Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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127 行
4.5 KiB

import threading
from mlagents.torch_utils import torch
from mlagents_envs.logging_util import get_logger
from mlagents.trainers.settings import SerializationSettings
logger = get_logger(__name__)
class exporting_to_onnx:
"""
Set this context by calling
```
with exporting_to_onnx():
```
Within this context, the variable exporting_to_onnx.is_exporting() will be true.
This implementation is thread safe.
"""
# local is_exporting flag for each thread
_local_data = threading.local()
_local_data._is_exporting = False
# global lock shared among all threads, to make sure only one thread is exporting at a time
_lock = threading.Lock()
def __enter__(self):
self._lock.acquire()
self._local_data._is_exporting = True
def __exit__(self, *args):
self._local_data._is_exporting = False
self._lock.release()
@staticmethod
def is_exporting():
if not hasattr(exporting_to_onnx._local_data, "_is_exporting"):
return False
return exporting_to_onnx._local_data._is_exporting
class ModelSerializer:
def __init__(self, policy):
# ONNX only support input in NCHW (channel first) format.
# Barracuda also expect to get data in NCHW.
# Any multi-dimentional input should follow that otherwise will
# cause problem to barracuda import.
self.policy = policy
batch_dim = [1]
seq_len_dim = [1]
vec_obs_size = 0
for sens_spec in self.policy.behavior_spec.observation_specs:
if len(sens_spec.shape) == 1:
vec_obs_size += sens_spec.shape[0]
num_vis_obs = sum(
1
for sens_spec in self.policy.behavior_spec.observation_specs
if len(sens_spec.shape) == 3
)
dummy_vec_obs = [torch.zeros(batch_dim + [vec_obs_size])]
# create input shape of NCHW
# (It's NHWC in self.policy.behavior_spec.observation_specs.shape)
dummy_vis_obs = [
torch.zeros(
batch_dim + [obs_spec.shape[2], obs_spec.shape[0], obs_spec.shape[1]]
)
for obs_spec in self.policy.behavior_spec.observation_specs
if len(obs_spec.shape) == 3
]
dummy_masks = torch.ones(
batch_dim + [sum(self.policy.behavior_spec.action_spec.discrete_branches)]
)
dummy_memories = torch.zeros(
batch_dim + seq_len_dim + [self.policy.export_memory_size]
)
self.dummy_input = (dummy_vec_obs, dummy_vis_obs, dummy_masks, dummy_memories)
self.input_names = (
["vector_observation"]
+ [f"visual_observation_{i}" for i in range(num_vis_obs)]
+ ["action_masks", "memories"]
)
self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}
self.output_names = ["version_number", "memory_size"]
if self.policy.behavior_spec.action_spec.continuous_size > 0:
self.output_names += [
"continuous_actions",
"continuous_action_output_shape",
]
self.dynamic_axes.update({"continuous_actions": {0: "batch"}})
if self.policy.behavior_spec.action_spec.discrete_size > 0:
self.output_names += ["discrete_actions", "discrete_action_output_shape"]
self.dynamic_axes.update({"discrete_actions": {0: "batch"}})
if (
self.policy.behavior_spec.action_spec.continuous_size == 0
or self.policy.behavior_spec.action_spec.discrete_size == 0
):
self.output_names += [
"action",
"is_continuous_control",
"action_output_shape",
]
self.dynamic_axes.update({"action": {0: "batch"}})
def export_policy_model(self, output_filepath: str) -> None:
"""
Exports a Torch model for a Policy to .onnx format for Unity embedding.
:param output_filepath: file path to output the model (without file suffix)
"""
onnx_output_path = f"{output_filepath}.onnx"
logger.info(f"Converting to {onnx_output_path}")
with exporting_to_onnx():
torch.onnx.export(
self.policy.actor_critic,
self.dummy_input,
onnx_output_path,
opset_version=SerializationSettings.onnx_opset,
input_names=self.input_names,
output_names=self.output_names,
dynamic_axes=self.dynamic_axes,
)
logger.info(f"Exported {onnx_output_path}")