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Merged
cregouby merged 2 commits into from
Feb 4, 2026
Merged

Remove detection-related attributes in dataset_coco_segmentation() #288

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2820dca
remove unwanted variables
Christophe-Regouby Feb 4, 2026
f9152b7
remove code duplication through `inherit = coco_detection_dataset`
Christophe-Regouby Feb 4, 2026
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128 changes: 5 additions & 123 deletions R/dataset-coco.R
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Original file line number Diff line number Diff line change
Expand Up @@ -215,19 +215,13 @@ coco_detection_dataset <- torch::dataset(
#' Loads the MS COCO dataset for instance segmentation tasks.
#'
#' @rdname coco_segmentation_dataset
#' @param root Root directory where the dataset is stored or will be downloaded to.
#' @param train Logical. If TRUE, loads the training split; otherwise, loads the validation split.
#' @param year Character. Dataset version year. One of \code{"2014"} or \code{"2017"}.
#' @param download Logical. If TRUE, downloads the dataset if it's not already present in the \code{root} directory.
#' @param transform Optional transform function applied to the image.
#' @param target_transform Optional transform function applied to the target.
#' @inheritParams coco_detection_dataset
#' @param target_transform Optional transform function applied to the target.
#' Use \code{target_transform_coco_masks} to convert polygon annotations to binary masks.
#'
#' @return An object of class `coco_segmentation_dataset`. Each item is a list:
#' - `x`: a `(C, H, W)` array representing the image.
#' - `y$boxes`: a `(N, 4)` `torch_tensor` of bounding boxes in the format \eqn{(x_{min}, y_{min}, x_{max}, y_{max})}.
#' - `y$labels`: an integer `torch_tensor` with the class label for each object.
#' - `y$area`: a float `torch_tensor` indicating the area of each object.
#' - `y$iscrowd`: a boolean `torch_tensor`, where `TRUE` marks the object as part of a crowd.
#' - `y$segmentation`: a list of segmentation polygons for each object.
#' - `y$masks`: a `(N, H, W)` boolean `torch_tensor` containing binary segmentation masks (when using target_transform_coco_masks).
Expand All @@ -239,8 +233,8 @@ coco_detection_dataset <- torch::dataset(
#'
#' @details
#' The returned image `x` is in CHW format (channels, height, width), matching the torch convention.
#' The dataset `y` offers instance segmentation annotations including bounding boxes, labels,
#' areas, crowd indicators, and segmentation masks from the official COCO annotations.
#' The dataset `y` offers instance segmentation annotations including labels,
#' crowd indicators, and segmentation masks from the official COCO annotations.
#'
#' Files are downloaded to a \code{coco} subdirectory in the torch cache directory for better organization.
#'
Expand All @@ -262,70 +256,10 @@ coco_detection_dataset <- torch::dataset(
#' }
#' @family segmentation_dataset
#' @seealso \code{\link{coco_detection_dataset}} for object detection tasks
#' @importFrom jsonlite fromJSON
#' @export
coco_segmentation_dataset <- torch::dataset(
name = "coco_segmentation_dataset",
resources = data.frame(
year = rep(c(2017, 2014), each = 4 ),
content = rep(c("image", "annotation"), time = 2, each = 2),
split = rep(c("train", "val"), time = 4),
url = c("http://images.cocodataset.org/zips/train2017.zip", "http://images.cocodataset.org/zips/val2017.zip",
rep("http://images.cocodataset.org/annotations/annotations_trainval2017.zip", time = 2),
"http://images.cocodataset.org/zips/train2014.zip", "http://images.cocodataset.org/zips/val2014.zip",
rep("http://images.cocodataset.org/annotations/annotations_trainval2014.zip", time = 2)),
size = c("800 MB", "800 MB", rep("770 MB", time = 2), "6.33 GB", "6.33 GB", rep("242 MB", time = 2)),
md5 = c(c("cced6f7f71b7629ddf16f17bbcfab6b2", "442b8da7639aecaf257c1dceb8ba8c80"),
rep("f4bbac642086de4f52a3fdda2de5fa2c", time = 2),
c("0da8cfa0e090c266b78f30e2d2874f1a", "a3d79f5ed8d289b7a7554ce06a5782b3"),
rep("0a379cfc70b0e71301e0f377548639bd", time = 2)),
stringsAsFactors = FALSE
),

initialize = function(
root = tempdir(),
train = TRUE,
year = c("2017", "2014"),
download = FALSE,
transform = NULL,
target_transform = NULL
) {

