Python TorchScript需要源代码访问才能对collections.deque执行编译

Python TorchScript需要源代码访问才能对collections.deque执行编译,python,deep-learning,pytorch,coreml,torchscript,Python,Deep Learning,Pytorch,Coreml,Torchscript,我正在尝试将PyTorch模型转换为TorchScript。当我开始用@torch.jit.script注释一些类时,我发现了一个错误: OSError:无法获取的源代码。TorchScript需要源代码访问才能执行编译,请确保原始.py文件可用。 据我所知,在CPython中实现的类因此不能被TorchScript编译器读取。我没有找到任何纯Python实现。我如何克服这个问题 下面是我试图注释的类: import queue import collections import threadi

我正在尝试将PyTorch模型转换为TorchScript。当我开始用
@torch.jit.script
注释一些类时,我发现了一个错误:

OSError:无法获取的源代码。TorchScript需要源代码访问才能执行编译,请确保原始.py文件可用。

据我所知,在CPython中实现的类因此不能被TorchScript编译器读取。我没有找到任何纯Python实现。我如何克服这个问题

下面是我试图注释的类:

import queue
import collections
import threading
import torch

@torch.jit.script
class SyncMaster(object):
    """An abstract `SyncMaster` object.

    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
    call `register(id)` and obtain an `SlavePipe` to communicate with the master.
    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
    and passed to a registered callback.
    - After receiving the messages, the master device should gather the information and determine to message passed
    back to each slave devices.
    """

    def __init__(self, master_callback):
        """

        Args:
            master_callback: a callback to be invoked after having collected messages from slave devices.
        """
        self._master_callback = master_callback
        self._queue = queue.Queue()
        self._registry = collections.OrderedDict()
        self._activated = False

    def __getstate__(self):
        return {'master_callback': self._master_callback}

    def __setstate__(self, state):
        self.__init__(state['master_callback'])

    def register_slave(self, identifier):
        """
        Register an slave device.

        Args:
            identifier: an identifier, usually is the device id.

        Returns: a `SlavePipe` object which can be used to communicate with the master device.

        """
        if self._activated:
            assert self._queue.empty(), 'Queue is not clean before next initialization.'
            self._activated = False
            self._registry.clear()
        future = FutureResult()
        self._registry[identifier] = _MasterRegistry(future)
        return SlavePipe(identifier, self._queue, future)

    def run_master(self, master_msg):
        """
        Main entry for the master device in each forward pass.
        The messages were first collected from each devices (including the master device), and then
        an callback will be invoked to compute the message to be sent back to each devices
        (including the master device).

        Args:
            master_msg: the message that the master want to send to itself. This will be placed as the first
            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.

        Returns: the message to be sent back to the master device.

        """
        self._activated = True

        intermediates = [(0, master_msg)]
        for i in range(self.nr_slaves):
            intermediates.append(self._queue.get())

        results = self._master_callback(intermediates)
        assert results[0][0] == 0, 'The first result should belongs to the master.'

        for i, res in results:
            if i == 0:
                continue
            self._registry[i].result.put(res)

        for i in range(self.nr_slaves):
            assert self._queue.get() is True

        return results[0][1]

    @property
    def nr_slaves(self):
        return len(self._registry)

将TorchScript的生成方法从
torch.jit.script
切换到
torch.jit.trace
,并且运行正常,无需任何注释。或者,
torch.onnx.export
有时也会起作用