cavis/libnd4j/include/ops/declarable/generic/nn/fusedBatchNorm.cpp

/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
// Created by raver119 on 29/10/17.
//

#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_fused_batch_norm)

#include <ops/declarable/CustomOperations.h>

namespace nd4j {
namespace ops {

    DECLARE_TYPES(fused_batch_norm) {
        getOpDescriptor()
                ->setAllowedInputTypes(nd4j::DataType::ANY)
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

CUSTOM_OP_IMPL(fused_batch_norm, 3, 3, false, 0, 2) {
    auto x      = INPUT_VARIABLE(0);                 // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
    auto scale  = INPUT_VARIABLE(1);                 // [iD]
    auto offset = INPUT_VARIABLE(2);                 // [iD]

    auto y = OUTPUT_VARIABLE(0);                     // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
    auto batchMean = OUTPUT_VARIABLE(1);             // [iD]
    auto batchVar  = OUTPUT_VARIABLE(2);             // [iD]

    const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
    const bool isTraining = (bool)INT_ARG(1);

    REQUIRE_TRUE(x->rankOf() == 4, 0, "CUSTOM_OP fused_batch_norm: the rank of input x array must be equal to 4, but got %i instead !", x->rankOf());

    int bS = x->sizeAt(0);              // batch size
    int iH, iW, iD;                     // input height, input width, input depth(number of channels)
    if(dataFormat) {
        iD = x->sizeAt(1);
        iH = x->sizeAt(2);
        iW = x->sizeAt(3);
    }
    else {
        iD = x->sizeAt(3);
        iH = x->sizeAt(1);
        iW = x->sizeAt(2);
    }

    REQUIRE_TRUE(scale->rankOf() == 1  && scale->sizeAt(0)  == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scale).c_str());
    REQUIRE_TRUE(offset->rankOf() == 1 && offset->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input offset array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(offset).c_str());

    NDArray *mean(nullptr), *variance(nullptr);
    if(!isTraining){
        mean     = INPUT_VARIABLE(3);
        variance = INPUT_VARIABLE(4);
        REQUIRE_TRUE(mean->rankOf() == 1     && mean->sizeAt(0) == iD,     0, "CUSTOM_OP fused_batch_norm: wrong shape of input mean array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(mean).c_str());
        REQUIRE_TRUE(variance->rankOf() == 1 && variance->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input variance array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(variance).c_str());
    }
    else {
        //REQUIRE_TRUE(block.width() == 3, 0, "CUSTOM_OP fused_batch_norm: when isTraining=true then number of input arrays must be equal to 3, but got %i instead !", block.width());
        std::vector<Nd4jLong> shape = {iD};
        mean = NDArrayFactory::create_(scale->ordering(), shape, scale->dataType(), block.launchContext());
        variance = NDArrayFactory::create_(scale->ordering(), shape, scale->dataType(), block.launchContext());
    }

    // FIXME: double?
    double epsilon;
    if(block.getTArguments()->size() > 0)
        epsilon = T_ARG(0) > 1.001e-5 ? T_ARG(0) : 1.001e-5;
    else
        epsilon = 0.001;

    const int restSize = x->lengthOf() / iD;
    auto xAffected = NDArrayFactory::create(x->ordering(), {restSize, iD}, mean->dataType(), block.launchContext());
    xAffected.assign(x);

    const int restSizeMinusOne = (restSize > 1) ? (restSize - 1) : 1;
    // FIXME: float?
    const double restSizeInv = 1.0 / restSize;
    const double restSizeAdjust = (double)restSize / restSizeMinusOne;

    if(isTraining) {
        auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
        sum *= restSizeInv;
        mean->assign(sum);
        *batchMean = *mean;
        //delete sum;
    }
    else
        *batchMean = 0.;

    xAffected -= *mean;

    if(isTraining) {
        int power = 2;
        xAffected.applyScalar(scalar::Pow, power, xAffected);
        auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
        sum *= restSizeInv;
        variance->assign(sum);
        *batchVar  = (*variance) * restSizeAdjust;
        //delete sum;
    }
    else
        *batchVar  = 0.;
    xAffected *= (*variance + epsilon).transform(transform::RSqrt) * (*scale) + (*offset);
    y->assign( xAffected );

    if(isTraining) {
        delete mean;
        delete variance;
    }

    return Status::OK();
}



DECLARE_SHAPE_FN(fused_batch_norm) {
    auto xShapeInfo     = inputShape->at(0);
    auto scaleShapeInfo = inputShape->at(1);

    const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
    const int iD = dataFormat ? xShapeInfo[2] : xShapeInfo[4];

    REQUIRE_TRUE(scaleShapeInfo[0] == 1  && scaleShapeInfo[1] == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scaleShapeInfo).c_str());

    Nd4jLong* outShapeInfo(nullptr), *batchMeanShapeInfo(nullptr), *batchVarShapeInfo(nullptr);

    COPY_SHAPE(xShapeInfo, outShapeInfo);
    COPY_SHAPE(scaleShapeInfo, batchMeanShapeInfo);
    COPY_SHAPE(scaleShapeInfo, batchVarShapeInfo);

    return SHAPELIST(CONSTANT(outShapeInfo), CONSTANT(batchMeanShapeInfo), CONSTANT(batchVarShapeInfo));
}




}
}

#endif