year <- match.arg(year)
split <- ifelse(train, "train", "val")

root <- fs::path_expand(root)
self$root <- root
self$year <- year
self$split <- split
self$transform <- transform
self$target_transform <- target_transform
self$archive_size <- self$resources[self$resources$year == year & self$resources$split == split & self$resources$content == "image", ]$size

self$data_dir <- fs::path(root, glue::glue("coco{year}"))

image_year <- ifelse(year == "2016", "2014", year)
self$image_dir <- fs::path(self$data_dir, glue::glue("{split}{image_year}"))
self$annotation_file <- fs::path(self$data_dir, "annotations",
glue::glue("instances_{split}{year}.json"))

if (download) {
cli_inform("Dataset {.cls {class(self)[[1]]}} (~{.emph {self$archive_size}}) will be downloaded and processed if not already available.")
self$download()
}

if (!self$check_exists()) {
runtime_error("Dataset not found. You can use `download = TRUE` to download it.")
}

self$load_annotations()

cli_inform("{.cls {class(self)[[1]]}} dataset loaded with {length(self$image_ids)} images.")
},

check_exists = function() {
fs::file_exists(self$annotation_file) && fs::dir_exists(self$image_dir)
},
inherit = coco_detection_dataset,

.getitem = function(index) {
image_id <- self$image_ids[index]
Expand All @@ -341,28 +275,18 @@ coco_segmentation_dataset <- torch::dataset(
anns <- self$annotations[self$annotations$image_id == image_id, ]

if (nrow(anns) > 0) {
boxes_wh <- torch::torch_tensor(do.call(rbind, anns$bbox), dtype = torch::torch_float())
boxes <- box_xywh_to_xyxy(boxes_wh)

label_ids <- anns$category_id
labels <- as.character(self$categories$name[match(label_ids, self$categories$id)])

area <- torch::torch_tensor(anns$area, dtype = torch::torch_float())
iscrowd <- torch::torch_tensor(as.logical(anns$iscrowd), dtype = torch::torch_bool())

} else {
# empty annotation
boxes <- torch::torch_zeros(c(0, 4), dtype = torch::torch_float())
labels <- character()
area <- torch::torch_empty(0, dtype = torch::torch_float())
iscrowd <- torch::torch_empty(0, dtype = torch::torch_bool())
anns$segmentation <- list()
}

y <- list(
boxes = boxes,
labels = labels,
area = area,
iscrowd = iscrowd,
segmentation = anns$segmentation
)
Expand All @@ -384,48 +308,6 @@ coco_segmentation_dataset <- torch::dataset(
class(result) <- c("image_with_segmentation_mask", class(result))
}
result
},

.length = function() {
length(self$image_ids)
},

download = function() {
annotation_filter <- self$resources$year == self$year & self$resources$split == self$split & self$resources$content == "annotation"
image_filter <- self$resources$year == self$year & self$resources$split == self$split & self$resources$content == "image"

cli_inform("Downloading {.cls {class(self)[[1]]}}...")

ann_zip <- download_and_cache(self$resources[annotation_filter, ]$url, prefix = "coco")
archive <- download_and_cache(self$resources[image_filter, ]$url, prefix = "coco")

if (tools::md5sum(archive) != self$resources[image_filter, ]$md5) {
runtime_error("Corrupt file! Delete the file in {archive} and try again.")
}

utils::unzip(ann_zip, exdir = self$data_dir)
utils::unzip(archive, exdir = self$data_dir)

cli_inform("Dataset {.cls {class(self)[[1]]}} downloaded and extracted successfully.")
},

load_annotations = function() {
data <- jsonlite::fromJSON(self$annotation_file)

self$image_metadata <- setNames(
split(data$images, seq_len(nrow(data$images))),
as.character(data$images$id)
)

self$annotations <- data$annotations
self$categories <- data$categories
self$category_names <- setNames(self$categories$name, self$categories$id)

ids <- as.numeric(names(self$image_metadata))
image_paths <- fs::path(self$image_dir,
sapply(ids, function(id) self$image_metadata[[as.character(id)]]$file_name))
exist <- fs::file_exists(image_paths)
self$image_ids <- ids[exist]
}
)

Expand Down
6 changes: 2 additions & 4 deletions man/coco_segmentation_dataset.Rd
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6 changes: 3 additions & 3 deletions tests/testthat/test-dataset-coco.R
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Expand Up @@ -46,7 +46,7 @@ test_that("coco_detection_dataset loads a single example correctly", {
expect_false("segmentation" %in% names(y))
})

test_that("coco_ dataset loads a single segmentation example correctly", {
test_that("coco_segmentation_dataset loads a single segmentation example correctly", {
skip_if(Sys.getenv("TEST_LARGE_DATASETS", unset = 0) != 1,
"Skipping test: set TEST_LARGE_DATASETS=1 to enable tests requiring large downloads.")

