tf_op_mapper_nhwc.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.tf_decoder import TFGraph, TFGraphNode
from x2paddle.core.op_mapper import OpMapper
from x2paddle.core.util import *
import inspect
import numpy
import sys


# compute padding size for SAME mode
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
    if pad_size < 0:
        pad_size = 0
    pad0 = int(pad_size / 2)
    pad1 = pad_size - pad0
    return [pad0, pad1]


def process_pack_shape(graph, param, shape_value):
    pack_inputs = [
        graph.get_node(name, copy=True) for name in param.layer.input
    ]
    all_const_value = 0
    for i in range(len(pack_inputs)):
        if pack_inputs[i].layer_type == "Const":
            pack_inputs[i] = pack_inputs[i].value
            all_const_value += 1
        elif shape_value[i] > 0:
            pack_inputs[i] = shape_value[i]
            all_const_value += 1
        else:
            if hasattr(pack_inputs[i], "index"):
                index = pack_inputs[i].index
                pack_inputs[i] = pack_inputs[i].layer_name + "[{}]".format(
                    index)
            else:
                pack_inputs[i] = pack_inputs[i].layer_name

    string_params = "["
    for i in range(len(pack_inputs)):
        string_params += "{}, ".format(pack_inputs[i])
    string_params = string_params.strip(", ") + "]"
    return string_params


class TFOpMapperNHWC(OpMapper):
    directly_map_ops = {
        'Relu': ['relu'],
        'Relu6': ['relu6'],
        'Shape': ['shape'],
        'Abs': ['abs'],
        'Sigmoid': ['sigmoid'],
        'Exp': ['exp'],
        'Rsqrt': ['rsqrt'],
        'swish_f32': ['swish'],
        'Tanh': ['tanh'],
        'LeakyRelu': ['leaky_relu', {
            'alpha': 'alpha'
        }]
    }
    elementwise_ops = {
        'Add': 'elementwise_add',
        'RealDiv': 'elementwise_div',
        'Sub': 'elementwise_sub',
        'Maximum': 'elementwise_max',
        'Mul': 'elementwise_mul',
        'FloorDiv': 'elementwise_floordiv'
    }

    def __init__(self, decoder):
        super(TFOpMapperNHWC, self).__init__()
        self.decoder = decoder
        self.graph = decoder.tf_graph
        self.weights = dict()
        self.batch_node = None
        self.omit_nodes = list()
        self.used_custom_layers = dict()

        not_placeholder = list()
        for name in self.graph.input_nodes:
            if self.graph.get_node(name).layer_type != "Placeholder":
                not_placeholder.append(name)
        for name in not_placeholder:
            idx = self.graph.input_nodes.index(name)
            del self.graph.input_nodes[idx]

        unsupported_ops = set()
        sys.stderr.write("Total nodes: {}\n".format(len(self.graph.topo_sort)))
        for i, node_name in enumerate(self.graph.topo_sort):
            sys.stderr.write("\rConverting node {} ...     ".format(i + 1))
            node = self.graph.get_node(node_name)
            op = node.layer_type
            if op in self.directly_map_ops:
                if len(unsupported_ops) > 0:
                    continue
                self.directly_map(node)
            elif op in self.elementwise_ops:
                if len(unsupported_ops) > 0:
                    continue
                self.elementwise_map(node)
            elif hasattr(self, op):
                if len(unsupported_ops) > 0:
                    continue
                func = getattr(self, op)
                func(node)
            else:
                unsupported_ops.add(op)
                continue
        if len(unsupported_ops) > 0:
            print("========= {} OPs are not supported yet ===========".format(
                len(unsupported_ops)))
            for op in unsupported_ops:
                print("========== {} ============".format(op))
            sys.exit(-1)
        sys.stderr.write("\nDone!\n")

    def add_omit_nodes(self, in_node_name, out_node_name):
        in_node = self.graph.get_node(in_node_name)
        out_node = self.graph.get_node(out_node_name)
        index = in_node.outputs.index(out_node_name)
        del in_node.outputs[index]
        index = out_node.inputs.index(in_node_name)
        del out_node.inputs[index]
        self.omit_nodes.append(in_node.layer_name)

    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_node(node.layer.input[0], copy=True)
        attr = dict()
        for param in op_info[1:]:
            tf_param_name = list(param.keys())[0]
            pd_param_name = list(param.values())[0]
            tf_param = node.get_attr(tf_param_name)
            attr[pd_param_name] = tf_param

        node.fluid_code.add_layer(op_info[0],
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def elementwise_map(self, node):
        assert node.layer_type in self.elementwise_ops
        op_type = self.elementwise_ops[node.layer_type]
        x = self.graph.get_node(node.layer.input[0], copy=True)
        y = self.graph.get_node(node.layer.input[1], copy=True)
        x_shape = x.out_shapes[0]
        y_shape = y.out_shapes[0]
        if len(x_shape) == 0:
            x_shape = [1]
        if len(y_shape) == 0:
            y_shape = [1]
        # incomplement broadcasting support for paddle
        x_input = x
        y_input = y
        if len(x_shape) < len(y_shape):
            unrevertable_ops = [
                "elementwise_sub", "elementwise_div", "elementwise_floordiv",
                "elementwise_mod", "elementwise_pow"
            ]
            if op_type not in unrevertable_ops:
                x_input = y
                y_input = x
                x_shape = y.out_shapes[0]
                if len(x_shape) == 0:
                    x_shape = [1]
                y_shape = x.out_shapes[0]
                if len(y_shape) == 0:
                    y_shape = [1]
            else:
                raise Exception("Unexpected situation happend")

        if len(x_shape) == 4 and len(y_shape) == 1:
            inputs = {"x": x_input, "y": y_input}
            node.fluid_code.add_layer(op_type, inputs=inputs, output=node)
            return

        is_sub_seq = True
        for i in range(len(y_shape)):
            index = -1 * i - 1
            if y_shape[index] != x_shape[index]:
                is_sub_seq = False
        if not is_sub_seq:
            x_expand_times = [1] * len(x_shape)
            y_expand_times = [1] * len(y_shape)
            x_need_expand = False
            y_need_expand = False
            for i in range(len(y_shape)):
                index = -1 * i - 1
                if y_shape[index] != x_shape[index]:
                    if y_shape[index] == 1:
                        y_expand_times[index] = x_shape[index]
                        y_need_expand = True
                    elif x_shape[index] == 1:
                        x_expand_times[index] = y_shape[index]
                        x_need_expand = True
                    else:
                        raise Exception("Unexpected situation happend")
            if x_need_expand:
                attr = {"expand_times": x_expand_times}
                node.fluid_code.add_layer("expand",
                                          inputs=x_input,
                                          output="x_tmp",
                                          param_attr=attr)
                x_input = "x_tmp"
            if y_need_expand:
                attr = {"expand_times": y_expand_times}
                node.fluid_code.add_layer("expand",
                                          inputs=y_input,
                                          output="y_tmp",
                                          param_attr=attr)
                y_input = "y_tmp"
        inputs = {"x": x_input, "y": y_input}
        node.fluid_code.add_layer(op_type,
                                  inputs=inputs,
                                  output=node,
                                  param_attr=None)

    def Placeholder(self, node):
        shape = node.out_shapes[0]
        assert len(shape) != 0, "Unknown shape of input nodes[{}].".format(
            node.layer_name)
        dtype = node.dtype
        if shape[0] < 0:
            self.batch_node = node
        attr = {
            'dtype': string(dtype),
            'shape': shape,
            'name': string(node.layer_name),
            'append_batch_size': False
        }

        node.fluid_code.add_layer("data",
                                  inputs=None,
                                  output=node,
                                  param_attr=attr)

