//
//  MNN.fbs
//  MNN
//
//  Created by MNN on 2019/1/4.
//  Copyright © 2018, Alibaba Group Holding Limited
//

include "CaffeOp.fbs";
include "Tensor.fbs";
include "Type.fbs";
namespace MNN;

enum FusedActivation : byte {
	kTfLiteActNone = 0,
	kTfLiteActRelu,
	kTfLiteActRelu1,
	kTfLiteActRelu6,
	kTfLiteActTanh,
	kTfLiteActSignBit,
	kTfLiteActSigmoid,
}

table QuantizedParam {
    zeroPoint: int;
    scale: float;
}

table QuantizedAdd {
	activationType: FusedActivation;
	input1QuantizedParam: QuantizedParam;
	input2QuantizedParam: QuantizedParam;
	outputQuantizedParam: QuantizedParam;
}

enum ModeFormat : byte {
    TENSORFLOW = 0,
    TFLITE
}

enum QuantizeMode : byte {
    MIN_COMBINED = 0,
    MIN_FIRST,
    SCALED
}

table Dequantize {
	inputQuantizedParam: QuantizedParam;
	mode: QuantizeMode;
	modelFormat: ModeFormat = TENSORFLOW;
	type: DataType;
}

table QuantizedAvgPool {
	kernelX: int;
	kernelY: int;
	modelFormat: ModeFormat = TENSORFLOW;
	outputActivationMax: int;
	outputActivationMin: int;
	padType: PoolPadType;
	padX: int;
	padY: int;
	strideX: int;
	strideY: int;
	type: DataType;
}

table QuantizedBiasAdd {
	bias: [int32];
	inputType: DataType;
	max: int32;
	min: int32;
	outputType: DataType;
}

table QuantizedConcat {
	activationType: FusedActivation;
	axis: int;
	inputScale: [float];
	inputZeroPoint: [int];
	outputQuantizedParam: QuantizedParam;
}

table QuantizedLogistic {
	inputQuantizedParam: QuantizedParam;
	outputQuantizedParam: QuantizedParam;
}

table QuantizedMatMul {
	transposeA: bool;
	transposeB: bool;
}

table QuantizedMaxPool {
	kernelX: int;
	kernelY: int;
	modelFormat: ModeFormat = TENSORFLOW;
	outputActivationMax: int;
	outputActivationMin: int;
	padType: PoolPadType;
	padX: int;
	padY: int;
	strideX: int;
	strideY: int;
	type: DataType;
}


table QuantizedRelu {
	type: DataType;
}

table QuantizedRelu6 {
	type: DataType;
}

table QuantizedReshape {
	dims: [int];
	modelFormat: ModeFormat = TENSORFLOW;
}

table QuantizedSoftmax {
	beta: float;
	inputScale: float;
}

enum QuantizeRoundMode : byte {
    HALF_AWAY_FROM_ZERO = 0,
    HALF_TO_EVEN
}


table QuantizeV2 {
	type: DataType;
	mode: QuantizeMode;
	roundMode: QuantizeRoundMode;
}

table RequantizationRange { }

table Requantize { }

table TfQuantizedConv2D {
	bias: [int32];
	biasflag: bool;
	common: Convolution2DCommon;
	weight: [ubyte];
	// tflite
	activationType: FusedActivation;
	multiplier: int32;
	outMax: int32;
	outMin: int32;
	shift: int32;
	// for depthwise_conv2D
	biasQuantizedParam: QuantizedParam;
	depthMultiplier: int;
	filterQuantizedParam: QuantizedParam;
	inputQuantizedParam: QuantizedParam;
	modelFormat: ModeFormat = TENSORFLOW;
	outputQuantizedParam: QuantizedParam;
}