Export a fitted PhysioML torch model to ONNX

Uses the governed R torch to TorchScript to Python PyTorch bridge. Only the batch axis may be dynamic. The graph and canonical model card are validated before they are atomically landed.

Usage

exportONNX(
  model,
  path,
  opset = 17L,
  dynamic_batch = TRUE,
  validate = TRUE,
  metadata_path = paste0(path, ".json"),
  overwrite = FALSE
)

Arguments

model

A fitted physio_dl_model.

path

Destination .onnx path.

opset

Positive ONNX opset; the initial default is 17.

dynamic_batch

Whether the batch axis is dynamic.

validate

Whether to enforce the full ONNX checker, graph/card consistency, and logit parity tolerance. Basic graph parsing, I/O contract checks, and CPU inference are always required.

metadata_path

Destination canonical JSON model-card path.

overwrite

Whether an existing graph/card pair may be replaced.

Value

Invisibly, a plain physio_onnx_export list.

Examples

arr <- array(stats::rnorm(50 * 2 * 6), dim = c(50, 2, 6))
pe <- PhysioExperiment::PhysioExperiment(
  assays = list(raw = arr),
  colData = S4Vectors::DataFrame(label = c("C3", "C4")),
  samplingRate = 100
)
path <- tempfile(fileext = ".onnx")
# \donttest{
# Export needs torch plus the governed torch->ONNX Python bridge.
train <- peDataset(
  pe, targets = rep(c("a", "b"), 3),
  task = "classification", class_levels = c("a", "b")
)
#> Error: the R package `torch` is installed but LibTorch is unavailable; install it explicitly with `torch::install_torch()`
fit <- trainModel(train, model = "cnn1d", epochs = 1L)
#> Error: the R package `torch` is installed but LibTorch is unavailable; install it explicitly with `torch::install_torch()`
exportONNX(fit, path)
#> Error: the R package `torch` is installed but LibTorch is unavailable; install it explicitly with `torch::install_torch()`
# }