improve docs

examples/ResNet/imagenet-resnet.py

@@ -80,7 +80,7 @@ def get_config(model, fake=False):
             EstimatedTimeLeft(),
             ScheduledHyperParamSetter(
                 'learning_rate', [(30, BASE_LR * 1e-1), (60, BASE_LR * 1e-2),
-                                  (85, BASE_LR * 1e-3), (95, BASE_LR * 1e-4), (105, BASE_LR * 1e-5)]),
+                                  (90, BASE_LR * 1e-3), (100, BASE_LR * 1e-4)]),
         ]
         if BASE_LR > 0.1:
             callbacks.append(
@@ -102,7 +102,7 @@ def get_config(model, fake=False):
         dataflow=dataset_train,
         callbacks=callbacks,
         steps_per_epoch=100 if args.fake else 1280000 // args.batch,
-        max_epoch=110,
+        max_epoch=105,
     )
 
 
@@ -115,7 +115,7 @@ if __name__ == '__main__':
     parser.add_argument('--data_format', help='specify NCHW or NHWC',
                         type=str, default='NCHW')
     parser.add_argument('-d', '--depth', help='resnet depth',
-                        type=int, default=18, choices=[18, 34, 50, 101, 152])
+                        type=int, default=50, choices=[18, 34, 50, 101, 152])
     parser.add_argument('--eval', action='store_true')
     parser.add_argument('--batch', default=256, type=int,
                         help='total batch size. 32 per GPU gives best accuracy, higher values should be similarly good')

tensorpack/callbacks/inference_runner.py

@@ -78,10 +78,8 @@ class InferenceRunnerBase(Callback):
 
         try:
             self._size = input.size()
-            logger.info("InferenceRunner will eval {} iterations".format(input.size()))
         except NotImplementedError:
             self._size = 0
-            logger.warn("InferenceRunner got an input with unknown size! It will iterate until OutOfRangeError!")
 
         self._hooks = []
 
@@ -95,6 +93,10 @@ class InferenceRunnerBase(Callback):
     def _before_train(self):
         self._hooked_sess = HookedSession(self.trainer.sess, self._hooks)
         self._input_callbacks.before_train()
+        if self._size > 0:
+            logger.info("InferenceRunner will eval {} iterations".format(self._size))
+        else:
+            logger.warn("InferenceRunner got an input with unknown size! It will iterate until OutOfRangeError!")
 
     def _after_train(self):
         self._input_callbacks.after_train()

tensorpack/callbacks/misc.py

@@ -94,4 +94,5 @@ class EstimatedTimeLeft(Callback):
 
         average_epoch_time = np.mean(self._times)
         time_left = (self._max_epoch - self.epoch_num) * average_epoch_time
+        if time_left > 0:
             logger.info("Estimated Time Left: " + humanize_time_delta(time_left))

tensorpack/dataflow/dataset/ilsvrc.py

@@ -114,7 +114,7 @@ def _guess_dir_structure(dir):
     else:
         dir_structure = 'original'
     logger.info(
-        "Assuming directory {} has {} structure.".format(
+        "[ILSVRC12] Assuming directory {} has '{}' structure.".format(
             dir, dir_structure))
     return dir_structure
 

tensorpack/graph_builder/training.py

@@ -12,7 +12,6 @@ from six.moves import zip, range
 
 from ..utils import logger
 from ..tfutils.tower import TowerContext
-from ..tfutils.common import get_tf_version_number
 from ..tfutils.gradproc import ScaleGradient
 
 from .utils import (
@@ -39,16 +38,10 @@ class DataParallelBuilder(GraphBuilder):
             towers(list[int]): list of GPU ids.
         """
         if len(towers) > 1:
-            logger.info("Training a model of {} towers".format(len(towers)))
-            DataParallelBuilder._check_tf_version()
+            logger.info("[DataParallel] Training a model of {} towers.".format(len(towers)))
 
