Merge branch 'master' into cpp

apache · Apr 16, 2017 · 2e66d77 · 2e66d77
2 parents 96eb4f5 + 1722b08
commit 2e66d77
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Showing 27 changed files with 432 additions and 162 deletions.
diff --git a/R-package/R/callback.R b/R-package/R/callback.R
@@ -12,7 +12,7 @@ mx.callback.log.train.metric <- function(period, logger=NULL) {
     if (nbatch %% period == 0 && !is.null(env$metric)) {
       result <- env$metric$get(env$train.metric)
       if (nbatch != 0 & verbose)
-        cat(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value, "\n"))
+        message(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value))
       if (!is.null(logger)) {
         if (class(logger) != "mx.metric.logger") {
           stop("Invalid mx.metric.logger.")
@@ -21,7 +21,7 @@ mx.callback.log.train.metric <- function(period, logger=NULL) {
         if (!is.null(env$eval.metric)) {
           result <- env$metric$get(env$eval.metric)
           if (nbatch != 0 & verbose)
-            cat(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value, "\n"))
+            message(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value))
           logger$eval <- c(logger$eval, result$value)
         }
       }
@@ -49,8 +49,8 @@ mx.callback.log.speedometer <- function(batch.size, frequency=50){
         speed <- frequency*batch.size/time
         result <- env$metric$get(env$train.metric)
         if (nbatch != 0 & verbose)
-          cat(paste0("Batch [", nbatch, "] Speed: ", speed, " samples/sec Train-",
-                     result$name, "=", result$value, "\n"))
+          message(paste0("Batch [", nbatch, "] Speed: ", speed, " samples/sec Train-",
+                     result$name, "=", result$value))
         env$tic = Sys.time()
       }      
     } else {
@@ -69,7 +69,7 @@ mx.callback.save.checkpoint <- function(prefix, period=1) {
   function(iteration, nbatch, env, verbose=TRUE) {
     if (iteration %% period == 0) {
       mx.model.save(env$model, prefix, iteration)
-      if(verbose) cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
+      if(verbose) message(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
     }
     return(TRUE)
   }
@@ -139,7 +139,7 @@ mx.callback.early.stop <- function(train.metric = NULL, eval.metric = NULL, bad.
 
           if (mx.best.iter == bad.steps) {
             if (verbose) {
-              cat(paste0("Best score=", mx.best.score, ", iteration [", iteration - bad.steps, "] \n"))
+              message(paste0("Best score=", mx.best.score, ", iteration [", iteration - bad.steps, "]"))
             }
             return(FALSE)
           } else {

diff --git a/R-package/R/lr_scheduler.R b/R-package/R/lr_scheduler.R
@@ -19,12 +19,12 @@ mx.lr_scheduler.FactorScheduler <- function(step, factor_val, stop_factor_lr=1e-
       lr    <- lr * factor_val
       if(lr < stop_factor_lr){
         lr <- stop_factor_lr
-        if(verbose) cat(paste0("Update[", num_update, 
+        if(verbose) message(paste0("Update[", num_update, 
                                "]: now learning rate arrived at ", lr, 
-                               "will not change in the future\n"))
+                               "will not change in the future"))
       } else{
-        if(verbose) cat(paste0("Update[", num_update, 
-                               "]: learning rate is changed to ", lr, "\n"))
+        if(verbose) message(paste0("Update[", num_update, 
+                               "]: learning rate is changed to ", lr))
       }
       optimizerEnv$lr    <- lr
       optimizerEnv$count <- count      
@@ -62,12 +62,12 @@ mx.lr_scheduler.MultiFactorScheduler <- function(step, factor_val, stop_factor_l
         lr <-  lr * factor_val
         if(lr < stop_factor_lr){
           lr <- stop_factor_lr
-          if(verbose) cat(paste0("Update[", num_update, 
+          if(verbose) message(paste0("Update[", num_update, 
                                  "]: now learning rate arrived at ", lr, 
-                                 "will not change in the future\n"))
+                                 "will not change in the future"))
         } else{
-          if(verbose) cat(paste0("Update[", num_update, 
-                                 "]: learning rate is changed to ", lr, "\n"))
+          if(verbose) message(paste0("Update[", num_update, 
+                                 "]: learning rate is changed to ", lr))
 
