fix tuning operation with torch models

R/api_torch.R

@@ -368,22 +368,24 @@
 .is_torch_model <- function(ml_model) {
     inherits(ml_model, "torch_model")
 }
+
 .torch_has_cuda <- function(){
     torch::cuda_is_available()
 }
+
 .torch_has_mps <- function(){
     torch::backends_mps_is_available()
 }
 
 .torch_mem_info <- function() {
-    if (.torch_has_cuda()){
-        # Get memory summary
+    mem_sum <-  0
+
+    if (.torch_has_cuda()) {
+        # get current memory info in GB
         mem_sum <- torch::cuda_memory_stats()
-        # Return current memory info in GB
-        mem_sum[["allocated_bytes"]][["all"]][["current"]] / 10^9
-    } else {
-        mem_sum <-  0
+        mem_sum <- mem_sum[["allocated_bytes"]][["all"]][["current"]] / 10^9
     }
+
     return(mem_sum)
 }
 #' @title Verify if torch works on CUDA

R/sits_classify.R

@@ -35,7 +35,7 @@
 #'                           (integer, min = 1, max = 16384).
 #' @param  multicores        Number of cores to be used for classification
 #'                           (integer, min = 1, max = 2048).
-#' @param  gpu_memory        Memory available in GPU in GB (default = 16)
+#' @param  gpu_memory        Memory available in GPU in GB (default = 4)
 #' @param  n_sam_pol         Number of time series per segment to be classified
 #'                           (integer, min = 10, max = 50).
 #' @param  output_dir        Valid directory for output file.

R/sits_lighttae.R

@@ -339,10 +339,12 @@ sits_lighttae <- function(samples = NULL,
             values <- array(
                 data = as.matrix(values), dim = c(n_samples, n_times, n_bands)
             )
-            # Load into GPU
-            if (.torch_has_cuda()){
+            # Get GPU memory
+            gpu_memory <- sits_env[["gpu_memory"]]
+            # if CUDA is available and gpu memory is defined, transform values
+            # to torch dataloader
+            if (.torch_has_cuda() && .has(gpu_memory)) {
                 # set the batch size according to the GPU memory
-                gpu_memory <- sits_env[["gpu_memory"]]
                 b_size <- 2^gpu_memory
                 # transfor the input array to a dataset
                 values <- .as_dataset(values)

R/sits_mlp.R

@@ -289,11 +289,12 @@ sits_mlp <- function(samples = NULL,
             values <- .pred_normalize(pred = values, stats = ml_stats)
             # Transform input into matrix
             values <- as.matrix(values)
-            # if CUDA is available, transform to torch data set
-            # Load into GPU
-            if (.torch_has_cuda()){
+            # Get GPU memory
+            gpu_memory <- sits_env[["gpu_memory"]]
+            # if CUDA is available and gpu memory is defined, transform values
+            # to torch dataloader
+            if (.torch_has_cuda() && .has(gpu_memory)) {
                 # set the batch size according to the GPU memory
-                gpu_memory <- sits_env[["gpu_memory"]]
                 b_size <- 2^gpu_memory
                 # transfor the input array to a dataset
                 values <- .as_dataset(values)

R/sits_tae.R

@@ -307,13 +307,12 @@ sits_tae <- function(samples = NULL,
             values <- array(
                 data = as.matrix(values), dim = c(n_samples, n_times, n_bands)
             )
-            # Predict using GPU if available
-            # If not, use CPU
-            # if CUDA is available, transform to torch data set
-            # Load into GPU
-            if (.torch_has_cuda()){
+            # Get GPU memory
+            gpu_memory <- sits_env[["gpu_memory"]]
+            # if CUDA is available and gpu memory is defined, transform values
+            # to torch dataloader
+            if (.torch_has_cuda() && .has(gpu_memory)) {
                 # set the batch size according to the GPU memory
-                gpu_memory <- sits_env[["gpu_memory"]]
                 b_size <- 2^gpu_memory
                 # transfor the input array to a dataset
                 values <- .as_dataset(values)

R/sits_tempcnn.R

@@ -358,15 +358,16 @@ sits_tempcnn <- function(samples = NULL,
             values <- array(
                 data = as.matrix(values), dim = c(n_samples, n_times, n_bands)
             )
-            # if CUDA is available, transform to torch data set
-            # Load into GPU
-            if (.torch_has_cuda()){
+            # Get GPU memory
+            gpu_memory <- sits_env[["gpu_memory"]]
+            # if CUDA is available and gpu memory is defined, transform values
+            # to torch dataloader
+            if (.torch_has_cuda() && .has(gpu_memory)) {
                 # set the batch size according to the GPU memory
-                gpu_memory <- sits_env[["gpu_memory"]]
                 b_size <- 2^gpu_memory
                 # transfor the input array to a dataset
                 values <- .as_dataset(values)
-                # To the data set to a torcj  transform in a dataloader to use the batch size
+                # To the data set to a torch transform in a dataloader to use the batch size
                 values <- torch::dataloader(values, batch_size = b_size)
                 # Do GPU classification with dataloader
                 values <- .try(

R/sits_tuning.R

@@ -29,9 +29,9 @@
 #'   \code{ml_method}. User can use \code{uniform}, \code{choice},
 #'   \code{randint}, \code{normal}, \code{lognormal}, \code{loguniform},
 #'   and \code{beta} distribution functions to randomize parameters.
-#' @param trials Number of random trials to perform the random search.
-#' @param progress           Show progress bar?
-#' @param multicores         Number of cores to process in parallel
+#' @param trials        Number of random trials to perform the random search.
+#' @param progress      Show progress bar?
+#' @param multicores    Number of cores to process in parallel.
 #'
 #' @return
 #' A tibble containing all parameters used to train on each trial
@@ -87,7 +87,6 @@ sits_tuning <- function(samples,
         # check validation_split parameter if samples_validation is not passed
         .check_num_parameter(validation_split, exclusive_min = 0, max = 0.5)
     }
-
     # check 'ml_functions' parameter
     ml_function <- substitute(ml_method, env = environment())
     if (is.call(ml_function))