         self.towers = towers
 
-    @staticmethod
-    def _check_tf_version():
-        assert get_tf_version_number() >= 1.1, \
-            "TF version {} is too old to run multi GPU training!".format(tf.VERSION)
-
     @staticmethod
     def _check_grad_list(grad_list):
         """
@@ -103,7 +96,7 @@ class DataParallelBuilder(GraphBuilder):
                     index=idx,
                     vs_name=tower_names[idx] if usevs else ''):
                 if len(str(device)) < 10:   # a device function doesn't have good string description
-                    logger.info("Building graph for training tower {} on device {}...".format(idx, device))
+                    logger.info("Building graph for training tower {} on device {} ...".format(idx, device))
                 else:
                     logger.info("Building graph for training tower {} ...".format(idx))
 

tensorpack/models/regularize.py

@@ -78,8 +78,8 @@ def regularize_cost(regex, func, name='regularize_cost'):
                 return name[prefixlen:]
             return name
         names = list(map(f, names))
-    logger.info("regularize_cost() found {} tensors.".format(len(names)))
-    _log_once("Applying regularizer for {}".format(', '.join(names)))
+    logger.info("regularize_cost() applying regularizers on {} tensors.".format(len(names)))
+    _log_once("The following tensors will be regularized: {}".format(', '.join(names)))
 
     return tf.add_n(costs, name=name)
 
@@ -106,7 +106,8 @@ def regularize_cost_from_collection(name='regularize_cost'):
     else:
         losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
     if len(losses) > 0:
-        logger.info("regularize_cost_from_collection() found {} tensors in REGULARIZATION_LOSSES.".format(len(losses)))
+        logger.info("regularize_cost_from_collection() applying regularizers on "
+                    "{} tensors in REGULARIZATION_LOSSES.".format(len(losses)))
         reg_loss = tf.add_n(losses, name=name)
         return reg_loss
     else:

tensorpack/utils/utils.py

@@ -24,42 +24,38 @@ def humanize_time_delta(sec):
     """Humanize timedelta given in seconds
 
     Args:
-        sec (float): time difference in seconds.
+        sec (float): time difference in seconds. Must be positive.
 
-    Examples:
+    Returns:
+        str - time difference as a readable string
 
-        Several time differences as a human readable string
+    Examples:
 
     .. code-block:: python
 
-        print humanize_seconds(1)                                   # 1 second
-        print humanize_seconds(60 + 1)                              # 1 minute 1 second
-        print humanize_seconds(87.6)                                # 1 minute 27 seconds
-        print humanize_seconds(0.01)                                # 0.01 seconds
-        print humanize_seconds(60 * 60 + 1)                         # 1 hour 0 minutes 1 second
-        print humanize_seconds(60 * 60 * 24 + 1)                    # 1 day 0 hours 0 minutes 1 second
-        print humanize_seconds(60 * 60 * 24 + 60 * 2 + 60*60*9+ 3)  # 1 day 9 hours 2 minutes 3 seconds
-
-    Returns:
-        time difference as a readable string
+        print(humanize_time_delta(1))                                   # 1 second
+        print(humanize_time_delta(60 + 1))                              # 1 minute 1 second
+        print(humanize_time_delta(87.6))                                # 1 minute 27 seconds
+        print(humanize_time_delta(0.01))                                # 0.01 seconds
+        print(humanize_time_delta(60 * 60 + 1))                         # 1 hour 1 second
+        print(humanize_time_delta(60 * 60 * 24 + 1))                    # 1 day 1 second
+        print(humanize_time_delta(60 * 60 * 24 + 60 * 2 + 60*60*9 + 3)) # 1 day 9 hours 2 minutes 3 seconds
     """
+    assert sec >= 0, sec
+    if sec == 0:
+        return "0 second"
     time = datetime(2000, 1, 1) + timedelta(seconds=int(sec))
     units = ['day', 'hour', 'minute', 'second']
-    vals = [time.day - 1, time.hour, time.minute, time.second]
+    vals = [int(sec // 86400), time.hour, time.minute, time.second]
     if sec < 60:
         vals[-1] = sec
 
     def _format(v, u):
         return "{:.3g} {}{}".format(v, u, "s" if v > 1 else "")
 
-    required = False
     ans = []
     for v, u in zip(vals, units):
-        if not required:
         if v > 0:
-                required = True
-                ans.append(_format(v, u))
-        else:
             ans.append(_format(v, u))
     return " ".join(ans)