feat(task): add cifar 10 and cifar 100

DESCRIPTION

@@ -127,6 +127,7 @@ Collate:
     'PipeOpTorchReshape.R'
     'PipeOpTorchSoftmax.R'
     'Select.R'
+    'TaskClassif_cifar.R'
     'TaskClassif_lazy_iris.R'
     'TaskClassif_melanoma.R'
     'TaskClassif_mnist.R'

R/TaskClassif_cifar.R

@@ -0,0 +1,175 @@
+#' @title CIFAR Classification Tasks
+#'
+#' @name mlr_tasks_cifar
+#'
+#' @format [R6::R6Class] inheriting from [mlr3::TaskClassif].
+#' @include aaa.R
+#'
+#' @description
+#' The CIFAR-10 and CIFAR-100 datasets. A subset of the 80 million tiny images dataset
+#' with noisy labels was supplied to student labelers, who were asked to filter out
+#' incorrectly labeled images.
+#'
+#' CIFAR-10 contains 10 classes. CIFAR-100 contains 100 classes, which may be partitioned into 20 superclasses of 5 classes each.
+#' The CIFAR-10 and CIFAR-100 classes are mutually exclusive.
+#' See Chapter 3.1 of [the technical report](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) for more details.
+#'
+#' The data is obtained from [`torchvision::cifar10_dataset()`] (or `torchvision::cifar100_dataset()`).
+#'
+#' @section Construction:
+#' ```
+#' tsk("cifar10")
+#' tsk("cifar100")
+#' ```
+#'
+#' @template task_download
+#'
+#' @section Properties:
+#' `r rd_info_task_torch("cifar10", missings = FALSE)`
+#'
+#' @references
+#' `r format_bib("cifar2009")`
+#' @examples
+#' task_cifar10 = tsk("cifar10")
+#' task_cifar100 = tsk("cifar100")
+#' print(task_cifar10)
+#' print(task_cifar100)
+NULL
+
+cifar_ds_generator = torch::dataset(
+  initialize = function(images) {
+    self$images = images
+  },
+  .getitem = function(idx) {
+    force(idx)
+
+    x = torch_tensor(self$images[idx, , , ])
+
+    return(list(x = x))
+  },
+  .length = function() {
+    dim(self$images)[1L]
+  }
+)
+
+constructor_cifar = function(path, type = 10) {
+  if (type == 10) {
+    d_train = torchvision::cifar10_dataset(root = path, train = TRUE, download = TRUE)
+    d_test = torchvision::cifar10_dataset(root = path, train = FALSE, download = FALSE)
+    class_names = readLines(file.path(path, "cifar-10-batches-bin", "batches.meta.txt"))
+    class_names = class_names[class_names != ""]
+  } else if (type == 100) {
+    d_train = torchvision::cifar100_dataset(root = path, train = TRUE, download = TRUE)
+    d_test = torchvision::cifar100_dataset(root = path, train = FALSE, download = FALSE)
+    class_names = readLines(file.path(path, "cifar-100-binary", "fine_label_names.txt"))
+  }
+
+  classes = c(d_train$y, d_test$y)
+  images = array(NA, dim = c(60000, 3, 32, 32))
+  # original data has channel dimension at the end
+  perm_idx = c(1, 4, 2, 3)
+  images[1:50000, , , ] = aperm(d_train$x, perm_idx, resize = TRUE)
+  images[50001:60000, , , ] = aperm(d_test$x, perm_idx, resize = TRUE)
+
+  return(list(class = factor(classes, labels = class_names), images = images))
+}
+
+constructor_cifar10 = function(path) {
+  require_namespaces("torchvision")
+
+  return(constructor_cifar(path, type = 10))
+}
+
+load_task_cifar10 = function(id = "cifar10") {
+  cached_constructor = function(backend) {
+    data <- cached(constructor_cifar10, "datasets", "cifar10")$data
+
+    cifar10_ds = cifar_ds_generator(data$images)
+
+    dd = as_data_descriptor(cifar10_ds, list(x = c(NA, 3, 32, 32)))
+    lt = lazy_tensor(dd)
+
+    dt = data.table(
+      class = data$class,
+      image = lt,
+      split = factor(rep(c("train", "test"), c(50000, 10000))),
+      ..row_id = seq_len(60000)
+    )
+
+    DataBackendDataTable$new(data = dt, primary_key = "..row_id")
+  }
+
+  backend = DataBackendLazy$new(
+    constructor = cached_constructor,
+    rownames = seq_len(60000),
+    col_info = load_col_info("cifar10"),
+    primary_key = "..row_id"
+  )
+
+  task = TaskClassif$new(
+    backend = backend,
+    id = "cifar10",
+    target = "class",
+    label = "CIFAR-10 Classification"
+  )
+
+  task$col_roles$feature = "image"
+
+  backend$hash = "mlr3torch::mlr_tasks_cifar10"
+  task$man = "mlr3torch::mlr_tasks_cifar"
+
+  return(task)
+}
+
+register_task("cifar10", load_task_cifar10)
+
+constructor_cifar100 = function(path) {
+  require_namespaces("torchvision")
+
+  return(constructor_cifar(path, type = 100))
+}
+
+load_task_cifar100 = function(id = "cifar100") {
+  cached_constructor = function(backend) {
+    data = cached(constructor_cifar100, "datasets", "cifar100")$data
+
+    cifar100_ds = cifar_ds_generator(data$images)
+
+    dd = as_data_descriptor(cifar100_ds, list(x = c(NA, 3, 32, 32)))
+    lt = lazy_tensor(dd)
+
+    dt = data.table(
+      class = data$class,
+      image = lt,
+      split = factor(rep(c("train", "test"), c(50000, 10000))),
+      ..row_id = seq_len(60000)
+    )
+
+    DataBackendDataTable$new(data = dt, primary_key = "..row_id")
+  }
+
+  backend = DataBackendLazy$new(
+    constructor = cached_constructor,
+    rownames = seq_len(60000),
+    col_info = load_col_info("cifar100"),
+    primary_key = "..row_id"
+  )
+
+  task = TaskClassif$new(
+    backend = backend,
+    id = "cifar100",
+    target = "class",
+    label = "CIFAR-100 Classification"
+  )
+
+  task$col_roles$feature = "image"
+
+  backend$hash = "mlr3torch::mlr_tasks_cifar100"
+  task$man = "mlr3torch::mlr_tasks_cifar"
+
+  return(task)
+}
+
+register_task("cifar100", load_task_cifar100)
+
+

R/TaskClassif_melanoma.R

@@ -37,6 +37,7 @@
 #'
 #' @references
 #' `r format_bib("melanoma2021")`
+#' @examples
 #' task = tsk("melanoma")
 #' task
 NULL

R/TaskClassif_tiny_imagenet.R

@@ -7,7 +7,7 @@
 #' The data is obtained from [`torchvision::tiny_imagenet_dataset()`].
 #'
 #' The underlying [`DataBackend`][mlr3::DataBackend] contains columns `"class"`, `"image"`, `"..row_id"`, `"split"`, where the last column
-#' indicates whether the row belongs to the train, validation or test set that defined provided in torchvision.
+#' indicates whether the row belongs to the train, validation or test set that are provided in torchvision.
 #'
 #' There are no labels for the test rows, so by default, these observations are inactive, which means that the task
 #' uses only 110000 of the 120000 observations that are defined in the underlying data backend.

R/bibentries.R

@@ -121,6 +121,12 @@ bibentries = c(# nolint start
     pages = "34",
     year = "2021",
     doi = "10.1038/s41597-021-00815-z"
+  ),
+  cifar2009 = bibentry("article",
+    title = "Learning Multiple Layers of Features from Tiny Images",
+    author = "Krizhevsky, Alex",
+    journal= "Master's thesis, Department of Computer Science, University of Toronto",,
+    year = "2009",
   )
 ) # nolint end
 

data-raw/cifar.R

@@ -0,0 +1,110 @@
+devtools::load_all()
+
+library(mlr3misc)
+library(data.table)
+library(torchvision)
+
+# cached
+cifar_ds_generator = torch::dataset(
+  initialize = function(images) {
+    self$images = images
+  },
+  .getitem = function(idx) {
+    force(idx)
+
+    x = torch_tensor(self$images[idx, , , ])
+
+    return(list(x = x))
+  },
+  .length = function() {
+    dim(self$images)[1L]
+  }
+)
+
+constructor_cifar = function(path, type = 10) {
+  if (type == 10) {
+    d_train = torchvision::cifar10_dataset(root = path, train = TRUE, download = TRUE)
+    d_test = torchvision::cifar10_dataset(root = path, train = FALSE, download = FALSE)
+    class_names = readLines(file.path(path, "cifar-10-batches-bin", "batches.meta.txt"))
+    class_names = class_names[class_names != ""]
+  } else if (type == 100) {
+    d_train = torchvision::cifar100_dataset(root = path, train = TRUE, download = TRUE)
+    d_test = torchvision::cifar100_dataset(root = path, train = FALSE, download = FALSE)
+    class_names = readLines(file.path(path, "cifar-100-binary", "fine_label_names.txt"))
+  }
+
+  classes = c(d_train$y, d_test$y)
+  images = array(NA, dim = c(60000, 3, 32, 32))
+  # original data has channel dimension at the end
+  perm_idx = c(1, 4, 2, 3)
+  images[1:50000, , , ] = aperm(d_train$x, perm_idx, resize = TRUE)
+  images[50001:60000, , , ] = aperm(d_test$x, perm_idx, resize = TRUE)
+
+  return(list(class = factor(classes, labels = class_names), images = images))
+}
+
+constructor_cifar10 = function(path) {
+  require_namespaces("torchvision")
+
+  return(constructor_cifar(path, type = 10))
+}
+
+withr::local_options(mlr3torch.cache = TRUE)
+path = file.path(get_cache_dir(), "datasets", "cifar10", "raw")
+
+# begin CIFAR-10
+data <- constructor_cifar10(path)
+
+cifar10_ds = cifar_ds_generator(data$images)
+
+dd = as_data_descriptor(cifar10_ds, list(x = c(NA, 3, 32, 32)))
+lt = lazy_tensor(dd)
+
+tsk_dt = data.table(
+  class = data$class,
+  image = lt,
+  split = factor(rep(c("train", "test"), c(50000, 10000))),
+  ..row_id = seq_len(60000)
+)
+
+# tsk_dt = cbind(data, data.table(image = lt))
+
+tsk_cifar10 = as_task_classif(tsk_dt, target = "class", id = "cifar10")
+tsk_cifar10$col_roles$feature = "image"
+
+ci = col_info(tsk_cifar10$backend)
+
+saveRDS(ci, here::here("inst/col_info/cifar10.rds"))
+# end CIFAR-10
+
+path = file.path(get_cache_dir(), "datasets", "cifar100", "raw")
+
+# begin CIFAR-100
+constructor_cifar100 = function(path) {
+  require_namespaces("torchvision")
+
+  return(constructor_cifar(path, type = 100))
+}
+
+data = constructor_cifar100(path)
+
+cifar100_ds = cifar_ds_generator(data$images)
+
+dd = as_data_descriptor(cifar100_ds, list(x = c(NA, 3, 32, 32)))
+lt = lazy_tensor(dd)
+
+dt = data.table(
+  class = data$class,
+  image = lt,
+  split = factor(rep(c("train", "test"), c(50000, 10000))),
+  ..row_id = seq_len(60000)
+)
+
+task = as_task_classif(dt, target = "class")
+
+task$col_roles$feature = "image"
+
+ci = col_info(task$backend)
+
+saveRDS(ci, here::here("inst/col_info/cifar100.rds"))
+