always use reset_state

examples/DisturbLabel/disturb.py

@@ -9,6 +9,8 @@ class DisturbLabel(ProxyDataFlow):
     def __init__(self, ds, prob):
         super(DisturbLabel, self).__init__(ds)
         self.prob = prob
+
+    def reset_state(self):
         self.rng = get_rng(self)
 
     def get_data(self):

scripts/dump_train_config.py

@@ -29,6 +29,7 @@ args = parser.parse_args()
 
 get_config_func = imp.load_source('config_script', args.config).get_config
 config = get_config_func()
+config.dataset.reset_state()
 
 if args.output:
     mkdir_p(args.output)

tensorpack/RL/expreplay.py

@@ -24,6 +24,9 @@ class ExpReplay(DataFlow, Callback):
     """
     Implement experience replay in the paper
     `Human-level control through deep reinforcement learning`.
+
+    This implementation provides the interface as an DataFlow.
+    This DataFlow is not fork-safe (doesn't support multiprocess prefetching)
     """
     def __init__(self,
             predictor,
@@ -80,9 +83,6 @@ class ExpReplay(DataFlow, Callback):
                 pbar.update()
         self._init_memory_flag.set()
 
-    def reset_state(self):
-        raise RuntimeError("Don't run me in multiple processes")
-
     def _populate_exp(self):
         """ populate a transition by epsilon-greedy"""
         old_s = self.player.current_state()

tensorpack/callbacks/inference.py

@@ -106,6 +106,7 @@ class InferenceRunner(Callback):
             vc.before_inference()
 
         sess = tf.get_default_session()
+        self.ds.reset_state()
         with tqdm(total=self.ds.size(), ascii=True) as pbar:
             for dp in self.ds.get_data():
                 #feed = dict(zip(self.input_vars, dp))   # TODO custom dp mapping?

tensorpack/dataflow/base.py

@@ -29,7 +29,7 @@ class DataFlow(object):
 
     def reset_state(self):
         """
-        Reset state of the dataflow,
+        Reset state of the dataflow. Will always be called before consuming data points.
         for example, RNG **HAS** to be reset here if used in the DataFlow.
         Otherwise it may not work well with prefetching, because different
         processes will have the same RNG state.
@@ -39,9 +39,6 @@ class DataFlow(object):
 
 class RNGDataFlow(DataFlow):
     """ A dataflow with rng"""
-    def __init__(self):
-        self.rng = get_rng(self)
-
     def reset_state(self):
         self.rng = get_rng(self)
 

tensorpack/dataflow/common.py

@@ -306,11 +306,7 @@ class JoinData(DataFlow):
 class LocallyShuffleData(ProxyDataFlow, RNGDataFlow):
     def __init__(self, ds, cache_size):
         ProxyDataFlow.__init__(self, ds)
-        RNGDataFlow.__init__(self)
         self.q = deque(maxlen=cache_size)
-        self.ds_wrap = RepeatedData(ds, -1)
-        self.ds_itr = self.ds_wrap.get_data()
-        self.current_cnt = 0
 
     def reset_state(self):
         ProxyDataFlow.reset_state(self)

tensorpack/dataflow/dataset/bsds500.py

@@ -9,7 +9,7 @@ import numpy as np
 
 from ...utils import logger, get_rng, get_dataset_dir
 from ...utils.fs import download
-from ..base import DataFlow
+from ..base import RNGDataFlow
 
 try:
     from scipy.io import loadmat
@@ -21,7 +21,7 @@ except ImportError:
 DATA_URL = "http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz"
 IMG_W, IMG_H = 481, 321
 
