| #------------------------------------------------------------- |
| # |
| # 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]) |
| } |
| |