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Posted to commits@mxnet.apache.org by qk...@apache.org on 2017/08/10 15:19:32 UTC
[incubator-mxnet] branch master updated: [R][MISC] update
Makefile/Jenkinsfile; use mx.ctx.default() in R test (#7401)
This is an automated email from the ASF dual-hosted git repository.
qkou pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 613132e [R][MISC] update Makefile/Jenkinsfile; use mx.ctx.default() in R test (#7401)
613132e is described below
commit 613132e2b6c6573b184b467aed5cf50dbb91aa47
Author: Qiang Kou (KK) <qk...@qkou.info>
AuthorDate: Thu Aug 10 15:19:28 2017 +0000
[R][MISC] update Makefile/Jenkinsfile; use mx.ctx.default() in R test (#7401)
---
Jenkinsfile | 4 +-
Makefile | 1 +
R-package/README.md | 6 --
R-package/tests/testthat.R | 4 --
R-package/tests/testthat/get_data.R | 13 ++++
R-package/tests/testthat/test_img_seg.R | 7 +-
R-package/tests/testthat/test_lstm.R | 7 +-
R-package/tests/testthat/test_model.R | 117 +++++++++++++++++++++++++++++---
R-package/tests/testthat/test_ndarray.R | 9 ++-
example/captcha/README.md | 2 +-
10 files changed, 142 insertions(+), 28 deletions(-)
diff --git a/Jenkinsfile b/Jenkinsfile
index 370c2b3..632789a 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -325,11 +325,11 @@ try {
init_git()
unpack_lib('gpu')
timeout(time: max_time, unit: 'MINUTES') {
- sh "${docker_run} cpu rm -rf .Renviron"
+ sh "${docker_run} gpu rm -rf .Renviron"
sh "${docker_run} gpu mkdir -p /workspace/ut-r-gpu/site-library"
sh "${docker_run} gpu make rpkg USE_BLAS=openblas R_LIBS=/workspace/ut-r-gpu/site-library"
sh "${docker_run} gpu R CMD INSTALL --library=/workspace/ut-r-gpu/site-library mxnet_current_r.tar.gz"
- sh "${docker_run} gpu make rpkgtest R_LIBS=/workspace/ut-r-gpu/site-library"
+ sh "${docker_run} gpu make rpkgtest R_LIBS=/workspace/ut-r-gpu/site-library R_GPU_ENABLE=1"
}
}
}
diff --git a/Makefile b/Makefile
index ed74214..560b77a 100644
--- a/Makefile
+++ b/Makefile
@@ -398,6 +398,7 @@ rpkg:
Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"
R CMD build --no-build-vignettes R-package
rm -rf mxnet_current_r.tar.gz
+ rm -rf R-package/src/image_recordio.h
mv mxnet_*.tar.gz mxnet_current_r.tar.gz
rpkgtest:
diff --git a/R-package/README.md b/R-package/README.md
index e150f17..6576700 100644
--- a/R-package/README.md
+++ b/R-package/README.md
@@ -1,7 +1,5 @@
<img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/mxnetR.png width=155/> Deep Learning for R
==========================
-[](https://travis-ci.org/dmlc/mxnet)
-[](http://mxnet.readthedocs.io/en/latest/api/r/index.html)
You have found MXNet R Package! The MXNet R packages brings flexible and efficient GPU
computing and state-of-art deep learning to R.
@@ -12,10 +10,6 @@ computing and state-of-art deep learning to R.
Sounds exciting? This page contains links to all the related documentation of the R package.
-Resources
----------
-* [MXNet R Package Document](http://mxnet.io/get_started/install.html)
- - Check this out for detailed documents, examples and installation guides.
