get_global_step -> get_global_step_value to avoid confusion with tf.train.get_global_step

tensorpack/callbacks/saver.py

@@ -9,7 +9,6 @@ import shutil
 from .base import Callback
 from ..utils import logger
 from ..tfutils.varmanip import get_savename_from_varname
-from ..tfutils import get_global_step
 
 __all__ = ['ModelSaver', 'MinSaver', 'MaxSaver']
 
@@ -76,7 +75,7 @@ due to an alternative in a different tower".format(v.name, var_dict[name].name))
             self.saver.save(
                 tf.get_default_session(),
                 self.path,
-                global_step=get_global_step(),
+                global_step=tf.train.get_global_step(),
                 write_meta_graph=False)
             logger.info("Model saved to %s" % tf.train.get_checkpoint_state(self.checkpoint_dir).model_checkpoint_path)
         except (OSError, IOError):   # disk error sometimes.. just ignore it

tensorpack/callbacks/stats.py

@@ -8,7 +8,7 @@ import json
 
 from .base import Callback
 from ..utils import logger
-from ..tfutils.common import get_global_step
+from ..tfutils.common import get_global_step_value
 
 __all__ = ['StatHolder', 'StatPrinter', 'SendStat']
 
@@ -134,7 +134,7 @@ class StatPrinter(Callback):
 
     def _trigger_epoch(self):
         # by default, add this two stat
-        self._stat_holder.add_stat('global_step', get_global_step())
+        self._stat_holder.add_stat('global_step', get_global_step_value())
         self._stat_holder.finalize()
         self._stat_holder.add_stat('epoch_num', self.epoch_num + 1)
 

tensorpack/tfutils/common.py

@@ -10,7 +10,7 @@ import six
 from contextlib import contextmanager
 
 __all__ = ['get_default_sess_config',
-           'get_global_step',
+           'get_global_step_value',
            'get_global_step_var',
            'get_op_tensor_name',
            'get_tensors_by_names',
@@ -56,16 +56,16 @@ def get_global_step_var():
         assert scope.name == '', \
             "Creating global_step_var under a variable scope would cause problems!"
         with tf.variable_scope(scope, reuse=False):
-            var = tf.get_variable(GLOBAL_STEP_OP_NAME, shape=[],
-                                  initializer=tf.constant_initializer(dtype=tf.int32),
+            var = tf.get_variable(GLOBAL_STEP_OP_NAME,
+                                  initializer=0,
                                   trainable=False, dtype=tf.int32)
         return var
 
 
-def get_global_step():
+def get_global_step_value():
     """
     Returns:
-        float: global_step value in current graph and session"""
+        int: global_step value in current graph and session"""
     return tf.train.global_step(
         tf.get_default_session(),
         get_global_step_var())

tensorpack/tfutils/symbolic_functions.py

@@ -140,8 +140,7 @@ def get_scalar_var(name, init_value, summary=False, trainable=False):
     Returns:
         tf.Variable: the variable
     """
-    ret = tf.get_variable(name, shape=[],
-                          initializer=tf.constant_initializer(init_value),
+    ret = tf.get_variable(name, initializer=init_value,
                           trainable=trainable)
     if summary:
         # this is recognized in callbacks.StatHolder

tensorpack/train/base.py

@@ -13,7 +13,7 @@ from .config import TrainConfig
 from ..utils import logger
 from ..utils.timer import timed_operation
 from ..callbacks import StatHolder
-from ..tfutils import get_global_step, get_global_step_var
+from ..tfutils import get_global_step_var, get_global_step_value
 from ..tfutils.modelutils import describe_model
 from ..tfutils.summary import create_scalar_summary
 
@@ -121,7 +121,7 @@ class Trainer(object):
                 if val.tag.endswith(suffix):
                     val.tag = val.tag[:-len(suffix)]
                 self.stat_holder.add_stat(val.tag, val.simple_value)
-        self.summary_writer.add_summary(summary, get_global_step())
+        self.summary_writer.add_summary(summary, get_global_step_value())
 
     def add_scalar_summary(self, name, val):
         """
@@ -144,7 +144,7 @@ class Trainer(object):
         """
         self._setup()
         describe_model()
-        get_global_step_var()
+        get_global_step_var()   # ensure such var exists
         # some final operations that might modify the graph
         logger.info("Setup callbacks ...")
         self.config.callbacks.setup_graph(weakref.proxy(self))
@@ -178,12 +178,12 @@ class Trainer(object):
         with self.sess.as_default():
             try:
                 callbacks.before_train()
-                logger.info("Start training with global_step={}".format(get_global_step()))
+                logger.info("Start training with global_step={}".format(get_global_step_value()))
                 for self.epoch_num in range(
                         self.config.starting_epoch, self.config.max_epoch + 1):
                     with timed_operation(
                         'Epoch {} (global_step {})'.format(
-                            self.epoch_num, get_global_step() + self.config.step_per_epoch),
+                            self.epoch_num, get_global_step_value() + self.config.step_per_epoch),
                             log_start=True):
                         for self.step_num in range(self.config.step_per_epoch):
                             if self.coord.should_stop():

tensorpack/utils/naming.py

@@ -3,6 +3,7 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import tensorflow as tf
+# this is also the name used by tf.train.get_global_step
 GLOBAL_STEP_OP_NAME = 'global_step'
 GLOBAL_STEP_VAR_NAME = 'global_step:0'