
+            inputs={"x": self.get_node_name(val_x)}, 
+            outputs=layer_outputs, 
+            **layer_attrs)
+
+    @print_mapping_info
+    def Expand(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_shape = self.graph.get_input_node(node, idx=1, copy=True)
+        val_x_dtype = val_x.dtype
+        name_ones = node.layer_name + '_ones'
+        attr_ones = {
+            'shape': val_shape.layer_name,
+            'dtype': string(val_x_dtype),
+            'fill_value': 1
+        }
+        self.paddle_graph.add_layer(
+            'paddle.full',
+            inputs={},
+            outputs=[name_ones],
+            **attr_ones)
+        inputs_dict = {'x': name_ones, 
+                       'y': self.get_node_name(val_x)}
+        self.paddle_graph.add_layer(
+            'paddle.multiply',
+            inputs=inputs_dict,
+            outputs=[node.layer_name])
+
+    @print_mapping_info
+    def Gather(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        indices = self.graph.get_input_node(node, idx=1, copy=True)
+        indices_shape = indices.out_shapes[0]
+        axis = node.get_attr('axis', 0)
+        #assert len(
+        #    indices_shape) <= 2, "Gather op don't support dim of indice >2 "
+        if axis == 0 and len(indices_shape) <= 1:
+            if len(val_x.out_shapes[0]) <= 1:
+                self.paddle_graph.add_layer(
+                    'paddle.gather',
+                    inputs={'x': self.get_node_name(val_x),
+                            'index': self.get_node_name(indices)},
+                    outputs=[node.layer_name])
+            elif len(val_x.out_shapes[0]) > 1:
+                if len(indices_shape) == 0:
+                    gather_ = node.layer_name + '_1'
+                    self.paddle_graph.add_layer(
+                        'paddle.gather',
+                        inputs={'x': self.get_node_name(val_x),
+                                'index': self.get_node_name(indices)},
+                        outputs=[gather_])
+                    self.paddle_graph.add_layer(
+                        'paddle.squeeze',
+                        inputs={'x': gather_},
+                        outputs=[node.layer_name],
+                        axis=[0])
+                else:
+                    self.paddle_graph.add_layer(
+                        'paddle.gather',
+                        inputs={'x': self.get_node_name(val_x),
+                                'index': self.get_node_name(indices)},
+                        outputs=[node.layer_name])
+        elif axis > 0 and len(indices_shape) <= 1:
+            perm = list(range(len(val_x.out_shapes[0])))
+            perm = [axis] + perm[:axis] + perm[axis + 1:]
+            name_trans = val_x.layer_name + '_trans'
+            self.paddle_graph.add_layer(
+                'paddle.transpose',
+                inputs={"x": self.get_node_name(val_x)},
+                outputs=[name_trans],
+                perm=perm)
+            self.paddle_graph.add_layer(
+                'paddle.gather',
+                inputs={'x': name_trans,
+                        'index': self.get_node_name(indices)},
+                outputs=[node.layer_name])
+            self.paddle_graph.add_layer(
+                'paddle.transpose', 
+                inputs={"x": node.layer_name}, 
+                outputs=[node.layer_name], 
+                perm=perm)
+            if len(indices_shape) < 1:
+                self.paddle_graph.add_layer(
+                    'paddle.squeeze',
+                    inputs={'x': node.layer_name},
+                    outputs=[node.layer_name],
+                    axis=[axis])
+        elif axis == 0 and len(indices_shape) > 1:
+            if val_x.out_shapes[0] is not None and isinstance(
+                    val_x, ONNXGraphDataNode):
+                indices_cast = indices.layer_name + '_cast'
+                self.paddle_graph.add_layer(
+                    'paddle.cast',
+                    inputs={"x": self.get_node_name(indices)},
+                    outputs=indices_cast,
+                    dtype=string('int64'))
+                op_name = name_generator("embedding", self.nn_name2id)
+                output_name = node.layer_name
+                layer_outputs = [op_name, output_name]
+                self.paddle_graph.add_layer(
+                    'paddle.nn.Embedding',
+                    inputs={"x": indices_cast},
+                    outputs=layer_outputs,
+                    param_attr=string(val_x.layer_name),
+                    size=val_x.out_shapes[0])
+            else:
+                from functools import reduce
+                reshape_shape = reduce(lambda x, y: x * y, indices_shape)
+                indices_reshape = indices.layer_name + '_shape'
+                self.paddle_graph.add_layer(
+                    'paddle.reshape',
+                    inputs={"x": self.get_node_name(indices)},
+                    outputs=[indices_reshape],
+                    shape=[reshape_shape, ])
+
+                perm = list(range(len(val_x.out_shapes[0])))
+                self.paddle_graph.add_layer(
+                    'paddle.gather',
+                    inputs={'x': self.get_node_name(val_x),
+                            'index': indices_reshape},
+                    outputs=[node.layer_name])
+                val_x_shape = val_x.out_shapes[0]
+                reshaped_shape = []
+                for i in perm:
+                    reshaped_shape.append(indices_shape[i])
+                for i in val_x_shape[:axis] + val_x_shape[axis + 1:]:
+                    reshaped_shape.append(i)
+                self.paddle_graph.add_layer(
+                    'paddle.reshape',
+                    inputs={"x": node.layer_name},
+                    outputs=[node.layer_name],
+                    shape=reshaped_shape)
