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6a03641cde
Summary: Running RunNet() in python in a loop can be a performance issue if the python code is doing a lot of other processing, such as data input, because python's Global Interpreter lock (GIL) will prevent the RunNet() to be called. This can easily be fixed by making RunNet() run multiple iterations inside the C++ land. (Another way to accomplish the same thing is to use Caffe2's "execution plans", but that requires more setup). + fixed timing reporting in my OC workflow + improved one error log in data_workers.py Sorry for piggypagging those small changes, but landing diffs currently is slow... Reviewed By: rpenggithub Differential Revision: D4523575 fbshipit-source-id: 039a647576efad5dd9afda74df478ac22b43c103
302 lines
9.3 KiB
Python
302 lines
9.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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'''
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This module provides a python-land multithreaded data input mechanism
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for Caffe2 nets.
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Basic usage is as follows:
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coordinator = data_workers.init_data_input_workers(
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net,
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["data", "label"],
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my_fetch_fun,
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batch_size=32,
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input_source_name="train"
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)
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...
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coordinator.start()
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First argument is the Caffe2 net (or model helper), and second argument
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is list of input blobs that are to be fed.
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Argument 'input_source_name' is used to distinguish different sources of data,
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such as train or test data. This is to ensure the data does not get mixed up,
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although two nets would share blobs.
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To do the actual data loading, one defines a "fetcher function"
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that has call signature
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my_fetch_fun(worker_id, batch_size)
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This function returns a list of numpy arrays corresponding to the different
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input blobs. In the example above, it would return two arrays, one for the
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data blob and another for the labels. These arrays can have arbitrary number
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of elements (i.e they do not need to match the batch size). The batch size
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is provided for the function as a hint only.
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For example, fetcher function could download images from a remote service or
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load random images from a directory on a file system.
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For a dummy example, see the data_workers_test unit test.
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Note that for data_parallel_models, init_data_input_workers will be called
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for each GPU. Note that the 'coordinator' returned by the function is same
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each time.
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'''
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import Queue
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import logging
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import threading
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import atexit
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import numpy as np
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from caffe2.python import workspace, core, scope
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from caffe2.proto import caffe2_pb2
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log = logging.getLogger("data_workers")
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def init_data_input_workers(
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net,
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input_blob_names,
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fetch_fun,
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batch_size,
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num_worker_threads=2,
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input_source_name="train",
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max_buffered_batches=100,
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):
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global global_coordinator
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device_option = scope.CurrentDeviceScope()
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if (device_option is None):
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device_option = caffe2_pb2.DeviceOption(device_type=caffe2_pb2.CPU)
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# Create coordinator object
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coordinator = DataInputCoordinator(
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net,
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input_blob_names,
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batch_size,
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device_option,
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scope.CurrentNameScope(),
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input_source_name,
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max_buffered_batches,
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)
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# Launch fetch worker threads
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workers = [
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threading.Thread(
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target=fetcher,
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args=[coordinator, global_coordinator._fetcher_id_seq + i,
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fetch_fun, batch_size, input_blob_names],
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) for i in range(num_worker_threads)
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]
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global_coordinator._fetcher_id_seq += num_worker_threads
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workers.append(threading.Thread(
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target=enqueuer,
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args=[coordinator]))
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coordinator._workers = workers
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global_coordinator.add(coordinator)
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return global_coordinator
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class DataInputCoordinator(object):
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def __init__(self, net, input_blob_names, batch_size,
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device_option, namescope, input_source_name,
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max_buffered_batches):
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self._net = net
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self._input_blob_names = input_blob_names
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self._batch_size = batch_size
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self._internal_queue = Queue.Queue(maxsize=max_buffered_batches)
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self._queues = []
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self._device_option = device_option
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self._namescope = namescope
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self._active = True
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self._started = False
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self._workers = []
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self._input_source_name = input_source_name
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self._create_caffe2_queues_and_ops()
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def is_active(self):
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return self._active
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def _start(self):
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if self._started:
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return
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self._active = True
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self._started = True
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for w in self._workers:
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w.daemon = True
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w.start()
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def _stop(self, reason=None):
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self._active = False
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if reason is not None:
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log.error("Data input failed due to an error: {}".format(reason))
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self._started = False
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def _wait_finish(self):
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log.info("Wait for workers to die")
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for w in self._workers:
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if w != threading.current_thread():
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w.join(1.0) # don't wait forever, thread may be blocked in i/o
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log.info("...finished")
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def _get(self):
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while self.is_active():
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try:
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return self._internal_queue.get(block=True, timeout=0.5)
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except Queue.Empty:
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continue
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return None
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def put(self, chunk):
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while self.is_active():
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try:
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self._internal_queue.put(chunk, block=True, timeout=0.5)
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return
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except Queue.Full:
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log.debug("Queue full: stalling fetchers...")
