FM / python

/usr/local/lib64/python3.6/site-packages/caffe2/python/

/usr/local/lib64/python3.6/site-packages/caffe2/python

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docs/	-	0755	rm
examples/	-	0755	rm
fakelowp/	-	0755	rm
helpers/	-	0755	rm
ideep/	-	0755	rm
layers/	-	0755	rm
mint/	-	0755	rm
mkl/	-	0755	rm
modeling/	-	0755	rm
models/	-	0755	rm
onnx/	-	0755	rm
operator_test/	-	0755	rm
predictor/	-	0755	rm
rnn/	-	0755	rm
serialized_test/	-	0755	rm
test/	-	0755	rm
trt/	-	0755	rm
__pycache__/	-	0755	rm
allcompare_test.py	2255	0644	edit dl rm
attention.py	12359	0644	edit dl rm
benchmark_generator.py	4912	0644	edit dl rm
binarysize.py	5521	0644	edit dl rm
brew.py	4762	0644	edit dl rm
brew_test.py	11739	0644	edit dl rm
build.py	153	0644	edit dl rm
cached_reader.py	4394	0644	edit dl rm
caffe2_pybind11_state.cpython-36m-x86_64-linux-gnu.so	48299712	0755	edit dl rm
caffe2_pybind11_state_gpu.cpython-36m-x86_64-linux-gnu.so	49048144	0755	edit dl rm
caffe_translator.py	35227	0644	edit dl rm
caffe_translator_test.py	3553	0644	edit dl rm
checkpoint.py	32101	0644	edit dl rm
checkpoint_test.py	13405	0644	edit dl rm
cnn.py	7626	0644	edit dl rm
context.py	2841	0644	edit dl rm
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convnet_benchmarks_test.py	839	0644	edit dl rm
core.py	119400	0644	edit dl rm
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crf.py	13250	0644	edit dl rm
crf_predict.py	1159	0644	edit dl rm
crf_viterbi_test.py	1663	0644	edit dl rm
dataio.py	23532	0644	edit dl rm
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dataset.py	12886	0644	edit dl rm
data_parallel_model.py	83100	0644	edit dl rm
data_parallel_model_test.py	56145	0644	edit dl rm
data_workers.py	15941	0644	edit dl rm
data_workers_test.py	6561	0644	edit dl rm
db_file_reader.py	6608	0644	edit dl rm
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device_checker.py	5157	0644	edit dl rm
dyndep.py	1533	0644	edit dl rm
embedding_generation_benchmark.py	5256	0644	edit dl rm
experiment_util.py	3625	0644	edit dl rm
extension_loader.py	744	0644	edit dl rm
fakefp16_transform_lib.py	322	0644	edit dl rm
filler_test.py	748	0644	edit dl rm
functional.py	4415	0644	edit dl rm
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fused_8bit_rowwise_conversion_ops_test.py	3945	0644	edit dl rm
gradient_checker.py	15377	0644	edit dl rm
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layers_test.py	92931	0644	edit dl rm
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layer_model_instantiator.py	3935	0644	edit dl rm
layer_parameter_sharing_test.py	9148	0644	edit dl rm
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lengths_reducer_fused_8bit_rowwise_ops_test.py	7575	0644	edit dl rm
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lstm_benchmark.py	10649	0644	edit dl rm
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mkl_test_util.py	1142	0644	edit dl rm
model_device_test.py	4777	0644	edit dl rm
model_helper.py	23492	0644	edit dl rm
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modifier_context.py	1772	0644	edit dl rm
muji.py	8131	0644	edit dl rm
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net_builder.py	27679	0644	edit dl rm
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python_op_test.py	9169	0644	edit dl rm
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schema.py	45621	0644	edit dl rm
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scope.py	3623	0644	edit dl rm
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session.py	7642	0644	edit dl rm
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sparse_to_dense_mask_test.py	6565	0644	edit dl rm
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tt_core.py	9349	0644	edit dl rm
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utils.py	14181	0644	edit dl rm
utils_test.py	1399	0644	edit dl rm
visualize.py	6315	0644	edit dl rm
workspace.py	25263	0644	edit dl rm
workspace_test.py	34844	0644	edit dl rm
_import_c_extension.py	2250	0644	edit dl rm
__init__.py	3925	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/data_workers.py (15941B)

## @package data_workers
# Module caffe2.python.data_workers

'''
This module provides a python-land multithreaded data input mechanism
for Caffe2 nets.

Basic usage is as follows:
   coordinator = data_workers.init_data_input_workers(
      net,
      ["data", "label"],
      my_fetch_fun,
      batch_size=32,
      input_source_name="train",
      dont_rebatch=False
   )
   ...
   coordinator.start()

First argument is the Caffe2 net (or model helper), and second argument
is list of input blobs that are to be fed.

