/**
 * Copyright 2019-2020 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.
 */

#ifndef GE_OP_NN_OPS_H_
#define GE_OP_NN_OPS_H_

#include "graph/operator_reg.h"
#include "graph/operator.h"

namespace ge {

REG_OP(FractionalMaxPoolGrad)
    .INPUT(orig_input, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .INPUT(orig_output, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .INPUT(out_backprop, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .INPUT(row_pooling_sequence, TensorType({ DT_INT64 }))
    .INPUT(col_pooling_sequence, TensorType({ DT_INT64 }))
    .OUTPUT(y, TensorType({ DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64 }))
    .ATTR(overlapping, Bool, false)
    .OP_END_FACTORY_REG(FractionalMaxPoolGrad)

REG_OP(FractionalAvgPool)
    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .OUTPUT(row_pooling_sequence, TensorType({DT_INT64}))
    .OUTPUT(col_pooling_sequence, TensorType({DT_INT64}))
    .ATTR(pooling_ratio, ListFloat, {})
    .ATTR(pseudo_random, Bool, false)
    .ATTR(overlapping, Bool, false)
    .ATTR(deterministic, Bool, false)
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(FractionalAvgPool)

REG_OP(FractionalMaxPool)
    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .OUTPUT(row_pooling_sequence, TensorType({DT_INT64}))
    .OUTPUT(col_pooling_sequence, TensorType({DT_INT64}))
    .ATTR(pooling_ratio, ListFloat, {})
    .ATTR(pseudo_random, Bool, false)
    .ATTR(overlapping, Bool, false)
    .ATTR(deterministic, Bool, false)
    .ATTR(seed, Int, 0)
    .ATTR(seed2, Int, 0)
    .OP_END_FACTORY_REG(FractionalMaxPool)

REG_OP(NthElement)
    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
                          DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_DOUBLE}))
    .INPUT(n, TensorType({DT_INT32}))
    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
                          DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_DOUBLE}))
    .ATTR(reverse, Bool, false)
    .OP_END_FACTORY_REG(NthElement)

REG_OP(FractionalAvgPoolGrad)
    .INPUT(orig_input_tensor_shape, TensorType({DT_INT64}))
    .INPUT(out_backprop, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .INPUT(row_pooling_sequence, TensorType({DT_INT64}))
    .INPUT(col_pooling_sequence, TensorType({DT_INT64}))
    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
    .ATTR(overlapping, Bool, false)
    .OP_END_FACTORY_REG(FractionalAvgPoolGrad)

REG_OP(DataFormatVecPermute)
    .INPUT(x, TensorType({ DT_INT32, DT_INT64 }))
    .OUTPUT(y, TensorType({ DT_INT32, DT_INT64 }))
    .ATTR(src_format, String, "NHWC")
    .ATTR(dst_format, String, "NCHW")
    .OP_END_FACTORY_REG(DataFormatVecPermute)

}  // namespace ge

#endif  // GE_OP_NN_OPS_H_