    def Const(self, node):
        shape = node.out_shapes[0]
        dtype = node.dtype
        value = node.value
        initializer = "Constant(0.0)"

        if len(shape) == 0:
            assert value.size == 1, "Unexpected situation happend"
            shape = [1]
            initializer = "Constant({})".format(value)

        self.weights[node.layer_name] = node.value

        attr = {
            'dtype': string(dtype),
            'shape': shape,
            'name': string(node.layer_name),
            'default_initializer': initializer
        }
        node.fluid_code.add_layer("create_parameter",
                                  inputs=None,
                                  output=node,
                                  param_attr=attr)

    def Transpose(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        perm = self.graph.get_node(node.layer.input[1], copy=True)
        assert perm.layer_type == "Const", "Perm of transpose OP should be Const"
        del self.weights[perm.layer_name.replace('/', '_')]
        perm.fluid_code.clear()
        perm = perm.value.tolist()

        attr = {'perm': perm}
        node.fluid_code.add_layer("transpose",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def MaxPool(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)

        k_size = node.get_attr("ksize")
        strides = node.get_attr("strides")
        data_format = node.get_attr("data_format").decode()
        pad_mode = node.get_attr("padding").decode()
        channel_first = data_format == "NCHW"

        attr = {
            "pool_size": k_size[1:3],
            "pool_type": string("max"),
            "pool_stride": strides[1:3],
            "pool_padding": string(pad_mode),
            "data_format": string("NHWC")
        }
        node.fluid_code.add_layer("pool2d",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Conv2D(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        kernel = self.graph.get_node(node.layer.input[1], copy=True)
        assert kernel.layer_type == "Const", "Kernel of Conv2D should be Const"
        self.add_omit_nodes(kernel.layer_name, node.layer_name)

        in_shape = input.out_shapes[0]
        if in_shape[3] < 0:
            in_shape = self.decoder.infer_tensor(input).shape
        k_size = kernel.out_shapes[0]
        if k_size.count(-1) > 0:
            k_size = self.decoder.infer_tensor(kernel).shape

        strides = node.get_attr("strides")
        dilations = node.get_attr("dilations")
        pad_mode = node.get_attr("padding").decode()

        self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose(
            kernel.value, (3, 2, 0, 1))

        attr = {
            "bias_attr": False,
            "param_attr": string(kernel.layer_name),
            "num_filters": k_size[3],
            "filter_size": k_size[0:2],
            "stride": strides[1:3],
            "dilation": dilations[1:3],
            "padding": string(pad_mode),
            "data_format": string("NHWC")
        }
        node.fluid_code.add_layer("conv2d",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def BiasAdd(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        bias = self.graph.get_node(node.layer.input[1], copy=True)
        inputs = {"x": input, "y": bias}
        node.fluid_code.add_layer("elementwise_add",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=None)

    def FusedBatchNorm(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        gamma = self.graph.get_node(node.layer.input[1], copy=True)
        beta = self.graph.get_node(node.layer.input[2], copy=True)
        moving_mean = self.graph.get_node(node.layer.input[3], copy=True)
        moving_var = self.graph.get_node(node.layer.input[4], copy=True)

        assert gamma.layer_type == "Const"
        assert beta.layer_type == "Const"
        assert moving_mean.layer_type == "Const"
        assert moving_var.layer_type == "Const"
        self.add_omit_nodes(gamma.layer_name, node.layer_name)
        self.add_omit_nodes(beta.layer_name, node.layer_name)
        self.add_omit_nodes(moving_mean.layer_name, node.layer_name)
        self.add_omit_nodes(moving_var.layer_name, node.layer_name)

        attr = {
            "epsilon": node.get_attr("epsilon"),
            "param_attr": string(gamma.layer_name),
            "bias_attr": string(beta.layer_name),
            "moving_mean_name": string(moving_mean.layer_name),
            "moving_variance_name": string(moving_var.layer_name),
            "is_test": True,
            "data_layout": string("NHWC")
        }