Installation
------------
diff --git a/R-package/tests/testthat.R b/R-package/tests/testthat.R
deleted file mode 100644
index f002e4c..0000000
--- a/R-package/tests/testthat.R
+++ /dev/null
@@ -1,4 +0,0 @@
-library(testthat)
-library(mxnet)
-
-test_check("mxnet")
diff --git a/R-package/tests/testthat/get_data.R b/R-package/tests/testthat/get_data.R
index 555e5e9..6d8de85 100644
--- a/R-package/tests/testthat/get_data.R
+++ b/R-package/tests/testthat/get_data.R
@@ -92,3 +92,16 @@ GetISBI_data <- function() {
file.remove('data/ISBI.zip')
}
}
+
+GetCaptcha_data <- function() {
+ if (!dir.exists("data")) {
+ dir.create("data/")
+ }
+ if (!file.exists('data/captcha_example/captcha_train.rec') |
+ !file.exists('data/captcha_example/captcha_test.rec')) {
+ download.file('https://s3-us-west-2.amazonaws.com/apache-mxnet/R/data/captcha_example.zip',
+ destfile = 'data/captcha_example.zip')
+ unzip('data/captcha_example.zip', exdir = 'data/')
+ file.remove('data/captcha_example.zip')
+ }
+}
diff --git a/R-package/tests/testthat/test_img_seg.R b/R-package/tests/testthat/test_img_seg.R
index ba5c9cd..fbca92e 100644
--- a/R-package/tests/testthat/test_img_seg.R
+++ b/R-package/tests/testthat/test_img_seg.R
@@ -2,6 +2,11 @@ require(mxnet)
source("get_data.R")
+if (Sys.getenv("R_GPU_ENABLE") != "" & as.integer(Sys.getenv("R_GPU_ENABLE")) == 1) {
+ mx.ctx.default(new = mx.gpu())
+ message("Using GPU for testing.")
+}
+
print_inferred_shape <- function(net) {
slist <- mx.symbol.infer.shape(symbol = net, data = c(168, 168, 1, 2))
print(slist$out.shapes)
@@ -116,7 +121,7 @@ test_that("UNET", {
train.y.array = train.y
dim(train.y.array) = c(IMG_SIZE, IMG_SIZE, 1, 30)
- devices <- mx.cpu()
+ devices <- mx.ctx.default()
mx.set.seed(0)
net <- get_unet()
diff --git a/R-package/tests/testthat/test_lstm.R b/R-package/tests/testthat/test_lstm.R
index 24b1a59..4a5cdbe 100644
--- a/R-package/tests/testthat/test_lstm.R
+++ b/R-package/tests/testthat/test_lstm.R
@@ -1,5 +1,10 @@
require(mxnet)
+if (Sys.getenv("R_GPU_ENABLE") != "" & as.integer(Sys.getenv("R_GPU_ENABLE")) == 1) {
+ mx.ctx.default(new = mx.gpu())
+ message("Using GPU for testing.")
+}
+
context("lstm models")
get.nll <- function(s) {
@@ -26,7 +31,7 @@ test_that("training error decreasing", {
X.train <- list(data=array(1:16, dim=c(2,8)), label=array(2:17, dim=c(2,8)))
s <- capture.output(model <- mx.lstm( X.train,
- ctx=mx.cpu(),
+ ctx=mx.ctx.default(),
num.round=num.round,
update.period=update.period,
num.lstm.layer=num.lstm.layer,
diff --git a/R-package/tests/testthat/test_model.R b/R-package/tests/testthat/test_model.R
index 73a2127..8cdd396 100644
--- a/R-package/tests/testthat/test_model.R
+++ b/R-package/tests/testthat/test_model.R
@@ -4,6 +4,11 @@ source("get_data.R")
context("models")
+if (Sys.getenv("R_GPU_ENABLE") != "" & as.integer(Sys.getenv("R_GPU_ENABLE")) == 1) {
+ mx.ctx.default(new = mx.gpu())
+ message("Using GPU for testing.")