+        elif axis > 0 and len(indices_shape) > 1:
+            from functools import reduce
+            reshape_shape = reduce(lambda x, y: x * y, indices_shape)
+            indices_reshape = indices.layer_name + '_shape'
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": self.get_node_name(indices)},
+                outputs=[indices_reshape],
+                shape=[reshape_shape, ])
+
+            perm = list(range(len(val_x.out_shapes[0])))
+            perm = [axis] + perm[:axis] + perm[axis + 1:]
+            name_trans = val_x.layer_name + '_transpose'
+            self.paddle_graph.add_layer(
+                'paddle.transpose',
+                inputs={"x": self.get_node_name(val_x)},
+                outputs=[name_trans],
+                perm=perm)
+            self.paddle_graph.add_layer(
+                'paddle.gather',
+                inputs={'x': name_trans,
+                        'index': indices_reshape},
+                outputs=[node.layer_name])
+            input_transpose = node.layer_name + '_transpose'
+            self.paddle_graph.add_layer(
+                'paddle.transpose',
+                inputs={"x": node.layer_name},
+                outputs=[input_transpose],
+                perm=perm)
+            val_x_shape = val_x.out_shapes[0]
+            reshaped_shape = []
+            for i in perm:
+                reshaped_shape.append(indices_shape[i])
+            for i in val_x_shape[:axis] + val_x_shape[axis + 1:]:
+                reshaped_shape.append(i)
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": input_transpose},
+                outputs=[node.layer_name],
+                shape=reshaped_shape)
+
+    @print_mapping_info
+    def ScatterND(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        indices = self.graph.get_input_node(node, idx=1, copy=True)
+        updates = self.graph.get_input_node(node, idx=2, copy=True)
+        if len(indices.out_shapes[0]) == 1:
+            self.paddle_graph.add_layer(
+                'paddle.scatter',
+                inputs={'x': self.get_node_name(val_x),
+                        'index': self.get_node_name(indices),
+                        'updates': self.get_node_name(updates)},
+                outputs=[node.layer_name])
+        else:
+            input_inner_indices = node.layer_name + '_input_inner_indices'
+            shape = val_x.out_shapes[0]
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": self.get_node_name(indices)},
+                outputs=[self.get_node_name(indices)],
+                shape=indices.out_shapes[0])
+
+            zeros_like_val_x = val_x.layer_name + '_zeros'
+            self.paddle_graph.add_layer(
+                'paddle.zeros_like',
+                inputs={"x": self.get_node_name(val_x)},
+                outputs=[zeros_like_val_x])
+            self.paddle_graph.add_layer(
+                'paddle.scatter_nd_add',
+                inputs={
+                    'x': zeros_like_val_x,
+                    'index': self.get_node_name(indices),
+                    'updates': self.get_node_name(updates)
+                },
+                outputs=[input_inner_indices])
+            indices_mask = node.layer_name + '_indices_mask'
+            constant_minus_one = node.layer_name + '_constant_minus_one'
+            # full_like support create tensor shape like input tensor
+            self.paddle_graph.add_layer(
+                'paddle.full_like',
+                inputs={"x": self.get_node_name(updates)},
+                outputs=[constant_minus_one],
+                dtype=string(updates.dtype),
+                fill_value=-1)
+            self.paddle_graph.add_layer(
+                'paddle.scatter_nd_add',
+                inputs={
+                    'x': zeros_like_val_x,
+                    'index': self.get_node_name(indices),
+                    'updates': constant_minus_one
+                },
+                outputs=[indices_mask])
+            constant_one = node.layer_name + '_constant_1'
+            # full_like support create tensor shape like input tensor
+            self.paddle_graph.add_layer(
+                'paddle.full_like',
+                inputs={"x": self.get_node_name(val_x)},
+                outputs=[constant_one],
+                dtype=string(val_x.dtype),
+                fill_value=1)
+            input_out_indices_mask = node.layer_name + '_input_out_indices_mask'
+            self.paddle_graph.add_layer(
+                "paddle.add",
+                inputs={"x": indices_mask,
+                        "y": constant_one},
+                outputs=[input_out_indices_mask])
+
+            input_out_indices = node.layer_name + '_input_out_indices'
+            self.paddle_graph.add_layer(
+                "paddle.multiply",
+                inputs={"x": self.get_node_name(val_x),
+                        "y": input_out_indices_mask},
+                outputs=[input_out_indices])
+
+            self.paddle_graph.add_layer(
+                "paddle.add",
+                inputs={"x": input_inner_indices,
+                        "y": input_out_indices},
+                outputs=[node.layer_name])
+
+    @print_mapping_info
+    def Range(self, node):
+        val_start = self.graph.get_input_node(node, idx=0, copy=True)
+        val_limit = self.graph.get_input_node(node, idx=1, copy=True)
+        val_delta = self.graph.get_input_node(node, idx=2, copy=True)
+        dtype = val_start.dtype
+        inputs = {'start': self.get_node_name(val_start), 
+                  'end': self.get_node_name(val_limit), 
+                  'step': self.get_node_name(val_delta)}
+        self.paddle_graph.add_layer(
+            'paddle.arange',
+            inputs=inputs,
+            outputs=[node.layer_name],
+            dtype=string(dtype))
+
+    @print_mapping_info
+    def Slice(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        starts, ends, axes, steps = None, None, None, None
+        layer_attrs = {}
+        if len(node.inputs) > 1:
+            starts = self.graph.get_input_node(node, idx=1, copy=True)
+            ends = self.graph.get_input_node(node, idx=2, copy=True)
+            starts_value = _const_weight_or_none(starts)
+            ends_value = _const_weight_or_none(ends)
+
+            if len(node.inputs) > 3:
+                axes = self.graph.get_input_node(node, idx=3, copy=True)
+                axes = _const_weight_or_none(axes, necessary=True)
+            if len(node.inputs) > 4:
+                steps = self.graph.get_input_node(node, idx=4, copy=True)
+                steps = _const_weight_or_none(steps)
+            layer_attrs = {
+                "axes": axes,
+                "starts": starts.layer_name,
+                "ends": ends.layer_name
+            }
+            if starts_value is not None and ends_value is not None:
+                starts_value = starts_value.copy()
+                ends_value = ends_value.copy()
+                #for idx in range(len(ends_value)):
+                #    if ends_value[idx] > 2**31 - 1:
+                #        ends_value[idx] = 2**31 - 1
+                #print(val_x.out_shapes)
+                for idx in range(len(ends_value)):
+                    if starts_value[idx] >= val_x.out_shapes[0][axes[idx]]:
+                        starts_value[idx] = val_x.out_shapes[0][axes[idx]] - 1
+                        ends_value[idx] = val_x.out_shapes[0][axes[idx]]
+                        starts_value[idx] = val_x.out_shapes[0][axes[idx]] - 1
+                    elif ends_value[idx] > 2**31 - 1:
+                        ends_value[idx] = 2**31 - 1
+                layer_attrs = {
+                    "axes": axes,
+                    "starts": starts_value,
+                    "ends": ends_value
+                }
+            else:
+                if starts.dtype != 'int32':
+                    starts_cast = starts.layer_name + '_cast'
+                    self.paddle_graph.add_layer(
+                        'paddle.cast',
+                        inputs={"x": self.get_node_name(starts)},
+                        outputs=[starts_cast],
+                        dtype=string('int32'))
+                    layer_attrs['starts'] = starts_cast
+                if ends.dtype != 'int32':
+                    ends_cast = ends.layer_name + '_cast'
+                self.paddle_graph.add_layer(
+                    'paddle.cast',
+                    inputs={"x": self.get_node_name(ends)},
+                    outputs=[ends_cast],
+                    dtype=string('int32'))
+                layer_attrs['ends'] = ends_cast
+        else:
+            starts = node.get_attr('starts')
+            ends = node.get_attr('ends')
+            axes = node.get_attr('axes')
+            for idx in range(len(ends)):
+                if ends[idx] > 2**31 - 1:
+                    ends[idx] = 2**31 - 1
+            layer_attrs = {"axes": axes, "starts": starts, "ends": ends}
+
+        if steps is not None:
+            layer_attrs['strides'] = steps
+            self.paddle_graph.add_layer(
+                'paddle.strided_slice', 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[node.layer_name], 
+                **layer_attrs)
+        else:
+            self.paddle_graph.add_layer(
+                'paddle.slice', 
+                inputs={"input": self.get_node_name(val_x)}, 
+                outputs=[node.layer_name],  
+                **layer_attrs)
+
+    @print_mapping_info
+    def ConstantOfShape(self, node):
+        val_shape = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_node(node.layer.output[0], copy=True)
+
+        value = node.get_attr('value')
+        dtype = value.dtype
+        value = value.tolist()
+        assert len(value) == 1, ('given value not Scalar, shape of value > 1, '
+                                 'this is not supported')
+        if len(value) == 1:
+            value = value[0]
+            layer_attrs = {
+                'shape': val_shape.layer_name,
+                'dtype': string(dtype),
+                'fill_value': value
+            }
+            self.paddle_graph.add_layer(
+                "paddle.full", 
+                inputs={}, 
+                outputs=[node.layer_name],
+                **layer_attrs)
+
+    @print_mapping_info
+    def Clip(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_node(node.layer.output[0], copy=True)
+        max_value, min_value = None, None
+        if len(node.inputs) == 1:
+            max_value = node.get_attr('max')
+            min_value = node.get_attr('min')
+            layer_attrs = {
+                'max': max_value,
+                'min': min_value,
+            }
+            self.paddle_graph.add_layer(
+                'paddle.clip', 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[node.layer_name], 
+                **layer_attrs)
+        else:
+            max_ipt = self.graph.get_input_node(node, idx=1, copy=True)
+            min_ipt = self.graph.get_input_node(node, idx=2, copy=True)
+            max_value = _const_weight_or_none(max_ipt)
+            min_value = _const_weight_or_none(min_ipt)
+            if max_value.shape == (1, ):
+                max_value = max_value[0]
+            if min_value.shape == (1, ):
+                min_value = min_value[0]
+        if max_value is not None and min_value is not None:
+            layer_attrs = {'max': max_value, 'min': min_value}
+            self.paddle_graph.add_layer(
+                'paddle.clip', 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[node.layer_name], 
+                **layer_attrs)
+        else:
+            raise
+
+    @print_mapping_info
+    def Split(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+
+        paddle_op = 'split'
