update docs

docs/tutorial/input-source.md

@@ -63,12 +63,12 @@ Both are supported in tensorpack, while we recommend using Python.
 ### TensorFlow Reader: Cons
 The disadvantage of TF reader is obvious and it's huge: it's __too complicated__.
 
-Reading data is a complicated and badly-structured job than running the model.
+Unlike running a mathematical model, reading data is a complicated and badly-structured task.
 You need to handle different data format, handle corner cases in noisy data,
 which all require condition operations, loops, sometimes even exception handling. These operations
 are __naturally not suitable__ for a symbolic graph.
 
-Let's take a look at what users are asking for:
+Let's take a look at what users are asking for `tf.data`:
 * Different ways to [pad data](https://github.com/tensorflow/tensorflow/issues/13969), [shuffle data](https://github.com/tensorflow/tensorflow/issues/14518)
 * [Handle none values in data](https://github.com/tensorflow/tensorflow/issues/13865)
 * [Handle dataset that's not a multiple of batch size](https://github.com/tensorflow/tensorflow/issues/13745)
@@ -76,16 +76,16 @@ Let's take a look at what users are asking for:
 * [Sort/skip some data](https://github.com/tensorflow/tensorflow/issues/14250)
 * [Write data to files](https://github.com/tensorflow/tensorflow/issues/15014)
 
-To support all these features which could've been done with 3 lines of code in Python, you need either a new TF
+To support all these features which could've been done with __3 lines of code in Python__, you need either a new TF
 API, or ask [Dataset.from_generator](https://www.tensorflow.org/versions/r1.4/api_docs/python/tf/contrib/data/Dataset#from_generator)
 (i.e. Python again) to the rescue.
 
 It only makes sense to use TF to read data, if your data is originally very clean and well-formated.
-If not, you may feel like writing a script to clean your data, but then you're almost writing a Python loader already!
+If not, you may feel like writing a script to format your data, but then you're almost writing a Python loader already!
 
-Think about it: it's a waste of time to write a Python script to transform from raw data to clean format (e.g. TFRecords),
+Think about it: it's a waste of time to write a Python script to transform from raw data to TF-friendly format,
 then a TF script to transform from this format to tensors.
-The intermediate step (TFRecords) doesn't have to exist.
+The intermediate format doesn't have to exist.
 You just need the right interface to connect Python to the graph directly, efficiently.
 `tensorpack.InputSource` is such an interface.
 
@@ -95,13 +95,16 @@ You just need the right interface to connect Python to the graph directly, effic
 For example,
 
 1. [FeedInput](../modules/input_source.html#tensorpack.input_source.FeedInput):
-	Come from a DataFlow and get fed to the graph.
+	Come from a DataFlow and get fed to the graph (slow).
 2. [QueueInput](../modules/input_source.html#tensorpack.input_source.QueueInput):
   Come from a DataFlow and get prefetched on CPU by a TF queue.
 3. [StagingInput](../modules/input_source.html#tensorpack.input_source.StagingInput):
 	Come from some `InputSource`, then prefetched on GPU by a TF StagingArea.
-4. Come from a DataFlow, and further processed by `tf.data.Dataset`.
-5. [TensorInput](../modules/input_source.html#tensorpack.input_source.TensorInput):
-	Come from some TF reading ops.
-6. Come from some ZMQ pipe, where the load/preprocessing may happen on a different machine.
-
+4. [TFDatasetInput](http://tensorpack.readthedocs.io/en/latest/modules/input_source.html#tensorpack.input_source.TFDatasetInput)
+	Come from a `tf.data.Dataset`.
+5. [dataflow_to_dataset](http://tensorpack.readthedocs.io/en/latest/modules/input_source.html#tensorpack.input_source.TFDatasetInput.dataflow_to_dataset)
+	Come from a DataFlow, and further processed by `tf.data.Dataset`.
+6. [TensorInput](../modules/input_source.html#tensorpack.input_source.TensorInput):
+	Come from some tensors you wrote.
+7. [ZMQInput](http://tensorpack.readthedocs.io/en/latest/modules/input_source.html#tensorpack.input_source.ZMQInput)
+	Come from some ZeroMQ pipe, where the load/preprocessing may happen on a different machine.

tensorpack/callbacks/inference_runner.py

@@ -170,14 +170,16 @@ class DataParallelInferenceRunner(InferenceRunnerBase):
     """
     Inference with data-parallel support on multiple GPUs.
     It will build one predict tower on each GPU, and run prediction
-    with a larger batch.
+    with a large total batch.
     """
     def __init__(self, input, infs, gpus):
         """
         Args:
             input (DataFlow or QueueInput)
-            gpus (list[int]): list of GPU id
+            gpus (int or list[int]): #gpus, or list of GPU id
         """
+        if isinstance(gpus, int):
+            gpus = list(range(gpus))
         self._tower_names = ['InferenceTower{}'.format(k) for k in range(len(gpus))]
         if isinstance(input, DataFlow):
             input = QueueInput(input)

tensorpack/callbacks/param.py

@@ -217,9 +217,9 @@ class ScheduledHyperParamSetter(HyperParamSetter):
             param: same as in :class:`HyperParamSetter`.
             schedule (list): with the format ``[(epoch1, val1), (epoch2, val2), (epoch3, val3)]``.
                 Each ``(ep, val)`` pair means to set the param
-                to "val" __after__ the completion of epoch `ep`.
+                to "val" **after** the completion of epoch `ep`.
                 If ep == 0, the value will be set before the first epoch
-                (by default the first is epoch 1).
+                (because by default the first is epoch 1).
             interp: None: no interpolation. 'linear': linear interpolation
 
         Example: