use positional args instead of list for add_param_summary

examples/Atari2600/DQN.py

@@ -134,8 +134,8 @@ class Model(ModelDesc):
 
         self.cost = tf.truediv(symbf.huber_loss(target - pred_action_value),
                                tf.cast(BATCH_SIZE, tf.float32), name='cost')
-        summary.add_param_summary([('conv.*/W', ['histogram', 'rms']),
-                                   ('fc.*/W', ['histogram', 'rms'])])   # monitor all W
+        summary.add_param_summary(('conv.*/W', ['histogram', 'rms']),
+                                  ('fc.*/W', ['histogram', 'rms']))   # monitor all W
         add_moving_summary(self.cost)
 
     def update_target_param(self):

examples/DoReFa-Net/alexnet-dorefa.py

@@ -157,7 +157,7 @@ class Model(ModelDesc):
         # weight decay on all W of fc layers
         wd_cost = regularize_cost('fc.*/W', l2_regularizer(5e-6))
 
-        add_param_summary([('.*/W', ['histogram', 'rms'])])
+        add_param_summary(('.*/W', ['histogram', 'rms']))
         self.cost = tf.add_n([cost, wd_cost], name='cost')
         add_moving_summary(cost, wd_cost, self.cost)
 

examples/DoReFa-Net/svhn-digit-dorefa.py

@@ -122,7 +122,7 @@ class Model(ModelDesc):
         # weight decay on all W of fc layers
         wd_cost = regularize_cost('fc.*/W', l2_regularizer(1e-7))
 
-        add_param_summary([('.*/W', ['histogram', 'rms'])])
+        add_param_summary(('.*/W', ['histogram', 'rms']))
         self.cost = tf.add_n([cost, wd_cost], name='cost')
         add_moving_summary(cost, wd_cost, self.cost)
 

examples/HED/hed.py

@@ -89,7 +89,7 @@ class Model(ModelDesc):
             wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
             costs.append(wd_cost)
 
-            add_param_summary([('.*/W', ['histogram'])])   # monitor W
+            add_param_summary(('.*/W', ['histogram']))   # monitor W
             self.cost = tf.add_n(costs, name='cost')
             add_moving_summary(costs + [wrong, self.cost])
 

examples/Inception/inception-bn.py

@@ -115,7 +115,7 @@ class Model(ModelDesc):
                                           80000, 0.7, True)
         wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='l2_regularize_loss')
 
-        add_param_summary([('.*/W', ['histogram'])])   # monitor W
+        add_param_summary(('.*/W', ['histogram']))   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')
         add_moving_summary(wd_cost, self.cost)
 

examples/ResNet/cifar10-resnet.py

@@ -103,7 +103,7 @@ class Model(ModelDesc):
         wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
         add_moving_summary(cost, wd_cost)
 
-        add_param_summary([('.*/W', ['histogram'])])   # monitor W
+        add_param_summary(('.*/W', ['histogram']))   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')
 
 

examples/char-rnn/char-rnn.py

@@ -91,7 +91,7 @@ class Model(ModelDesc):
         xent_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
             logits, symbolic_functions.flatten(nextinput))
         self.cost = tf.reduce_mean(xent_loss, name='cost')
-        summary.add_param_summary([('.*/W', ['histogram'])])   # monitor histogram of all W
+        summary.add_param_summary(('.*/W', ['histogram']))   # monitor histogram of all W
         summary.add_moving_summary(self.cost)
 
     def get_gradient_processor(self):

examples/cifar-convnet.py

@@ -71,7 +71,7 @@ class Model(ModelDesc):
                          name='regularize_loss')
         add_moving_summary(cost, wd_cost)
 
-        add_param_summary([('.*/W', ['histogram'])])   # monitor W
+        add_param_summary(('.*/W', ['histogram']))   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')
 
 

examples/mnist-convnet.py

@@ -107,9 +107,9 @@ class Model(ModelDesc):
             summary.add_moving_summary(cost)
 