+        split = node.get_attr('split')
+        axis = node.get_attr('axis', 0)
+        layer_attrs = {
+            'num_or_sections': split,
+            'axis': axis,
+        }
+        outputs_list = list()
+        if isinstance(split, list) or isinstance(split, tuple):
+            for i, s in enumerate(split):
+                outputs_list.append("{}_{}".format(node.layer_name, i))
+        else:
+            outputs_list.append(node.layer_name)
+
+        self.paddle_graph.add_layer(
+            'paddle.split', 
+            inputs={"x": self.get_node_name(val_x)}, 
+            outputs=outputs_list, 
+            **layer_attrs)
+
+    @print_mapping_info
+    def Reshape(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_shape = self.graph.get_input_node(node, idx=1, copy=True)
+        val_reshaped = self.graph.get_node(node.layer.output[0], copy=True)
+        shape_value = _const_weight_or_none(val_shape)
+        shape_dims = len(val_shape.out_shapes[0])
+
+        if shape_value is not None:
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={'x': self.get_node_name(val_x)},
+                outputs=[node.layer_name],
+                shape=shape_value.tolist())
+        elif len(node.out_shapes[0]) > 0 and _is_static_shape(node.out_shapes[
+                0]):
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={'x': self.get_node_name(val_x)},
+                outputs=[node.layer_name],
+                shape=node.out_shapes[0])
+        else:
+            # shape may be [], come form Gather by scalar indices
+            if len(val_shape.out_shapes[0]) > 0:
+                self.paddle_graph.add_layer(
+                    'paddle.reshape',
+                    inputs={'x': self.get_node_name(val_shape)},
+                    outputs=[self.get_node_name(val_shape)],
+                    shape=val_shape.out_shapes[0])
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={'x': self.get_node_name(val_x),
+                        'shape': self.get_node_name(val_shape)},
+                outputs=node)
+
+    @print_mapping_info
+    def Cast(self, node):
+        val_input = self.graph.get_input_node(node, idx=0, copy=True)
+        val_output = self.graph.get_node(node.layer.output[0], copy=True)
+
+        dtype = node.get_attr('to')
+        if not isinstance(dtype, np.dtype):
+            dtype = TENSOR_TYPE_TO_NP_TYPE[dtype]
+
+        output_dtype = val_output.dtype
+        if output_dtype:
+            assert dtype == output_dtype, 'dtype of to unmatches output'
+        self.paddle_graph.add_layer(
+            'paddle.cast', 
+            inputs={'x': self.get_node_name(val_input)}, 
+            outputs=[node.layer_name], 
+            dtype=string(dtype))
+
+    @print_mapping_info
+    def Not(self, node):
+        val_input = self.graph.get_input_node(node, idx=0, copy=True)
+        self.paddle_graph.add_layer('paddle.logical_not', 
+                                    inputs={'x': self.get_node_name(val_input)}, 
+                                    outputs=[node.layer_name])
+
+    @print_mapping_info
+    def AveragePool(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+
+        auto_pad = node.get_attr('auto_pad', 'NOTSET')
+        kernel_shape = node.get_attr("kernel_shape")
+        poolnd = len(kernel_shape)
+        strides = node.get_attr("strides")
+        pad_mode = node.get_attr("pads")
+        ceil_mode = bool(node.get_attr('ceil_mode', 0))
+        pads = node.get_attr('pads', [0] * (poolnd * 2))
+
+        paddings, val_x = self._pad_if_asymmetric(node, pads, val_x)
+
+        if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
+            input_shape = val_x.out_shapes[0]
+            pad_h = _get_same_padding(input_shape[2], kernel_shape[0],
+                                      strides[0])
+            pad_w = _get_same_padding(input_shape[3], kernel_shape[1],
+                                      strides[1])
+            paddings = pad_h + pad_w
+
+        paddle_op = 'fluid.layers.pool{}d'.format(poolnd)
+        assert 2 <= poolnd <= 3, 'only pool2d and pool3d are supported'
+        layer_attrs = {
+            "pool_size": kernel_shape,
+            "pool_type": string('avg'),
+            "pool_stride": strides,
+            "pool_padding": paddings,
+            "ceil_mode": ceil_mode,
+            "exclusive": 'True',
+            "name": string(node.layer_name)
+        }
+        self.paddle_graph.add_layer(
+            paddle_op, 
+            inputs={'input': val_x if isinstance(val_x, str) else self.get_node_name(val_x)}, 
+            outputs=[node.layer_name], 
+            **layer_attrs)
+        # TODO(syf): op has diff
+#         op_name = name_generator("pool", self.nn_name2id)
+#         output_name = node.layer_name
+#         layer_outputs = [op_name, output_name]
+#         paddle_op = 'paddle.nn.Pool{}D'.format(poolnd)
+#         assert 1 <= poolnd <= 3, 'only Pool1D, Pool2D and Pool3D are supported'
+#         layer_attrs = {
+#             "kernel_size": kernel_shape,
+#             "stride": strides,
+#             "padding": paddings,
+#             "ceil_mode": ceil_mode,
+#             "exclusive": 'True',
+#         }
+#         self.paddle_graph.add_layer(
+#             paddle_op, 
+#             inputs={'x': self.get_node_name(val_x)}, 
+#             outputs=layer_outputs, 
+#             **layer_attrs)
+
+    @print_mapping_info
+    def Concat(self, node):
+        inputs_list = []
+        dtypes = set()
+        for i in range(len(node.layer.input)):
+            ipt = self.graph.get_input_node(node, idx=i, copy=True)
+            inputs_list.append(self.get_node_name(ipt))
+            dtypes.add(ipt.dtype)
+        if len(dtypes) > 1:
+            assert 'Unspported situation happened, please create issue on https://github.com/PaddlePaddle/X2Paddle/issues.'
