| #------------------------------------------------------------- |
| # |
| # 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") |
| imgSize=as.integer(args[1]) |
| numImg=as.integer(args[2]) |
| numChannels=as.integer(args[3]) |
| numFilters=as.integer(args[4]) |
| filterSize=as.integer(args[5]) |
| stride=as.integer(args[6]) |
| pad=as.integer(args[7]) |
| |
| # Assumption: NCHW image format |
| x=matrix(seq(1, numImg*numChannels*imgSize*imgSize), numImg, numChannels*imgSize*imgSize, byrow=TRUE) |
| w=matrix(seq(1, numFilters*numChannels*filterSize*filterSize), numFilters, numChannels*filterSize*filterSize, byrow=TRUE) |
| |
| if(as.logical(args[9])) { |
| zero_mask = (x - mean(x)*1.5) > 0 |
| x = x * zero_mask |
| } else { |
| x = x - mean(x) |
| } |
| if(as.logical(args[10])) { |
| zero_mask = (w - mean(w)*1.5) > 0 |
| w = w * zero_mask |
| } else { |
| w = w - mean(w) |
| } |
| pad_image <- function(img, Hin, Win, padh, padw){ |
| C = nrow(img) |
| img_padded = matrix(0, C, (Hin+2*padh)*(Win+2*padw), byrow=TRUE) # 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 |
| } |
| |
| conv2d <- function(X, W, C, Hin, Win, Hf, Wf, strideh, stridew, padh, padw) { |
| N = nrow(X) |
| F = nrow(W) |
| Hout = as.integer((Hin + 2 * padh - Hf) / strideh + 1) |
| Wout = as.integer((Win + 2 * padw - Wf) / stridew + 1) |
| |
| # Create output volume |
| out = matrix(0, N, F*Hout*Wout, byrow=TRUE) |
| |
| # Convolution - im2col implementation |
| for (n in 1:N) { # all examples |
| Xn = matrix(X[n,], C, Hin*Win, byrow=TRUE) # reshape |
| |
| # Pad image |
| Xn_padded = pad_image(Xn, Hin, Win, padh, padw) # shape (C, (Hin+2*padh)*(Win+2*padw)) |
| |
| # Extract local image patches into columns with im2col, of shape (C*Hf*Wf, Hout*Wout) |
| Xn_padded_cols = im2col(Xn_padded, Hin+2*padh, Win+2*padw, Hf, Wf, strideh, stridew) |
| |
| # Convolve patches with filters |
| outn = W %*% Xn_padded_cols # shape (F, Hout*Wout) |
| out[n,] = matrix(t(outn), 1, F*Hout*Wout) # reshape |
| } |
| |
| out |
| } |
| |
| R = conv2d(x, w, numChannels, imgSize, imgSize, filterSize, filterSize, stride, stride, pad, pad); |
| Hout = as.integer((imgSize + 2 * pad - filterSize) / stride + 1) |
| Wout = Hout |
| |
| b = matrix(seq(1, numFilters), numFilters, 1, byrow=TRUE) |
| for(k in 0:(numFilters-1)) { |
| start = k*Hout^2; |
| R[,(start+1):(start+Hout^2)] = R[,(start+1):(start+Hout^2)] + b[k+1,1] |
| } |
| |
| writeMM(as(R,"CsparseMatrix"), paste(args[8], "B", sep="")) |