Argument 'input_source_name' is used to distinguish different sources of data,
such as train or test data. This is to ensure the data does not get mixed up,
although two nets would share blobs.

To do the actual data loading, one defines a "fetcher function"
that has call signature
   my_fetch_fun(worker_id, batch_size)

Optionally, one can define a "init function" that is called once before
threads start, and has call signature:
   my_init_fun(data_coordinator, global_coordinator)

If dont_rebatch is set to True, the data input is not batched into equal sized
chunks but data directly provided by fetchers is used.

'batch_columns' can be used to specify which dimension is the batch dimension,
for each of the inputs. Default is 0 for all iputs.

'timeout' is the timeout in seconds after which if no data is available, the
net will fail (default 600s = 10 mins).

This function returns a list of numpy arrays corresponding to the different
input blobs. In the example above, it would return two arrays, one for the
data blob and another for the labels. These arrays can have arbitrary number
of elements (i.e they do not need to match the batch size). The batch size
is provided for the function as a hint only.

For example, fetcher function could download images from a remote service or
load random images from a directory on a file system.

For a dummy example, see the data_workers_test unit test.

Note that for data_parallel_models, init_data_input_workers will be called
for each GPU. Note that the 'coordinator' returned by the function is same
each time.
'''

import queue as Queue
from itertools import chain
import logging
import threading
import numpy as np
import time

from caffe2.python import workspace, core, scope, utils
from caffe2.proto import caffe2_pb2
from caffe2.python.parallel_workers import Metrics, State, \
    WorkerCoordinator, GlobalWorkerCoordinator, Worker, run_worker

log = logging.getLogger("data_workers")
log.setLevel(logging.INFO)
LOG_INT_SECS = 60

def get_worker_ids(num_workers):
    return list(range(0, num_workers))

def init_data_input_workers(
    net,
    input_blob_names,
    fetch_fun,
    batch_size,
    num_worker_threads=2,
    input_source_name="train",
    max_buffered_batches=800,
    init_fun=None,
    external_loggers=None,
    dont_rebatch=False,
    batch_columns=None,
    timeout=600
):
    global global_coordinator
    device_option = scope.CurrentDeviceScope()
    if (device_option is None):
        device_option = caffe2_pb2.DeviceOption(device_type=caffe2_pb2.CPU)

metrics = Metrics(external_loggers)
    batch_feeder = BatchFeeder(
        net,
        input_blob_names,
        batch_size,
        device_option,
        scope.CurrentNameScope(),
        input_source_name,
        global_coordinator.get_queue(input_source_name, max_buffered_batches),
        metrics,
        dont_rebatch,
        batch_columns,
        timeout=timeout
    )

# Launch fetch worker threads
    worker_ids = [
        global_coordinator.get_new_worker_id()
        for i in range(num_worker_threads)
    ]

# Create coordinator object
    coordinator = WorkerCoordinator(
        input_source_name, worker_ids, init_fun, batch_feeder)

workers = [
        threading.Thread(
            target=run_worker,
            name="data_workers fetcher id {}".format(worker_id),
            args=[coordinator,
                  DataWorker(coordinator, worker_id, fetch_fun, metrics,
                             batch_size, batch_feeder)],
        ) for worker_id in worker_ids
    ]

workers.append(threading.Thread(
        target=enqueuer,
        name="Enqueuer {} {}".format(input_source_name, scope.CurrentNameScope()),
        args=[coordinator, batch_feeder]))
    coordinator._workers = workers
    global_coordinator.add(coordinator)

return global_coordinator

class BatchFeeder(State):
    def __init__(self, net, input_blob_names, batch_size,
                 device_option, namescope, input_source_name, queue,
                 metrics, dont_rebatch, batch_columns, timeout=600):
        self._counter = 0
        self._input_blob_names = input_blob_names
        self._batch_size = batch_size
        self._internal_queue = queue
        self._queues = []
        self._device_option = device_option
        self._namescope = namescope
        self._timeout = timeout
        self._input_source_name = input_source_name
        self._c2_queue_capacity = 4
        self._create_caffe2_queues(net)
        self._create_caffe2_ops(net)
        self._inputs = 0
        self._prev_seconds = 0
        self._last_warning = time.time()
        self._dont_rebatch = dont_rebatch
        self._init_scratch()
        self._metrics = metrics

if batch_columns is None:
            batch_columns = [0 for _ in input_blob_names]
        self._batch_columns = batch_columns

def start(self):
        self._inputs = 0
        self._prev_seconds = time.time()

def stop(self):
        try:
            for q in self._queues:
                workspace.RunOperatorOnce(
                    core.CreateOperator("CloseBlobsQueue", [q], [])
                )
        finally:
            self._log_inputs_per_interval(0, force=True)

def cleanup(self):
        utils.ResetBlobs(self._scratch_blob.values())
        utils.ResetBlobs(self._scratch_status.values())

def _get(self, data_input_coordinator):
        start_time = time.time()
        last_warning = time.time()
        while data_input_coordinator.is_active():
            try:
                return self._internal_queue.get(block=True, timeout=0.5)
            except Queue.Empty:
                if time.time() - last_warning > 10.0:
                    log.warning("** Data input is slow: (still) no data in {} secs.".format(
                        time.time() - start_time))
                    last_warning = time.time()
                continue
        return None

def _validate_chunk(self, chunk):
        if chunk is None:
            log.warning("Fetcher function returned None")
            return False

assert len(chunk) == len(self._input_blob_names), \
            "Expecting data blob for each input"
        for d in chunk:
            assert isinstance(d, np.ndarray), \
                "Fetcher function must return a numpy array"
        if not self._dont_rebatch:
            j = 1
            for d in chunk[1:]:
                assert d.shape[self._batch_columns[j]] == \
                    chunk[0].shape[self._batch_columns[0]], \
                    "Each returned input must have equal number of samples"
                j += 1

if len(chunk) == 0:
            log.warning("Worker provided zero length input")
            return False

return True

def put(self, chunk, data_input_coordinator):
        if not self._validate_chunk(chunk):
            return

while data_input_coordinator.is_active():
            try:
                qsize = self._internal_queue.qsize()
                if qsize < 2 and (time.time() - self._last_warning) > LOG_INT_SECS:
                    log.warning("Warning, data loading lagging behind: " +
                                "queue size={}, name={}".format(qsize, self._input_source_name))
                    self._last_warning = time.time()
                self._counter += 1
                self._internal_queue.put(chunk, block=True, timeout=0.5)
                self._log_inputs_per_interval(chunk[0].shape[0])
                return
            except Queue.Full:
                log.debug("Queue full: stalling fetchers...")
                continue

def _enqueue_batch_direct(self, data_input_coordinator):
        data = self._get(data_input_coordinator)
        if data is None:
            return
        if data_input_coordinator.is_active():
            for b, q, c in zip(self._input_blob_names, self._queues, data):
                self._enqueue(b, q, c)

def _enqueue_batch(self, data_input_coordinator):
        '''
        This pulls data from the python-side queue and collects them
        into batch-sized pieces, unless dont_rebatch is set to true.
        '''
        if self._dont_rebatch:
            self._enqueue_batch_direct(data_input_coordinator)
            return

cur_batch = [np.array([]) for d in self._input_blob_names]
        first_batch_col = self._batch_columns[0]

# Collect data until we have a full batch size
        while (
            cur_batch[0].shape[0] == 0 or
            cur_batch[0].shape[first_batch_col] < self._batch_size
        ) and data_input_coordinator.is_active():
            chunk = self._get(data_input_coordinator)
            if chunk is None:
                continue

for j, chunk_elem in enumerate(chunk):
                if cur_batch[j].shape[0] == 0:
                    cur_batch[j] = chunk_elem.copy()
                else:
                    cur_batch[j] = np.append(
                        cur_batch[j], chunk_elem, axis=self._batch_columns[j]
                    )

start_time = time.time()
        try:
            # Return data over the batch size back to queue
            if cur_batch[0].shape[0] > 0 and cur_batch[0].shape[
                first_batch_col
            ] > self._batch_size:
                leftover = []
                trimmed_batch = []
                for j, b in enumerate(cur_batch):
                    [c, l] = np.split(
                        b, [self._batch_size], axis=self._batch_columns[j]
                    )
                    leftover.append(l)
                    trimmed_batch.append(c)
                cur_batch = trimmed_batch
                try:
                    self._internal_queue.put(leftover, block=False)
                except Queue.Full:
                    pass

assert cur_batch[0].shape[first_batch_col] == self._batch_size

if data_input_coordinator.is_active():
                for b, q, c in zip(
                    self._input_blob_names, self._queues, cur_batch
                ):
                    self._enqueue(b, q, c)
        finally:
            self._metrics.put_metric('enqueue_time', time.time() - start_time)

def _init_scratch(self):
        self._scratch_blob = {}
        self._scratch_status = {}
        for blob_name in self._input_blob_names:
            scratch_name = self._namescope + blob_name + \
                "_scratch_" + self._input_source_name
            self._scratch_blob[blob_name] = core.BlobReference(scratch_name)
            self._scratch_status[blob_name] = core.BlobReference(
                scratch_name + "_status"
            )