+        axis = node.get_attr('axis')
+        self.paddle_graph.add_layer(
+            'paddle.concat', 
+            inputs={"x": inputs_list}, 
+            outputs=[node.layer_name], 
+            axis=axis)
+
+    @print_mapping_info
+    def Flatten(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        output_shape = node.out_shapes[0]
+        axis = node.get_attr('axis', 1)
+        shape_list = [1, 1]
+        if axis == 0:
+            for s in output_shape:
+                shape_list[1] *= s
+        else:
+            for s in output_shape[:axis]:
+                shape_list[0] *= s
+            for s in output_shape[axis:]:
+                shape_list[1] *= s
+        self.paddle_graph.add_layer(
+            'paddle.reshape', 
+            inputs={"x": self.get_node_name(val_x)}, 
+            outputs=[node.layer_name],
+            shape=shape_list)
+
+    @print_mapping_info
+    def Gemm(self, node):
+        val_a = self.graph.get_input_node(node, idx=0, copy=True)
+        val_b = self.graph.get_input_node(node, idx=1, copy=True)
+        val_c = self.graph.get_input_node(node, idx=2, copy=True)
+
+        alpha = node.get_attr('alpha', 1.)  # optional
+        beta = node.get_attr('beta', 1.)  # optional
+        trans_a = bool(node.get_attr('transA', 0))  # optional
+        trans_b = bool(node.get_attr('transB', 0))  # optional
+        val_mm = node.layer_name + '_mm'
+        matmul_inputs = {"x": self.get_node_name(val_a), 
+                         "y": self.get_node_name(val_b)}
+        attr_matmul = {
+            "transpose_x": trans_a,
+            "transpose_y": trans_b,
+        }
+        self.paddle_graph.add_layer(
+            'paddle.matmul',
+            inputs=matmul_inputs,
+            outputs=[val_mm],
+            **attr_matmul)
+        self.paddle_graph.add_layer(
+            "paddle.scale", 
+            inputs={"x": val_mm}, 
+            outputs=[val_mm],
+            scale=alpha)
+
+        if beta != 0:
+            if beta == 1.:
+                add_inputs = {"x": val_mm, 
+                              "y": self.get_node_name(val_c)}
+                self.paddle_graph.add_layer(
+                    "paddle.add",
+                    inputs=add_inputs,
+                    outputs=[node.layer_name])
+            else:
+                var_beta = node.layer_name + '_beta'
+                self.paddle_graph.add_layer(
+                    "paddle.scale",
+                    inputs={"x": self.get_node_name(val_c)},
+                    outputs=[var_beta],
+                    scale=beta)
+                add_inputs = {"x": val_mm, "y": var_beta}
+                self.paddle_graph.add_layer(
+                    "paddle.addd",
+                    inputs=add_inputs,
+                    outputs=[node.layer_name])
+
+    @print_mapping_info
+    def Sum(self, node):
+        val_inps = node.layer.input
+        inputs_dict = {
+            "x": self.get_node_name(
+                self.graph.get_input_node(
+                node, idx=0, copy=True)),
+            "y": self.get_node_name(
+                self.graph.get_input_node(
+                node, idx=1, copy=True)),
+        }
+        self.paddle_graph.add_layer("paddle.add", 
+                                    inputs=inputs_dict, 
+                                    outputs=[node.layer_name])
+
+        for idx, ipt in enumerate(val_inps[2:]):
+            y = self.graph.get_input_node(node, idx=idx, copy=True)
+            inputs_dict = {
+                "x": node.layer_name,
+                "y": self.get_node_name(y),
+            }
+            self.paddle_graph.add_layer(
+                "paddle.add", 
+                inputs=inputs_dict, 
+                outputs=[node.layer_name])
+
+    @print_mapping_info
+    def MatMul(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        x_shape = val_x.out_shapes[0]
+        y_shape = val_y.out_shapes[0]
+        inputs_dict = {"x": self.get_node_name(val_x), 
+                       "y": self.get_node_name(val_y)}
+        if y_shape[0] == 1 and x_shape[-1] != 1 and x_shape[0] != 1:
+            y_squeeze = val_y.layer_name + '_squeeze'
+            self.paddle_graph.add_layer(
+                "paddle.squeeze",
+                inputs={"x": self.get_node_name(val_y)},
+                outputs=[y_squeeze],
+                axis=[0])
+            inputs_dict['y'] = y_squeeze
+            self.paddle_graph.add_layer(
+                "paddle.matmul", 
+                inputs=inputs_dict, 
+                outputs=[node.layer_name])
+        else:
+            self.paddle_graph.add_layer(
+                "paddle.matmul", 
+                inputs=inputs_dict, 
+                outputs=[node.layer_name])
+
+    @print_mapping_info
+    def BatchNormalization(self, node):
+        op_name = name_generator("batchnorm", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_scale = self.graph.get_input_node(node, idx=1, copy=True)
+        val_b = self.graph.get_input_node(node, idx=2, copy=True)
+        val_mean = self.graph.get_input_node(node, idx=3, copy=True)
+        val_var = self.graph.get_input_node(node, idx=4, copy=True)
+
+        momentum = node.get_attr('momentum', .9)
+        epsilon = node.get_attr('epsilon', 1e-5)
+        c = val_x.out_shapes[0][1]
+
+        # Attribute: spatial is used in BatchNormalization-1,6,7
+        spatial = bool(node.get_attr('spatial'))
+        layer_attrs = {
+            "num_channels": c,
+            "momentum": momentum,
+            "epsilon": epsilon,
