remove the 'towerp' constant completely. Use custom predict tower names everywhere.

examples/PennTreebank/PTB-LSTM.py

@@ -146,7 +146,7 @@ def get_config():
             RunOp(lambda: M.reset_lstm_state()),
             InferenceRunner(
                 test_data,
-                [ScalarStats(['cost'], prefix='test')], prefix='test'),
+                [ScalarStats(['cost'], prefix='test')], tower_name='InferenceTowerTest'),
             RunOp(lambda: M.reset_lstm_state()),
             CallbackFactory(
                 trigger_epoch=lambda self:

tensorpack/callbacks/inference_runner.py

@@ -58,12 +58,12 @@ def summary_inferencer(trainer, infs):
 @six.add_metaclass(ABCMeta)
 class InferenceRunnerBase(Callback):
     """ Base methods for inference runner"""
-    def __init__(self, input, infs, prefix='', extra_hooks=None):
+    def __init__(self, input, infs, tower_name='InferenceTower', extra_hooks=None, prefix=None):
         """
         Args:
             input (InputSource): the input to use. Must have ``size()``.
             infs (list[Inferencer]): list of :class:`Inferencer` to run.
-            prefix(str): an prefix used to build the tower. Must be set
+            tower_name(str): name scope to build the tower. Must be set
                 differently if more than one :class:`InferenceRunner` are used.
             extra_hooks (list[SessionRunHook]): extra :class:`SessionRunHook` to run with the evaluation.
         """
@@ -79,7 +79,9 @@ class InferenceRunnerBase(Callback):
             self._size = input.size()
         except NotImplementedError:
             raise ValueError("Input used in InferenceRunner must have a size!")
-        self._prefix = prefix
+        self._tower_name = tower_name
+        if prefix is not None:
+            self._tower_name = 'InferenceTower' + prefix
 
         if extra_hooks is None:
             extra_hooks = []
@@ -90,14 +92,9 @@ class InferenceRunnerBase(Callback):
         tower_id = self.trainer.config.predict_tower[0]
         device = '/gpu:{}'.format(tower_id) if tower_id >= 0 else '/cpu:0'
 
-        # TODO this cannot be InferenceRunner? fix it. check name
-        tower_name = 'InferenceRunnerTower'
-        if self._prefix:
-            tower_name += '_' + self._prefix
-
         self._input_source.setup(self.trainer.model.get_inputs_desc())
         with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-            self._tower_handle = self.trainer.predictor_factory.build(tower_name, device, self._input_source)
+            self._tower_handle = self.trainer.predictor_factory.build(self._tower_name, device, self._input_source)
 
         self._hooks = [self._build_hook(inf) for inf in self.infs]
         cbs = self._input_source.get_callbacks()
@@ -127,7 +124,7 @@ class InferenceRunner(InferenceRunnerBase):
     A callback that runs a list of :class:`Inferencer` on some :class:`InputSource`.
     """
 
-    def __init__(self, input, infs, prefix='', extra_hooks=None):
+    def __init__(self, input, infs, tower_name='InferenceTower', extra_hooks=None):
         """
         Args:
             input (InputSource or DataFlow): The :class:`InputSource` to run
@@ -140,7 +137,7 @@ class InferenceRunner(InferenceRunnerBase):
         if isinstance(input, FeedfreeInput):    # TODO support other input
             assert isinstance(input, TensorInput), "InferenceRunner only accepts TensorInput or FeedInput!"
         super(InferenceRunner, self).__init__(
-            input, infs, prefix=prefix, extra_hooks=extra_hooks)
+            input, infs, tower_name=tower_name, extra_hooks=extra_hooks)
 
     def _build_hook(self, inf):
         out_names = inf.get_output_tensors()
@@ -153,7 +150,6 @@ def FeedfreeInferenceRunner(*args, **kwargs):
     return InferenceRunner(*args, **kwargs)
 
