/*
 * Copyright (c) 2018 Samsung Electronics Co., Ltd. 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.
 */

NNFW_KERNEL(convFloat32, bool, 
            (const float* inputData, const Shape& inputShape,
                 const float* filterData, const Shape& filterShape,
                 const float* biasData, const Shape& biasShape,
                 int32_t padding_left, int32_t padding_right,
                 int32_t padding_top, int32_t padding_bottom,
                 int32_t stride_width, int32_t stride_height,
                 int32_t activation,
                 float* outputData, const Shape& outputShape)
           );

NNFW_KERNEL(depthwiseConvFloat32, bool,
             (const float* inputData, const Shape& inputShape,
              const float* filterData, const Shape& filterShape,
              const float* biasData, const Shape& biasShape,
              int32_t padding_left, int32_t padding_right,
              int32_t padding_top, int32_t padding_bottom,
              int32_t stride_width, int32_t stride_height,
              int32_t depth_multiplier, int32_t activation,
              float* outputData, const Shape& outputShape)
            );

NNFW_KERNEL(averagePoolFloat32, bool,
            (const float* inputData, const Shape& inputShape,
             int32_t padding_left, int32_t padding_right,
             int32_t padding_top, int32_t padding_bottom,
             int32_t stride_width, int32_t stride_height,
             int32_t filter_width, int32_t filter_height, int32_t activation,
             float* outputData, const Shape& outputShape)
           );

NNFW_KERNEL(maxPoolFloat32, bool,
            (const float* inputData, const Shape& inputShape,
             int32_t padding_left, int32_t padding_right,
             int32_t padding_top, int32_t padding_bottom,
             int32_t stride_width, int32_t stride_height,
             int32_t filter_width, int32_t filter_height, int32_t activation,
             float* outputData, const Shape& outputShape)
           );

NNFW_KERNEL(softmaxFloat32, bool,
            (const float* inputData, const Shape& inputShape,
             const float beta,
             float* outputData, const Shape& outputShape)
           );

NNFW_KERNEL(fullyConnectedFloat32, bool,
            (const float* inputData, const Shape& inputShape,
             const float* weights, const Shape& weightsShape,
             const float* biasData, const Shape& biasShape,
             int32_t activation,
             float* outputData, const Shape& outputShape)
           );

NNFW_KERNEL(concatenationFloat32, bool,
            (const std::vector<const float*>& inputDataPtrs,
             const std::vector<Shape>& inputShapes, int32_t axis,
             float* outputData, const Shape& outputShape)
           );

NNFW_KERNEL(reshapeGeneric, bool,
            (const void* inputData, const Shape& inputShape,
                    void* outputData, const Shape& outputShape)
           );