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109 行
3.6 KiB
109 行
3.6 KiB
import os
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import threading
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from mlagents.torch_utils 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 exporting_to_onnx:
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"""
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Set this context by calling
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```
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with exporting_to_onnx():
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```
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Within this context, the variable exporting_to_onnx.is_exporting() will be true.
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This implementation is thread safe.
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"""
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# local is_exporting flag for each thread
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_local_data = threading.local()
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_local_data._is_exporting = False
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# global lock shared among all threads, to make sure only one thread is exporting at a time
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_lock = threading.Lock()
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def __enter__(self):
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self._lock.acquire()
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self._local_data._is_exporting = True
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def __exit__(self, *args):
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self._local_data._is_exporting = False
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self._lock.release()
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@staticmethod
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def is_exporting():
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if not hasattr(exporting_to_onnx._local_data, "_is_exporting"):
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return False
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return exporting_to_onnx._local_data._is_exporting
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class ModelSerializer:
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def __init__(self, policy):
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# ONNX only support input in NCHW (channel first) format.
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# Barracuda also expect to get data in NCHW.
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# Any multi-dimentional input should follow that otherwise will
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# cause problem to barracuda import.
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self.policy = policy
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batch_dim = [1]
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seq_len_dim = [1]
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dummy_vec_obs = [torch.zeros(batch_dim + [self.policy.vec_obs_size])]
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# create input shape of NCHW
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# (It's NHWC in self.policy.behavior_spec.observation_shapes)
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dummy_vis_obs = [
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torch.zeros(batch_dim + [shape[2], shape[0], shape[1]])
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for shape in self.policy.behavior_spec.observation_shapes
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if len(shape) == 3
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]
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dummy_masks = torch.ones(
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batch_dim + [sum(self.policy.behavior_spec.action_spec.discrete_branches)]
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)
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dummy_memories = torch.zeros(
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batch_dim + seq_len_dim + [self.policy.export_memory_size]
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)
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self.dummy_input = (dummy_vec_obs, dummy_vis_obs, dummy_masks, dummy_memories)
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self.input_names = (
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["vector_observation"]
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+ [f"visual_observation_{i}" for i in range(self.policy.vis_obs_size)]
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+ ["action_masks", "memories"]
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)
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self.output_names = [
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"action",
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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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self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}
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self.dynamic_axes.update({"action": {0: "batch"}})
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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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"""
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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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with exporting_to_onnx():
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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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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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