threaded mapdata

tensorpack/callbacks/concurrency.py

@@ -48,3 +48,4 @@ class StartProcOrThread(Callback):
             elif isinstance(k, StoppableThread):
                 logger.info("Stopping {} ...".format(k.name))
                 k.stop()
+                k.join()

tensorpack/callbacks/group.py

@@ -102,7 +102,7 @@ class Callbacks(Callback):
                 continue
             for f in fetch:
                 ret.append(f)
-                self._cbid_to_fetchid[idx].append(len(ret)-1)
+                self._cbid_to_fetchid[idx].append(len(ret) - 1)
         self._extra_fetches_cache = ret
         return ret
 

tensorpack/dataflow/common.py

@@ -205,9 +205,8 @@ class MapData(ProxyDataFlow):
                 yield ret
 
 
-class MapDataComponent(ProxyDataFlow):
+class MapDataComponent(MapData):
     """ Apply a mapper/filter on a datapoint component"""
-
     def __init__(self, ds, func, index=0):
         """
         Args:
@@ -217,16 +216,13 @@ class MapDataComponent(ProxyDataFlow):
                 Note that if you use the filter feature, ``ds.size()`` will be incorrect.
             index (int): index of the component.
         """
-        super(MapDataComponent, self).__init__(ds)
-        self.func = func
-        self.index = index
-
-    def get_data(self):
-        for dp in self.ds.get_data():
-            repl = self.func(dp[self.index])
-            if repl is not None:
-                dp[self.index] = repl   # NOTE modifying
-                yield dp
+        def f(dp):
+            r = func(dp[index])
+            if r is None:
+                return None
+            dp[index] = r
+            return dp
+        super(MapDataComponent, self).__init__(ds, f)
 
 
 class RepeatedData(ProxyDataFlow):

tensorpack/dataflow/image.py

@@ -60,8 +60,23 @@ class AugmentImageComponent(MapDataComponent):
             self.augs = augmentors
         else:
             self.augs = AugmentorList(augmentors)
+
+        self._nr_error = 0
+
+        def func(x):
+            try:
+                ret = self.augs.augment(x)
+            except KeyboardInterrupt:
+                raise
+            except Exception:
+                self._nr_error += 1
+                if self._nr_error % 1000 == 0:
+                    logger.warn("Got {} augmentation errors.".format(self._nr_error))
+                return None
+            return ret
+
         super(AugmentImageComponent, self).__init__(
-            ds, lambda x: self.augs.augment(x), index)
+            ds, func, index)
 
     def reset_state(self):
         self.ds.reset_state()

tensorpack/dataflow/prefetch.py

@@ -5,23 +5,24 @@
 from __future__ import print_function
 import multiprocessing as mp
 import itertools
-from six.moves import range, zip
+from six.moves import range, zip, queue
 import uuid
 import os
 import zmq
 
 from .base import ProxyDataFlow
+from .common import RepeatedData
 from ..utils.concurrency import (ensure_proc_terminate,
-                                 mask_sigint, start_proc_mask_signal)
+                                 mask_sigint, start_proc_mask_signal,
+                                 StoppableThread)
 from ..utils.serialize import loads, dumps
 from ..utils import logger
 from ..utils.gpu import change_gpu
 
-__all__ = ['PrefetchData', 'BlockParallel', 'PrefetchDataZMQ', 'PrefetchOnGPUs']
+__all__ = ['PrefetchData', 'PrefetchDataZMQ', 'PrefetchOnGPUs']
 
 
 class PrefetchProcess(mp.Process):
-
     def __init__(self, ds, queue, reset_after_spawn=True):
         """
         :param ds: ds to take data from
@@ -49,7 +50,6 @@ class PrefetchData(ProxyDataFlow):
         This is significantly slower than :class:`PrefetchDataZMQ` when data
         is large.
     """
-
     def __init__(self, ds, nr_prefetch, nr_proc=1):
         """
         Args:
@@ -83,31 +83,18 @@ class PrefetchData(ProxyDataFlow):
         pass
 
 
-def BlockParallel(ds, queue_size):
-    """
-    Insert ``BlockParallel`` in dataflow pipeline to block parallelism on ds.
-
-    :param ds: a `DataFlow`
-    :param queue_size: size of the queue used
-    """
-    return PrefetchData(ds, queue_size, 1)
-
-
 class PrefetchProcessZMQ(mp.Process):
-    def __init__(self, ds, conn_name):
-        """
-        :param ds: a `DataFlow` instance.
-        :param conn_name: the name of the IPC connection
-        """
+    def __init__(self, ds, conn_name, hwm):
         super(PrefetchProcessZMQ, self).__init__()
         self.ds = ds
         self.conn_name = conn_name
+        self.hwm = hwm
 
     def run(self):
         self.ds.reset_state()
         self.context = zmq.Context()
         self.socket = self.context.socket(zmq.PUSH)
-        self.socket.set_hwm(5)
+        self.socket.set_hwm(self.hwm)
         self.socket.connect(self.conn_name)
         while True:
             for dp in self.ds.get_data():
@@ -119,13 +106,14 @@ class PrefetchDataZMQ(ProxyDataFlow):
     Prefetch data from a DataFlow using multiple processes, with ZMQ for
     communication.
     """
-    def __init__(self, ds, nr_proc=1, pipedir=None):
+    def __init__(self, ds, nr_proc=1, pipedir=None, hwm=50):
         """
         Args:
             ds (DataFlow): input DataFlow.
             nr_proc (int): number of processes to use.
             pipedir (str): a local directory where the pipes should be put.
                 Useful if you're running on non-local FS such as NFS or GlusterFS.
+            hwm (int): the zmq "high-water mark" for both sender and receiver.
         """
         super(PrefetchDataZMQ, self).__init__(ds)
         try:
@@ -141,10 +129,10 @@ class PrefetchDataZMQ(ProxyDataFlow):
             pipedir = os.environ.get('TENSORPACK_PIPEDIR', '.')
         assert os.path.isdir(pipedir), pipedir
         self.pipename = "ipc://{}/dataflow-pipe-".format(pipedir.rstrip('/')) + str(uuid.uuid1())[:6]
-        self.socket.set_hwm(5)  # a little bit faster than default, don't know why
+        self.socket.set_hwm(hwm)
         self.socket.bind(self.pipename)
 
-        self.procs = [PrefetchProcessZMQ(self.ds, self.pipename)
+        self.procs = [PrefetchProcessZMQ(self.ds, self.pipename, hwm)
                       for _ in range(self.nr_proc)]
         self.start_processes()
         # __del__ not guranteed to get called at exit
@@ -206,3 +194,71 @@ class PrefetchOnGPUs(PrefetchDataZMQ):
             for gpu, proc in zip(self.gpus, self.procs):
                 with change_gpu(gpu):
                     proc.start()
+
+
+class ThreadedMapData(ProxyDataFlow):
+    """
+    Same as :class:`MapData`, but start threads to run the mapping function.
+    This is useful when the mapping function is the bottleneck, but you don't
+    want to start processes for the entire dataflow pipeline.
+
+    With threads, there are tiny communication overhead, but due to GIL, you
+    should avoid starting the threads in your main process.
+    Note that the threads will only start in the process which calls
+    `reset_state()`.
+    """
+    class WorkerThread(StoppableThread):
+        def __init__(self, inq, outq, map_func):
+            self.inq = inq
+            self.outq = outq
+            self.func = map_func
+
+        def run(self):
+            while not self.stopped():
+                dp = self.queue_get_stoppable(self.inq)
+                dp = self.func(dp)
+                if dp is not None:
+                    self.queue_put_stoppable(self.outq, dp)
+
+    def __init__(self, ds, nr_thread, map_func, buffer_size=200):
+        """
+        Args:
+            pass
+        """
+        super(ThreadedMapData, self).__init__(ds)
+        self.infinite_ds = RepeatedData(ds, -1)
+        self.nr_thread = nr_thread
+        self.buffer_size = buffer_size
+        self.map_func = map_func
+        self._threads = []
+
+    def reset_state(self):
+        super(ThreadedMapData, self).reset_state()
+        for t in self._threads:
+            t.stop()
+            t.join()
+        self._in_queue = queue.Queue()
+        self._out_queue = queue.Queue()
+        self._threads = [ThreadedMapData.WorkerThread(
+            self._in_queue, self._out_queue, self.map_func)
+            for _ in range(self.nr_thread)]
+        for t in self._threads:
+            t.start()
+
+        # fill the buffer
+        self._itr = self.infinite_ds.get_data()
+        self._fill_buffer()
+
+    def _fill_buffer(self):
+        n = self.buffer_size - self._in_queue.qsize() - self._out_queue.qsize()
+        if n <= 0:
+            return
+        for _ in range(n):
+            self._in_queue.put(next(self._itr))
+
+    def get_data(self):
+        self._fill_buffer()
+        sz = self.size()
+        for _ in range(sz):
+            self._in_queue.put(next(self._itr))
+            yield self._out_queue.get()

tensorpack/tfutils/__init__.py

@@ -22,7 +22,7 @@ _TO_IMPORT = set([
     'gradproc',
     'argscope',
     'tower'
-    ])
+])
 
 _CURR_DIR = os.path.dirname(__file__)
 for _, module_name, _ in iter_modules(

tensorpack/train/base.py

@@ -157,8 +157,8 @@ class Trainer(object):
     def global_step(self):
         try:
             return self._starting_step + \
-                    self.config.steps_per_epoch * (self.epoch_num - 1) + \
-                    self.local_step + 1
+                self.config.steps_per_epoch * (self.epoch_num - 1) + \
+                self.local_step + 1
         except AttributeError:
             return get_global_step_value()
 

tensorpack/train/config.py

@@ -5,9 +5,9 @@
 import tensorflow as tf
 
 from ..callbacks import (
-        Callbacks, MovingAverageSummary,
-        StatPrinter, ProgressBar,
-        MaintainStepCounter)
+    Callbacks, MovingAverageSummary,
+    StatPrinter, ProgressBar,
+    MaintainStepCounter)
 from ..dataflow.base import DataFlow
 from ..models import ModelDesc
 from ..utils import logger
@@ -86,9 +86,9 @@ class TrainConfig(object):
         assert_type(callbacks, list)
         if extra_callbacks is None:
             extra_callbacks = [
-                    MovingAverageSummary(),
-                    ProgressBar(),
-                    StatPrinter()]
+                MovingAverageSummary(),
+                ProgressBar(),
+                StatPrinter()]
         self.callbacks = [MaintainStepCounter()] + callbacks + extra_callbacks
         assert_type(self.callbacks, list)
         self.callbacks = Callbacks(self.callbacks)

tox.ini

 [flake8]
 max-line-length = 120
+ignore = E265
 exclude = .git,
 					tensorpack/__init__.py,
 					setup.py,