| ### |
| # Reproducing parper: |
| # Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks" |
| ### |
| |
| library(mxnet) |
| |
| residual_unit <- function(data, num_filter, stride, dim_match, name, bottle_neck=TRUE, bn_mom=0.9, workspace=512){ |
| if(bottle_neck){ |
| bn1 <- mx.symbol.BatchNorm(data=data, fix_gamma=FALSE, eps=2e-5, |
| momentum=bn_mom, name=paste0(name,'_bn1')) |
| act1 <- mx.symbol.Activation(data=bn1, act_type='relu', |
| name=paste0(name, '_relu1')) |
| conv1 <- mx.symbol.Convolution(data=act1, num_filter=as.integer(num_filter*0.25), |
| kernel=c(1,1), stride=c(1,1), pad=c(0,0), |
| no_bias=TRUE, workspace=workspace, |
| name=paste0(name,'_conv1')) |
| bn2 <- mx.symbol.BatchNorm(data=conv1, fix_gamma=FALSE, eps=2e-5, |
| momentum=bn_mom, name=paste0(name, '_bn2')) |
| act2 <- mx.symbol.Activation(data=bn2, act_type='relu', name=paste0(name, '_relu2')) |
| conv2 <- mx.symbol.Convolution(data=act2, num_filter=as.integer(num_filter*0.25), |
| kernel=c(3,3), stride=stride, pad=c(1,1), |
| no_bias=TRUE, workspace=workspace, |
| name=paste0(name, '_conv2')) |
| bn3 <- mx.symbol.BatchNorm(data=conv2, fix_gamma=FALSE, eps=2e-5, |
| momentum=bn_mom, name=paste0(name, '_bn3')) |
| act3 <- mx.symbol.Activation(data=bn3, act_type='relu', name=paste0(name,'_relu3')) |
| conv3 <- mx.symbol.Convolution(data=act3, num_filter=num_filter, kernel=c(1,1), |
| stride=c(1,1), pad=c(0,0), no_bias=TRUE, |
| workspace=workspace, name=paste0(name, '_conv3')) |
| if (dim_match){ |
| shortcut <- data |
| } else{ |
| shortcut <- mx.symbol.Convolution(data=act1, num_filter=num_filter, |
| kernel=c(1,1), stride=stride, no_bias=TRUE, |
| workspace=workspace, name=paste0(name,'_sc')) |
| } |
| return (conv3 + shortcut) |
| } else{ |
| bn1 <- mx.symbol.BatchNorm(data=data, fix_gamma=FALSE, momentum=bn_mom, |
| eps=2e-5, name=paste0(name,'_bn1')) |
| act1 <- mx.symbol.Activation(data=bn1, act_type='relu', name=paste0(name, '_relu1')) |
| conv1 <- mx.symbol.Convolution(data=act1, num_filter=num_filter, kernel=c(3,3), |
| stride=stride, pad=c(1,1), no_bias=TRUE, |
| workspace=workspace, name=paste0(name,'_conv1')) |
| bn2 <- mx.symbol.BatchNorm(data=conv1, fix_gamma=FALSE, momentum=bn_mom, |
| eps=2e-5, name=paste0(name, '_bn2')) |
| act2 <- mx.symbol.Activation(data=bn2, act_type='relu', |
| name=paste0(name, '_relu2')) |
| conv2 <- mx.symbol.Convolution(data=act2, num_filter=num_filter, kernel=c(3,3), |
| stride=c(1,1), pad=c(1,1), no_bias=TRUE, |
| workspace=workspace, name=paste0(name, '_conv2')) |
| if (dim_match){ |
| shortcut = data |
| } else { |
| shortcut <- mx.symbol.Convolution(data=act1, num_filter=num_filter, kernel=c(1,1), |
| stride=stride, no_bias=TRUE, |
| workspace=workspace, name=paste0(name,'_sc')) |
| } |
| return (conv2 + shortcut) |
| } |
| } |
| |
| |
| |
