update

tensorpack/dataflow/common.py

@@ -154,6 +154,7 @@ class MapData(ProxyDataFlow):
         :param ds: a :mod:`DataFlow` instance.
         :param func: a function that takes a original datapoint, returns a new
             datapoint. return None to skip this data point.
+            Note that if you use filter, ds.size() won't be correct.
         """
         super(MapData, self).__init__(ds)
         self.func = func
@@ -171,6 +172,7 @@ class MapDataComponent(ProxyDataFlow):
         :param ds: a :mod:`DataFlow` instance.
         :param func: a function that takes a datapoint component dp[index], returns a
             new value of dp[index]. return None to skip this datapoint.
+            Note that if you use filter, ds.size() won't be correct.
         """
         super(MapDataComponent, self).__init__(ds)
         self.func = func

tensorpack/models/_common.py

@@ -16,6 +16,13 @@ from ..utils import logger
 # make sure each layer is only logged once
 _layer_logged = set()
 
+def disable_layer_logging():
+    class ContainEverything:
+        def __contains__(self, x):
+            return True
+    # can use nonlocal in python3, but how
+    globals()['_layer_logged'] = ContainEverything()
+
 def layer_register(summary_activation=False, log_shape=True):
     """
     Register a layer.

tensorpack/predict.py

@@ -115,9 +115,13 @@ class PredictWorker(multiprocessing.Process):
         self.config = config
 
     def run(self):
+        logger.info("Worker {} use GPU {}".format(self.idx, self.gpuid))
         os.environ['CUDA_VISIBLE_DEVICES'] = self.gpuid
         G = tf.Graph()     # build a graph for each process, because they don't need to share anything
         with G.as_default(), tf.device('/gpu:0'):
+            if self.idx != 0:
+                from tensorpack.models._common import disable_layer_logging
+                disable_layer_logging()
             self.func = get_predict_func(self.config)
             if self.idx == 0:
                 describe_model()
@@ -173,13 +177,13 @@ class DatasetPredictor(object):
                 die_cnt = 0
                 while True:
                     res = self.result_queue.get()
+                    pbar.update()
                     if res[0] != DIE:
                         yield res[1]
                     else:
                         die_cnt += 1
                         if die_cnt == self.nr_gpu:
                             break
-                    pbar.update()
                 self.inqueue_proc.join()
                 self.inqueue_proc.terminate()
                 for p in self.workers:

tensorpack/train/trainer.py

@@ -4,6 +4,7 @@
 
 import tensorflow as tf
 import threading
+import time
 import copy
 import re
 import functools