add build_placeholder method in InputDesc

tensorpack/models/model_desc.py

@@ -4,40 +4,70 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 from abc import ABCMeta, abstractmethod
+from collections import namedtuple
 import tensorflow as tf
 import pickle
 import six
 
 from ..utils import logger
-from ..utils.naming import INPUTS_KEY
 from ..utils.argtools import memoized
 from .regularize import regularize_cost_from_collection
 
 __all__ = ['InputDesc', 'InputVar', 'ModelDesc']
 
 
-class InputDesc(object):
-    """ Store metadata about input placeholders. """
-    def __init__(self, type, shape, name, sparse=False):
-        """
-        Args:
-            type: tf type of the tensor.
-            shape (list):
-            name (str):
-            sparse (bool): whether to use ``tf.sparse_placeholder``.
-        """
-        self.type = type
-        self.shape = shape
-        self.name = name
-        self.sparse = sparse
+class InputDesc(
+        namedtuple('InputDescTuple', ['type', 'shape', 'name'])):
+    """
+    Metadata about an input entry point to the graph.
+    This metadata can be later used to build placeholders or other types of
+    input source.
+    """
 
     def dumps(self):
+        """
+        Returns:
+            str: serialized string
+        """
         return pickle.dumps(self)
 
     @staticmethod
     def loads(buf):
+        """
+        Args:
+            buf (str): serialized string
+
+        Returns:
+            InputDesc:
+        """
         return pickle.loads(buf)
 
+    def build_placeholder(self, prefix=''):
+        """
+        Build a tf.placeholder from the metadata, with an optional prefix.
+
+        Args:
+            prefix(str): the name of the placeholder will be ``prefix + self.name``
+
+        Returns:
+            tf.Tensor:
+        """
+        with tf.name_scope(None):   # clear any name scope it might get called in
+            return tf.placeholder(
+                self.type, shape=self.shape,
+                name=prefix + self.name)
+
+    # TODO cache results from build_placeholder, and skip it in serialization
+    @memoized
+    def build_placeholder_reuse(self):
+        """
+        Build a tf.placeholder from the metadata, or return an old one.
+
+        Returns:
+            tf.Tensor:
+        """
+        return self.build_placeholder()
+
 
 class InputVar(InputDesc):
     def __init__(self, *args, **kwargs):
@@ -70,17 +100,12 @@ class ModelDesc(object):
             list[tf.Tensor]: the list of built placeholders.
         """
         inputs = self._get_inputs()
-        for v in inputs:
-            tf.add_to_collection(INPUTS_KEY, v.dumps())
         ret = []
-        with tf.name_scope(None):   # clear any name scope it might get called in
-            for v in inputs:
-                placehdr_f = tf.placeholder if not v.sparse else tf.sparse_placeholder
-                ret.append(placehdr_f(
-                    v.type, shape=v.shape,
-                    name=prefix + v.name))
+        for v in inputs:
+            ret.append(v.build_placeholder(prefix))
         return ret
 
+    @memoized
     def get_inputs_desc(self):
         """
         Returns:
@@ -150,33 +175,3 @@ class ModelDesc(object):
 
     def _get_gradient_processor(self):
         return []
-
-
-class ModelFromMetaGraph(ModelDesc):
-    """
-    Load the exact TF graph from a saved meta_graph.
-    Only useful for inference.
-    """
-
-    # TODO this class may not be functional anymore. don't use
-
-    def __init__(self, filename):
-        """
-        Args:
-            filename (str): file name of the saved meta graph.
-        """
-        tf.train.import_meta_graph(filename)
-        all_coll = tf.get_default_graph().get_all_collection_keys()
-        for k in [INPUTS_KEY, tf.GraphKeys.TRAINABLE_VARIABLES,
-                  tf.GraphKeys.GLOBAL_VARIABLES]:
-            if k not in all_coll:
-                logger.warn("Collection {} not found in metagraph!".format(k))
-
-    def _get_inputs(self):
-        col = tf.get_collection(INPUTS_KEY)
-        col = [InputDesc.loads(v) for v in col]
-        return col
-
-    def _build_graph(self, _, __):
-        """ Do nothing. Graph was imported already """
-        pass

tensorpack/train/base.py

@@ -38,6 +38,7 @@ class Trainer(object):
         config (TrainConfig): the config used in this trainer.
         model (ModelDesc)
         sess (tf.Session): the current session in use.
+        hooked_sess (tf.MonitoredSession): the session with hooks.
         monitors (Monitors): the monitors. Callbacks can use it for logging.
 
         epoch_num (int): the number of epochs that have finished.
@@ -107,9 +108,6 @@ class Trainer(object):
         """ Abstract method: run one iteration. Subclass should define what is "iteration".
         """
 
-    def _trigger_epoch(self):
-        pass
-
     def setup(self):
         """
         Setup the trainer and be ready for the main loop.
@@ -192,7 +190,6 @@ class Trainer(object):
                         self._epoch_num, self.global_step, time.time() - start_time))
 
                     # trigger epoch outside the timing region.
-                    self._trigger_epoch()
                     self._callbacks.trigger_epoch()
                 logger.info("Training has finished!")
             except (StopTraining, tf.errors.OutOfRangeError):

tensorpack/utils/naming.py

@@ -16,9 +16,6 @@ PREDICT_TOWER = 'towerp'
 # extra variables to summarize during training in a moving-average way
 MOVING_SUMMARY_OPS_KEY = 'MOVING_SUMMARY_OPS'
 
-# metainfo for input tensors
-INPUTS_KEY = 'INPUTS_METAINFO'
-
 SUMMARY_BACKUP_KEYS = [tf.GraphKeys.SUMMARIES, MOVING_SUMMARY_OPS_KEY]
 
 TOWER_FREEZE_KEYS = SUMMARY_BACKUP_KEYS + [tf.GraphKeys.UPDATE_OPS]