ml-agents/ml-agents/mlagents/trainers/torch/model_serialization.py

from typing import Tuple
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 TensorNames:
    batch_size_placeholder = "batch_size"
    sequence_length_placeholder = "sequence_length"
    vector_observation_placeholder = "vector_observation"
    recurrent_in_placeholder = "recurrent_in"
    visual_observation_placeholder_prefix = "visual_observation_"
    observation_placeholder_prefix = "obs_"
    previous_action_placeholder = "prev_action"
    action_mask_placeholder = "action_masks"
    random_normal_epsilon_placeholder = "epsilon"

    value_estimate_output = "value_estimate"
    recurrent_output = "recurrent_out"
    memory_size = "memory_size"
    version_number = "version_number"
    continuous_action_output_shape = "continuous_action_output_shape"
    discrete_action_output_shape = "discrete_action_output_shape"
    continuous_action_output = "continuous_actions"
    discrete_action_output = "discrete_actions"

    # Deprecated TensorNames entries for backward compatibility
    is_continuous_control_deprecated = "is_continuous_control"
    action_output_deprecated = "action"
    action_output_shape_deprecated = "action_output_shape"

    @staticmethod
    def get_visual_observation_name(index: int) -> str:
        """
        Returns the name of the visual observation with a given index
        """
        return TensorNames.visual_observation_placeholder_prefix + str(index)

    @staticmethod
    def get_observation_name(index: int) -> str:
        """
        Returns the name of the observation with a given index
        """
        return TensorNames.observation_placeholder_prefix + str(index)


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
        observation_specs = self.policy.behavior_spec.observation_specs
        batch_dim = [1]
        seq_len_dim = [1]
        num_obs = len(observation_specs)

        dummy_obs = [
            torch.zeros(
                batch_dim + list(ModelSerializer._get_onnx_shape(obs_spec.shape))
            )
            for obs_spec in observation_specs
        ]

        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_obs, dummy_masks, dummy_memories)

        self.input_names = [TensorNames.get_observation_name(i) for i in range(num_obs)]
        self.input_names += [
            TensorNames.action_mask_placeholder,
            TensorNames.recurrent_in_placeholder,
        ]

        self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}

        self.output_names = [TensorNames.version_number, TensorNames.memory_size]
        if self.policy.behavior_spec.action_spec.continuous_size > 0:
            self.output_names += [
                TensorNames.continuous_action_output,
                TensorNames.continuous_action_output_shape,
            ]
            self.dynamic_axes.update(
                {TensorNames.continuous_action_output: {0: "batch"}}
            )
        if self.policy.behavior_spec.action_spec.discrete_size > 0:
            self.output_names += [
                TensorNames.discrete_action_output,
                TensorNames.discrete_action_output_shape,
            ]
            self.dynamic_axes.update({TensorNames.discrete_action_output: {0: "batch"}})

        if self.policy.export_memory_size > 0:
            self.output_names += [TensorNames.recurrent_output]

    @staticmethod
    def _get_onnx_shape(shape: Tuple[int, ...]) -> Tuple[int, ...]:
        """
        Converts the shape of an observation to be compatible with the NCHW format
        of ONNX
        """
        if len(shape) == 3:
            return shape[2], shape[0], shape[1]
        return shape

    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.debug(f"Converting to {onnx_output_path}")

        with exporting_to_onnx():
            torch.onnx.export(
                self.policy.actor,
                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}")