-class BSDS500(DataFlow):
+class BSDS500(RNGDataFlow):
     """
     `Berkeley Segmentation Data Set and Benchmarks 500
     <http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html#bsds500>`_.
@@ -53,10 +53,6 @@ class BSDS500(DataFlow):
         self.shuffle = shuffle
         assert name in ['train', 'test', 'val']
         self._load(name)
-        self.rng = get_rng(self)
-
-    def reset_state(self):
-        self.rng = get_rng(self)
 
     def _load(self, name):
         image_glob = os.path.join(self.data_root, 'images', name, '*.jpg')

tensorpack/dataflow/dataset/cifar.py

@@ -15,7 +15,7 @@ import logging
 
 from ...utils import logger, get_rng, get_dataset_dir
 from ...utils.fs import download
-from ..base import DataFlow
+from ..base import RNGDataFlow
 
 __all__ = ['Cifar10', 'Cifar100']
 
@@ -77,7 +77,7 @@ def get_filenames(dir, cifar_classnum):
                      os.path.join(dir, 'cifar-100-python', 'test')]
     return filenames
 
-class CifarBase(DataFlow):
+class CifarBase(RNGDataFlow):
     """
     Return [image, label],
         image is 32x32x3 in the range [0,255]
@@ -106,10 +106,6 @@ class CifarBase(DataFlow):
         self.data = read_cifar(self.fs, cifar_classnum)
         self.dir = dir
         self.shuffle = shuffle
-        self.rng = get_rng(self)
-
-    def reset_state(self):
-        self.rng = get_rng(self)
 
     def size(self):
         return 50000 if self.train_or_test == 'train' else 10000

tensorpack/dataflow/dataset/ilsvrc.py

@@ -11,7 +11,7 @@ from six.moves import range
 from ...utils import logger, get_rng, get_dataset_dir, memoized
 from ...utils.loadcaffe import get_caffe_pb
 from ...utils.fs import mkdir_p, download
-from ..base import DataFlow
+from ..base import RNGDataFlow
 
 __all__ = ['ILSVRCMeta', 'ILSVRC12']
 
@@ -79,7 +79,7 @@ class ILSVRCMeta(object):
             arr = cv2.resize(arr, size[::-1])
         return arr
 
-class ILSVRC12(DataFlow):
+class ILSVRC12(RNGDataFlow):
     def __init__(self, dir, name, meta_dir=None, shuffle=True):
         """
         :param dir: A directory containing a subdir named `name`, where the
@@ -119,17 +119,10 @@ class ILSVRC12(DataFlow):
         self.shuffle = shuffle
         self.meta = ILSVRCMeta(meta_dir)
         self.imglist = self.meta.get_image_list(name)
-        self.rng = get_rng(self)
 
     def size(self):
         return len(self.imglist)
 
-    def reset_state(self):
-        """
-        reset rng for shuffle
-        """
-        self.rng = get_rng(self)
-
     def get_data(self):
         """
         Produce original images or shape [h, w, 3], and label

tensorpack/dataflow/dataset/mnist.py

@@ -11,7 +11,7 @@ from six.moves import urllib, range
 
 from ...utils import logger, get_dataset_dir
 from ...utils.fs import download
-from ..base import DataFlow
+from ..base import RNGDataFlow
 
 __all__ = ['Mnist']
 
@@ -92,7 +92,7 @@ class DataSet(object):
     def num_examples(self):
         return self._num_examples
 
-class Mnist(DataFlow):
+class Mnist(RNGDataFlow):
     """
     Return [image, label],
         image is 28x28 in the range [0,1]
@@ -136,7 +136,7 @@ class Mnist(DataFlow):
         ds = self.train if self.train_or_test == 'train' else self.test
         idxs = list(range(ds.num_examples))
         if self.shuffle:
-            random.shuffle(idxs)
+            self.rng.shuffle(idxs)
         for k in idxs:
             img = ds.images[k].reshape((28, 28))
             label = ds.labels[k]

tensorpack/dataflow/dataset/svhn.py

@@ -9,7 +9,7 @@ import numpy as np
 from six.moves import range
 
 from ...utils import logger, get_rng, get_dataset_dir
-from ..base import DataFlow
+from ..base import RNGDataFlow
 
 try:
     import scipy.io
@@ -20,7 +20,7 @@ except ImportError:
 