Expand All @@ -64,7 +64,7 @@ test_that("coco_ dataset loads a single segmentation example correctly", {
expect_length(dim(item$x), 3)

expect_type(y, "list")
expect_named(y, c("boxes", "labels", "area", "iscrowd", "segmentation", "image_height", "image_width", "masks"))
expect_named(y, c("labels", "iscrowd", "segmentation", "image_height", "image_width", "masks"))

expect_tensor(y$masks)
expect_equal(y$masks$ndim, 3)
Expand All @@ -85,7 +85,7 @@ test_that("coco_detection_dataset batches correctly using dataloader", {
expect_true(all(vapply(batch$x, is_torch_tensor, logical(1))))

expect_type(batch$y, "list")
expect_named(batch$y[[1]], c("boxes", "labels", "area", "iscrowd", "segmentation"))
expect_named(batch$y[[1]], c("boxes", "labels", "area", "iscrowd"))
expect_tensor(batch$y[[1]]$boxes)
expect_equal(batch$y[[1]]$boxes$ndim, 2)
expect_equal(batch$y[[1]]$boxes$size(2), 4)
Expand Down
26 changes: 6 additions & 20 deletions tests/testthat/test-segmentation-transforms.R
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Expand Up @@ -66,7 +66,7 @@ test_that("target_transform_coco_masks() handles multiple objects", {
expect_equal(result$masks$shape, c(2, 100, 100))
})

# Trimap mask transformation tests
# Trimap mask transformation tests

test_that("target_transform_trimap_masks() converts trimap to masks", {
skip_if_not_installed("torch")
Expand All @@ -89,12 +89,12 @@ test_that("target_transform_trimap_masks() creates mutually exclusive masks", {

# Dataset behavior tests (skipped unless TEST_LARGE_DATASETS=1)

test_that("coco_detection_dataset with target_transform produces masks", {
test_that("coco_segmentation_dataset with target_transform produces masks", {
skip_on_cran()
skip_if(Sys.getenv("TEST_LARGE_DATASETS", unset = 0) != 1,
"Set TEST_LARGE_DATASETS=1 to enable")
tmp <- withr::local_tempdir()
ds <- coco_detection_dataset(root = tmp, train = FALSE, year = "2017",
ds <- coco_segmentation_dataset(root = tmp, train = FALSE, year = "2017",
download = TRUE, target_transform = target_transform_coco_masks)
y <- ds[1]$y
expect_true("masks" %in% names(y))
Expand All @@ -103,21 +103,7 @@ test_that("coco_detection_dataset with target_transform produces masks", {
expect_tensor_dtype(y$masks, torch::torch_bool())
})

test_that("oxfordiiitpet with target_transform produces masks", {
skip_on_cran()
skip_if(Sys.getenv("TEST_LARGE_DATASETS", unset = 0) != 1,
"Set TEST_LARGE_DATASETS=1 to enable")
tmp <- withr::local_tempdir()
ds <- oxfordiiitpet_segmentation_dataset(root = tmp, train = TRUE,
download = TRUE, target_transform = target_transform_trimap_masks)
y <- ds[1]$y
expect_true("masks" %in% names(y))
expect_tensor(y$masks)
expect_tensor_shape(y$masks, c(3, NA, NA))
expect_tensor_dtype(y$masks, torch::torch_bool())
})

# Integration with draw_segmentation_masks()
# Integration with draw_segmentation_masks()

test_that("transformed masks work with draw_segmentation_masks()", {
skip_if_not_installed("torch")
Expand All @@ -132,7 +118,7 @@ test_that("transformed masks work with draw_segmentation_masks()", {
expect_equal(result$shape[1], 3)
})

# Existing helper tests
# Existing helper tests

test_that("coco_polygon_to_mask handles single polygon", {
skip_if_not_installed("torch")
Expand All @@ -150,5 +136,5 @@ test_that("coco_polygon_to_mask handles empty polygon", {
skip_if_not_installed("magick")
mask <- coco_polygon_to_mask(list(), 100, 100)
expect_tensor(mask)
expect_equal(as.numeric(mask$sum()$item()), 0)
expect_equal_to_r(mask$sum(), 0)
})
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