| 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) |
| } |