        node.fluid_code.add_layer("batch_norm",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def DepthwiseConv2dNative(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        kernel = self.graph.get_node(node.layer.input[1], copy=True)
        assert kernel.layer_type == "Const", "Kernel of DepthwiseConv2DNative should be Const"
        self.add_omit_nodes(kernel.layer_name, node.layer_name)

        in_shape = input.out_shapes[0]
        if in_shape[3] < 0:
            in_shape = self.decoder.infer_tensor(input).shape
        k_size = kernel.out_shapes[0]
        if k_size.count(-1) > 0:
            k_size = self.decoder.infer_tensor(kernel).shape

        strides = node.get_attr("strides")
        dilations = node.get_attr("dilations")
        data_format = node.get_attr("data_format").decode()
        pad_mode = node.get_attr("padding").decode()
        channel_first = data_format == "NCHW"

        self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose(
            kernel.value, (2, 3, 0, 1))

        attr = {
            "bias_attr": False,
            "param_attr": string(kernel.layer_name),
            "num_filters": in_shape[3],
            "filter_size": k_size[0:2],
            "stride": strides[1:3],
            "dilation": dilations[1:3],
            "groups": k_size[3] * in_shape[3],
            "use_cudnn": False,
            "padding": string(pad_mode),
            "data_format": string("NHWC")
        }
        node.fluid_code.add_layer("conv2d",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Reshape(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        param = self.graph.get_node(node.layer.input[1], copy=True)
        attr = None
        if param.layer_type == "Const":
            attr = {"shape": param.value.tolist()}
            inputs = {"x": input}
            self.add_omit_nodes(param.layer_name, node.layer_name)
        else:
            inputs = {"x": input, "shape": param}
            shape_value = self.decoder.infer_shape_tensor(param)
            if param.layer_type == "Pack":
                pack_inputs = [
                    self.graph.get_node(name, copy=True)
                    for name in param.layer.input
                ]
                all_const_value = 0
                for i in range(len(pack_inputs)):
                    if pack_inputs[i].layer_type == "Const":
                        pack_inputs[i] = pack_inputs[i].value
                        all_const_value += 1
                    elif shape_value[i] > 0:
                        pack_inputs[i] = shape_value[i]
                        all_const_value += 1
                    else:
                        if hasattr(pack_inputs[i], "index"):
                            index = pack_inputs[i].index
                            pack_inputs[i] = pack_inputs[
                                i].layer_name + "[{}]".format(index)
                        else:
                            pack_inputs[i] = pack_inputs[i].layer_name

                ### special optimize for paddle-lite
                in_size = 1
                in_shape = input.out_shapes[0]
                for i in range(len(in_shape)):
                    in_size *= in_shape[i]
                if all_const_value == len(pack_inputs) and in_size > 0:
                    if pack_inputs[0] > 0 and pack_inputs.count(-1) == 1:
                        for i in range(len(pack_inputs)):
                            in_size /= pack_inputs[i]
                        index = pack_inputs.index(-1)
                        pack_inputs[index] = in_size * -1
                        pack_inputs[0] = -1

                if all_const_value == len(pack_inputs) and pack_inputs.count(
                        -1) == 0:
                    pack_inputs[0] = -1
                ###################################

                string_params = "["
                for i in range(len(pack_inputs)):
                    string_params += "{}, ".format(pack_inputs[i])
                string_params = string_params.strip(", ") + "]"
                inputs["shape"] = string_params

        node.fluid_code.add_layer("reshape",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=attr)

    def AvgPool(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)

        k_size = node.get_attr("ksize")
        strides = node.get_attr("strides")
        pad_mode = node.get_attr("padding").decode()

