deprecate _get_input_vars

tensorpack/callbacks/inference_runner.py

@@ -97,7 +97,7 @@ class InferenceRunner(Triggerable):
 
     def _find_input_tensors(self):
         if self.input_tensors is None:
-            input_vars = self.trainer.model.get_reuse_placehdrs()
+            input_vars = self.trainer.model.get_reused_placehdrs()
             # TODO even if it works here, sparse still is unavailable
             # because get_tensor_by_name doesn't work for sparse
 
@@ -198,7 +198,7 @@ class FeedfreeInferenceRunner(Triggerable):
         self._input_data._setup(self.trainer)
         # only 1 prediction tower will be used for inference
         self._input_tensors = self._input_data.get_input_tensors()
-        model_placehdrs = self.trainer.model.get_reuse_placehdrs()
+        model_placehdrs = self.trainer.model.get_reused_placehdrs()
         if self._input_names is not None:
             raise NotImplementedError("Random code. Not tested.")
             assert len(self._input_names) == len(self._input_tensors), \

tensorpack/dataflow/format.py

@@ -66,7 +66,7 @@ class LMDBData(RNGDataFlow):
         Args:
             lmdb_path (str): a directory or a file.
             shuffle (bool): shuffle the keys or not.
-            keys (list of str or str): list of str as the keys, used only when shuffle is True.
+            keys (list[str] or str): list of str as the keys, used only when shuffle is True.
                 It can also be a format string e.g. ``{:0>8d}`` which will be
                 formatted with the indices from 0 to *total_size - 1*.
 

tensorpack/models/model_desc.py

@@ -8,17 +8,17 @@ import tensorflow as tf
 import pickle
 import six
 
-from ..utils import logger, INPUT_VARS_KEY
+from ..utils import logger, INPUTS_KEY
 from ..tfutils.gradproc import CheckGradient
 from ..tfutils.summary import add_moving_summary
 from ..tfutils.tower import get_current_tower_context
 
-__all__ = ['ModelDesc', 'InputVar', 'ModelFromMetaGraph']
+__all__ = ['InputDesc', 'InputVar', 'ModelDesc', 'ModelFromMetaGraph']
 
+# TODO "variable" is not the right name to use for input here.
 
-# TODO "variable" is not a right name to use across this file.
 
-class InputVar(object):
+class InputDesc(object):
     """ Store metadata about input placeholders. """
     def __init__(self, type, shape, name, sparse=False):
         """
@@ -41,13 +41,16 @@ class InputVar(object):
         return pickle.loads(buf)
 
 
+InputVar = InputDesc
+
+
 @six.add_metaclass(ABCMeta)
 class ModelDesc(object):
     """ Base class for a model description """
 
-    def get_input_vars(self):
+    def get_reused_placehdrs(self):
         """
-        Create or return (if already created) raw input TF placeholder vars in the graph.
+        Create or return (if already created) raw input TF placeholders in the graph.
 
         Returns:
             list[tf.Tensor]: the list of input placeholders in the graph.
@@ -58,20 +61,21 @@ class ModelDesc(object):
         self.reuse_input_vars = ret
         return ret
 
-    # alias
-    get_reuse_placehdrs = get_input_vars
+    def get_input_vars(self):
+        logger.warn("[Deprecated] get_input_vars() was renamed to get_reused_placehdrs()!")
+        return self.get_reused_placehdrs()
 
     def build_placeholders(self, prefix=''):
         """
-        For each InputVar, create new placeholders with optional prefix and
+        For each input, create new placeholders with optional prefix and
         return them. Useful when building new towers.
 