| resnet <- function(units, num_stage, filter_list, num_class, bottle_neck=TRUE, |
| bn_mom=0.9, workspace=512){ |
| num_unit <- length(units) |
| if(num_unit != num_stage) stop("Number of units different from num_stage") |
| data <- mx.symbol.Variable(name='data') |
| data <- mx.symbol.BatchNorm(data=data, fix_gamma=TRUE, eps=2e-5, momentum=bn_mom, |
| name='bn_data') |
| body <- mx.symbol.Convolution(data=data, num_filter=filter_list[1], kernel=c(7, 7), |
| stride=c(2,2), pad=c(3, 3), |
| no_bias=TRUE, name="conv0", workspace=workspace) |
| body <- mx.symbol.BatchNorm(data=body, fix_gamma=FALSE, eps=2e-5, |
| momentum=bn_mom, name='bn0') |
| body <- mx.symbol.Activation(data=body, act_type='relu', name='relu0') |
| body <- mx.symbol.Pooling(data=body, kernel=c(3, 3), stride=c(2,2), |
| pad=c(1,1), pool_type='max') |
| |
| |
| for(i in 1:num_stage){ |
| if(i==1) stride <- c(1,1) |
| else stride <- c(2,2) |
| body <- residual_unit(body, filter_list[i+1], stride, FALSE, |
| name=paste0('stage', i, '_unit1') , |
| bottle_neck=bottle_neck, workspace=workspace) |
| for(j in 1:(units[i]-1)){ |
| body <- residual_unit(body, filter_list[i+1], c(1,1), |
| TRUE, name=paste0('stage',i, '_unit', j + 1), |
| bottle_neck=bottle_neck, |
| workspace=workspace) |
| } |
| } |
| bn1 <- mx.symbol.BatchNorm(data=body, fix_gamma=FALSE, eps=2e-5, |
| momentum=bn_mom, name='bn1') |
| relu1 <- mx.symbol.Activation(data=bn1, act_type='relu', name='relu1') |
| # Although kernel is not used here when global_pool=TRUE, we should put one |
| pool1 <- mx.symbol.Pooling(data=relu1, global_pool=TRUE, kernel=c(7, 7), |
| pool_type='avg', name='pool1') |
| flat <- mx.symbol.Flatten(data=pool1) |
| fc1 <- mx.symbol.FullyConnected(data=flat, num_hidden=num_class, name='fc1') |
| resnet <- mx.symbol.SoftmaxOutput(data=fc1, name='softmax') |
| return(resnet) |
| } |
| |
| get_symbol <- function(num_class, depth=18){ |
| if (depth == 18){ |
| units <- c(2, 2, 2, 2) |
| } else if (depth == 34){ |
| units = c(3, 4, 6, 3) |
| } else if (depth == 50){ |
| units = c(3, 4, 6, 3) |
| } else if (depth == 101){ |
| units = c(3, 4, 23, 3) |
| } else if (depth == 152){ |
| units = c(3, 8, 36, 3) |
| } else if (depth == 200){ |
| units = c(3, 24, 36, 3) |
| } else if (depth == 269){ |
| units = c(3, 30, 48, 8) |
| } else{ |
| stop(paste0("no experiments done on depth ", depth)) |
| } |
| |
| if (depth >=50){ |
| filter_list <- c(64, 256, 512, 1024, 2048) |
| bottle_neck <- TRUE |
| } else{ |
| filter_list <- c(64, 64, 128, 256, 512) |
| bottle_neck <- FALSE |
| } |
| bn_mom <- 0.9 #momentum of batch normalization |
| workspace <- 500 |
| symbol <- resnet(units=units, num_stage=4, filter_list=filter_list, |
| num_class=num_class, bottle_neck=bottle_neck, |
| bn_mom=bn_mom, workspace=workspace) |
| return(symbol) |
| } |
| |
| |
| |
| |
| |