        attr = {
            "pool_size": k_size[1:3],
            "pool_type": string("avg"),
            "pool_stride": strides[1:3],
            "pool_padding": string(pad_mode),
            "data_format": string("NHWC")
        }
        node.fluid_code.add_layer("pool2d",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def SplitV(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        num_sections = self.graph.get_node(node.layer.input[1], copy=True)
        dim = self.graph.get_node(node.layer.input[2], copy=True)
        assert num_sections.layer_type == "Const"
        assert dim.layer_type == "Const"
        self.add_omit_nodes(num_sections.layer_name, node.layer_name)
        self.add_omit_nodes(dim.layer_name, node.layer_name)
        dim = dim.value
        attr = {
            "num_or_sections": num_sections.value.tolist(),
            "dim": dim.value
        }
        node.fluid_code.add_layer("split",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def ConcatV2(self, node):
        inputs = [
            self.graph.get_node(name, copy=True)
            for name in node.layer.input[:-1]
        ]
        axis = self.graph.get_node(node.layer.input[-1], copy=True)
        assert axis.layer_type == "Const"
        self.add_omit_nodes(axis.layer_name, node.layer_name)
        axis = axis.value
        if axis < 0:
            axis += len(inputs[0].out_shapes[0])

        attr = {"axis": axis}
        node.fluid_code.add_layer("concat",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=attr)

    def Tile(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        expand_times = self.graph.get_node(node.layer.input[1], copy=True)
        if expand_times.layer_type == "Const":
            expand_times = expand_times.value.tolist()
            self.add_omit_nodes(expand_times.layer_name, node.layer_name)
        attr = {"expand_times": expand_times}
        node.fluid_code.add_layer("expand",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Pack(self, node):
        inputs = [
            self.graph.get_node(name, copy=True) for name in node.layer.input
        ]
        if len(inputs) == 1 and len(inputs[0].out_shapes[0]) == 0:
            input_name = inputs[0].layer_name
            if hasattr(inputs[0], "index"):
                input_name += "[{}]".format(inputs[0].index)
            node.fluid_code.add_note("{} = {}".format(node.layer_name,
                                                      input_name))
            return
        axis = node.get_attr("axis")
        attr = {"axis": axis}
        node.fluid_code.add_layer("stack",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=attr)
        input_shape_sample = inputs[0].out_shapes[0]
        if len(input_shape_sample) == 0:
            attr = {"shape": [-1]}
            node.fluid_code.add_layer("reshape",
                                      inputs=node,
                                      output=node,
                                      param_attr=attr)

    def Pad(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        paddings = self.graph.get_node(node.layer.input[1], copy=True)
        assert paddings.layer_type == "Const", "Padding should be Const"
        self.add_omit_nodes(paddings.layer_name, node.layer_name)
        paddings = paddings.value.flatten().tolist()

        if len(input.out_shapes[0]) == 4:
            new_padding = None
            if paddings[0] + paddings[1] + paddings[6] + paddings[7] == 0:
                new_padding = paddings[2:6]
            if new_padding is not None:
                attr = {"paddings": new_padding, "data_format": string("NHWC")}
                node.fluid_code.add_layer("pad2d",
                                          inputs=input,
                                          output=node,
                                          param_attr=attr)
                return

        attr = {"paddings": paddings}
        node.fluid_code.add_layer("pad",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Range(self, node):
        start = self.graph.get_node(node.layer.input[0], copy=True)
        limit = self.graph.get_node(node.layer.input[1], copy=True)
        delta = self.graph.get_node(node.layer.input[2], copy=True)
        all_param_const = -2
        if start.layer_type == "Const":
            self.add_omit_nodes(start.layer_name, node.layer_name)
            start = start.value
            all_param_const += 1
        if limit.layer_type == "Const":
            self.add_omit_nodes(limit.layer_name, node.layer_name)
            limit = limit.value
            all_param_const += 1
        if delta.layer_type == "Const":
            self.add_omit_nodes(delta.layer_name, node.layer_name)
            delta = delta.value
            all_param_const += 1
        dtype = node.dtype
        inputs = {
            "start": start,
            "end": limit,
            "step": delta,
            "dtype": string(dtype)
        }
        attr = {"dtype": string(node.dtype)}
        node.fluid_code.add_layer("range",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=None)