+            "is_test": True,
+            "param_attr": string(self.get_node_name(val_scale)),
+            "bias_attr": string(self.get_node_name(val_b)),
+            "moving_mean_name": string(self.get_node_name(val_mean)),
+            "moving_variance_name": string(self.get_node_name(val_var)),
+            "use_global_stats": False,
+        }
+        self.paddle_graph.add_layer(
+            "paddle.nn.BatchNorm", 
+            inputs={"x": self.get_node_name(val_x)}, 
+            outputs=layer_outputs, 
+            **layer_attrs)
+
+    @print_mapping_info
+    def Transpose(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        perm = node.get_attr('perm')
+        self.paddle_graph.add_layer(
+            "paddle.transpose", 
+            inputs={"x": self.get_node_name(val_x)},
+            outputs=[node.layer_name], 
+            perm=perm)
+
+    @print_mapping_info
+    def PRelu(self, node):
+        op_name = name_generator("prelu", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_slope = self.graph.get_input_node(node, idx=1, copy=True)
+
+        mode = 'channel'
+        shape_slope = val_slope.out_shapes[0]
+        if shape_slope == [1]:
+            mode = 'all'
+        elif len(shape_slope) > 2:
+            mode = 'element'
+
+        if mode == 'channel' and len(shape_slope) == 1:
+            # paddle params shape need be [1, channel]
+            slope_data = _const_weight_or_none(val_slope)
+            slope_data = np.reshape(slope_data, [1] + shape_slope)
+            self.weights[val_slope.layer_name] = slope_data
+
+        layer_attrs = {
+            "param_attr": string(val_slope.layer_name),
+            'mode': string(mode),
+            "channel": val_x.out_shapes[0][1] if mode == "channel" else None,
+            "input_shape": val_x.out_shapes[0] if mode == "element" else None,
+        }
+        self.paddle_graph.add_layer(
+            "paddle.nn.PReLU", 
+            inputs={"x": self.get_node_name(val_x)}, 
+            outputs=layer_outputs, 
+            **layer_attrs)
+
+    @print_mapping_info
+    def Squeeze(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        axes = node.get_attr('axes')
+        if len(val_x.out_shapes[0]) == 1:
+            self.paddle_graph.add_layer(
+                "paddle.cast",
+                inputs={"x": self.get_node_name(val_x)},
+                outputs=[node.layer_name],
+                dtype=string(val_x.dtype))
+        else:
+            self.paddle_graph.add_layer(
+                "paddle.squeeze", 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[node.layer_name], 
+                axis=axes)
+
+    @print_mapping_info
+    def Equal(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        self.paddle_graph.add_layer(
+            "paddle.equal",
+            inputs={'x': self.get_node_name(val_x),
+                    'y': self.get_node_name(val_y)},
+            outputs=[node.layer_name])
+
+    @print_mapping_info
+    def Greater(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        self.paddle_graph.add_layer(
+            "paddle.greater_than",
+            inputs={'x': self.get_node_name(val_x),
+                    'y': self.get_node_name(val_y)},
+            outputs=node,
+            param_attr=None)
+
+    @print_mapping_info
+    def Where(self, node):
+        condition = self.graph.get_input_node(node, idx=0, copy=True)
+        val_x = self.graph.get_input_node(node, idx=1, copy=True)
+        val_y = self.graph.get_input_node(node, idx=2, copy=True)
+
+        not_condition = condition.layer_name + '_not'
+        self.paddle_graph.add_layer(
+            "paddle.logical_not",
+            inputs={"x": self.get_node_name(condition)},
+            outputs=[not_condition])
+        cast_not_condition = not_condition + '_cast'
+        self.paddle_graph.add_layer(
+            "paddle.cast",
+            inputs={"x": not_condition},
+            outputs=[cast_not_condition],
+            dtype=string(val_x.dtype))
+        cast_condition = condition.layer_name + '_cast'
+        self.paddle_graph.add_layer(
+            "paddle.cast",
+            inputs={"x": self.get_node_name(condition)},
+            outputs=[cast_condition],
+            dtype=string(val_x.dtype))
+        mul_val_x = val_x.layer_name + '_mul'
+        self.paddle_graph.add_layer(
+            "paddle.multiply",
+            inputs={'x': self.get_node_name(val_x),
+                    'y': cast_condition},
+            outputs=[mul_val_x])
+        mul_val_y = val_y.layer_name + '_mul'
+        self.paddle_graph.add_layer(
+            "paddle.multiply",
+            inputs={'x': self.get_node_name(val_y),
+                    'y': cast_not_condition},
+            outputs=[mul_val_y])
+
+        self.paddle_graph.add_layer(
+            "paddle.add",
+            inputs={'x': mul_val_x,
+                    'y': mul_val_y},
+            outputs=[node.layer_name])
+
+    @print_mapping_info
+    def NonZero(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_x_dim = len(val_x.out_shapes[0])
+        if val_x_dim == 1:
+            self.paddle_graph.add_layer(
+                "paddle.nonzero", 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[self.get_node_name(val_x)])
+            self.paddle_graph.add_layer(
+                "paddle.transpose",
+                inputs={"x": self.get_node_name(val_x)},
+                outputs=[node.layer_naem],
+                perm=[1, 0])
+        if val_x_dim > 1:
+            self.paddle_graph.add_layer(
