better variable name management

tensorpack/callbacks/common.py

@@ -8,6 +8,7 @@ import re
 
 from .base import Callback
 from ..utils import *
+from ..tfutils.varmanip import get_savename_from_varname
 
 __all__ = ['ModelSaver']
 
@@ -15,37 +16,42 @@ class ModelSaver(Callback):
     """
     Save the model to logger directory.
     """
-    def __init__(self, keep_recent=10, keep_freq=0.5):
+    def __init__(self, keep_recent=10, keep_freq=0.5,
+            var_collections=tf.GraphKeys.VARIABLES):
         """
         :param keep_recent: see `tf.train.Saver` documentation.
         :param keep_freq: see `tf.train.Saver` documentation.
         """
         self.keep_recent = keep_recent
         self.keep_freq = keep_freq
+        if not isinstance(var_collections, list):
+            var_collections = [var_collections]
+        self.var_collections = var_collections
 
     def _setup_graph(self):
+        vars = []
+        for key in self.var_collections:
+            vars.extend(tf.get_collection(key))
         self.path = os.path.join(logger.LOG_DIR, 'model')
         self.saver = tf.train.Saver(
-            var_list=ModelSaver._get_vars(),
+            var_list=ModelSaver._get_var_dict(vars),
             max_to_keep=self.keep_recent,
             keep_checkpoint_every_n_hours=self.keep_freq)
         self.meta_graph_written = False
 
     @staticmethod
-    def _get_vars():
-        vars = tf.all_variables()
+    def _get_var_dict(vars):
         var_dict = {}
         for v in vars:
-            name = v.name
-            if re.match('tower[p1-9]', name):
-                #logger.info("Skip {} when saving model.".format(name))
-                continue
-            if 'tower0/' in name:
-                new_name = name.replace('tower0/', '')
-                logger.info(
-                    "{} renamed to {} when saving model.".format(name, new_name))
-                name = new_name
-            var_dict[name] = v
+            name = get_savename_from_varname(v.name)
+            if name not in var_dict:
+                if name != v.name:
+                    logger.info(
+                        "{} renamed to {} when saving model.".format(v.name, name))
+                var_dict[name] = v
+            else:
+                logger.warn("Variable {} won't be saved \
+because {} will be saved".format(v.name, var_dict[name].name))
         return var_dict
 
     def _trigger_epoch(self):

tensorpack/callbacks/param.py

@@ -15,7 +15,7 @@ from ..tfutils import get_op_var_name
 
 __all__ = ['HyperParamSetter', 'HumanHyperParamSetter',
            'ScheduledHyperParamSetter',
-           'NonDecreasingStatMonitorParamSetter',
+           'StatMonitorParamSetter',
            'HyperParam', 'GraphVarParam', 'ObjAttrParam']
 
 class HyperParam(object):
@@ -176,14 +176,15 @@ class ScheduledHyperParamSetter(HyperParamSetter):
                 return v
         return None
 
-class NonDecreasingStatMonitorParamSetter(HyperParamSetter):
+class StatMonitorParamSetter(HyperParamSetter):
     """
     Set hyperparameter by a func, if a specific stat wasn't
-    monotonically decreasing $a$ times out of the last $b$ epochs
+    monotonically decreasing/increasing $a$ times out of the last $b$ epochs
     """
     def __init__(self, param, stat_name, value_func,
             last_k=5,
-            min_non_decreasing=2
+            min_non_decreasing=2,
+            reverse=False
             ):
         """
         Change param by `new_value = value_func(old_value)`,
@@ -192,6 +193,8 @@ class NonDecreasingStatMonitorParamSetter(HyperParamSetter):
 
         For example, if error wasn't decreasing, anneal the learning rate:
             NonDecreasingStatMonitorParamSetter('learning_rate', 'val-error', lambda x: x * 0.2)
+
+        If reverse==True, use 'increasing' instead of decreasing
         """
         super(NonDecreasingStatMonitorParamSetter, self).__init__(param)
         self.stat_name = stat_name
@@ -200,6 +203,11 @@ class NonDecreasingStatMonitorParamSetter(HyperParamSetter):
         self.min_non_decreasing = min_non_decreasing
         self.last_changed_epoch = 0
 
+        if not reverse:
+            self.less_than = lambda x, y: x <= y
+        else:
+            self.less_than = lambda x, y: x >= y
+
     def _get_value_to_set(self):
         holder = self.trainer.stat_holder
         hist = holder.get_stat_history(self.stat_name)
@@ -209,10 +217,10 @@ class NonDecreasingStatMonitorParamSetter(HyperParamSetter):
         hist = hist[-self.last_k-1:]    # len==last_k+1
         cnt = 0
         for k in range(self.last_k):
-            if hist[k] <= hist[k+1]:
+            if self.less_than(hist[k], hist[k+1]):
                 cnt += 1
         if cnt >= self.min_non_decreasing \
-                and hist[-1] >= hist[0]:
+                and self.less_than(hist[0], hist[-1]):
             return self.value_func(self.get_current_value())
         return None
 

tensorpack/tfutils/varmanip.py

@@ -36,7 +36,6 @@ def get_savename_from_varname(
         name = savename_prefix + '/' + name
     return name
 
-
 class SessionUpdate(object):
     """ Update the variables in a session """
     def __init__(self, sess, vars_to_update):
@@ -87,7 +86,10 @@ def dump_session_params(path):
     var.extend(tf.get_collection(EXTRA_SAVE_VARS_KEY))
     result = {}
     for v in var:
-        name = v.name.replace(":0", "")
+        name = get_savename_from_varname(v.name)
+        if name in result:
+            logger.info("Variable {} would be stored instead of another with \
+the same name".format(v.name))
         result[name] = v.eval()
     logger.info("Variables to save to {}:".format(path))
     logger.info(str(result.keys()))

tensorpack/utils/naming.py

@@ -11,7 +11,7 @@ MOVING_SUMMARY_VARS_KEY = 'MOVING_SUMMARY_VARIABLES'
 # placeholders for input variables
 INPUT_VARS_KEY = 'INPUT_VARIABLES'
 
-# variables that need to be saved, apart from trainable variables
+# variables that need to be saved for inference, apart from trainable variables
 EXTRA_SAVE_VARS_KEY = 'EXTRA_SAVE_VARIABLES'
 
 import tensorflow as tf