    def Mean(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        reduce_idx = self.graph.get_node(node.layer.input[1], copy=True)
        assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]"
        dims = reduce_idx.value.tolist()
        keep_dims = node.get_attr("keep_dims")

        attr = {"dim": dims, "keep_dim": keep_dims}
        node.fluid_code.add_layer("reduce_mean",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def MatMul(self, node):
        x = self.graph.get_node(node.layer.input[0], copy=True)
        y = self.graph.get_node(node.layer.input[1], copy=True)
        transpose_a = node.get_attr('transpose_a')
        transpose_b = node.get_attr('transpose_b')
        inputs = {"x": x, "y": y}
        # fix paddle shape infer problem
        # should be removed after paddle 1.6
        x_last_dim = x.out_shapes[0][-1]
        y_last_dim = y.out_shapes[0][0]
        certain_dim = x_last_dim if x_last_dim > y_last_dim else y_last_dim
        shape = x.out_shapes[0]
        shape[-1] = certain_dim
        attr = {"shape": shape}
        node.fluid_code.add_layer("reshape",
                                  inputs=x,
                                  output=x,
                                  param_attr=attr)
        shape = y.out_shapes[0]
        shape[0] = certain_dim
        attr = {"shape": shape}
        node.fluid_code.add_layer("reshape",
                                  inputs=y,
                                  output=y,
                                  param_attr=attr)

        attr = {"transpose_x": transpose_a, "transpose_y": transpose_b}
        node.fluid_code.add_layer("matmul",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=attr)

    def ArgMax(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        axis = self.graph.get_node(node.layer.input[1], copy=True)
        assert axis.layer_type == "Const", "ArgMax only support Const parameter"
        self.add_omit_nodes(axis.layer_name, node.layer_name)
        axis = axis.value
        attr = {"axis": axis}
        node.fluid_code.add_layer("argmax",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def StridedSlice(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        begin = self.graph.get_node(node.layer.input[1], copy=True)
        end = self.graph.get_node(node.layer.input[2], copy=True)
        strides = self.graph.get_node(node.layer.input[3], copy=True)
        assert begin.layer_type == "Const"
        assert end.layer_type == "Const"
        assert strides.layer_type == "Const"
        self.add_omit_nodes(begin.layer_name, node.layer_name)
        self.add_omit_nodes(end.layer_name, node.layer_name)
        self.add_omit_nodes(strides.layer_name, node.layer_name)
        strides = strides.value.tolist()
        assert len(set(strides)) == 1 and strides[
            0] == 1, "Only support strides be 1 in StridedSlice OP"

        begin = begin.value.tolist()
        end = end.value.tolist()

        for i in range(len(end)):
            if end[i] == 0:
                end[i] = 999999

        begin_mask = node.get_attr('begin_mask')
        end_mask = node.get_attr('end_mask')
        ellipsis_mask = node.get_attr('ellipsis_mask')
        new_axis_mask = node.get_attr('new_axis_mask')
        shrink_axis_mask = node.get_attr('shrink_axis_mask')

        assert ellipsis_mask == 0, "(OP:{} Name:{})Only support ellipsis_mask be 0[now: {}] n StridedSlice OP".format(
            node.layer_type, node.layer.name, ellipsis_mask)