         Returns:
             list[tf.Tensor]: the list of built placeholders.
         """
-        input_vars = self._get_input_vars()
+        input_vars = self._get_inputs()
         for v in input_vars:
-            tf.add_to_collection(INPUT_VARS_KEY, v.dumps())
+            tf.add_to_collection(INPUTS_KEY, v.dumps())
         ret = []
         for v in input_vars:
             placehdr_f = tf.placeholder if not v.sparse else tf.sparse_placeholder
@@ -80,20 +84,21 @@ class ModelDesc(object):
                 name=prefix + v.name))
         return ret
 
-    def get_input_vars_desc(self):
+    def get_inputs_desc(self):
         """
         Returns:
-            list[:class:`InputVar`]: list of the underlying :class:`InputVar`.
+            list[:class:`InputDesc`]: list of the underlying :class:`InputDesc`.
         """
-        return self._get_input_vars()
+        return self._get_inputs()
 
-    def _get_input_vars(self):  # keep backward compatibility
+    def _get_inputs(self):  # this is a better name than _get_input_vars
         """
-        :returns: a list of InputVar
+        :returns: a list of InputDesc
         """
-        return self._get_inputs()
+        logger.warn("[Deprecated] _get_input_vars() is renamed to _get_inputs()")
+        return self._get_input_vars()
 
-    def _get_inputs(self):  # this is a better name than _get_input_vars
+    def _get_input_vars(self):  # keep backward compatibility
         raise NotImplementedError()
 
     def build_graph(self, model_inputs):
@@ -102,7 +107,7 @@ class ModelDesc(object):
 
         Args:
             model_inputs (list[tf.Tensor]): a list of inputs, corresponding to
-                InputVars of this model.
+                InputDesc of this model.
         """
         self._build_graph(model_inputs)
 
@@ -169,14 +174,14 @@ class ModelFromMetaGraph(ModelDesc):
         """
         tf.train.import_meta_graph(filename)
         all_coll = tf.get_default_graph().get_all_collection_keys()
-        for k in [INPUT_VARS_KEY, tf.GraphKeys.TRAINABLE_VARIABLES,
+        for k in [INPUTS_KEY, tf.GraphKeys.TRAINABLE_VARIABLES,
                   tf.GraphKeys.GLOBAL_VARIABLES]:
             assert k in all_coll, \
                 "Collection {} not found in metagraph!".format(k)
 
     def _get_inputs(self):
-        col = tf.get_collection(INPUT_VARS_KEY)
-        col = [InputVar.loads(v) for v in col]
+        col = tf.get_collection(INPUTS_KEY)
+        col = [InputDesc.loads(v) for v in col]
         return col
 
     def _build_graph(self, _, __):

tensorpack/predict/base.py

@@ -123,7 +123,7 @@ class OfflinePredictor(OnlinePredictor):
         """
         self.graph = tf.Graph()
         with self.graph.as_default():
-            input_placehdrs = config.model.get_input_vars()
+            input_placehdrs = config.model.get_reused_placehdrs()
             with TowerContext('', False):
                 config.model.build_graph(input_placehdrs)
 

tensorpack/predict/config.py

@@ -47,7 +47,7 @@ class PredictConfig(object):
         self.input_names = input_names
         if self.input_names is None:
             # neither options is set, assume all inputs
-            raw_vars = self.model.get_input_vars_desc()
+            raw_vars = self.model.get_inputs_desc()
             self.input_names = [k.name for k in raw_vars]
         self.output_names = output_names
         assert_type(self.output_names, list)

tensorpack/predict/multigpu.py

@@ -28,7 +28,7 @@ class MultiTowerOfflinePredictor(OnlinePredictor):
         with self.graph.as_default():
             # TODO backup summary keys?
             def fn(_):
-                config.model.build_graph(config.model.get_input_vars())
+                config.model.build_graph(config.model.get_reused_placehdrs())
             build_prediction_graph(fn, towers)
 
             self.sess = tf.Session(config=config.session_config)

tensorpack/train/input_data.py

@@ -39,14 +39,14 @@ class FeedInput(InputData):
         return self.ds.size()
 
     def _setup(self, trainer):
-        self.input_vars = trainer.model.get_input_vars()
+        self.input_placehdrs = trainer.model.get_reused_placehdrs()
         rds = RepeatedData(self.ds, -1)
         rds.reset_state()
         self.data_producer = rds.get_data()
 
     def next_feed(self):
         data = next(self.data_producer)
-        feed = dict(zip(self.input_vars, data))
+        feed = dict(zip(self.input_placehdrs, data))
         self._last_feed = feed
         return feed
 
