/**
 * Copyright 2019 Huawei Technologies Co., Ltd
 *
 * 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.
 */

namespace mindspore.predict;

enum ResizeMethod: byte {
    UNKNOW = -1,
    BILINEAR = 0,
    NEAREST_NEIGHBOR = 1
}

enum DataFormatType : byte {
    UNKNOW = -1,
    NCHW = 0,
    NHWC = 1,
    HWC = 2,   // for input image or resize
    CHW = 3,   // for input image or resize
}

enum ActivationType : byte {
    NO_ACTIVATION = 0,
    RELU = 1,
    SIGMOID = 2,
    RELU6 = 3,
    ELU = 4,
    LEAKY_RELU = 5,
    ABS = 6,
    RELU1 = 7,
    SOFTSIGN = 8,
    SOFTPLUS = 9,
    TANH = 10,
    UNKNOW = 11
}

enum PoolMode : byte {
    MAX_POOLING = 0,
    MEAN_POOLING = 1,
    GLOBAL_POOING = 2
}

enum EltwiseMode : byte {
    PROD = 0,
    SUM = 1,
    MAXIMUM = 2
}

enum PadMode : byte {
    NOTSET=0,
    SAME=1,
    VALID=2,
    CAFFE_CEIL_NEW=4
}

enum PaddingMode : byte {
    CONSTANT = 0,
    REFLECT = 1,
    SYMMETRIC = 2,
    MODE_RESERVED = 3
}

table Pad {
    paddingmode: PaddingMode;
    paddings: [int];
}

table Maximum {
    format: DataFormatType = 0;
}

table Concat {
    axis: int;
    n: int;
}

table SoftMax {
    axis: [int];
}

table Activation {
    type: ActivationType = 0;
}

table Conv2D {
    format: DataFormatType = 0;
    group: int;
    channelIn: int;
    channelOut: int;
    kernelW: int;
    kernelH: int;
    strideW: int;
    strideH: int;
    padMode: PadMode;
    padUp: int;
    padDown: int;
    padLeft: int;
    padRight: int;
    dilateW: int;
    dilateH: int;
    hasBias: bool = false;
    activationType: ActivationType = 0;
}

table FusedBatchNorm {
    epsilon: float;   // eg. epsilon=0.001
}

table CaffeBatchNorm {
    epsilon: float;   // eg. epsilon=0.001
}

table Squeeze {
    axis: [int];
}

table BiasAdd {
    axis: [int];
}

table Pooling {
    format: DataFormatType = 0;
    poolingMode: PoolMode;
    windowW: int;
    windowH: int;
    strideW: int;
    strideH: int;
    padMode: PadMode;
    padUp: int;
    padDown: int;
    padLeft: int;
    padRight: int;
    caffeMode: bool = false;
}

table DepthwiseConv2D {
    format: DataFormatType = 0;
    channelIn: int;
    channelMultiplier: int;
    kernelW: int;
    kernelH: int;
    strideW: int;
    strideH: int;
    padMode: PadMode;
    padUp: int;
    padDown: int;
    padLeft: int;
    padRight: int;
    dilateW: int;
    dilateH: int;
    hasBias: bool  = false;
    activationType: ActivationType = 0;
}

table DeDepthwiseConv2D {
    format: DataFormatType = 0;
    channelIn: int;
    channelMultiplier: int;
    kernelW: int;
    kernelH: int;
    strideW: int;
    strideH: int;
    padMode: PadMode;
    padUp: int;
    padDown: int;
    padLeft: int;
    padRight: int;
    dilateW: int;
    dilateH: int;
    hasBias: bool  = false;
    activationType: ActivationType = 0;
}


table Resize {
    format: DataFormatType = 0;
    method: ResizeMethod;
    newHeight: long;
    newWidth: long;
    alignCorners: bool = false;
    preserveAspectRatio: bool = false;
}

table DetectionPostProcess {
    format: DataFormatType = 0;
    inputSize: int;
    hScale: float;
    wScale: float;
    xScale: float;
    yScale: float;
    NmsIouThreshold: float;
    NmsScoreThreshold: float;
    MaxDetections: long;
    DetectionsPreClass: long;
    MaxClassesPreDetection: long;
    NumClasses: long;
    UseRegularNms: bool;
}

table FullConnection {
    format: DataFormatType = 0;
    hasBias: bool;
    axis: int;
}

// Mean(input_tensor, axis, keep_dims)
table Mean {
    axis: [int];
    keepDims: bool = false;
}

table DeConv2D {
    format: DataFormatType = 0;
    group: int;
    channelIn: int;
    channelOut: int;
    kernelW: int;
    kernelH: int;
    strideW: int;
    strideH: int;
    padMode: PadMode;
    padUp: int;
    padDown: int;
    padLeft: int;
    padRight: int;
    dilateW: int;
    dilateH: int;
    hasBias: bool = false;
    activationType: ActivationType = 0;
}

table Scale {
    format: DataFormatType = 0;
}

table Eltwise {
    format: DataFormatType = 0;
    mode: EltwiseMode;
}

table Add {
    format: DataFormatType = 0;
}

table Slice {
    format: DataFormatType = 0;
    begin: [int];
    end: [int];
    stride: [int];
}

table Mul {
}

table Exp {
}

table Reshape {
    format: DataFormatType = 0;
    shape: [long];
}

table Power {
    power: float;
    scale: float;
    shift: float;
}

table ArgMax {
    axis: int;
    outMaxValue: bool;
    topK: int;
    keepDims: bool;
    axisType: int;
}

table NetOutput {
    format: DataFormatType = 0;
}

table MatMul {
    transposeA : bool = false;
    transposeB : bool = false;
}

table CaffePReLU {
    channelShared : bool = false;
}

table StridedSlice {
    beginMask: int;
    endMask: int;
    ellipsisMask: int;
    newAxisMask: int;
    shrinkAxisMask: int;
    begin: [int];
    end: [int];
    stride: [int];
    isScale: [int];
}

table Stack {
    axis: int;
    n: int;
    isScale: [int];
}

table Range {
    start: int;
    limit: int;
    delta: int;
}

table ExpandDims {
    dim: int;
}

table Tile {
    multiples: [int];
}

table Cast {
    srcT: int;
    dstT: int;
}

table Split {
    numberSplit: int;
    sizeSplits: [int];
    splitDim: int;
}

table CaffeCrop {
    axis : long;
    offsets : [long];
}

table Permute {
    order: [long];
}