remove some mentions of tensorpack in dataflow

tensorpack/dataflow/__init__.py

@@ -48,8 +48,8 @@ for _, module_name, __ in iter_modules(
         _global_import(module_name)
 
 
-globals()['dataset'] = LazyLoader('dataset', globals(), 'tensorpack.dataflow.dataset')
-globals()['imgaug'] = LazyLoader('imgaug', globals(), 'tensorpack.dataflow.imgaug')
+globals()['dataset'] = LazyLoader('dataset', globals(), __name__ + '.dataset')
+globals()['imgaug'] = LazyLoader('imgaug', globals(), __name__ + '.imgaug')
 
 del LazyLoader
 

tensorpack/dataflow/imgaug/base.py

@@ -48,7 +48,7 @@ class Augmentor(object):
         is responsible for calling this method (once or more times) in the **process that uses the augmentor**
         before using it.
 
-        If you use tensorpack's built-in augmentation dataflow (:class:`AugmentImageComponent`, etc),
+        If you use a built-in augmentation dataflow (:class:`AugmentImageComponent`, etc),
         this method will be called in the dataflow's own `reset_state` method.
 
         If you use Python≥3.7 on Unix, this method will be automatically called after fork,

tensorpack/dataflow/imgaug/external.py

@@ -24,8 +24,9 @@ class IAAugmentor(ImageAugmentor):
 
     .. code-block:: python
 
-        from tensorpack import imgaug  # this is not the aleju/imgaug library
         from imgaug import augmentors as iaa  # this is the aleju/imgaug library
+        from tensorpack import imgaug  # this is not the aleju/imgaug library
+        # or from dataflow import imgaug  # if you're using the standalone version of dataflow
         myaug = imgaug.IAAugmentor(
             iaa.Sequential([
                 iaa.Sharpen(alpha=(0, 1), lightness=(0.75, 1.5)),
@@ -68,6 +69,7 @@ class Albumentations(ImageAugmentor):
     .. code-block:: python
 
         from tensorpack import imgaug
+        # or from dataflow import imgaug  # if you're using the standalone version of dataflow
         import albumentations as AB
         myaug = imgaug.Albumentations(AB.RandomRotate90(p=1))
     """

tensorpack/dataflow/remote.py

@@ -34,7 +34,7 @@ def send_dataflow_zmq(df, addr, hwm=50, format=None, bind=False):
         addr: a ZMQ socket endpoint.
         hwm (int): ZMQ high-water mark (buffer size)
         format (str): The serialization format.
-             Default format uses :mod:`tensorpack.utils.serialize`.
+             Default format uses :mod:`utils.serialize`.
              This format works with :class:`dataflow.RemoteDataZMQ`.
              An alternate format is 'zmq_ops', used by https://github.com/tensorpack/zmq_ops
              and :class:`input_source.ZMQInput`.

tensorpack/utils/develop.py

@@ -69,7 +69,7 @@ def create_dummy_func(func, dependency):
 def building_rtfd():
     """
     Returns:
-        bool: if tensorpack is being imported to generate docs now.
+        bool: if the library is being imported to generate docs now.
     """
     return os.environ.get('READTHEDOCS') == 'True' \
         or os.environ.get('DOC_BUILDING')

tensorpack/utils/utils.py

@@ -103,8 +103,6 @@ def fix_rng_seed(seed):
 
     .. code-block:: python
 
-            import tensorpack.utils.utils as utils
-
             seed = 42
             utils.fix_rng_seed(seed)
             tesnorflow.set_random_seed(seed)