@@ -134,7 +134,7 @@ class QueueInput(FeedfreeInput):
         return self.ds.size()
 
     def _setup(self, trainer):
-        self.input_placehdrs = trainer.model.get_input_vars()
+        self.input_placehdrs = trainer.model.get_reused_placehdrs()
         assert len(self.input_placehdrs) > 0, \
             "QueueInput can only be used with input placeholders!"
         if self.queue is None:
@@ -182,7 +182,7 @@ class BatchQueueInput(FeedfreeInput):
         return self.ds.size() // self.batch_size
 
     def _setup(self, trainer):
-        self.input_placehdrs = trainer.model.get_input_vars()
+        self.input_placehdrs = trainer.model.get_reused_placehdrs()
         assert len(self.input_placehdrs) > 0, \
             "QueueInput can only be used with input placeholders!"
 
@@ -194,7 +194,7 @@ class BatchQueueInput(FeedfreeInput):
                 name=get_op_tensor_name(p.name)[0] + '-nobatch'))
 
         # dequeue_many requires fully-defined shapes
-        shape_err = "Use of BatchQueueInput requires input variables to have fully-defined "
+        shape_err = "Use of BatchQueueInput requires inputs to have fully-defined "
         "shapes except for the batch dimension"
         shapes = []
         for p in placehdrs_nobatch:
@@ -226,7 +226,7 @@ class BatchQueueInput(FeedfreeInput):
 
 
 class DummyConstantInput(FeedfreeInput):
-    """ Input some constant variables. Only for debugging performance issues """
+    """ Input some constant tensor. Only for debugging performance issues """
 
     def __init__(self, shapes):
         self.shapes = shapes
@@ -238,9 +238,10 @@ class DummyConstantInput(FeedfreeInput):
         ret = []
         for idx, p in enumerate(placehdrs):
             with tf.device('/gpu:0'):
-                ret.append(tf.get_variable('dummy-' + p.op.name,
-                                           shape=self.shapes[idx], dtype=p.dtype, trainable=False,
-                                           initializer=tf.constant_initializer()))
+                ret.append(tf.get_variable(
+                    'dummy-' + p.op.name, shape=self.shapes[idx],
+                    dtype=p.dtype, trainable=False,
+                    initializer=tf.constant_initializer()))
         return ret
 
 

tensorpack/train/trainer.py

@@ -47,7 +47,7 @@ class PredictorFactory(object):
                 freeze_collection(SUMMARY_BACKUP_KEYS), \
                 tf.variable_scope(tf.get_variable_scope(), reuse=True):
             def fn(_):
-                self.model.build_graph(self.model.get_input_vars())
+                self.model.build_graph(self.model.get_reused_placehdrs())
             build_prediction_graph(fn, self.towers)
         self.tower_built = True
 
@@ -79,7 +79,7 @@ class SimpleTrainer(Trainer):
     def _setup(self):
         self._input_method._setup(self)
         model = self.model
-        self.input_vars = model.get_input_vars()
+        self.input_vars = model.get_reused_placehdrs()
         with TowerContext('', is_training=True):
             model.build_graph(self.input_vars)
             cost_var = model.get_cost()

tensorpack/utils/naming.py

@@ -19,8 +19,8 @@ PREDICT_TOWER = 'towerp'
 # extra variables to summarize during training in a moving-average way
 MOVING_SUMMARY_VARS_KEY = 'MOVING_SUMMARY_VARIABLES'
 
-# placeholders for input variables
-INPUT_VARS_KEY = 'INPUT_VARIABLES'
+# metainfo for input tensors
+INPUTS_KEY = 'INPUTS_METAINFO'
 
 SUMMARY_BACKUP_KEYS = [tf.GraphKeys.SUMMARIES, MOVING_SUMMARY_VARS_KEY]