use global_step. replace step_per_epoch by steps_per_epoch

CHANGES.md

@@ -2,12 +2,13 @@
 ## Breaking API changes.
 
 tensorpack is still in early development, and API changes can happen.
-Usually the backward compatibilty is __preserved for several months__, with a deprecation warning,
+The backward compatibilty will be __preserved for several months__, with a deprecation warning,
 so you won't need to look at here very often.
 
 Here are a list of things that were changed, starting from an early version.
 TensorFlow itself also changes API and those are not listed here.
 
+* 2017/01/27. `TrainConfig(step_per_epoch)` was renamed to `steps_per_epoch`.
 * 2017/01/25. Argument order of `models.ConcatWith` is changed to follow the API change in
 	TensorFlow upstream. See [commit](https://github.com/ppwwyyxx/tensorpack/commit/2df3dcf401a99fe61c699ad719e95528872d3abe).
 * 2017/01/25. `TrainConfig(callbacks=)` now takes a list of `Callback` instances. See [commit](https://github.com/ppwwyyxx/tensorpack/commit/243e957fe6d62a0cfb5728bd77fb3e005d6603e4)

examples/mnist-convnet.py

@@ -141,7 +141,7 @@ def get_config():
         dataflow=dataset_train,  # the DataFlow instance for training
         optimizer=tf.train.AdamOptimizer(lr),
         callbacks=[
-            PeriodicTrigger(ModelSaver(), every_k_steps=100),   # save the model after every epoch
+            ModelSaver(),   # save the model after every epoch
             InferenceRunner(    # run inference(for validation) after every epoch
                 dataset_test,   # the DataFlow instance used for validation
                 # Calculate both the cost and the error for this DataFlow

tensorpack/callbacks/base.py

@@ -5,7 +5,7 @@
 import tensorflow as tf
 from abc import ABCMeta
 import six
-from ..tfutils.common import get_op_or_tensor_by_name
+from ..tfutils.common import get_op_or_tensor_by_name, get_global_step_value
 
 __all__ = ['Callback', 'PeriodicCallback', 'ProxyCallback', 'CallbackFactory']
 
@@ -15,8 +15,10 @@ class Callback(object):
     """ Base class for all callbacks
 
     Attributes:
-        epoch_num(int): the epoch that have completed the update.
-        local_step(int): the local step number in the current epoch.
+        epoch_num(int): the current epoch num, starting from 1.
+        local_step(int): the current local step number (1-based) in the current epoch.
+            which is also the number of steps that have finished.
+        global_step(int): the number of global steps that have finished.
         trainer(Trainer): the trainer.
         graph(tf.Graph): the graph.
 
@@ -33,6 +35,7 @@ class Callback(object):
         Args:
             trainer(Trainer): the trainer which calls the callback
         """
+        self._steps_per_epoch = trainer.config.steps_per_epoch
         self.trainer = trainer
         self.graph = tf.get_default_graph()
         with tf.name_scope(type(self).__name__):
@@ -45,6 +48,7 @@ class Callback(object):
         """
         Called right before the first iteration.
         """
+        self._starting_step = get_global_step_value()
         self._before_train()
 
     def _before_train(self):
@@ -111,7 +115,14 @@ class Callback(object):
 
     @property
     def local_step(self):
-        return self.trainer.local_step
+        # inside trainer, we're still in the 'local_step' loop, so the number is off by 1
+        return self.trainer.local_step + 1
+
+    @property
+    def global_step(self):
+        return self._starting_step + \
+                self._steps_per_epoch * (self.epoch_num - 1) + \
+                self.local_step
 
     def __str__(self):
         return type(self).__name__

tensorpack/callbacks/steps.py

@@ -56,7 +56,7 @@ class MaintainStepCounter(Callback):
                 gs_var, 1,
                 name=GLOBAL_STEP_INCR_OP_NAME)
             tf.mod(
-                self.gs_incr_var, self.trainer.config.step_per_epoch,
+                self.gs_incr_var, self.trainer.config.steps_per_epoch,
                 name=LOCAL_STEP_OP_NAME)
 
