see if rtd works better

docs/conf.py

@@ -34,7 +34,8 @@ import mock
                 #+ ', '.join(["{}={}".format(k,v) for k,v in kwargs.items()]) + ')'
 
 MOCK_MODULES = ['numpy', 'scipy', 'tensorflow', 'scipy.misc', 'h5py', 'nltk',
-                'cv2', 'scipy.io', 'dill', 'zmq']
+                'cv2', 'scipy.io', 'dill', 'zmq', 'subprocess32', 'lmdb',
+                'tornado']
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = mock.Mock(name=mod_name)
 

tensorpack/models/_common.py

@@ -5,8 +5,7 @@
 import tensorflow as tf
 from functools import wraps
 import six
-import copy
-#from decorator import decorator
+import copy, os
 
 from ..tfutils import *
 from ..tfutils.modelutils import *
@@ -31,7 +30,8 @@ def layer_register(summary_activation=False, log_shape=True):
         Can be overriden when creating the layer.
     :param log_shape: log input/output shape of this layer
     """
-    #@decorator only enable me when building docs.
+
+
     def wrapper(func):
         @wraps(func)
         def wrapped_func(name, inputs, *args, **kwargs):
@@ -69,6 +69,13 @@ def layer_register(summary_activation=False, log_shape=True):
                 return outputs
         wrapped_func.f = func   # attribute to access the underlining function object
         return wrapped_func
+
+    # need some special handling for RTD to work with the arguments
+    on_rtd = os.environ.get('READTHEDOCS') == 'True'
+    if on_rtd:
+        from decorator import decorator
+        wrapper = decorator(wrapper)
+
     return wrapper
 
 def shape2d(a):

tensorpack/tfutils/modelutils.py

@@ -18,7 +18,7 @@ def describe_model():
         msg.append("{}: shape={}, dim={}".format(
             v.name, shape.as_list(), ele))
     size_mb = total * 4 / 1024.0**2
-    msg.append("Total param={} ({:01f} MB)".format(total, size_mb))
+    msg.append("Total param={} ({:01f} MB assuming all float32)".format(total, size_mb))
     logger.info("Model Params: {}".format('\n'.join(msg)))