| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| args <- commandArgs(TRUE) |
| library("Matrix") |
| library("matrixStats") |
| imgSize=as.integer(args[1]) |
| numImg=as.integer(args[2]) |
| numChannels=as.integer(args[3]) |
| poolSize1=as.integer(args[4]) |
| poolSize2=as.integer(args[5]) |
| stride=as.integer(args[6]) |
| pad=as.integer(args[7]) |
| P=as.integer(args[8]) |
| Q=as.integer(args[9]) |
| |
| # Assumption: NCHW image format |
| x=matrix(seq(1, numImg*numChannels*imgSize*imgSize), numImg, numChannels*imgSize*imgSize, byrow=TRUE) |
| dout=matrix(seq(1, numImg*numChannels*P*Q), numImg, numChannels*P*Q, byrow=TRUE) |
| if(as.logical(args[11])) { |
| zero_mask = (x - mean(x)*1.5) > 0 |
| x = x * zero_mask |
| } else { |
| x = x - mean(x) |
| } |
| if(as.logical(args[12])) { |
| zero_mask = (dout - mean(dout)*1.5) > 0 |
| dout = dout * zero_mask |
| } else { |
| dout = dout - mean(dout) |
| } |
| max_pool_backward <- function(dout, Hout, Wout, X, C, |
| Hin, Win, Hf, Wf, strideh, stridew) |
| { |
| N = nrow(X) |
| |
| # Create gradient volume |
| dX = matrix(0, N, C*Hin*Win, byrow=TRUE) |
| |
| # Gradient of max pooling |
| for (n in 1:N) { # all examples |
| img = matrix(X[n,], C, Hin*Win, byrow=TRUE) |
| dimg = matrix(0, C, Hin*Win, byrow=TRUE) |
| for (c in 1:C) { # all channels |
| img_slice = matrix(img[c,], Hin, Win, byrow=TRUE) |
| dimg_slice = matrix(0, Hin, Win, byrow=TRUE) |
| for (hout in 1:Hout) { # all output rows |
| hin = (hout-1) * strideh + 1 |
| for (wout in 1:Wout) { # all output columns |
| win = (wout-1) * stridew + 1 |
| img_slice_patch = img_slice[hin:(hin+Hf-1), win:(win+Wf-1)] |
| max_val = max(img_slice_patch) |
| max_val_ind = matrix(0, nrow(img_slice_patch), ncol(img_slice_patch)) |
| max_val_ind[which.max(img_slice_patch)] = 1 # first max value indicator |
| # gradient passes through only for the max value in this patch |
| dimg_slice_patch = max_val_ind * dout[n, (c-1)*Hout*Wout + (hout-1)*Wout + wout] |
| dimg_slice[hin:(hin+Hf-1), win:(win+Wf-1)] = |
| dimg_slice[hin:(hin+Hf-1), win:(win+Wf-1)] + dimg_slice_patch |
| } |
| } |
| dimg[c,] = matrix(t(dimg_slice), 1, Hin*Win) |
| } |
| dX[n,] = matrix(t(dimg), 1, C*Hin*Win) |
| } |
| |
| dX |
| } |
| |
| output = max_pool_backward(dout, P, Q, x, numChannels, imgSize, imgSize, poolSize1, poolSize2, stride, stride) |
| writeMM(as(output,"CsparseMatrix"), paste(args[10], "B", sep="")) |