+}
+
test_that("MNIST", {
# # Network configuration
GetMNIST_ubyte()
@@ -36,13 +41,10 @@ test_that("MNIST", {
silent=0)
mx.set.seed(0)
- devices = lapply(1:2, function(i) {
- mx.cpu(i)
- })
-
+
# create the model
model <- mx.model.FeedForward.create(softmax, X=dtrain, eval.data=dtest,
- ctx=devices, num.round=1,
+ ctx = mx.ctx.default(), num.round=1,
learning.rate=0.1, momentum=0.9,
initializer=mx.init.uniform(0.07),
epoch.end.callback=mx.callback.save.checkpoint("chkpt"),
@@ -83,12 +85,30 @@ test_that("Regression", {
})
mx.set.seed(0)
model <- mx.model.FeedForward.create(lro, X = train.x, y = train.y,
- ctx = mx.cpu(), num.round = 5,
+ ctx = mx.ctx.default(), num.round = 5,
array.batch.size = 20,
learning.rate = 2e-6,
momentum = 0.9,
eval.metric = demo.metric.mae)
+ train.x <- data.matrix(BostonHousing[train.ind, -(13:14)])
+ train.y <- BostonHousing[train.ind, c(13:14)]
+ test.x <- data.matrix(BostonHousing[-train.ind, -(13:14)])
+ test.y <- BostonHousing[-train.ind, c(13:14)]
+
+ data <- mx.symbol.Variable("data")
+ fc2 <- mx.symbol.FullyConnected(data, num_hidden=2)
+ lro2 <- mx.symbol.LinearRegressionOutput(fc2)
+
+ mx.set.seed(0)
+ train_iter = mx.io.arrayiter(data = t(train.x), label = t(train.y))
+
+ model <- mx.model.FeedForward.create(lro2, X = train_iter,
+ ctx = mx.ctx.default(),
+ num.round = 50,
+ array.batch.size = 20,
+ learning.rate = 2e-6,
+ momentum = 0.9)
})
@@ -141,7 +161,7 @@ test_that("Fine-tune", {
arg_params_new[["fc1_bias"]] <- fc1_bias_new
#model <- mx.model.FeedForward.create(symbol = new_soft, X = train_iter, eval.data = val_iter,
- # ctx = mx.cpu(), eval.metric = mx.metric.accuracy,
+ # ctx = mx.ctx.default(), eval.metric = mx.metric.accuracy,
# num.round = 2, learning.rate = 0.05, momentum = 0.9,
# wd = 0.00001, kvstore = "local",
# batch.end.callback = mx.callback.log.train.metric(50),
@@ -171,9 +191,7 @@ test_that("Matrix Factorization", {
pred1 <- mx.symbol.sum_axis(pred, axis = 1, name = "pred1")
pred2 <- mx.symbol.Flatten(pred1, name = "pred2")
pred3 <- mx.symbol.LinearRegressionOutput(data = pred2, label = score, name = "pred3")
- devices = lapply(1:2, function(i) {
- mx.cpu(i)
- })
+
mx.set.seed(123)
CustomIter <- setRefClass( "CustomIter", fields = c("iter1", "iter2"),
@@ -216,7 +234,7 @@ test_that("Matrix Factorization", {
train_iter <- CustomIter$new(user_iter, item_iter)
- model <- mx.model.FeedForward.create(pred3, X = train_iter, ctx = devices,
+ model <- mx.model.FeedForward.create(pred3, X = train_iter, ctx = mx.ctx.default(),
num.round = 5, initializer = mx.init.uniform(0.07),
learning.rate = 0.07,
eval.metric = mx.metric.rmse,
@@ -225,3 +243,80 @@ test_that("Matrix Factorization", {
input.names = c("user", "item"),
output.names = "score")
})
+
+test_that("Captcha", {
+ GetCaptcha_data()
+ data.shape <- c(80, 30, 3)
+ batch_size <- 40
+ train <- mx.io.ImageRecordIter(
+ path.imgrec = "./data/captcha_example/captcha_train.rec",
+ path.imglist = "./data/captcha_example/captcha_train.lst",
+ batch.size = batch_size,
+ label.width = 4,
+ data.shape = data.shape,
+ mean.img = "mean.bin")
+
+ val <- mx.io.ImageRecordIter(
+ path.imgrec = "./data/captcha_example/captcha_test.rec",
+ path.imglist = "./data/captcha_example/captcha_test.lst",
+ batch.size = batch_size,
+ label.width = 4,
+ data.shape = data.shape,
+ mean.img = "mean.bin")
+
+ data <- mx.symbol.Variable("data")
+ label <- mx.symbol.Variable("label")
+ conv1 <- mx.symbol.Convolution(data = data, kernel = c(5, 5), num_filter = 32)
+ pool1 <- mx.symbol.Pooling(data = conv1, pool_type = "max", kernel = c(2, 2), stride = c(1, 1))
+ relu1 <- mx.symbol.Activation(data = pool1, act_type = "relu")
+
+ conv2 <- mx.symbol.Convolution(data = relu1, kernel = c(5, 5), num_filter = 32)