         # monitor histogram of all weight (of conv and fc layers) in tensorboard
-        summary.add_param_summary([('.*/W', ['histogram', 'rms']),
-                                   ('.*/weights', ['histogram', 'rms'])  # to also work with slim
-                                   ])
+        summary.add_param_summary(('.*/W', ['histogram', 'rms']),
+                                  ('.*/weights', ['histogram', 'rms'])  # to also work with slim
+                                  )
 
 
 def get_data():

examples/svhn-digit-convnet.py

@@ -57,7 +57,7 @@ class Model(ModelDesc):
         wd_cost = regularize_cost('fc.*/W', l2_regularizer(0.00001))
         add_moving_summary(cost, wd_cost)
 
-        add_param_summary([('.*/W', ['histogram', 'rms'])])   # monitor W
+        add_param_summary(('.*/W', ['histogram', 'rms']))   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')
 
 

tensorpack/tfutils/modelutils.py

@@ -11,7 +11,7 @@ __all__ = ['describe_model', 'get_shape_str']
 
 
 def describe_model():
-    """ print a description of the current model parameters """
+    """ Print a description of the current model parameters """
     train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
     msg = [""]
     total = 0
@@ -29,8 +29,10 @@ def describe_model():
 
 def get_shape_str(tensors):
     """
-    :param tensors: a tensor or a list of tensors
-    :returns: a string to describe the shape
+    Args:
+        tensors (list or tf.Tensor): a tensor or a list of tensors
+    Returns:
+        str: a string to describe the shape
     """
     if isinstance(tensors, (list, tuple)):
         for v in tensors:

tensorpack/tfutils/summary.py

@@ -7,6 +7,7 @@ import tensorflow as tf
 import re
 
 from ..utils.argtools import memoized
+from ..utils import logger
 from ..utils.naming import MOVING_SUMMARY_VARS_KEY
 from .tower import get_current_tower_context
 from . import get_global_step_var
@@ -18,7 +19,8 @@ __all__ = ['create_summary', 'add_param_summary', 'add_activation_summary',
 
 def create_summary(name, v):
     """
-    Return a tf.Summary object with name and simple scalar value v
+    Returns:
+        tf.Summary: a tf.Summary object with name and simple scalar value v.
     """
     assert isinstance(name, six.string_types), type(name)
     v = float(v)
@@ -29,8 +31,10 @@ def create_summary(name, v):
 
 def add_activation_summary(x, name=None):
     """
-    Add summary to graph for an activation tensor x.
-    If name is None, use x.name.
+    Add summary for an activation tensor x.  If name is None, use x.name.
+
+    Args:
+        x (tf.Tensor): the tensor to summary.
     """
     ctx = get_current_tower_context()
     if ctx is not None and not ctx.is_main_training_tower:
@@ -47,16 +51,20 @@ def add_activation_summary(x, name=None):
         tf.summary.scalar(name + '-rms', rms(x))
 
 
-def add_param_summary(summary_lists):
+def add_param_summary(*summary_lists):
     """
-    Add summary for all trainable variables matching the regex
+    Add summary Ops for all trainable variables matching the regex.
 
-    :param summary_lists: list of (regex, [list of summary type to perform]).
-        Type can be 'mean', 'scalar', 'histogram', 'sparsity', 'rms'
+    Args:
+        summary_lists (list): each is (regex, [list of summary type to perform]).
+        Summary type can be 'mean', 'scalar', 'histogram', 'sparsity', 'rms'
     """
     ctx = get_current_tower_context()
     if ctx is not None and not ctx.is_main_training_tower:
         return
+    if len(summary_lists) == 0 and isinstance(summary_lists[0], list):
+        logger.warn("[Deprecated] Use positional args to call add_param_summary() instead of a list.")
+        summary_lists = summary_lists[0]
 
     def perform(var, action):
         ndim = var.get_shape().ndims