clean some deprecations and rename a file

README.md

@@ -43,7 +43,7 @@ It's Yet Another TF wrapper, but different in:
 	+ Data-parallel distributed training is off-the-shelf to use. It is as slow as Google's [benchmark code](https://github.com/tensorflow/benchmarks).
 
 3. Focus on large datasets.
-	+ It's painful to read/preprocess data from TF. Use __DataFlow__ to efficiently process large datasets such as ImageNet in __pure Python__.
+	+ It's painful to read/preprocess data from TF. Use __DataFlow__ to load large datasets (e.g. ImageNet) in __pure Python__ with multi-process prefetch.
 	+ DataFlow has a unified interface, so you can compose and reuse them to perform complex preprocessing.
 
 4. Interface of extensible __Callbacks__.

tensorpack/predict/base.py

@@ -8,7 +8,6 @@ import tensorflow as tf
 import six
 
 from ..utils import logger
-from ..utils.develop import deprecated
 from ..utils.argtools import memoized
 from ..utils.naming import TOWER_FREEZE_KEYS
 from ..tfutils import get_tensors_by_names, TowerContext, get_op_tensor_name
@@ -16,7 +15,6 @@ from ..tfutils.collection import freeze_collection
 
 __all__ = ['PredictorBase', 'AsyncPredictorBase',
            'OnlinePredictor', 'OfflinePredictor',
-           'get_predict_func',
            'PredictorTowerBuilder',
            'build_prediction_graph',
            ]
@@ -147,14 +145,6 @@ class OfflinePredictor(OnlinePredictor):
                 input_tensors, output_tensors, config.return_input, sess)
 
 
-@deprecated("Use OfflinePredictor instead!", "2017-05-20")
-def get_predict_func(config):
-    """
-    Equivalent to ``OfflinePredictor(config)``.
-    """
-    return OfflinePredictor(config)
-
-
 class PredictorTowerBuilder(object):
     """
     A builder which caches the predictor tower it has built.

tensorpack/train/base.py

@@ -13,7 +13,6 @@ import tensorflow as tf
 from .predict import PredictorFactory
 from .config import TrainConfig
 from ..utils import logger
-from ..utils.develop import deprecated
 from ..callbacks import Callback, Callbacks, MaintainStepCounter
 from ..callbacks.monitor import Monitors, TrainingMonitor
 from ..tfutils import get_global_step_value
@@ -206,6 +205,7 @@ class Trainer(object):
                 self._callbacks.after_train()
                 self.hooked_sess.close()
 
+    # Predictor related methods:    TODO
     @property
     def vs_name_for_predictor(self):
         """
@@ -213,7 +213,6 @@ class Trainer(object):
         """
         return ""
 
-    # Predictor related methods:    TODO
     def get_predictor(self, input_names, output_names, tower=0):
         """
         Args:
@@ -236,11 +235,3 @@ class Trainer(object):
     def get_predictors(self, input_names, output_names, n):
         """ Return n predictors. """
         return [self.get_predictor(input_names, output_names, k) for k in range(n)]
-
-    @deprecated("Use get_predictor instead!", "2017-05-20")
-    def get_predict_func(self, input_names, output_names, tower=0):
-        return self.get_predictor(input_names, output_names, tower)
-
-    @deprecated("Use get_predictors instead!", "2017-05-20")
-    def get_predict_funcs(self, input_names, output_names, n):
-        return self.get_predictors(input_names, output_names, n)