        # TODO codes without validation
        # Use it carefully
        new_begin = list()
        new_end = list()
        new_axes = list()
        shrink_axes = list()
        for i, item in enumerate(begin):
            mask = (new_axis_mask >> i) & 1
            if mask != 0:
                new_axes.append(i)
                continue

            mask = (shrink_axis_mask >> i) & 1
            if mask != 0:
                shrink_axes.append(i)

            mask = (begin_mask >> i) & 1
            if mask != 0:
                new_begin.append(0)
            else:
                new_begin.append(item)

            mask = (end_mask >> i) & 1
            if mask != 0:
                new_end.append(999999)
            else:
                new_end.append(end[i])

        attr = {
            "axes": [i for i in range(len(new_begin))],
            "starts": new_begin,
            "ends": new_end
        }
        node.fluid_code.add_layer("slice",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)
        if len(new_axes) > 0:
            attr = {"axes": new_axes}
            node.fluid_code.add_layer("unsqueeze",
                                      inputs=node,
                                      output=node,
                                      param_attr=attr)
        if len(shrink_axes) > 0:
            if len(input.out_shapes[0]) + len(new_axes) <= 1:
                pass
            else:
                attr = {"axes": shrink_axes}
                node.fluid_code.add_layer("squeeze",
                                          inputs=node,
                                          output=node,
                                          param_attr=attr)

    def Slice(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        begin = self.graph.get_node(node.layer.input[1], copy=True)
        size = self.graph.get_node(node.layer.input[2], copy=True)
        attr = dict()
        inputs = {"x": input}
        if begin.layer_type == "Const":
            self.add_omit_nodes(begin.layer_name, node.layer_name)
            begin = begin.value.tolist()
            attr["offsets"] = begin
        else:
            inputs["offsets"] = begin
        if size.layer_type == "Const":
            self.add_omit_nodes(size.layer_name, node.layer_name)
            size = size.value.tolist()
            attr["shape"] = size
        else:
            inputs["shape"] = size

        if isinstance(begin, TFGraphNode) and begin.layer_type == "Pack":
            begin = process_pack_shape(self.graph, begin,
                                       self.decoder.infer_shape_tensor(begin))
            inputs["offsets"] = begin
        if isinstance(size, TFGraphNode) and size.layer_type == "Pack":
            size = process_pack_shape(self.graph, size,
                                      self.decoder.infer_shape_tensor(size))
            inputs["shape"] = size

        node.fluid_code.add_layer("crop_tensor",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=attr)

    def Conv2DBackpropInput(self, node):
        out_shape = self.graph.get_node(node.layer.input[0], copy=True)
        kernel = self.graph.get_node(node.layer.input[1], copy=True)
        input = self.graph.get_node(node.layer.input[2], copy=True)

        assert kernel.layer_type == "Const", "Kernel of Conv2DBackpropInput should be Const"

        self.add_omit_nodes(kernel.layer_name, node.layer_name)
        self.add_omit_nodes(out_shape.layer_name, node.layer_name)

        if out_shape.layer_type == "Const":
            out_shape = out_shape.value.tolist()
        else:
            out_shape = self.decoder.infer_shape_tensor(out_shape,
                                                        node.out_shapes[0])

        in_shape = input.out_shapes[0]
        if in_shape[3] < 0:
            in_shape = self.decoder.infer_tensor(input).shape
        k_size = kernel.out_shapes[0]
        if k_size.count(-1) > 0:
            k_size = self.decoder.infer_tensor(kernel).shape

        pad_mode = node.get_attr("padding").decode()
        strides = node.get_attr("strides")
        dilations = node.get_attr("dilations")
        data_format = node.get_attr("data_format").decode()
        channel_first = data_format == "NCHW"

        self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose(
            kernel.value, (3, 2, 0, 1))

        attr = {
            "bias_attr": False,
            "param_attr": string(kernel.layer_name),
            "num_filters": k_size[2],
            "filter_size": k_size[0:2],
            "stride": strides[1:3],
            "dilation": dilations[1:3],
            "output_size": out_shape[1:3],
            "padding": string(pad_mode),
            "data_format": string("NHWC")
        }
        node.fluid_code.add_layer("conv2d_transpose",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Max(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        reduce_idx = self.graph.get_node(node.layer.input[1], copy=True)
        assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]"
        keep_dims = node.get_attr("keep_dims")
        dim = reduce_idx.value.tolist()