 
-# TODO some scripts to test
 class DataParallelInferenceRunner(InferenceRunnerBase):
     """
     Inference by feeding datapoints in a data-parallel way to multiple GPUs.
@@ -166,7 +162,7 @@ class DataParallelInferenceRunner(InferenceRunnerBase):
             input (DataParallelFeedInput or DataFlow)
             gpus (list[int]): list of GPU id
         """
-        self._tower_names = ['InferenceRunner{}'.format(k) for k in range(len(gpus))]
+        self._tower_names = ['InferenceTower{}'.format(k) for k in range(len(gpus))]
         if isinstance(input, DataFlow):
             input = DataParallelFeedInput(input, self._tower_names)
         assert isinstance(input, DataParallelFeedInput), input

tensorpack/callbacks/monitor.py

@@ -117,7 +117,7 @@ class Monitors(TrainingMonitor):
         for val in summary.value:
             if val.WhichOneof('value') == 'simple_value':
                 val.tag = re.sub('tower[p0-9]+/', '', val.tag)   # TODO move to subclasses
-                suffix = '-summary'  # issue#6150
+                suffix = '-summary'  # tensorflow#6150, tensorboard#59
                 if val.tag.endswith(suffix):
                     val.tag = val.tag[:-len(suffix)]
                 self._dispatch(lambda m: m.put_scalar(val.tag, val.simple_value))

tensorpack/tfutils/distributions.py

@@ -21,7 +21,7 @@ def class_scope(func):
 
     def get_name_scope_name():
         if get_tf_version_number() > 1.2:
-            return tf.get_name_scope().name
+            return tf.get_default_graph().get_name_scope()
         else:
             g = tf.get_default_graph()
             s = "RANDOM_STR_ABCDEFG"

tensorpack/tfutils/scope_utils.py

@@ -15,14 +15,14 @@ else:
 __all__ = ['get_name_scope_name', 'auto_reuse_variable_scope']
 
 
-@deprecated("Use tf.get_name_scope() (available since 1.2.1).")
+@deprecated("Use tf.get_default_graph().get_name_scope() (available since 1.2.1).")
 def get_name_scope_name():
     """
     Returns:
         str: the name of the current name scope, without the ending '/'.
     """
     if get_tf_version_number() > 1.2:
-        return tf.get_name_scope().name
+        return tf.get_default_graph().get_name_scope()
     else:
         g = tf.get_default_graph()
         s = "RANDOM_STR_ABCDEFG"

tensorpack/tfutils/tower.py

@@ -4,6 +4,7 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
+from .common import get_tf_version_number
 
 __all__ = ['get_current_tower_context', 'TowerContext']
 
@@ -16,8 +17,7 @@ class TowerContext(object):
     def __init__(self, tower_name, is_training=None, index=0, vs_name=''):
         """
         Args:
-            tower_name (str): The name scope of the tower. Currently used
-                values are like: 'tower0', 'towerp0', or ''
+            tower_name (str): The name scope of the tower.
             is_training (bool): if None, automatically determine from tower_name.
             index (int): index of this tower.
             vs_name (str): Open a variable scope with this name, if given.
@@ -106,6 +106,12 @@ class TowerContext(object):
         for c in self._ctxs:
             c.__enter__()
 
+        if get_tf_version_number() >= 1.2:
+            ns = tf.get_default_graph().get_name_scope()
+            assert ns == self._name, \
+                "Name conflict: name_scope inside tower '{}' becomes '{}'!".format(self._name, ns) \
+                + " You may need a different name for the tower!"
+
     def __exit__(self, exc_type, exc_val, exc_tb):
         global _CurrentTowerContext
         _CurrentTowerContext = None

tensorpack/tfutils/varmanip.py

@@ -9,7 +9,6 @@ import tensorflow as tf
 from collections import defaultdict
 import numpy as np
 from ..utils import logger
-from ..utils.naming import PREDICT_TOWER
 from .common import get_op_tensor_name
 
 __all__ = ['SessionUpdate', 'dump_session_params', 'dump_chkpt_vars',
@@ -29,11 +28,6 @@ def get_savename_from_varname(
         str: the name used to save the variable
     """
     name = varname
-    # TODO PREDICT_TOWER is not used anymore
-    if PREDICT_TOWER in name:
-        logger.error("No variable under '{}' name scope should be saved!".format(PREDICT_TOWER))
-        # don't overwrite anything in the current prediction graph
-        return None
     if varname_prefix is not None \
             and name.startswith(varname_prefix):
         name = name[len(varname_prefix) + 1:]

tensorpack/utils/naming.py

@@ -10,9 +10,6 @@ GLOBAL_STEP_INCR_VAR_NAME = 'global_step_incr:0'
 LOCAL_STEP_OP_NAME = 'local_step'
 LOCAL_STEP_VAR_NAME = 'local_step:0'
 
-# prefix of predict tower
-PREDICT_TOWER = 'towerp'
-
 # extra variables to summarize during training in a moving-average way
 MOVING_SUMMARY_OPS_KEY = 'MOVING_SUMMARY_OPS'