use MapData to implement LMDBDataDecoder and LMDBDataPoint

examples/GAN/GAN.py

@@ -14,17 +14,21 @@ from tensorpack.dataflow import DataFlow
 
 
 class GANModelDesc(ModelDesc):
-    def collect_variables(self):
-        """Extract variables by prefix
+    def collect_variables(self, g_scope='gen', d_scope='discrim'):
+        """
+        Assign self.g_vars to the parameters under scope `g_scope`,
+        and same with self.d_vars.
         """
         all_vars = tf.trainable_variables()
-        self.g_vars = [v for v in all_vars if v.name.startswith('gen/')]
-        self.d_vars = [v for v in all_vars if v.name.startswith('discrim/')]
+        self.g_vars = [v for v in all_vars if v.name.startswith(g_scope + '/')]
+        self.d_vars = [v for v in all_vars if v.name.startswith(d_scope + '/')]
+        # TODO after TF1.0.0rc1
+        # self.g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, g_scope)
+        # self.d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, d_scope)
 
     def build_losses(self, logits_real, logits_fake):
         """D and G play two-player minimax game with value function V(G,D)
 
-
           min_G max _D V(D, G) = IE_{x ~ p_data} [log D(x)] + IE_{z ~ p_fake} [log (1 - D(G(z)))]
 
         Note, we swap 0, 1 labels as suggested in "Improving GANs".

tensorpack/callbacks/steps.py

@@ -86,12 +86,13 @@ class ProgressBar(Callback):
     def _before_train(self):
         self._total = self.trainer.config.steps_per_epoch
         self._tqdm_args = get_tqdm_kwargs(leave=True)
-        if self._names is not []:
+        if len(self._names):
             self._tqdm_args['bar_format'] = self._tqdm_args['bar_format'] + "{postfix} "
 
     def _trigger_step(self, *args):
         if self.local_step == 1:
             self._bar = tqdm.trange(self._total, **self._tqdm_args)
+        if len(self._names):
             self._bar.set_postfix(zip(self._tags, args))
         self._bar.update()
 

tensorpack/dataflow/format.py

@@ -13,6 +13,7 @@ from ..utils.loadcaffe import get_caffe_pb
 from ..utils.serialize import loads
 from ..utils.argtools import log_once
 from .base import RNGDataFlow
+from .common import MapData
 
 __all__ = ['HDF5Data', 'LMDBData', 'LMDBDataDecoder', 'LMDBDataPoint',
            'CaffeLMDB', 'SVMLightData']
@@ -133,55 +134,54 @@ class LMDBData(RNGDataFlow):
                 yield [k, v]
 
 
-class LMDBDataDecoder(LMDBData):
+class LMDBDataDecoder(MapData):
     """ Read a LMDB database and produce a decoded output."""
-    def __init__(self, lmdb_path, decoder, shuffle=True, keys=None):
+    def __init__(self, lmdb_data, decoder):
         """
         Args:
-            lmdb_path, shuffle, keys: same as :class:`LMDBData`.
+            lmdb_data: a :class:`LMDBData` instance.
             decoder (k,v -> dp | None): a function taking k, v and returning a datapoint,
                 or return None to discard.
         """
-        super(LMDBDataDecoder, self).__init__(lmdb_path, shuffle=shuffle, keys=keys)
-        self.decoder = decoder
-
-    def get_data(self):
-        for dp in super(LMDBDataDecoder, self).get_data():
-            v = self.decoder(dp[0], dp[1])
-            if v:
-                yield v
+        def f(dp):
+            return decoder(dp[0], dp[1])
+        super(LMDBDataDecoder, self).__init__(lmdb_data, f)
 
 
-class LMDBDataPoint(LMDBDataDecoder):
+class LMDBDataPoint(MapData):
     """ Read a LMDB file and produce deserialized values.
         This can work with :func:`tensorpack.dataflow.dftools.dump_dataflow_to_lmdb`. """
 
-    def __init__(self, lmdb_path, shuffle=True, keys=None):
+    def __init__(self, lmdb_data):
         """
         Args:
-            lmdb_path (str): a directory or a file.
-            shuffle (bool): shuffle the keys or not.
-            keys (list): list of keys for lmdb file or the key format `'{:0>8d}'`
+            lmdb_data: a :class:`LMDBData` instance.
         """
-        super(LMDBDataPoint, self).__init__(
-            lmdb_path, decoder=lambda k, v: loads(v), shuffle=shuffle, keys=keys)
+        def f(dp):
+            return loads(dp[1])
+        super(LMDBDataPoint, self).__init__(lmdb_data, f)
 
 
-class CaffeLMDB(LMDBDataDecoder):
+def CaffeLMDB(lmdb_path, shuffle=True, keys=None):
     """
     Read a Caffe LMDB file where each value contains a ``caffe.Datum`` protobuf.
     Produces datapoints of the format: [HWC image, label].
 
-    Example:
-        ds = CaffeLMDB("/tmp/validation", keys='{:0>8d}')
-    """
+    Note that Caffe LMDB format is not efficient: it stores serialized raw
+    arrays rather than JPEG images.
 
-    def __init__(self, lmdb_path, shuffle=True, keys=None):
-        """
     Args:
         lmdb_path, shuffle, keys: same as :class:`LMDBData`.
+
+    Returns:
+        a :class:`LMDBDataDecoder` instance.
+
+    Example:
+        ds = CaffeLMDB("/tmp/validation", keys='{:0>8d}')
     """
+
     cpb = get_caffe_pb()
+    lmdb_data = LMDBData(lmdb_path, shuffle, keys)
 
     def decoder(k, v):
         try:
@@ -193,9 +193,7 @@ class CaffeLMDB(LMDBDataDecoder):
             log_once("Cannot read key {}".format(k), 'warn')
             return None
         return [img.transpose(1, 2, 0), datum.label]
-
-        super(CaffeLMDB, self).__init__(
-            lmdb_path, decoder=decoder, shuffle=shuffle, keys=keys)
+    return LMDBDataDecoder(lmdb_data, decoder)
 
 
 class SVMLightData(RNGDataFlow):

tensorpack/models/batch_norm.py

@@ -121,14 +121,8 @@ def BatchNorm(x, use_local_stat=None, decay=0.9, epsilon=1e-5):
     * ``variance/EMA``: the moving average of variance.
 
     Note:
-        * In multi-tower training, only the first training tower maintains a moving average.
+        In multi-tower training, only the first training tower maintains a moving average.
         This is consistent with most frameworks.
-
-        * It automatically selects :meth:`BatchNormV1` or :meth:`BatchNormV2`
-          according to availability.
-
-        * This is a slightly faster but equivalent version of BatchNormV1. It uses
-          ``fused_batch_norm`` in training.
     """
     shape = x.get_shape().as_list()
     assert len(shape) in [2, 4]