pneuma-godot/addons/godot_rl_agents/onnx/wrapper/ONNX_wrapper.gd
2024-05-22 08:45:43 +02:00

51 lines
1.7 KiB
GDScript

extends Resource
class_name ONNXModel
var inferencer_script = load("res://addons/godot_rl_agents/onnx/csharp/ONNXInference.cs")
var inferencer = null
## How many action values the model outputs
var action_output_size: int
## Used to differentiate models
## that only output continuous action mean (e.g. sb3, cleanrl export)
## versus models that output mean and logstd (e.g. rllib export)
var action_means_only: bool
## Whether action_means_value has been set already for this model
var action_means_only_set: bool
# Must provide the path to the model and the batch size
func _init(model_path, batch_size):
inferencer = inferencer_script.new()
action_output_size = inferencer.Initialize(model_path, batch_size)
# This function is the one that will be called from the game,
# requires the observation as an array and the state_ins as an int
# returns an Array containing the action the model takes.
func run_inference(obs: Array, state_ins: int) -> Dictionary:
if inferencer == null:
printerr("Inferencer not initialized")
return {}
return inferencer.RunInference(obs, state_ins)
func _notification(what):
if what == NOTIFICATION_PREDELETE:
inferencer.FreeDisposables()
inferencer.free()
# Check whether agent uses a continuous actions model with only action means or not
func set_action_means_only(agent_action_space):
action_means_only_set = true
var continuous_only: bool = true
var continuous_actions: int
for action in agent_action_space:
if not agent_action_space[action]["action_type"] == "continuous":
continuous_only = false
break
else:
continuous_actions += agent_action_space[action]["size"]
if continuous_only:
if continuous_actions == action_output_size:
action_means_only = true