# Feed empty arrays to the scratch blobs here, so that there won't be
        # race conditions when calling FeedBlob (which calls wworkspace
        # CreateBlob()) from enqueue threads
        for b in chain(
            self._scratch_blob.values(), self._scratch_status.values()
        ):
            workspace.FeedBlob(
                b,
                np.array([]).astype(np.float32),
                device_option=self._device_option,
            )

def _enqueue(self, blob_name, queue, data_arr):
        '''
        Enqueue the correctly sized batch arrays to Caffe2's queue.
        '''
        workspace.FeedBlob(
            self._scratch_blob[blob_name],
            data_arr,
            device_option=self._device_option
        )

op = core.CreateOperator(
            "SafeEnqueueBlobs",
            [queue, self._scratch_blob[blob_name]],
            [self._scratch_blob[blob_name], self._scratch_status[blob_name]],
            device_option=self._device_option
        )
        workspace.RunOperatorOnce(op)

def _create_caffe2_queues(self, net):
        '''
        Creates queues on caffe2 side
        '''
        def create_queue(queue_name, num_blobs, capacity):
            workspace.RunOperatorOnce(
                core.CreateOperator(
                    "CreateBlobsQueue",
                    [], [queue_name],
                    num_blobs=1,
                    capacity=capacity))
            return core.ScopedBlobReference(queue_name)

for blob_name in self._input_blob_names:
            qname = blob_name + "_c2queue" + "_" + self._input_source_name
            q = create_queue(
                qname, num_blobs=1, capacity=self._c2_queue_capacity
            )
            self._queues.append(q)

def _create_caffe2_ops(self, net):
        '''
        Creates dequeue-ops on caffe2 side
        '''
        for q, blob_name in zip(self._queues, self._input_blob_names):
            # Add operator to the Caffe2 network to dequeue
            net.DequeueBlobs(q, blob_name, timeout_secs=float(self._timeout))

def _log_inputs_per_interval(self, inputs, force=False):
        self._inputs += inputs
        current_seconds = time.time()
        delta_seconds = current_seconds - self._prev_seconds
        if delta_seconds >= LOG_INT_SECS or force:
            inputs_per_sec = int(self._inputs / delta_seconds)
            qsize = self._internal_queue.qsize()
            log.info("{}/{}: {} inputs/sec".format(
                self._input_source_name,
                self._namescope,
                inputs_per_sec,
            ))
            log.info("-- queue: {} batches".format(qsize))
            # log and reset perf metrics
            self._metrics.put_metric(
                'inputs_per_sec', inputs_per_sec, False)
            self._metrics.put_metric('queue_size', qsize, False)
            self._metrics.put_metric(
                'time_elapsed', delta_seconds, False)
            self._metrics.log_metrics()
            self._metrics.reset_metrics()
            self._inputs = 0
            self._prev_seconds = current_seconds

class GlobalCoordinator(GlobalWorkerCoordinator):
    def __init__(self):
        GlobalWorkerCoordinator.__init__(self)
        self._queues = {}

def get_queue(self, queue_name, max_buffered_batches):
        assert isinstance(max_buffered_batches, int)
        if queue_name not in self._queues:
            self._queues[queue_name] = Queue.Queue(maxsize=max_buffered_batches)
        return self._queues[queue_name]

def reset_data_input(self, namescope, name, net, batch_size):
        log.info("Reset data input {}, batch size {}: ".format(name, batch_size))
        for c in self._coordinators:
            if c._worker_name == name and c._state._namescope == namescope:
                c._state._batch_size = batch_size
                c._state._create_caffe2_ops(net)

class DataWorker(Worker):
    def __init__(
        self,
        coordinator,
        worker_id,
        worker_fun,
        metrics,
        batch_size,
        batch_feeder
    ):
        Worker.__init__(self, coordinator, worker_id, worker_fun=worker_fun,
                        metrics=metrics)
        self._batch_size = batch_size
        self._batch_feeder = batch_feeder

def run(self):
        input_data = self._worker_fun(self._worker_id, self._batch_size)

self._batch_feeder.put(input_data, self._coordinator)

def finish(self):
        self._metrics.put_metric(
            'fetcher_time', time.time() - self._start_time)

global_coordinator = GlobalCoordinator()

def enqueuer(coordinator, batch_feeder):
    while coordinator.is_active():
        batch_feeder._enqueue_batch(coordinator)

cmd: