| # 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. |
| |
| library(mxnet) |
| |
| get_conv <- function(name, data, num_filter, kernel, stride, |
| pad, with_relu, bn_momentum) { |
| conv = mx.symbol.Convolution(name = name, data = data, num_filter = num_filter, |
| kernel = kernel, stride = stride, pad = pad, no_bias = TRUE) |
| bn = mx.symbol.BatchNorm(name = paste(name, '_bn', sep = ''), data = conv, |
| fix_gamma = FALSE, momentum = bn_momentum, eps = 2e-5) |
| if (with_relu) { |
| return(mx.symbol.Activation(name = paste(name, '_relu', sep = ''), |
| data = bn, act_type = 'relu')) |
| } else { |
| return(bn) |
| } |
| } |
| |
| make_block <- function(name, data, num_filter, dim_match, bn_momentum) { |
| if (dim_match) { |
| conv1 = get_conv(name = paste(name, '_conv1', sep = ''), data = data, |
| num_filter = num_filter, kernel = c(3, 3), stride = c(1, 1), |
| pad = c(1, 1), with_relu = TRUE, bn_momentum = bn_momentum) |
| } else { |
| conv1 = get_conv(name = paste(name, '_conv1', sep = ''), data = data, |
| num_filter = num_filter, kernel = c(3, 3), stride = c(2, 2), |
| pad = c(1, 1), with_relu = TRUE, bn_momentum = bn_momentum) |
| } |
| |
| conv2 = get_conv(name = paste(name, '_conv2', sep = ''), data = conv1, |
| num_filter = num_filter, kernel = c(3, 3), stride = c(1, 1), |
| pad = c(1, 1), with_relu = FALSE, bn_momentum = bn_momentum) |
| if (dim_match) { |
| shortcut = data |
| } else { |
| shortcut = mx.symbol.Convolution(name = paste(name, '_proj', sep = ''), |
| data = data, num_filter = num_filter, kernel = c(2, 2), |
| stride = c(2, 2), pad = c(0, 0), no_bias = TRUE) |
| } |
| fused = shortcut + conv2 |
| return(mx.symbol.Activation(name = paste(name, '_relu', sep = ''), data = fused, act_type = 'relu')) |
| } |
| |
| get_body <- function(data, num_level, num_block, num_filter, bn_momentum) { |
| for (level in 1:num_level) { |
| for (block in 1:num_block) { |
| data = make_block( |
| name = paste('level', level, '_block', block, sep = ''), |
| data = data, |
| num_filter = num_filter * 2 ^ (level - 1), |
| dim_match = (level == 1 || block > 1), |
| bn_momentum = bn_momentum |
| ) |
| } |
| } |
| return(data) |
| } |
| |
| get_symbol <- function(num_class, num_level = 3, num_block = 9, |
| num_filter = 16, bn_momentum = 0.9, pool_kernel = c(8, 8)) { |
| data = mx.symbol.Variable(name = 'data') |
| zscore = mx.symbol.BatchNorm(name = 'zscore', data = data, |
| fix_gamma = TRUE, momentum = bn_momentum) |
| conv = get_conv(name = 'conv0', data = zscore, num_filter = num_filter, |
| kernel = c(3, 3), stride = c(1, 1), pad = c(1, 1), |
| with_relu = TRUE, bn_momentum = bn_momentum) |
| body = get_body(conv, num_level, num_block, num_filter, bn_momentum) |
| pool = mx.symbol.Pooling(data = body, kernel = pool_kernel, pool_type = 'avg') |
| flat = mx.symbol.Flatten(data = pool) |
| fc = mx.symbol.FullyConnected(data = flat, num_hidden = num_class, name = 'fc') |
| return(mx.symbol.SoftmaxOutput(data = fc, name = 'softmax')) |
| } |
