some improvements in logging and fix #118.

tensorpack/__init__.py

@@ -15,6 +15,3 @@ from tensorpack.predict import *
 
 if int(numpy.__version__.split('.')[1]) < 9:
     logger.warn("Numpy < 1.9 could be extremely slow on some tasks.")
-
-if get_tf_version() < 10:
-    logger.error("tensorpack requires TensorFlow >= 0.10")

tensorpack/callbacks/base.py

@@ -168,7 +168,6 @@ class PeriodicCallback(ProxyCallback):
 
     def _trigger_epoch(self):
         if self.epoch_num % self.period == 0:
-            self.cb.epoch_num = self.epoch_num - 1
             self.cb.trigger_epoch()
 
     def __str__(self):

tensorpack/callbacks/concurrency.py

@@ -27,6 +27,6 @@ class StartProcOrThread(Callback):
 
     def _before_train(self):
         logger.info("Starting " +
-                    ', '.join([k.name for k in self._procs_threads]))
+                    ', '.join([k.name for k in self._procs_threads]) + ' ...')
         # avoid sigint get handled by other processes
         start_proc_mask_signal(self._procs_threads)

tensorpack/tfutils/modelutils.py

@@ -13,6 +13,9 @@ __all__ = ['describe_model', 'get_shape_str']
 def describe_model():
     """ Print a description of the current model parameters """
     train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
+    if len(train_vars) == 0:
+        logger.info("No trainable variables in the graph!")
+        return
     msg = [""]
     total = 0
     for v in train_vars:

tensorpack/train/base.py

@@ -68,25 +68,14 @@ class Trainer(object):
     def run_step(self):
         """ Abstract method. Run one iteration. """
 
-    def get_predict_func(self, input_names, output_names):
+    def get_extra_fetches(self):
         """
-        Args:
-            input_names (list), output_names(list): list of names
-
         Returns:
-            an OnlinePredictor
-        """
-        raise NotImplementedError()
+            list: list of tensors/ops to fetch in each step.
 
-    def get_predict_funcs(self, input_names, output_names, n):
-        """ Return n predictors.
-            Can be overwritten by subclasses to exploit more
-            parallelism among predictors.
+        This function should only get called after :meth:`setup()` has finished.
         """
-        if len(self.config.predict_tower) > 1:
-            logger.warn(
-                "[Speed] Have set multiple predict_tower, but only have naive `get_predict_funcs` implementation")
-        return [self.get_predict_func(input_names, output_names) for k in range(n)]
+        return self._extra_fetches
 
     def trigger_epoch(self):
         """
@@ -129,28 +118,21 @@ class Trainer(object):
         """
         self.add_summary(create_scalar_summary(name, val))
 
-    def get_extra_fetches(self):
-        """
-        Returns:
-            list: list of tensors/ops to fetch in each step.
-
-        This function should only get called after :meth:`setup()` has finished.
-        """
-        return self._extra_fetches
-
     def setup(self):
         """
         Setup the trainer and be ready for the main loop.
         """
-        self._setup()
+        if not hasattr(logger, 'LOG_DIR'):
+            raise RuntimeError("logger directory wasn't set!")
+
+        self._setup()   # subclass will setup the graph
+
         describe_model()
         # some final operations that might modify the graph
         logger.info("Setup callbacks ...")
         self.config.callbacks.setup_graph(weakref.proxy(self))
         self._extra_fetches = self.config.callbacks.extra_fetches()
 
-        if not hasattr(logger, 'LOG_DIR'):
-            raise RuntimeError("logger directory wasn't set!")
         self.summary_writer = tf.summary.FileWriter(logger.LOG_DIR, graph=self.sess.graph)
         self.summary_op = tf.summary.merge_all()
         # create an empty StatHolder
@@ -206,3 +188,23 @@ class Trainer(object):
                 self.coord.request_stop()
                 self.summary_writer.close()
                 self.sess.close()
+
+    def get_predict_func(self, input_names, output_names):
+        """
+        Args:
+            input_names (list), output_names(list): list of names
+
+        Returns:
+            an OnlinePredictor
+        """
+        raise NotImplementedError()
+
+    def get_predict_funcs(self, input_names, output_names, n):
+        """ Return n predictors.
+            Can be overwritten by subclasses to exploit more
+            parallelism among predictors.
+        """
+        if len(self.config.predict_tower) > 1:
+            logger.warn(
+                "[Speed] Have set multiple predict_tower, but only have naive `get_predict_funcs` implementation")
+        return [self.get_predict_func(input_names, output_names) for k in range(n)]