 SVHN_URL = "http://ufldl.stanford.edu/housenumbers/"
 
-class SVHNDigit(DataFlow):
+class SVHNDigit(RNGDataFlow):
     """
     SVHN Cropped Digit Dataset
     return img of 32x32x3, label of 0-9
@@ -33,7 +33,6 @@ class SVHNDigit(DataFlow):
         :param data_dir: a directory containing the original {train,test,extra}_32x32.mat
         """
         self.shuffle = shuffle
-        self.rng = get_rng(self)
 
         if name in SVHNDigit.Cache:
             self.X, self.Y = SVHNDigit.Cache[name]
@@ -54,9 +53,6 @@ class SVHNDigit(DataFlow):
     def size(self):
         return self.X.shape[0]
 
-    def reset_state(self):
-        self.rng = get_rng(self)
-
     def get_data(self):
         n = self.X.shape[0]
         idxs = np.arange(n)

tensorpack/dataflow/dftools.py

@@ -23,6 +23,7 @@ def dump_dataset_images(ds, dirname, max_count=None, index=0):
     mkdir_p(dirname)
     if max_count is None:
         max_count = sys.maxint
+    ds.reset_state()
     for i, dp in enumerate(ds.get_data()):
         if i % 100 == 0:
             print(i)
@@ -34,6 +35,7 @@ def dump_dataset_images(ds, dirname, max_count=None, index=0):
 def dataflow_to_process_queue(ds, size, nr_consumer):
     """
     Convert a `DataFlow` to a multiprocessing.Queue.
+    The dataflow will only be reset in the spawned process.
 
     :param ds: a `DataFlow`
     :param size: size of the queue
@@ -50,6 +52,7 @@ def dataflow_to_process_queue(ds, size, nr_consumer):
             self.q = q
 
         def run(self):
+            self.ds.reset_state()
             try:
                 for idx, dp in enumerate(self.ds.get_data()):
                     self.q.put((idx, dp))

tensorpack/dataflow/format.py

@@ -5,7 +5,7 @@
 from ..utils import logger, get_rng
 from ..utils.timer import timed_operation
 from ..utils.loadcaffe import get_caffe_pb
-from .base import DataFlow
+from .base import RNGDataFlow
 
 import random
 from tqdm import tqdm
@@ -31,7 +31,7 @@ else:
 Adapters for different data format.
 """
 
-class HDF5Data(DataFlow):
+class HDF5Data(RNGDataFlow):
     """
     Zip data from different paths in an HDF5 file. Will load all data into memory.
     """
@@ -55,19 +55,18 @@ class HDF5Data(DataFlow):
     def get_data(self):
         idxs = list(range(self._size))
         if self.shuffle:
-            random.shuffle(idxs)
+            self.rng.shuffle(idxs)
         for k in idxs:
             yield [dp[k] for dp in self.dps]
 
 
-class LMDBData(DataFlow):
+class LMDBData(RNGDataFlow):
     """ Read a lmdb and produce k,v pair """
     def __init__(self, lmdb_dir, shuffle=True):
         self._lmdb = lmdb.open(lmdb_dir, readonly=True, lock=False,
                 map_size=1099511627776 * 2, max_readers=100)
         self._txn = self._lmdb.begin()
         self._shuffle = shuffle
-        self.rng = get_rng(self)
         self._size = self._txn.stat()['entries']
         if shuffle:
             self.keys = self._txn.get('__keys__')
@@ -81,8 +80,8 @@ class LMDBData(DataFlow):
                             pbar.update()
 
     def reset_state(self):
+        super(LMDBData, self).reset_state()
         self._txn = self._lmdb.begin()
-        self.rng = get_rng(self)
 
