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symbol_inception-bn.R
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library(mxnet)
eps = 1e-10 + 1e-5
bn_mom = 0.9
fix_gamma = FALSE
ConvFactory <- function(data, num_filter, kernel, stride = c(1, 1),
pad = c(0, 0), name = '', suffix = '') {
conv <- mx.symbol.Convolution(data = data, num_filter = num_filter,
kernel = kernel, stride = stride, pad = pad,
name = paste('conv_', name, suffix, sep = ''))
bn <- mx.symbol.BatchNorm(data = conv, eps = eps, momentum = bn_mom, fix.gamma = fix_gamma, name = paste('bn_', name, suffix, sep = ''))
act <- mx.symbol.Activation(data = bn, act_type = 'relu', name = paste('relu_', name, suffix, sep = ''))
return(act)
}
InceptionFactoryA <- function(data, num_1x1, num_3x3red, num_3x3, num_d3x3red,
num_d3x3, pool, proj, name) {
# 1x1
c1x1 <- ConvFactory(data = data, num_filter = num_1x1, kernel = c(1, 1), name = paste(name, '_1x1', sep = '')
)
# 3x3 reduce + 3x3
c3x3r <- ConvFactory(data = data, num_filter = num_3x3red, kernel = c(1, 1),
name = paste(name, '_3x3', sep = ''), suffix = '_reduce')
c3x3 <- ConvFactory(data = c3x3r, num_filter = num_3x3, kernel = c(3, 3),
pad = c(1, 1), name = paste(name, '_3x3', sep = ''))
# double 3x3 reduce + double 3x3
cd3x3r <- ConvFactory(data = data, num_filter = num_d3x3red, kernel = c(1, 1),
name = paste(name, '_double_3x3', sep = ''), suffix = '_reduce')
cd3x3 <- ConvFactory(data = cd3x3r, num_filter = num_d3x3, kernel = c(3, 3),
pad = c(1, 1), name = paste(name, '_double_3x3_0', sep = ''))
cd3x3 <- ConvFactory(data = cd3x3, num_filter = num_d3x3, kernel = c(3, 3),
pad = c(1, 1), name = paste(name, '_double_3x3_1', sep = ''))
# pool + proj
pooling <- mx.symbol.Pooling(data = data, kernel = c(3, 3), stride = c(1, 1),
pad = c(1, 1), pool_type = pool,
name = paste(pool, '_pool_', name, '_pool', sep = ''))
cproj <- ConvFactory(data = pooling, num_filter = proj, kernel = c(1, 1),
name = paste(name, '_proj', sep = ''))
# concat
concat_lst <- list()
concat_lst <- c(c1x1, c3x3, cd3x3, cproj)
concat_lst$num.args = 4
concat_lst$name = paste('ch_concat_', name, '_chconcat', sep = '')
concat = mxnet:::mx.varg.symbol.Concat(concat_lst)
return(concat)
}
InceptionFactoryB <- function(data, num_3x3red, num_3x3, num_d3x3red, num_d3x3, name) {
# 3x3 reduce + 3x3
c3x3r <- ConvFactory(data = data, num_filter = num_3x3red, kernel = c(1, 1),
name = paste(name, '_3x3', sep = ''), suffix = '_reduce')
c3x3 <- ConvFactory(data = c3x3r, num_filter = num_3x3, kernel = c(3, 3),
pad = c(1, 1), stride = c(2, 2), name = paste(name, '_3x3', sep = ''))
# double 3x3 reduce + double 3x3
cd3x3r <- ConvFactory(data = data, num_filter = num_d3x3red, kernel = c(1, 1),
name = paste(name, '_double_3x3', sep = ''), suffix = '_reduce')
cd3x3 <- ConvFactory(data = cd3x3r, num_filter = num_d3x3, kernel = c(3, 3),
pad = c(1, 1), stride = c(1, 1), name = paste(name, '_double_3x3_0', sep = ''))
cd3x3 = ConvFactory(data = cd3x3, num_filter = num_d3x3, kernel = c(3, 3),
pad = c(1, 1), stride = c(2, 2), name = paste(name, '_double_3x3_1', sep = ''))
# pool + proj
pooling = mx.symbol.Pooling(data = data, kernel = c(3, 3), stride = c(2, 2),
pad = c(1, 1), pool_type = "max",
name = paste('max_pool_', name, '_pool', sep = ''))
# concat
concat_lst <- list()
concat_lst <- c(c3x3, cd3x3, pooling)
concat_lst$num.args = 3
concat_lst$name = paste('ch_concat_', name, '_chconcat', sep = '')
concat = mxnet:::mx.varg.symbol.Concat(concat_lst)
return(concat)
}
get_symbol <- function(num_classes = 1000) {
# data
data = mx.symbol.Variable(name = "data")
# stage 1
conv1 = ConvFactory(data = data, num_filter = 64, kernel = c(7, 7),
stride = c(2, 2), pad = c(3, 3), name = '1')
pool1 = mx.symbol.Pooling(data = conv1, kernel = c(3, 3), stride = c(2, 2),
name = 'pool_1', pool_type = 'max')
# stage 2
conv2red = ConvFactory(data = pool1, num_filter = 64, kernel = c(1, 1),
stride = c(1, 1), name = '2_red')
conv2 = ConvFactory(data = conv2red, num_filter = 192, kernel = c(3, 3),
stride = c(1, 1), pad = c(1, 1), name = '2')
pool2 = mx.symbol.Pooling(data = conv2, kernel = c(3, 3), stride = c(2, 2),
name = 'pool_2', pool_type = 'max')
# stage 2
in3a = InceptionFactoryA(pool2, 64, 64, 64, 64, 96, "avg", 32, '3a')
in3b = InceptionFactoryA(in3a, 64, 64, 96, 64, 96, "avg", 64, '3b')
in3c = InceptionFactoryB(in3b, 128, 160, 64, 96, '3c')
# stage 3
in4a = InceptionFactoryA(in3c, 224, 64, 96, 96, 128, "avg", 128, '4a')
in4b = InceptionFactoryA(in4a, 192, 96, 128, 96, 128, "avg", 128, '4b')
in4c = InceptionFactoryA(in4b, 160, 128, 160, 128, 160, "avg", 128, '4c')
in4d = InceptionFactoryA(in4c, 96, 128, 192, 160, 192, "avg", 128, '4d')
in4e = InceptionFactoryB(in4d, 128, 192, 192, 256, '4e')
# stage 4
in5a = InceptionFactoryA(in4e, 352, 192, 320, 160, 224, "avg", 128, '5a')
in5b = InceptionFactoryA(in5a, 352, 192, 320, 192, 224, "max", 128, '5b')
# global avg pooling
avg = mx.symbol.Pooling(data = in5b, kernel = c(7, 7), stride = c(1, 1),
name = "global_pool", pool_type = 'avg')
# linear classifier
flatten = mx.symbol.Flatten(data = avg, name = 'flatten')
fc1 = mx.symbol.FullyConnected(data = flatten,
num_hidden = num_classes,
name = 'fc1')
softmax = mx.symbol.SoftmaxOutput(data = fc1, name = 'softmax')
return(softmax)
}