+                "paddle.nonzero", 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[self.get_node_name(val_x)])
+            self.paddle_graph.add_layer(
+                "paddle.split",
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[self.get_node_name(val_x)],
+                num_or_sections=1,
+                axis=val_x_dim)
+            self.paddle_graph.add_layer(
+                "paddle.concat", 
+                inputs={"x": self.get_node_name(val_x)}, 
+                outputs=[node.layer_name])
+
+    @print_mapping_info
+    def Identity(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        self.paddle_graph.add_layer(
+            "paddle.assign", 
+            inputs={"x": self.get_node_name(val_x)}, 
+            outputs=[node.layer_name])
+
+    @print_mapping_info
+    def Tile(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_repeats = self.graph.get_input_node(node, idx=1, copy=True)
+        repeats = _const_weight_or_none(val_repeats)
+
+        if repeats is None:
+            repeats = val_repeats.layer_name
+            if val_repeats.dtype != 'int32':
+                self.paddle_graph.add_layer(
+                    "paddle.cast",
+                    inputs={"x": repeats},
+                    outputs=["{}.tmp".format(repeats)],
+                    dtype=string("int32"))
+                repeats = "{}.tmp".format(repeats)
+
+        elif isinstance(repeats, int):
+            repeats = [repeats]
+
+        attr = {
+            'expand_times': repeats,
+            "name": string(node.layer_name),
+        }
+        self.paddle_graph.add_layer(
+            "paddle.tile", 
+            inputs={"x": self.get_node_name(val_x)}, 
+                    outputs=[node.layer_name], 
+                    repeat_times=repeats)
+
+    @print_mapping_info
+    def MaxPool(self, node):
+        op_name = name_generator("pool", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        auto_pad = node.get_attr('auto_pad', 'NOTSET')
+        assert node.get_attr(
+            "dilations") is None, 'only dilations = 0 is supported'  # optional
+
+        kernel_shape = node.get_attr("kernel_shape")
+        poolnd = len(kernel_shape)
+        strides = node.get_attr("strides")
+        pad_mode = node.get_attr("pads")
+        ceil_mode = bool(node.get_attr('ceil_mode', 0))  # optional
+        pads = node.get_attr('pads', [0] * (poolnd * 2))  # optional
+        paddle_op = 'paddle.nn.MaxPool{}D'.format(poolnd)
+        assert 1 <= poolnd <= 3, 'only Pool1D, Pool2D and Pool3D are supported'
+
+        paddings, val_x = self._pad_if_asymmetric(node, pads, val_x)
+
+        if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
+            input_shape = val_x.out_shapes[0]
+            pad_h = _get_same_padding(input_shape[2], kernel_shape[0],
+                                      strides[0])
+            pad_w = _get_same_padding(input_shape[3], kernel_shape[1],
+                                      strides[1])
+            paddings = pad_h + pad_w
+            
+        layer_attrs = {
+            "kernel_size": kernel_shape,
+            "stride": strides,
+            "padding": paddings,
+            "ceil_mode": ceil_mode,
+        }
+        self.paddle_graph.add_layer(
+            paddle_op, 
+            inputs={'x': val_x if isinstance(val_x, str) else self.get_node_name(val_x)}, 
+            outputs=layer_outputs, 
+            **layer_attrs)
+
+    @print_mapping_info
+    def GlobalMaxPool(self, node):
+        op_name = name_generator("pool", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        input_shape = val_x.out_shapes[0]
+        if len(input_shape) == 4:
+            poolnd = 2
+        elif len(input_shape) == 5:
+            poolnd = 3
+        elif len(input_shape) == 3:
+            poolnd = 1
+        paddle_op = 'paddle.nn.AdaptiveMaxPool{}D'.format(poolnd)
+        assert 1 <= poolnd <= 3, 'only Pool1D, Pool2D and Pool3D are supported'
+        output_shape = node.out_shapes[0]
+        self.paddle_graph.add_layer(
+            paddle_op, 
+            inputs={'x': self.get_node_name(val_x)}, 
+            outputs=layer_outputs, 
+            output_size=output_shape[2:])
+
+    @print_mapping_info
+    def GlobalAveragePool(self, node):
+        op_name = name_generator("pool", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        input_shape = val_x.out_shapes[0]
+        if len(input_shape) == 4:
+            poolnd = 2
+        elif len(input_shape) == 5:
+            poolnd = 3
+        elif len(input_shape) == 3:
+            poolnd = 1
+        paddle_op = 'paddle.nn.AdaptiveAvgPool{}D'.format(poolnd)
+        assert 1 <= poolnd <= 3, 'only Pool1D, Pool2D and Pool3D are supported'
+        output_shape = node.out_shapes[0]
+        self.paddle_graph.add_layer(
+            paddle_op, 
+            inputs={'x': self.get_node_name(val_x)}, 
+            outputs=layer_outputs, 
+            output_size=output_shape[2:])
+
+    @print_mapping_info
+    def Conv(self, node):
+        op_name = name_generator("conv", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [op_name, output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_w = self.graph.get_input_node(node, idx=1, copy=True)
+        val_y = self.graph.get_node(node.layer.output[0], copy=True)
+        has_bias = len(node.layer.input) == 3
+        if has_bias:
+            val_b = self.graph.get_input_node(node, idx=2, copy=True)
+        auto_pad = node.get_attr('auto_pad', 'NOTSET')
+
+        kernel_shape = node.get_attr('kernel_shape')