         }
         optimizerEnv$lr           <- lr

diff --git a/R-package/R/model.R b/R-package/R/model.R
@@ -85,7 +85,7 @@ mx.model.create.kvstore <- function(kvstore, arg.params, ndevice, verbose=TRUE)
     } else {
       kvstore <- 'local_allreduce_cpu'
     }
-    if(verbose) cat(paste0("Auto-select kvstore type = ", kvstore, "\n"))
+    if(verbose) message(paste0("Auto-select kvstore type = ", kvstore))
   }
   return(mx.kv.create(kvstore))
 }
@@ -101,7 +101,7 @@ mx.model.train <- function(symbol, ctx, input.shape,
                            kvstore,
                            verbose=TRUE) {
   ndevice <- length(ctx)
-  if(verbose) cat(paste0("Start training with ", ndevice, " devices\n"))
+  if(verbose) message(paste0("Start training with ", ndevice, " devices"))
   # create the executors
   sliceinfo <- mx.model.slice.shape(input.shape, ndevice)
   train.execs <- lapply(1:ndevice, function(i) {
@@ -204,7 +204,7 @@ mx.model.train <- function(symbol, ctx, input.shape,
     train.data$reset()
     if (!is.null(metric)) {
       result <- metric$get(train.metric)
-      if(verbose) cat(paste0("[", iteration, "] Train-", result$name, "=", result$value, "\n"))
+      if(verbose) message(paste0("[", iteration, "] Train-", result$name, "=", result$value))
     }
     if (!is.null(eval.data)) {
       if (!is.null(metric)) {
@@ -238,7 +238,7 @@ mx.model.train <- function(symbol, ctx, input.shape,
       eval.data$reset()
       if (!is.null(metric)) {
         result <- metric$get(eval.metric)
-        if(verbose) cat(paste0("[", iteration, "] Validation-", result$name, "=", result$value, "\n"))
+        if(verbose) message(paste0("[", iteration, "] Validation-", result$name, "=", result$value))
       }
     } else {
       eval.metric <- NULL

diff --git a/R-package/R/rnn_model.R b/R-package/R/rnn_model.R
@@ -174,18 +174,18 @@ train.rnn <- function (model, train.data, eval.data,
 
             nbatch <- nbatch + seq.len
             if ((epoch.counter %% log.period) == 0) {
-                cat(paste0("Epoch [", epoch.counter,
+                message(paste0("Epoch [", epoch.counter,
                            "] Train: NLL=", train.nll / nbatch,
-                           ", Perp=", exp(train.nll / nbatch), "\n"))
+                           ", Perp=", exp(train.nll / nbatch)))
             }
         }
         train.data$reset()
         # end of training loop
         toc <- Sys.time()
-        cat(paste0("Iter [", iteration,
+        message(paste0("Iter [", iteration,
                    "] Train: Time: ", as.numeric(toc - tic, units="secs"),
                    " sec, NLL=", train.nll / nbatch,
-                   ", Perp=", exp(train.nll / nbatch), "\n"))
+                   ", Perp=", exp(train.nll / nbatch)))
 
         if (!is.null(eval.data)) {
             val.nll <- 0.0
@@ -216,9 +216,9 @@ train.rnn <- function (model, train.data, eval.data,
             }
             eval.data$reset()
             perp <- exp(val.nll / nbatch)
-            cat(paste0("Iter [", iteration,
+            message(paste0("Iter [", iteration,
                        "] Val: NLL=", val.nll / nbatch,
-                       ", Perp=", exp(val.nll / nbatch), "\n"))
+                       ", Perp=", exp(val.nll / nbatch)))
         }
     }
 
@@ -241,4 +241,4 @@ check.data <- function(data, batch.size, is.train) {
         if (!data$iter.next()) stop("Empty input")
     }
     return (data)
-}
+}
diff --git a/R-package/R/zzz.R b/R-package/R/zzz.R
@@ -25,9 +25,22 @@ NULL
 }
 
 .onUnload <- function(libpath) {
-  print("Start unload")
+  message("Start unload")
   mx.internal.notify.shutdown()
-  unloadModule("mxnet")
   library.dynam.unload("mxnet", libpath)
   library.dynam.unload("libmxnet", libpath)
+  message("MXNet shutdown")
+}
+
+.onAttach <- function(...) {
+  if (!interactive() || stats::runif(1) > 0.1) return()
+
+  tips <- c(
+    "Need help? Feel free to open an issue on https://github.com/dmlc/mxnet/issues",
+    "For more documents, please visit http://mxnet.io",
+    "Use suppressPackageStartupMessages() to eliminate package startup messages."
+  )
+
+  tip <- sample(tips, 1)
+  packageStartupMessage(paste(strwrap(tip), collapse = "\n"))
 }
diff --git a/R-package/src/mxnet.cc b/R-package/src/mxnet.cc
@@ -19,7 +19,7 @@ void SetSeed(int seed) {
   MX_CALL(MXRandomSeed(seed));
 }
 
-void NotifyShutdown(int seed) {
+void NotifyShutdown() {
   MX_CALL(MXNotifyShutdown());
 }
 
