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Author: sebffischer

R/PipeOpTaskPreprocTorch.R

@@ -449,7 +449,7 @@ pipeop_preproc_torch = function(id, fn, shapes_out = NULL, param_set = NULL, pac
   # we e.g. want torchvision in suggests, so we cannot already access the function.
   if (identical(shapes_out, "infer")) {
     shapes_out = crate(function(shapes_in, param_vals, task) {
-      getFromNamespace("infer_shapes", "mlr3torch")(shapes_in = shapes_in, param_vals = param_vals, output_names = self$output$name, fn = self$fn, rowwise = self$rowwise, id = self$id)
+      getFromNamespace("infer_shapes", "mlr3torch")(shapes_in = shapes_in, param_vals = param_vals, output_names = self$output$name, fn = self$fn, rowwise = self$rowwise, id = self$id) # nolint
     })
   } else if (is.function(shapes_out) || is.null(shapes_out)) {
     # nothing to do

R/PipeOpTorchReshape.R

@@ -5,7 +5,7 @@
 #' This internally calls [`torch::torch_reshape()`] with the given `shape`.
 #' @section Parameters:
 #' * `shape` :: `integer(1)`\cr
-#'   The desired output shape. Unknown dimension (one at most) can either be specified as `-1` or `NA`.
+#'   The desired output shape. Unknown dimension (one at most) can either be specified as `-1`.
 #' @templateVar id nn_reshape
 #' @template pipeop_torch_channels_default
 #' @template pipeop_torch

R/lazy_tensor.R

@@ -231,6 +231,7 @@ is_lazy_tensor = function(x) {
 #'   Is not cloned, so should be cloned beforehand.
 #' @param shape (`integer()` or `NULL`)\cr
 #'   The shape of the lazy tensor.
+#'   `NA`s indicate dimensions where the shape is not known.
 #' @param shape_predict (`integer()` or `NULL`)\cr
 #'   The shape of the lazy tensor if it was applied during `$predict()`.
 #'

R/shape.R

@@ -89,7 +89,7 @@ assert_rgb_shape = function(shape) {
 
 # grayscale or rgb image
 assert_grayscale_or_rgb = function(shape) {
-  assert_shape(shape, len = 4L, null_ok = FALSE)
+  assert_shape(shape, len = 4L, null_ok = FALSE, only_batch_unknown = TRUE)
   assert_true(shape[2L] == 3L || shape[2L] == 1L)
 }