+ pool2 <- mx.symbol.Pooling(data = conv2, pool_type = "avg", kernel = c(2, 2), stride = c(1, 1))
+ relu2 <- mx.symbol.Activation(data = pool2, act_type = "relu")
+
+ flatten <- mx.symbol.Flatten(data = relu2)
+ fc1 <- mx.symbol.FullyConnected(data = flatten, num_hidden = 120)
+ fc21 <- mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
+ fc22 <- mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
+ fc23 <- mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
+ fc24 <- mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
+ fc2 <- mx.symbol.Concat(c(fc21, fc22, fc23, fc24), dim = 0, num.args = 4)
+ label <- mx.symbol.transpose(data = label)
+ label <- mx.symbol.Reshape(data = label, target_shape = c(0))
+ captcha_net <- mx.symbol.SoftmaxOutput(data = fc2, label = label, name = "softmax")
+
+ mx.metric.acc2 <- mx.metric.custom("accuracy", function(label, pred) {
+ ypred <- max.col(t(pred)) - 1
+ ypred <- matrix(ypred, nrow = nrow(label), ncol = ncol(label), byrow = TRUE)
+ return(sum(colSums(label == ypred) == 4)/ncol(label))
+ })
+
+ mx.set.seed(42)
+
+ train$reset()
+ train$iter.next()
+
+ input.names <- "data"
+ input.shape <- sapply(input.names, function(n){dim(train$value()[[n]])}, simplify = FALSE)
+ arg_names <- arguments(captcha_net)
+ output.names <- "label"
+ output.shape <- sapply(output.names, function(n){dim(train$value()[[n]])}, simplify = FALSE)
+ params <- mx.model.init.params(captcha_net, input.shape, output.shape,
+ mx.init.Xavier(factor_type = "in", magnitude = 2.34),
+ mx.cpu())
+
+ #model <- mx.model.FeedForward.create(
+ # X = train,
+ # eval.data = val,
+ # ctx = mx.ctx.default(),
+ # symbol = captcha_net,
+ # eval.metric = mx.metric.acc2,
+ # num.round = 1,
+ # learning.rate = 1e-04,
+ # momentum = 0.9,
+ # wd = 1e-05,
+ # batch.end.callback = mx.callback.log.train.metric(50),
+ # initializer = mx.init.Xavier(factor_type = "in", magnitude = 2.34),
+ # optimizer = "sgd",
+ # clip_gradient = 10)
+})
diff --git a/R-package/tests/testthat/test_ndarray.R b/R-package/tests/testthat/test_ndarray.R
index 142c87e..326ea6c 100644
--- a/R-package/tests/testthat/test_ndarray.R
+++ b/R-package/tests/testthat/test_ndarray.R
@@ -2,9 +2,14 @@ require(mxnet)
context("ndarray")
+if (Sys.getenv("R_GPU_ENABLE") != "" & as.integer(Sys.getenv("R_GPU_ENABLE")) == 1) {
+ mx.ctx.default(new = mx.gpu())
+ message("Using GPU for testing.")
+}
+
test_that("element-wise calculation for vector", {
x = 1:10
- mat = mx.nd.array(as.array(x), mx.cpu(0))
+ mat = mx.nd.array(as.array(x), mx.ctx.default())
expect_equal(x, as.array(mat))
expect_equal(x + 1, as.array(mat + 1))
expect_equal(x - 10, as.array(mat - 10))
@@ -26,7 +31,7 @@ test_that("element-wise calculation for vector", {
test_that("element-wise calculation for matrix", {
x = matrix(1:4, 2, 2)
- mat = mx.nd.array(as.array(x), mx.cpu(0))
+ mat = mx.nd.array(as.array(x), mx.ctx.default())
expect_equal(x, as.array(mat))
expect_equal(x + 1, as.array(mat + 1))
expect_equal(x - 10, as.array(mat - 10))
diff --git a/example/captcha/README.md b/example/captcha/README.md
index 588d626..02e8726 100644
--- a/example/captcha/README.md
+++ b/example/captcha/README.md
@@ -2,4 +2,4 @@ This is the R version of [captcha recognition](http://blog.xlvector.net/2016-05/
-You can download the images and `.rec` files from [here](https://drive.google.com/open?id=0B_52ppM3wSXBdHctQmhUdmlTbDQ). Since each image has 4 labels, please remember to use `label_width=4` when generating the `.rec` files.
+You can download the images and `.rec` files from [here](https://s3-us-west-2.amazonaws.com/apache-mxnet/R/data/captcha_example.zip). Since each image has 4 labels, please remember to use `label_width=4` when generating the `.rec` files.
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