@@ -35,7 +35,7 @@ void ProfilerSetState(int state) {
 void InitRcppModule() {
   using namespace Rcpp;  // NOLINT(*)
   function("mx.internal.set.seed", &SetSeed);
-  function("mx.internal.notify.shudown", &NotifyShutdown);
+  function("mx.internal.notify.shutdown", &NotifyShutdown);
   function("mx.internal.profiler.config", &ProfilerSetConfig);
   function("mx.internal.profiler.state", &ProfilerSetState);
 }

diff --git a/R-package/src/symbol.cc b/R-package/src/symbol.cc
@@ -324,7 +324,6 @@ SEXP SymbolFunction::operator() (SEXP* args) {
 }
 
 void Symbol::InitRcppModule() {
-  RLOG_INFO << "Init Rcpp";
   using namespace Rcpp;  // NOLINT(*)
   class_<Symbol>("MXSymbol")
       .method("debug.str", &Symbol::DebugStr,

diff --git a/docs/tutorials/r/fiveMinutesNeuralNetwork.md b/docs/tutorials/r/fiveMinutesNeuralNetwork.md
@@ -87,7 +87,7 @@ Note that `mx.set.seed` controls the random process in `mxnet`. You can see the
 To get an idea of what is happening, view the computation graph from R:
 
  ```{r}
-    graph.viz(model$symbol$as.json())
+    graph.viz(model$symbol)
  ```
 
 [<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png">](https://github.com/dmlc/mxnet)

diff --git a/docs/zh/api/r/fiveMinutesNeuralNetwork.md b/docs/zh/api/r/fiveMinutesNeuralNetwork.md
@@ -94,7 +94,7 @@ model <- mx.mlp(train.x, train.y, hidden_node=10, out_node=2, out_activation="so
 想要知道具体发生了什么，我们可以在R中轻松看到计算图：
 
 ```{r}
-graph.viz(model$symbol$as.json())
+graph.viz(model$symbol)
 ```
 
 [![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png)](https://github.com/dmlc/mxnet)

diff --git a/example/README.md b/example/README.md
@@ -85,6 +85,7 @@ If you want to contribute to this list and the examples, please open a new pull
 * [Multi task tutorial](https://github.com/haria/mxnet-multi-task-example/blob/master/multi-task.ipynb) - A demo of how to train and predict multi-task network on both MNIST and your own dataset.
 * [class active maps](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/class_active_maps.ipynb) - A demo of how to localize the discriminative regions in an image using global average pooling (GAP) in CNNs.
 * [DMLC MXNet Notebooks](https://github.com/dmlc/mxnet-notebooks) DMLC's repo for various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications.
+* [AWS Seoul Summit 2017 Demos](https://github.com/sxjscience/aws-summit-2017-seoul) The demo codes and ipython notebooks in AWS Seoul Summit 2017.
 
 ### <a name="mobile-apps-examples"></a>Mobile App Examples
 -------------------

diff --git a/example/image-classification/train_mnist.R b/example/image-classification/train_mnist.R
@@ -95,6 +95,8 @@ parse_args <- function() {
                         help='the batch size')
     parser$add_argument('--lr', type='double', default=.05,
                         help='the initial learning rate')
+    parser$add_argument('--mom', type='double', default=.9,
+                        help='momentum for sgd')
     parser$add_argument('--model-prefix', type='character',
                         help='the prefix of the model to load/save')
     parser$add_argument('--num-round', type='integer', default=10,
@@ -113,6 +115,32 @@ if (args$network == 'mlp') {
     data_shape <- c(28, 28, 1)
     net <- get_lenet()
 }
+
 # train
-source("train_model.R")
-train_model.fit(args, net, get_iterator(data_shape))
+data_loader <- get_iterator(data_shape)
+data <- data_loader(args)
+train <- data$train
+val <- data$value 
+
+if (is.null(args$gpus)) {
+  devs <- mx.cpu()  
+} else {
+  devs <- lapply(unlist(strsplit(args$gpus, ",")), function(i) {
+    mx.gpu(as.integer(i))
+  })
+}
+
+mx.set.seed(0)
+
+model <- mx.model.FeedForward.create(
+  X                  = train,
+  eval.data          = val,
+  ctx                = devs,
+  symbol             = net,
+  num.round          = args$num_round,
+  array.batch.size   = args$batch_size,
+  learning.rate      = args$lr,
+  momentum           = args$mom,
+  eval.metric        = mx.metric.accuracy,
+  initializer        = mx.init.uniform(0.07),
+  batch.end.callback = mx.callback.log.train.metric(100))
diff --git a/example/image-classification/train_model.R b/example/image-classification/train_model.R
@@ -33,9 +33,9 @@ train_model.fit <- function(args, network, data_loader) {
     }
 
     # data
-    data <- data_loader
+    data <- data_loader(args)
     train <- data$train
-    val <- data$value  
+    val <- data$value 
 
     # devices
     if (is.null(args$gpus)) {