     def _before_train(self):
@@ -71,12 +71,12 @@ class MaintainStepCounter(Callback):
 class ProgressBar(Callback):
     """ A progress bar based on tqdm. Enabled by default. """
     def _before_train(self):
-        self._total = self.trainer.config.step_per_epoch
+        self._total = self.trainer.config.steps_per_epoch
         self._tqdm_args = get_tqdm_kwargs(leave=True)
 
     def _trigger_step(self, *args):
-        if self.local_step == 0:
+        if self.local_step == 1:
             self._bar = tqdm.trange(self._total, **self._tqdm_args)
         self._bar.update()
-        if self.local_step == self._total - 1:
+        if self.local_step == self._total:
             self._bar.close()

tensorpack/callbacks/trigger.py

@@ -76,5 +76,5 @@ class PeriodicTrigger(ProxyCallback):
     def _trigger_epoch(self, *args):
         if self._epoch_k is None:
             return
-        if self.local_step % self._epoch_k == 0:
+        if self.epoch_num % self._epoch_k == 0:
             self.cb.trigger()

tensorpack/train/base.py

@@ -163,7 +163,7 @@ class Trainer(object):
                         self.config.starting_epoch, self.config.max_epoch + 1):
                     logger.info("Start Epoch {} ...".format(self.epoch_num))
                     start_time = time.time()
-                    for self.local_step in range(self.config.step_per_epoch):
+                    for self.local_step in range(self.config.steps_per_epoch):
                         if self.coord.should_stop():
                             return
                         fetch_data = self.run_step()  # implemented by subclass

tensorpack/train/config.py

@@ -28,7 +28,7 @@ class TrainConfig(object):
                  callbacks=None, extra_callbacks=None,
                  session_config=get_default_sess_config(),
                  session_init=None,
-                 starting_epoch=1, step_per_epoch=None, max_epoch=99999,
+                 starting_epoch=1, steps_per_epoch=None, max_epoch=99999,
                  nr_tower=1, tower=None, predict_tower=[0],
                  **kwargs):
         """
@@ -48,7 +48,7 @@ class TrainConfig(object):
             session_config (tf.ConfigProto): the config used to instantiate the session.
             session_init (SessionInit): how to initialize variables of a session. Defaults to a new session.
             starting_epoch (int): The index of the first epoch.
-            step_per_epoch (int): the number of steps (defined by :meth:`Trainer.run_step`) to run in each epoch.
+            steps_per_epoch (int): the number of steps (defined by :meth:`Trainer.run_step`) to run in each epoch.
                 Defaults to the input data size.
             max_epoch (int): maximum number of epoch to run training.
             nr_tower (int): number of training towers.
@@ -103,21 +103,26 @@ class TrainConfig(object):
         self.session_init = session_init
         assert_type(self.session_init, SessionInit)
 
-        self.step_per_epoch = step_per_epoch
-        if self.step_per_epoch is None:
+        if steps_per_epoch is None:
+            steps_per_epoch = kwargs.pop('step_per_epoch', None)
+            if steps_per_epoch is not None:
+                # TODO deprecate @Mar.27
+                logger.warn("[Deprecated] Use steps_per_epoch instead of step_per_epoch!")
+        if steps_per_epoch is None:
             try:
                 if dataflow is not None:
-                    self.step_per_epoch = self.dataflow.size()
+                    steps_per_epoch = self.dataflow.size()
                 else:
-                    self.step_per_epoch = self.data.size()
+                    steps_per_epoch = self.data.size()
             except NotImplementedError:
-                logger.exception("You must set `step_per_epoch` if dataset.size() is not implemented.")
+                logger.exception("You must set `steps_per_epoch` if dataset.size() is not implemented.")
         else:
-            self.step_per_epoch = int(self.step_per_epoch)
+            steps_per_epoch = int(steps_per_epoch)
+        self.steps_per_epoch = steps_per_epoch
 
         self.starting_epoch = int(starting_epoch)
         self.max_epoch = int(max_epoch)
-        assert self.step_per_epoch >= 0 and self.max_epoch > 0
+        assert self.steps_per_epoch >= 0 and self.max_epoch > 0
 
         self.nr_tower = nr_tower
         if tower is not None: