bug fix

tensorpack/callbacks/concurrency.py

@@ -20,7 +20,7 @@ class StartProcOrThread(Callback):
         self._procs_threads = procs_threads
 
     def _before_train(self):
-        logger.info("Starting threads & procs: " +  \
-                ' .'.join([k.name for k in self._procs_threads]))
+        logger.info("Starting " +  \
+                ', '.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/gradproc.py

@@ -116,13 +116,15 @@ class ScaleGradient(MapGradient):
     """
     Scale certain gradient by a multiplier
     """
-    def __init__(self, multipliers):
+    def __init__(self, multipliers, log=True):
         """
         :param multipliers: list of (regex, float)
+        :param log: whether to do logging or not
         """
         if not isinstance(multipliers, list):
             multipliers = [multipliers]
         self.multipliers = multipliers
+        self._log = log
         super(ScaleGradient, self).__init__(self._mapper)
 
     def _mapper(self, grad, var):
@@ -133,7 +135,8 @@ class ScaleGradient(MapGradient):
                 regex = regex + '$'
 
             if re.match(regex, varname):
-                logger.info("Apply lr multiplier {} for {}".format(val, varname))
+                if self._log:
+                    logger.info("Apply lr multiplier {} for {}".format(val, varname))
                 if val != 0:    # skip zero to speed up
                     return grad * val
                 else:

tensorpack/train/base.py

@@ -15,6 +15,7 @@ from ..utils import logger, get_tqdm_kwargs
 from ..utils.timer import timed_operation
 from ..callbacks import StatHolder
 from ..tfutils import get_global_step, get_global_step_var
+from ..tfutils.modelutils import describe_model
 from ..tfutils.summary import create_summary
 
 __all__ = ['Trainer', 'StopTraining']
@@ -94,6 +95,7 @@ class Trainer(object):
 
     def setup(self):
         self._setup()
+        describe_model()
         # some final operations that might modify the graph
         logger.info("Setup callbacks ...")
         self.config.callbacks.setup_graph(weakref.proxy(self))

tensorpack/train/multigpu.py

@@ -10,11 +10,10 @@ from six.moves import zip, range
 from ..utils import logger
 from ..utils.naming import *
 from ..utils.concurrency import LoopThread
-from ..tfutils.summary import summary_moving_average
-from ..tfutils.modelutils import describe_model
+from ..tfutils.summary import summary_moving_average, add_moving_summary
 from ..tfutils import (backup_collection, restore_collection,
         get_global_step_var, TowerContext)
-from ..tfutils.gradproc import apply_grad_processors
+from ..tfutils.gradproc import apply_grad_processors, ScaleGradient
 
 from .trainer import QueueInputTrainer
 
@@ -89,7 +88,6 @@ class SyncMultiGPUTrainer(MultiGPUTrainer):
         self.train_op = tf.group(
             self.config.optimizer.apply_gradients(grads, get_global_step_var()),
             summary_moving_average(), name='train_op')
-        describe_model()
         # [debug]: do nothing in training
         #self.train_op = self.dequed_inputs[0][0] + self.dequed_inputs[1][0]
 
@@ -101,7 +99,8 @@ class AsyncMultiGPUTrainer(MultiGPUTrainer):
         gradprocs = self.model.get_gradient_processor()
         # pretend to average the grads, in order to make async and
         # sync have consistent effective learning rate
-        gradprocs.insert(0, ScaleGradient(('.*', 1.0 / self.config.nr_tower)))
+        if self.config.nr_tower > 1:
+            gradprocs.insert(0, ScaleGradient(('.*', 1.0 / self.config.nr_tower), log=False))
         grad_list = [apply_grad_processors(g, gradprocs) for g in grad_list]
 
         # use grad from the first tower for iteration in main thread
@@ -109,7 +108,6 @@ class AsyncMultiGPUTrainer(MultiGPUTrainer):
             self.config.optimizer.apply_gradients(
                 grad_list[0], get_global_step_var()),
             summary_moving_average(), name='train_op')
-        describe_model()
 
         self._start_async_threads(grad_list)
 

tensorpack/train/trainer.py

@@ -15,7 +15,6 @@ from ..utils import logger, SUMMARY_BACKUP_KEYS
 from ..tfutils import (get_vars_by_names, freeze_collection,
         get_global_step_var, TowerContext)
 from ..tfutils.summary import summary_moving_average, add_moving_summary
-from ..tfutils.modelutils import describe_model
 from ..predict import OnlinePredictor, build_multi_tower_prediction_graph
 from ..callbacks.concurrency import StartProcOrThread
 from ..tfutils.gradproc import apply_grad_processors
@@ -81,7 +80,6 @@ class SimpleTrainer(Trainer):
             self.config.optimizer.apply_gradients(grads, get_global_step_var()),
             summary_moving_average())
 
-        describe_model()
         # create an infinte data producer
         self.config.dataset.reset_state()
         self.data_producer = RepeatedData(self.config.dataset, -1).get_data()
@@ -204,7 +202,6 @@ class QueueInputTrainer(Trainer):
         grads = self._single_tower_grad()
         grads = apply_grad_processors(grads,
                 self.model.get_gradient_processor())
-        describe_model()
 
         self.train_op = tf.group(
             self.config.optimizer.apply_gradients(grads, get_global_step_var()),