Deprecate huber_loss used in DQN

docs/conf.py

@@ -369,7 +369,9 @@ def autodoc_skip_member(app, what, name, obj, skip, options):
         'GaussianDeform',
         'dump_chkpt_vars',
         'VisualQA',
-        'ParamRestore']:
+        'ParamRestore',
+        'huber_loss'
+        ]:
         return True
     if name in ['get_data', 'size', 'reset_state']:
         # skip these methods with empty docstring

examples/DeepQNetwork/DQNModel.py

@@ -5,12 +5,14 @@
 
 import abc
 import tensorflow as tf
+import tensorpack
 from tensorpack import ModelDesc, InputDesc
 from tensorpack.utils import logger
 from tensorpack.tfutils import (
     collection, summary, get_current_tower_context, optimizer, gradproc)
 from tensorpack.tfutils import symbolic_functions as symbf
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
+assert tensorpack.tfutils.common.get_tf_version_number() >= 1.2
 
 
 class Model(ModelDesc):
@@ -72,14 +74,14 @@ class Model(ModelDesc):
 
         target = reward + (1.0 - tf.cast(isOver, tf.float32)) * self.gamma * tf.stop_gradient(best_v)
 
-        self.cost = tf.reduce_mean(symbf.huber_loss(
-                                   target - pred_action_value), name='cost')
+        self.cost = tf.losses.huber_loss(
+            target, pred_action_value, reduction=tf.losses.Reduction.MEAN)
         summary.add_param_summary(('conv.*/W', ['histogram', 'rms']),
                                   ('fc.*/W', ['histogram', 'rms']))   # monitor all W
         summary.add_moving_summary(self.cost)
 
     def _get_optimizer(self):
-        lr = symbf.get_scalar_var('learning_rate', 1e-3, summary=True)
+        lr = tf.get_variable('learning_rate', initializer=1e-3, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
         return optimizer.apply_grad_processors(
             opt, [gradproc.GlobalNormClip(10), gradproc.SummaryGradient()])

tensorpack/RL/__init__.py

@@ -9,6 +9,11 @@ import os.path
 __all__ = []
 
 
+"""
+This module should be removed in the future.
+"""
+
+
 def _global_import(name):
     p = __import__(name, globals(), locals(), level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)

tensorpack/dataflow/base.py

@@ -23,7 +23,9 @@ class DataFlowTerminated(BaseException):
 
 class DataFlowReentrantGuard(object):
     """
-    A tool to enforce thread-level non-reentrancy on DataFlow.
+    A tool to enforce non-reentrancy.
+    Mostly used on DataFlow whose :meth:`get_data` is stateful,
+    so that multiple instances of the iterator cannot co-exist.
     """
     def __init__(self):
         self._lock = threading.Lock()
@@ -31,7 +33,7 @@ class DataFlowReentrantGuard(object):
     def __enter__(self):
         self._succ = self._lock.acquire(False)
         if not self._succ:
-            raise threading.ThreadError("This DataFlow cannot be reused under different threads!")
+            raise threading.ThreadError("This DataFlow is not reentrant!")
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         self._lock.release()

tensorpack/tfutils/symbolic_functions.py

@@ -6,6 +6,8 @@ import tensorflow as tf
 from contextlib import contextmanager
 import numpy as np
 
+from ..utils.develop import deprecated
+
 # __all__ = ['get_scalar_var']
 
 
@@ -124,6 +126,7 @@ def rms(x, name=None):
     return tf.sqrt(tf.reduce_mean(tf.square(x)), name=name)
 
 
+@deprecated("Please use tf.losses.huber_loss instead!")
 def huber_loss(x, delta=1, name='huber_loss'):
     r"""
     Huber loss of x.