blob: fdcba56ef83c2e8b94debda9c147e63e0564f3bc [file] [log] [blame]
#-------------------------------------------------------------
#
# 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])
mode=args[10]
# Assumption: NCHW image format
x=matrix(seq(1, numImg*numChannels*imgSize*imgSize), numImg, numChannels*imgSize*imgSize, byrow=TRUE)
if(as.logical(args[9])) {
zero_mask = (x - 1.5*mean(x)) > 0
x = x * zero_mask
} else {
x = x - mean(x)
}
pad_image <- function(img, Hin, Win, padh, padw){
C = nrow(img)
img_padded = matrix(0, C, (Hin+2*padh)*(Win+2*padw)) # zeros
for (c in 1:C) {
img_slice = matrix(img[c,], Hin, Win, byrow=TRUE) # depth slice C reshaped
img_padded_slice = matrix(0, Hin+2*padh, Win+2*padw)
img_padded_slice[(padh+1):(padh+Hin), (padw+1):(padw+Win)] = img_slice
img_padded[c,] = matrix(t(img_padded_slice), 1, (Hin+2*padh)*(Win+2*padw)) # reshape
}
img_padded
}
im2col <- function(img, Hin, Win, Hf, Wf, strideh, stridew) {
C = nrow(img)
Hout = as.integer((Hin - Hf) / strideh + 1)
Wout = as.integer((Win - Wf) / stridew + 1)
img_cols = matrix(0, C*Hf*Wf, Hout*Wout, byrow=TRUE) # zeros
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
# Extract a local patch of the input image corresponding spatially to the filter sizes.
img_patch = matrix(0, C, Hf*Wf, byrow=TRUE) # zeros
for (c in 1:C) { # all channels
img_slice = matrix(img[c,], Hin, Win, byrow=TRUE) # reshape
img_patch[c,] = matrix(t(img_slice[hin:(hin+Hf-1), win:(win+Wf-1)]), 1, Hf*Wf)
}
img_cols[,(hout-1)*Wout + wout] = matrix(t(img_patch), C*Hf*Wf, 1) # reshape
}
}
img_cols
}
max_pool <- function(X, N, C, Hin, Win, Hf, Wf,
strideh, stridew) {
Hout = as.integer((Hin - Hf) / strideh + 1)
Wout = as.integer((Win - Wf) / stridew + 1)
# Create output volume
out = matrix(0, N, C*Hout*Wout, byrow=TRUE)
# Max pooling - im2col implementation
for (n in 1:N) { # all examples
img = matrix(X[n,], C, Hin*Win, byrow=TRUE) # reshape
img_maxes = matrix(0, C, Hout*Wout, byrow=TRUE) # zeros
for (c in 1:C) { # all channels
# Extract local image slice patches into columns with im2col, of shape (Hf*Wf, Hout*Wout)
img_slice_cols = im2col(matrix(t(img[c,]), 1, Hin*Win) , Hin, Win, Hf, Wf, strideh, stridew)
# Max pooling on patches
img_maxes[c,] = colMaxs(img_slice_cols)
}
out[n,] = matrix(t(img_maxes), 1, C*Hout*Wout)
}
out
}
if( mode=="max" ) {
output = max_pool(x, numImg, numChannels, imgSize, imgSize, poolSize1, poolSize2, stride, stride)
} else {
output = max_pool(x, numImg, numChannels, imgSize*imgSize, 1, poolSize1, poolSize2, stride, stride)
}
writeMM(as(output,"CsparseMatrix"), paste(args[8], "B", sep=""))