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symbol_resnet-v2.R
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symbol_resnet-v2.R
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# 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.
###
# 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)
}