#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
#
#-------------------------------------------------------------

/*
 * 2D Average Pooling layer.
 *
 * This implementation uses a built-in operator for higher performance.
 */

forward = function(matrix[double] X, int C, int Hin, int Win, int Hf, int Wf,
                   int strideh, int stridew, int padh, int padw)
    return (matrix[double] out, int Hout, int Wout) {
  /*
   * Computes the forward pass for a 2D spatial average pooling layer.
   * The input data has N examples, each represented as a 3D volume
   * unrolled into a single vector.
   *
   * This implementation uses a built-in operator for higher
   * performance.
   *
   * Inputs:
   *  - X: Inputs, of shape (N, C*Hin*Win).
   *  - C: Number of input channels (dimensionality of input depth).
   *  - Hin: Input height.
   *  - Win: Input width.
   *  - Hf: Filter height.
   *  - Wf: Filter width.
   *  - strideh: Stride over height.
   *  - stridew: Stride over width.
   *  - padh: Padding for top and bottom sides.
   *      A typical value is 0.
   *  - padw: Padding for left and right sides.
   *      A typical value is 0.
   *
   * Outputs:
   *  - out: Outputs, of shape (N, C*Hout*Wout).
   *  - Hout: Output height.
   *  - Wout: Output width.
   */
  N = nrow(X)
  Hout = as.integer(floor((Hin + 2*padh - Hf)/strideh + 1))
  Wout = as.integer(floor((Win + 2*padw - Wf)/stridew + 1))

  # Max pooling - built-in implementation
  out = avg_pool(X, input_shape=[N,C,Hin,Win], pool_size=[Hf,Wf],
                 stride=[strideh,stridew], padding=[padh,padw])
}

backward = function(matrix[double] dout, int Hout, int Wout, matrix[double] X,
                    int C, int Hin, int Win, int Hf, int Wf,
                    int strideh, int stridew, int padh, int padw)
    return (matrix[double] dX) {
  /*
   * Computes the backward pass for a 2D spatial average pooling layer.
   * The input data has N examples, each represented as a 3D volume
   * unrolled into a single vector.
   *
   * Inputs:
   *  - dout: Gradient wrt `out` from upstream, of
   *      shape (N, C*Hout*Wout).
   *  - Hout: Output height.
   *  - Wout: Output width.
   *  - X: Inputs, of shape (N, C*Hin*Win).
   *  - C: Number of input channels (dimensionality of input depth).
   *  - Hin: Input height.
   *  - Win: Input width.
   *  - Hf: Filter height.
   *  - Wf: Filter width.
   *  - strideh: Stride over height.
   *  - stridew: Stride over width.
   *  - padh: Padding for top and bottom sides.
   *      A typical value is 0.
   *  - padw: Padding for left and right sides.
   *      A typical value is 0.
   *
   * Outputs:
   *  - dX: Gradient wrt `X`, of shape (N, C*Hin*Win).
   */
  N = nrow(X)

  # Gradient of average pooling
  dX = avg_pool_backward(X, dout, input_shape=[N,C,Hin,Win], pool_size=[Hf,Wf],
                         stride=[strideh,stridew], padding=[padh,padw])
}