        attr = {"dim": dim, "keep_dim": keep_dims}
        node.fluid_code.add_layer("reduce_max",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Sum(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        reduce_idx = self.graph.get_node(node.layer.input[1], copy=True)
        assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]"
        keep_dims = node.get_attr("keep_dims")
        dim = reduce_idx.value.tolist()

        attr = {"dim": dim, "keep_dim": keep_dims}
        node.fluid_code.add_layer("reduce_sum",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Cast(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        dtype = node.dtype_map[node.get_attr('DstT')]
        attr = {"dtype": string(dtype)}
        node.fluid_code.add_layer("cast",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Split(self, node):
        dim = self.graph.get_node(node.layer.input[0], copy=True)
        input = self.graph.get_node(node.layer.input[1], copy=True)
        assert dim.layer_type == "Const"
        self.add_omit_nodes(dim.layer_name, node.layer_name)
        num_split = node.get_attr('num_split')
        dim = dim.value

        attr = {"num_or_sections": num_split, "dim": dim}
        node.fluid_code.add_layer("split",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Squeeze(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        squeeze_dims = node.get_attr('squeeze_dims')
        attr = {"axes": squeeze_dims}
        node.fluid_code.add_layer("squeeze",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def Softmax(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        axis = node.get_attr("axis")
        attr = {"axis": axis}
        node.fluid_code.add_layer("softmax",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def ResizeNearestNeighbor(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        resize_shape = self.graph.get_node(node.layer.input[1], copy=True)
        if resize_shape.layer_type == "Const":
            self.add_omit_nodes(resize_shape.layer_name, node.layer_name)
            resize_shape = resize_shape.value.tolist()
        align_corners = node.get_attr("align_corners")
        attr = {
            "align_corners": align_corners,
            "out_shape": resize_shape,
            "data_format": string("NHWC")
        }
        node.fluid_code.add_layer("resize_nearest",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def ResizeBilinear(self, node):
        input = self.graph.get_node(node.layer.input[0], copy=True)
        resize_shape = self.graph.get_node(node.layer.input[1], copy=True)
        if resize_shape.layer_type == "Const":
            self.add_omit_nodes(resize_shape.layer_name, node.layer_name)
            resize_shape = resize_shape.value.tolist()
        align_corners = node.get_attr("align_corners")
        attr = {
            "align_corners": align_corners,
            "out_shape": resize_shape,
            "align_mode": 1,
            "data_format": string("NHWC")
        }
        node.fluid_code.add_layer("resize_bilinear",
                                  inputs=input,
                                  output=node,
                                  param_attr=attr)

    def GreaterEqual(self, node):
        x = self.graph.get_node(node.layer.input[0], copy=True)
        y = self.graph.get_node(node.layer.input[1], copy=True)
        inputs = {"x": x, "y": y}
        node.fluid_code.add_layer("greater_equal",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=None)

    def RandomUniform(self, node):
        shape = self.graph.get_node(node.layer.input[0], copy=True)
        if shape.layer_type == "Const":
            self.add_omit_nodes(shape.layer_name, node.layer_name)
            shape = shape.value.tolist()
        if not isinstance(shape, list):
            attr = {"dtype": string("int64")}
            node.fluid_code.add_layer("cast",
                                      inputs=shape,
                                      output=shape,
                                      param_attr=attr)
        attr = {"min": 0.0, "max": 0.9999}
        inputs = {"shape": shape}
        node.fluid_code.add_layer("uniform_random",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=attr)

    def SquaredDifference(self, node):
        x = self.graph.get_node(node.layer.input[0], copy=True)
        y = self.graph.get_node(node.layer.input[1], copy=True)
        inputs = {"x": x, "y": y}
        node.fluid_code.add_layer("elementwise_sub",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=None)
        inputs = {"x": node, "y": node}
        node.fluid_code.add_layer("elementwise_mul",
                                  inputs=inputs,
                                  output=node,
                                  param_attr=None)