hide deprecated methods from documentation

docs/conf.py

@@ -32,6 +32,7 @@ MOCK_MODULES = ['scipy', 'tabulate',
                 'gym', 'functools32']
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = mock.Mock(name=mod_name)
+sys.modules['cv2'].__version__ = '3.2.1'    # fake version
 
 import tensorpack
 
@@ -349,6 +350,18 @@ def process_signature(app, what, name, obj, options, signature,
     return signature, return_annotation
 
 def autodoc_skip_member(app, what, name, obj, skip, options):
+    for deprecate in [
+        'DistributedReplicatedTrainer',
+        'SingleCostFeedfreeTrainer',
+        'SimpleFeedfreeTrainer',
+        'FeedfreeTrainerBase',
+        'FeedfreeInferenceRunner',
+        'replace_get_variable',
+        'remap_get_variable',
+        'freeze_get_variable',
+        'ParamRestore']:
+        if deprecate in name:
+            return True
     if name in ['get_data', 'size', 'reset_state']:
         # skip these methods with empty docstring
         if not obj.__doc__ and inspect.isfunction(obj):

docs/tutorial/trainer.md

@@ -6,7 +6,7 @@ Tensorpack base trainer implements the logic of __running the iteration__.
 Users or derived trainers should implement __what the iteration is__.
 
 Most neural network training tasks are single-cost optimization.
-Tensorpack provides some trainer implementations for such tasks:
+Tensorpack provides some trainer implementations for such tasks.
 These trainers will build the graph based on the given `ModelDesc`, and minimizes `ModelDesc.cost`.
 
 Existing trainers were implemented with certain prefetch mechanism,

tensorpack/libinfo.py

 
+import os
+
+# issue#7378 may happen with custom opencv. It doesn't hurt to disable opencl
+os.environ['OPENCV_OPENCL_RUNTIME'] = ''
 try:
     # issue#1924 may happen on old systems
     import cv2  # noqa
+    if int(cv2.__version__.split('.')[0]) == 3:
+        cv2.ocl.setUseOpenCL(False)
 except ImportError:
     pass
-import os
-
-# issue#7378 may happen with custom opencv. It doesn't hurt to disable opencl
-os.environ['OPENCV_OPENCL_RUNTIME'] = ''
 
 os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'  # issue#9339
 os.environ['TF_AUTOTUNE_THRESHOLD'] = '3'   # use more warm-up

tensorpack/tfutils/scope_utils.py

@@ -5,8 +5,6 @@
 
 import tensorflow as tf
 import six
-from .common import get_tf_version_number
-from ..utils.develop import deprecated
 if six.PY2:
     import functools32 as functools
 else:
@@ -15,22 +13,6 @@ else:
 __all__ = ['auto_reuse_variable_scope']
 
 
-@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_default_graph().get_name_scope()
-    else:
-        g = tf.get_default_graph()
-        s = "RANDOM_STR_ABCDEFG"
-        unique = g.unique_name(s)
-        scope = unique[:-len(s)].rstrip('/')
-        return scope
-
-
 def auto_reuse_variable_scope(func):
     """
     A decorator which automatically reuse the current variable scope if the

tensorpack/train/distributed.py

@@ -13,7 +13,7 @@ from ..callbacks import RunOp
 from ..tfutils.sesscreate import NewSessionCreator
 from ..tfutils.common import get_global_step_var, get_op_tensor_name
 
-__all__ = ['DistributedReplicatedTrainer']
+__all__ = ['DistributedReplicatedTrainer', 'DistributedTrainerReplicated']
 
 
 class OverrideToLocalVariable(object):
@@ -34,7 +34,7 @@ class OverrideToLocalVariable(object):
         return getter(name, *args, **kwargs)
 
 
-class DistributedReplicatedTrainer(MultiGPUTrainerBase):
+class DistributedTrainerReplicated(MultiGPUTrainerBase):
     """
     Distributed replicated training.
     Each worker process builds the same model on one or more GPUs.
@@ -323,3 +323,8 @@ class DistributedReplicatedTrainer(MultiGPUTrainerBase):
     @property
     def vs_name_for_predictor(self):
         return "tower0"
+
+
+def DistributedReplicatedTrainer(*args, **kwargs):
+    logger.warn("DistributedReplicatedTrainer was renamed to DistributedTrainerReplicated!")
+    return DistributedTrainerReplicated(*args, **kwargs)