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continue
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def _enqueue_batch(self):
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'''
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This pulls data from the python-side queue and collects them
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into batch-sized pieces.
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'''
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cur_batch = [np.array([]) for d in self._input_blob_names]
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# Collect data until we have a full batch size
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while cur_batch[0].shape[0] < self._batch_size and self.is_active():
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chunk = self._get()
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if chunk is None:
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continue
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for j, chunk_elem in enumerate(chunk):
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if cur_batch[j].shape[0] == 0:
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cur_batch[j] = chunk_elem.copy()
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else:
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cur_batch[j] = np.append(cur_batch[j], chunk_elem, axis=0)
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# Return data over the batch size back to queue
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if cur_batch[0].shape[0] > self._batch_size:
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leftover = [c[self._batch_size:] for c in cur_batch]
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cur_batch = [c[:self._batch_size] for c in cur_batch]
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try:
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self._internal_queue.put(leftover, block=False)
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except Queue.Full:
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pass
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assert cur_batch[0].shape[0] == self._batch_size
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if self.is_active():
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for b, q, c in zip(self._input_blob_names, self._queues, cur_batch):
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self._enqueue(b, q, c)
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def _enqueue(self, blob_name, queue, data_arr):
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'''
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Enqueue the correctly sized batch arrays to Caffe2's queue.
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'''
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scratch_name = self._namescope + blob_name + \
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"_scratch_" + self._input_source_name
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blob = core.BlobReference(scratch_name)
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workspace.FeedBlob(
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blob,
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data_arr,
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device_option=self._device_option
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)
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op = core.CreateOperator(
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"EnqueueBlobs",
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[queue, blob],
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[blob],
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device_option=self._device_option
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)
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workspace.RunOperatorOnce(op)
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def _create_caffe2_queues_and_ops(self):
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'''
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Creates queues on caffe2 side, and respective operators
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to pull (dequeue) blobs from the queues.
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'''
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def create_queue(queue_name, num_blobs, capacity):
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workspace.RunOperatorOnce(
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core.CreateOperator(
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"CreateBlobsQueue",
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[], [queue_name],
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num_blobs=1,
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capacity=capacity))
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return core.ScopedBlobReference(queue_name)
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for blob_name in self._input_blob_names:
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qname = blob_name + "_c2queue" + "_" + self._input_source_name
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q = create_queue(qname, num_blobs=1, capacity=4)
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self._queues.append(q)
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log.info("Created queue: {}".format(q))
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# Add operator to the Caffe2 network to dequeue
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self._net.DequeueBlobs(q, blob_name)
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class GlobalCoordinator(object):
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def __init__(self):
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self._coordinators = []
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self._fetcher_id_seq = 0
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self.register_shutdown_handler()
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def add(self, coordinator):
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self._coordinators.append(coordinator)
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def start(self):
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for c in self._coordinators:
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c._start()
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def stop(self):
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for c in self._coordinators:
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c._stop()
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for c in self._coordinators:
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c._wait_finish()
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self._coordinators = []
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def register_shutdown_handler(self):
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def cleanup():
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self.stop()
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atexit.register(cleanup)
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global_coordinator = GlobalCoordinator()
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def fetcher(coordinator, worker_id, fetch_fun, batch_size, input_blob_names):
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while coordinator.is_active():
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try:
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input_data = fetch_fun(worker_id, batch_size)
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if input_data is None:
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log.warn("Fetcher function returned None")
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continue
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assert len(input_data) == len(input_blob_names), \
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"Expecting data blob for each input"
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for d in input_data:
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assert isinstance(d, np.ndarray), \
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"Fetcher function must return a numpy array"
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for d in input_data[1:]:
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assert d.shape[0] == input_data[0].shape[0], \
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"Each returned input must have equal number of samples"
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coordinator.put(input_data)
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except Exception as e:
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logging.exception("Exception in fetcher", e)
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coordinator._stop("Exception in fetcher {}: {}".format(
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worker_id, e
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))
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def enqueuer(coordinator):
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while coordinator.is_active():
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coordinator._enqueue_batch()
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