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Register backcompat s3 methods for keras2 #619

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83 changes: 82 additions & 1 deletion R/package.R
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Original file line number Diff line number Diff line change
Expand Up @@ -117,6 +117,12 @@ tf_v2 <- function() {
tryCatch(tf$python$util$deprecation$silence()$`__enter__`(),
error = function(e) NULL)

if (isNamespaceLoaded("keras")) {
keras2_backcompat_hook()
} else {
setHook(packageEvent("keras", "onLoad"), keras2_backcompat_hook)
}

# TODO: move this into .onAttach, where you either emit immediately if
# already loaded otherwise register emit hook for reticulate
# emit <- get("packageStartupMessage") # R CMD check
Expand All @@ -136,7 +142,6 @@ tf_v2 <- function() {
"a Python installation where the tensorflow module is installed.", call. = FALSE)
})


# provide a common base S3 class for tensors
reticulate::register_class_filter(function(classes) {
if (any(c("tensorflow.python.ops.variables.Variable",
Expand All @@ -160,6 +165,82 @@ is_string <- function(x) {
is.character(x) && length(x) == 1L && !is.na(x)
}


keras2_backcompat_hook <- #function(){}
function(...) {
message("Calling keras2 backcompat hooks")
keras_ns <- asNamespace("keras")

new_model_class_names <- c("keras.src.models.sequential.Sequential",
"keras.models.sequential.Sequential")

generic <- "compose_layer"
method <- keras_ns[["compose_layer.keras.models.Sequential"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

new_model_class_names <- c("keras.src.models.model.Model",
"keras.models.model.Model")

generic <- "fit"
method <- keras_ns[["fit.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "compile"
method <- keras_ns[["compile.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "predict"
method <- keras_ns[["predict.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "evaluate"
method <- keras_ns[["evaluate.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "export_savedmodel"
method <- keras_ns[["export_savedmodel.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "format"
method <- keras_ns[["format.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "print"
method <- keras_ns[["print.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "summary"
method <- keras_ns[["summary.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

generic <- "plot"
method <- keras_ns[["plot.keras.engine.training.Model"]]
envir <- environment(get(generic, keras_ns))
for(name in new_model_class_names)
registerS3method(generic, name, method, envir)

}



#' TensorFlow configuration information
#'
#' @return List with information on the current configuration of TensorFlow.
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