     def size(self):
         return self._size
@@ -96,8 +95,8 @@ class LMDBData(DataFlow):
                     yield [k, v]
         else:
             s = self.size()
-            for i in range(s):
-                k = self.rng.choice(self.keys)
+            self.rng.shuffle(self.keys)
+            for k in self.keys:
                 v = self._txn.get(k)
                 yield [k, v]
 

tensorpack/dataflow/prefetch.py

@@ -35,7 +35,7 @@ class PrefetchProcess(multiprocessing.Process):
         self.queue = queue
 
     def run(self):
-        # reset RNG of ds so each process will produce different data
+        # reset all ds so each process will produce different data
         self.ds.reset_state()
         while True:
             for dp in self.ds.get_data():
@@ -73,6 +73,10 @@ class PrefetchData(ProxyDataFlow):
             dp = self.queue.get()
             yield dp
 
+    def reset_state(self):
+        # do nothing. all ds are reset once and only once in spawned processes
+        pass
+
 class PrefetchProcessZMQ(multiprocessing.Process):
     def __init__(self, ds, conn_name):
         """
@@ -134,6 +138,10 @@ class PrefetchDataZMQ(ProxyDataFlow):
             dp = loads(self.socket.recv(copy=False))
             yield dp
 
+    def reset_state(self):
+        # do nothing. all ds are reset once and only once in spawned processes
+        pass
+
     def __del__(self):
         # on exit, logger may not be functional anymore
         try:

tensorpack/dataflow/remote.py

@@ -23,6 +23,7 @@ def serve_data(ds, addr):
     socket.bind(addr)
     ds = RepeatedData(ds, -1)
     try:
+        ds.reset_state()
         logger.info("Serving data at {}".format(addr))
         while True:
             for dp in ds.get_data():

tensorpack/tfutils/modelutils.py

@@ -19,7 +19,7 @@ def describe_model():
             v.name, shape.as_list(), ele))
     size_mb = total * 4 / 1024.0**2
     msg.append("Total param={} ({:01f} MB assuming all float32)".format(total, size_mb))
-    logger.info("Model Params: {}".format('\n'.join(msg)))
+    logger.info("Model Parameters: {}".format('\n'.join(msg)))
 
 
 def get_shape_str(tensors):

tensorpack/train/trainer.py

@@ -40,6 +40,7 @@ class SimpleTrainer(Trainer):
         self.init_session_and_coord()
         describe_model()
         # create an infinte data producer
+        self.config.dataset.reset_state()
         self.data_producer = RepeatedData(self.config.dataset, -1).get_data()
         self.main_loop()
 
@@ -62,21 +63,22 @@ class SimpleTrainer(Trainer):
         return func
 
 class EnqueueThread(threading.Thread):
-    def __init__(self, trainer, queue, enqueue_op, raw_input_var):
+    def __init__(self, trainer):
         super(EnqueueThread, self).__init__()
         self.sess = trainer.sess
         self.coord = trainer.coord
         self.dataflow = RepeatedData(trainer.config.dataset, -1)
 
-        self.input_vars = raw_input_var
-        self.op = enqueue_op
-        self.queue = queue
+        self.input_vars = trainer.input_vars
+        self.queue = trainer.input_queue
+        self.op = self.queue.enqueue(self.input_vars)
         self.close_op = self.queue.close(cancel_pending_enqueues=True)
 
         self.size_op = self.queue.size()
         self.daemon = True
 
     def run(self):
+        self.dataflow.reset_state()
         with self.sess.as_default():
             try:
                 while True:
@@ -155,8 +157,7 @@ class QueueInputTrainer(Trainer):
 
     def _build_enque_thread(self):
         """ create a thread that keeps filling the queue """
-        enqueue_op = self.input_queue.enqueue(self.input_vars)
-        self.input_th = EnqueueThread(self, self.input_queue, enqueue_op, self.input_vars)
+        self.input_th = EnqueueThread(self)
         self.extra_threads_procs.append(self.input_th)
 
     def train(self):