Source code for tensorpack.predict.concurrency

# -*- coding: utf-8 -*-
# File:

import multiprocessing
import numpy as np
import six
import tensorflow as tf
from six.moves import queue, range

from ..tfutils.model_utils import describe_trainable_vars
from ..utils import logger
from ..utils.concurrency import DIE, ShareSessionThread, StoppableThread
from .base import AsyncPredictorBase, OfflinePredictor, OnlinePredictor

__all__ = ['MultiProcessPredictWorker', 'MultiProcessQueuePredictWorker',

[docs]class MultiProcessPredictWorker(multiprocessing.Process): """ Base class for predict worker that runs offline in multiprocess"""
[docs] def __init__(self, idx, config): """ Args: idx (int): index of the worker. the 0th worker will print log. config (PredictConfig): the config to use. """ super(MultiProcessPredictWorker, self).__init__() = "MultiProcessPredictWorker-{}".format(idx) self.idx = idx self.config = config
def _init_runtime(self): """ Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll have workers that run on multiGPUs """ if self.idx != 0: from tensorpack.models.registry import disable_layer_logging disable_layer_logging() self.predictor = OfflinePredictor(self.config) if self.idx == 0: with self.predictor.graph.as_default(): describe_trainable_vars()
[docs]class MultiProcessQueuePredictWorker(MultiProcessPredictWorker): """ An offline predictor worker that takes input and produces output by queue. Each process will exit when they see :class:`DIE`. """
[docs] def __init__(self, idx, inqueue, outqueue, config): """ Args: idx, config: same as in :class:`MultiProcessPredictWorker`. inqueue (multiprocessing.Queue): input queue to get data point. elements are (task_id, dp) outqueue (multiprocessing.Queue): output queue to put result. elements are (task_id, output) """ super(MultiProcessQueuePredictWorker, self).__init__(idx, config) self.inqueue = inqueue self.outqueue = outqueue assert isinstance(self.inqueue, multiprocessing.queues.Queue) assert isinstance(self.outqueue, multiprocessing.queues.Queue)
[docs] def run(self): self._init_runtime() while True: tid, dp = self.inqueue.get() if tid == DIE: self.outqueue.put((DIE, None)) return else: self.outqueue.put((tid, self.predictor(*dp)))
class PredictorWorkerThread(StoppableThread, ShareSessionThread): def __init__(self, queue, pred_func, id, batch_size=5): super(PredictorWorkerThread, self).__init__() = "PredictorWorkerThread-{}".format(id) self.queue = queue self.func = pred_func self.daemon = True self.batch_size = batch_size = id def run(self): with self.default_sess(): while not self.stopped(): batched, futures = self.fetch_batch() try: outputs = self.func(*batched) except tf.errors.CancelledError: for f in futures: f.cancel() logger.warn("In PredictorWorkerThread id={}, call was cancelled.".format( return # print "Worker {} batched {} Queue {}".format( #, len(futures), self.queue.qsize()) # debug, for speed testing # if not hasattr(self, 'xxx'): # = outputs = self.func(batched) # else: # outputs = [[[0][0]] * len(batched[0]), [[1][0]] * len(batched[0])] for idx, f in enumerate(futures): f.set_result([k[idx] for k in outputs]) def fetch_batch(self): """ Fetch a batch of data without waiting""" inp, f = self.queue.get() nr_input_var = len(inp) batched, futures = [[] for _ in range(nr_input_var)], [] for k in range(nr_input_var): batched[k].append(inp[k]) futures.append(f) while len(futures) < self.batch_size: try: inp, f = self.queue.get_nowait() for k in range(nr_input_var): batched[k].append(inp[k]) futures.append(f) except queue.Empty: break # do not wait for k in range(nr_input_var): batched[k] = np.asarray(batched[k]) return batched, futures
[docs]class MultiThreadAsyncPredictor(AsyncPredictorBase): """ An multithreaded online async predictor which runs a list of OnlinePredictor. It would do an extra batching internally. """
[docs] def __init__(self, predictors, batch_size=5): """ Args: predictors (list): a list of OnlinePredictor available to use. batch_size (int): the maximum of an internal batch. """ assert len(predictors) self._need_default_sess = False for k in predictors: assert isinstance(k, OnlinePredictor), type(k) if k.sess is None: self._need_default_sess = True # TODO support predictors.return_input here assert not k.return_input self.input_queue = queue.Queue(maxsize=len(predictors) * 100) self.threads = [ PredictorWorkerThread( self.input_queue, f, id, batch_size=batch_size) for id, f in enumerate(predictors)] if six.PY2: # TODO XXX set logging here to avoid affecting TF logging import tornado.options as options options.parse_command_line(['--logging=debug']) logger.warn("MultiThreadAsyncPredictor is inefficient in Python 2! Switch to Python 3 instead.")
[docs] def start(self): if self._need_default_sess: assert tf.get_default_session() is not None, \ "Not session is bind to predictors, " \ "MultiThreadAsyncPredictor.start() has to be called under a default session!" for t in self.threads: t.start()
[docs] def put_task(self, dp, callback=None): """ Same as in :meth:`AsyncPredictorBase.put_task`. """ f = Future() if callback is not None: f.add_done_callback(callback) self.input_queue.put((dp, f)) return f
try: if six.PY2: from tornado.concurrent import Future else: from concurrent.futures import Future except ImportError: from ..utils.develop import create_dummy_class MultiThreadAsyncPredictor = create_dummy_class('MultiThreadAsyncPredictor', 'tornado.concurrent') # noqa