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| require(mxnet) | ||
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| source("get_data.R") | ||
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| print_inferred_shape <- function(net) { | ||
| slist <- mx.symbol.infer.shape(symbol = net, data = c(168, 168, 1, 2)) | ||
| print(slist$out.shapes) | ||
| } | ||
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| convolution_module <- function(net, kernel_size, pad_size, filter_count, | ||
| stride = c(1, 1), work_space = 2048, batch_norm = TRUE, | ||
| down_pool = FALSE, up_pool = FALSE, act_type = "relu", | ||
| convolution = TRUE) { | ||
| if (up_pool) { | ||
| net = mx.symbol.Deconvolution(net, kernel = c(2, 2), pad = c(0, 0), | ||
| stride = c(2, 2), num_filter = filter_count, | ||
| workspace = work_space) | ||
| net = mx.symbol.BatchNorm(net) | ||
| if (act_type != "") { | ||
| net = mx.symbol.Activation(net, act_type = act_type) | ||
| } | ||
| } | ||
| if (convolution) { | ||
| conv = mx.symbol.Convolution(data = net, kernel = kernel_size, stride = stride, | ||
| pad = pad_size, num_filter = filter_count, | ||
| workspace = work_space) | ||
| net = conv | ||
| } | ||
| if (batch_norm) { | ||
| net = mx.symbol.BatchNorm(net) | ||
| } | ||
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| if (act_type != "") { | ||
| net = mx.symbol.Activation(net, act_type = act_type) | ||
| } | ||
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| if (down_pool) { | ||
| pool = mx.symbol.Pooling(net, pool_type = "max", kernel = c(2, 2), stride = c(2, 2)) | ||
| net = pool | ||
| } | ||
| print_inferred_shape(net) | ||
| return(net) | ||
| } | ||
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| get_unet <- function() { | ||
| data = mx.symbol.Variable('data') | ||
| kernel_size = c(3, 3) | ||
| pad_size = c(1, 1) | ||
| filter_count = 32 | ||
| pool1 = convolution_module(data, kernel_size, pad_size, filter_count = filter_count, down_pool = TRUE) | ||
| net = pool1 | ||
| pool2 = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 2, down_pool = TRUE) | ||
| net = pool2 | ||
| pool3 = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4, down_pool = TRUE) | ||
| net = pool3 | ||
| pool4 = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4, down_pool = TRUE) | ||
| net = pool4 | ||
| net = mx.symbol.Dropout(net) | ||
| pool5 = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 8, down_pool = TRUE) | ||
| net = pool5 | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4, up_pool = TRUE) | ||
| net = convolution_module(net, kernel_size, pad_size = c(2, 2), filter_count = filter_count * 4, up_pool = TRUE) | ||
| net = mx.symbol.Crop(net, pool3, num.args = 2) | ||
| net = mx.symbol.concat(c(pool3, net), num.args = 2) | ||
| net = mx.symbol.Dropout(net) | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4) | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4, up_pool = TRUE) | ||
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| net = mx.symbol.Concat(c(pool2, net), num.args = 2) | ||
| net = mx.symbol.Dropout(net) | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4) | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4, up_pool = TRUE) | ||
| convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 4) | ||
| net = mx.symbol.Concat(c(pool1, net), num.args = 2) | ||
| net = mx.symbol.Dropout(net) | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 2) | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = filter_count * 2, up_pool = TRUE) | ||
| net = convolution_module(net, kernel_size, pad_size, filter_count = 1, batch_norm = FALSE, act_type = "") | ||
| net = mx.symbol.SoftmaxOutput(data = net, name = 'sm') | ||
| return(net) | ||
| } | ||
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| context("Image segmentation") | ||
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| test_that("UNET", { | ||
| list.of.packages <- c("imager") | ||
| new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] | ||
| if(length(new.packages)) install.packages(new.packages) | ||
| GetISBI_data() | ||
| library(imager) | ||
| IMG_SIZE <- 168 | ||
| files <- list.files(path = "data/ISBI/train-volume/") | ||
| a = 'data/ISBI/train-volume/' | ||
| filess = paste(a, files, sep = '') | ||
| list_of_images = lapply(filess, function(x) { | ||
| x <- load.image(x) | ||
| y <- resize(x, size_x = IMG_SIZE, size_y = IMG_SIZE) | ||
| }) | ||
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| train.x = do.call('cbind', lapply(list_of_images, as.vector)) | ||
| train.array <- train.x | ||
| dim(train.array) <- c(IMG_SIZE, IMG_SIZE, 1, 30) | ||
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| files <- list.files(path = "data/ISBI/train-labels") | ||
| b = 'data/ISBI/train-labels/' | ||
| filess = paste(b, files, sep = '') | ||
| list_of_images = lapply(filess, function(x) { | ||
| x <- load.image(x) | ||
| y <- resize(x, size_x = IMG_SIZE, size_y = IMG_SIZE) | ||
| }) | ||
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| train.y = do.call('cbind', lapply(list_of_images, as.vector)) | ||
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| train.y[which(train.y < 0.5)] = 0 | ||
| train.y[which(train.y > 0.5)] = 1 | ||
| train.y.array = train.y | ||
| dim(train.y.array) = c(IMG_SIZE, IMG_SIZE, 1, 30) | ||
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| devices <- mx.cpu() | ||
| mx.set.seed(0) | ||
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| net <- get_unet() | ||
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| model <- mx.model.FeedForward.create(net, X = train.array, y = train.y.array, | ||
| ctx = devices, num.round = 2, | ||
| initializer = mx.init.normal(sqrt(2 / 576)), | ||
| learning.rate = 0.05, | ||
| momentum = 0.99, | ||
| array.batch.size = 2) | ||
| }) |
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