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77 行
2.9 KiB
77 行
2.9 KiB
import os
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import torch
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from mlagents_envs.logging_util import get_logger
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from mlagents.trainers.settings import SerializationSettings
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logger = get_logger(__name__)
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class ModelSerializer:
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def __init__(self, policy):
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self.policy = policy
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batch_dim = [1]
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dummy_vec_obs = [torch.zeros(batch_dim + [self.policy.vec_obs_size])]
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dummy_vis_obs = (
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[torch.zeros(batch_dim + list(self.policy.vis_obs_shape))]
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if self.policy.vis_obs_size > 0
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else []
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)
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dummy_masks = torch.ones(batch_dim + [sum(self.policy.actor_critic.act_size)])
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dummy_memories = torch.zeros(batch_dim + [1] + [self.policy.m_size])
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# Need to pass all posslible inputs since currently keyword arguments is not
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# supported by torch.nn.export()
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self.dummy_input = (dummy_vec_obs, dummy_vis_obs, dummy_masks, dummy_memories)
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# Input names can only contain actual input used since in torch.nn.export
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# it maps input_names only to input nodes that exist in the graph
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self.input_names = []
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self.dynamic_axes = {"action": {0: "batch"}, "action_probs": {0: "batch"}}
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if self.policy.use_vec_obs:
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self.input_names.append("vector_observation")
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self.dynamic_axes.update({"vector_observation": {0: "batch"}})
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if self.policy.use_vis_obs:
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self.input_names.append("visual_observation")
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self.dynamic_axes.update({"visual_observation": {0: "batch"}})
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if not self.policy.use_continuous_act:
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self.input_names.append("action_masks")
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self.dynamic_axes.update({"action_masks": {0: "batch"}})
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if self.policy.use_recurrent:
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self.input_names.append("memories")
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self.dynamic_axes.update({"memories": {0: "batch"}})
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self.output_names = [
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"action",
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"action_probs",
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"version_number",
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"memory_size",
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"is_continuous_control",
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"action_output_shape",
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]
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def export_policy_model(self, output_filepath: str) -> None:
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"""
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Exports a Torch model for a Policy to .onnx format for Unity embedding.
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:param output_filepath: file path to output the model (without file suffix)
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:param brain_name: Brain name of brain to be trained
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"""
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if not os.path.exists(output_filepath):
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os.makedirs(output_filepath)
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onnx_output_path = f"{output_filepath}.onnx"
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logger.info(f"Converting to {onnx_output_path}")
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torch.onnx.export(
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self.policy.actor_critic,
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self.dummy_input,
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onnx_output_path,
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verbose=False,
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opset_version=SerializationSettings.onnx_opset,
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input_names=self.input_names,
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output_names=self.output_names,
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dynamic_axes=self.dynamic_axes,
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)
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logger.info(f"Exported {onnx_output_path}")
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