+        convnd = len(kernel_shape)
+        assert 2 <= convnd <= 3, 'only Conv2D and Conv3D is supported'
+        num_out_channels = val_w.out_shapes[0][0]
+        num_in_channels = val_w.out_shapes[0][1]
+        paddle_op = 'paddle.nn.Conv{}D'.format(convnd)
+
+        num_groups = node.get_attr('group', 1)
+        strides = node.get_attr('strides', [1] * convnd)
+        dilations = node.get_attr('dilations', [1] * convnd)
+        pads = node.get_attr('pads', [0] * (convnd * 2))
+
+        input_shape = val_x.out_shapes[0]
+        paddings, val_x = self._pad_if_asymmetric(node, pads, val_x)
+
+        if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
+            pad_h = _get_same_padding(input_shape[2], kernel_shape[0],
+                                      strides[0])
+            pad_w = _get_same_padding(input_shape[3], kernel_shape[1],
+                                      strides[1])
+            paddings = pad_h + pad_w
+
+        layer_attrs = {
+            "in_channels": num_in_channels * num_groups,
+            "out_channels": num_out_channels,
+            "kernel_size": kernel_shape,
+            "stride": strides,
+            "padding": paddings,
+            "dilation": dilations,
+            "groups": num_groups,
+            'weight_attr': string(val_w.layer_name),
+        }
+        if has_bias:
+            layer_attrs["bias_attr"] = string(val_b.layer_name)
+        else:
+            layer_attrs["bias_attr"] = False
+        self.paddle_graph.add_layer(
+            paddle_op, 
+            inputs={'x': val_x if isinstance(val_x, str) else self.get_node_name(val_x)}, 
+            outputs=layer_outputs, 
+            **layer_attrs)
+
+    @print_mapping_info
+    def ConvTranspose(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_w = self.graph.get_input_node(node, idx=1, copy=True)
+        val_b = None
+        if len(node.layer.input) > 2:
+            val_b = self.graph.get_input_node(node, idx=2, copy=True)
+        auto_pad = node.get_attr('auto_pad', 'NOTSET')
+        out_padding = node.get_attr('output_padding', [0, 0])
+        kernel_shape = node.get_attr('kernel_shape')
+        assert kernel_shape, 'kernel_shape not inferred'
+        convnd = len(kernel_shape)
+        assert 2 <= convnd <= 3, 'only Conv2DTranspose and Conv3DTranspose supported'
+        num_in_channels = val_w.out_shapes[0][0]
+        num_out_channels = val_w.out_shapes[0][1]
+        paddle_op = 'paddle.nn.functional.conv{}d_transpose'.format(convnd)
+
+        num_groups = node.get_attr('group', 1)
+        strides = node.get_attr('strides', [1] * convnd)
+        dilations = node.get_attr('dilations', [1] * convnd)
+        output_size = node.get_attr('output_shape', [])
+        pads = node.get_attr('pads', [0] * (convnd * 2))
+
+        paddings, var_x = self._pad_if_asymmetric(node, pads, val_x)
+
+        output_size = [0, 0]
+
+        output_size[0] = (val_x.out_shapes[0][2] - 1
+                          ) * strides[0] - 2 * paddings[0] + dilations[0] * (
+                              kernel_shape[0] - 1) + 1 + out_padding[0]
+        output_size[1] = (val_x.out_shapes[0][3] - 1
+                          ) * strides[1] - 2 * paddings[1] + dilations[1] * (
+                              kernel_shape[1] - 1) + 1 + out_padding[1]
+#         layer_attrs = {
+#             'in_channels': num_in_channels,
+#             'out_channels': num_out_channels,
+#             'output_size': output_size or None,
+#             'kernel_size': kernel_shape,
+#             'padding': paddings,
+#             'stride': strides,
+#             'dilation': dilations,
+#             'groups': num_groups,
+#             'weight_attr': string(val_w.layer_name),
+#             'bias_attr': None if val_b is None else string(val_b.layer_name),
+#         }
+#         self.paddle_graph.add_layer(
+#             paddle_op, 
+#             inputs={"x": self.get_node_name(val_x)}, 
+#             outputs=layer_outputs, 
+#             **layer_attrs)
+        inputs_dict = {'x': val_x if isinstance(val_x, str) else self.get_node_name(val_x),
+                       "weight": val_w.layer_name}
+        layer_attrs = {
+            "stride": strides,
+            "dilation": dilations,
+            "padding": paddings,
+            "groups": num_groups,
+            "output_size": node.out_shapes[0][2:]}
+        if val_b is not None:
+            inputs_dict["bias"] = val_b.layer_name
+        else:
+            layer_attrs["bias"] = None
+        self.paddle_graph.add_layer(
+            kernel="paddle.nn.functional.conv2d_transpose",
+            inputs=inputs_dict,
+            outputs=[node.layer_name],
+            **layer_attrs)

x2paddle/op_mapper/static/onnx2paddle/onnx_op_mapper.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from x2paddle.op_mapper.onnx2paddle.opset9 import OpSet9, custom_layers
+from x2paddle.op_mapper.static.onnx2paddle.opset9 import OpSet9, custom_layers
 from x2paddle.core.op_mapper import OpMapper
 from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
 

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

@@ -17,7 +17,7 @@ from x2paddle.core.graph import GraphNode
 from x2paddle.core.fluid_code import Layer
 from x2paddle.core.fluid_code import FluidCode
 from x2paddle.core.util import string
-from x2paddle.op_mapper.onnx2paddle.opset9.custom_layer import *
+from x2paddle.op_mapper.static.onnx2paddle.opset9.custom_layer import *
 from functools import reduce
 import numpy as np
 import onnx