fix old links to the docs (fix #1016)

README.md

@@ -3,7 +3,7 @@
 Tensorpack is a neural network training interface based on TensorFlow.
 
 [![Build Status](https://travis-ci.org/tensorpack/tensorpack.svg?branch=master)](https://travis-ci.org/tensorpack/tensorpack)
-[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io/en/latest/index.html)
+[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io)
 [![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users)
 [![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com)
 ## Features:

docs/casestudies/colorize.md

@@ -279,7 +279,7 @@ Note that you can certainly use `tf.summary.scalar(self.cost)`, but then you'll
 
 Let's summarize: we have a model and data.
 The missing piece which stitches these parts together is the training protocol.
-It is only a [configuration](http://tensorpack.readthedocs.io/en/latest/modules/tensorpack.train.html#tensorpack.train.TrainConfig)
+It is only a [configuration](http://tensorpack.readthedocs.io/modules/tensorpack.train.html#tensorpack.train.TrainConfig)
 
 For the dataflow, we already implemented `get_data` in the first part. Specifying the learning rate is done by
 

docs/tutorial/input-source.md

@@ -93,13 +93,13 @@ Some choices are:
     Data come from a DataFlow and get buffered on CPU by a TF queue.
 3. [StagingInput](../modules/input_source.html#tensorpack.input_source.StagingInput):
 	Come from some other `InputSource`, then prefetched on GPU by a TF StagingArea.
-4. [TFDatasetInput](http://tensorpack.readthedocs.io/en/latest/modules/input_source.html#tensorpack.input_source.TFDatasetInput)
+4. [TFDatasetInput](../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)
+5. [dataflow_to_dataset](../modules/input_source.html#tensorpack.input_source.TFDatasetInput.dataflow_to_dataset)
 	Come from a DataFlow, and then lfurther processed by utilities in `tf.data.Dataset`.
 6. [TensorInput](../modules/input_source.html#tensorpack.input_source.TensorInput):
 	Come from some tensors you define (can be reading ops, for example).
-7. [ZMQInput](http://tensorpack.readthedocs.io/en/latest/modules/input_source.html#tensorpack.input_source.ZMQInput)
+7. [ZMQInput](../modules/input_source.html#tensorpack.input_source.ZMQInput)
 	Come from some ZeroMQ pipe, where the reading/preprocessing may happen in a different process or even a different machine.
 
 Typically, we recommend using `DataFlow + QueueInput` as it's good for most use cases.

examples/ImageNetModels/README.md

@@ -3,7 +3,7 @@ ImageNet training code of ResNet, ShuffleNet, DoReFa-Net, AlexNet, Inception, VG
 
 To train any of the models, just do `./{model}.py --data /path/to/ilsvrc`.
 More options are available in `./{model}.py --help`.
-Expected format of data directory is described in [docs](http://tensorpack.readthedocs.io/en/latest/modules/dataflow.dataset.html#tensorpack.dataflow.dataset.ILSVRC12).
+Expected format of data directory is described in [docs](http://tensorpack.readthedocs.io/modules/dataflow.dataset.html#tensorpack.dataflow.dataset.ILSVRC12).
 Some pretrained models can be downloaded at [tensorpack model zoo](http://models.tensorpack.com/).
 
 ### ShuffleNet

examples/ImageNetModels/imagenet_utils.py

@@ -102,7 +102,7 @@ def get_imagenet_dataflow(
     Returns: A DataFlow which produces BGR images and labels.
 
     See explanations in the tutorial:
-    http://tensorpack.readthedocs.io/en/latest/tutorial/efficient-dataflow.html
+    http://tensorpack.readthedocs.io/tutorial/efficient-dataflow.html
     """
     assert name in ['train', 'val', 'test']
     isTrain = name == 'train'

examples/ResNet/README.md

@@ -25,7 +25,7 @@ baseline and they actually cannot beat this ResNet recipe.
 | ResNet152   | 5.78%       | 21.51%      | [:arrow_down:](http://models.tensorpack.com/ResNet/ImageNet-ResNet152.npz)   |
 
 To reproduce the above results,
-first decompress ImageNet data into [this structure](http://tensorpack.readthedocs.io/en/latest/modules/dataflow.dataset.html#tensorpack.dataflow.dataset.ILSVRC12), then:
+first decompress ImageNet data into [this structure](http://tensorpack.readthedocs.io/modules/dataflow.dataset.html#tensorpack.dataflow.dataset.ILSVRC12), then:
 ```bash
 ./imagenet-resnet.py --data /path/to/original/ILSVRC -d 50 [--mode resnet/preact/se] --batch 256
 # See ./imagenet-resnet.py -h for other options.
@@ -35,7 +35,7 @@ You should be able to see good GPU utilization (95%~99%), if your data is fast e
 With batch=64x8, it can finish 100 epochs in 16 hours on AWS p3.16xlarge (8 V100s).
 
 The default data pipeline is probably OK for machines with SSD & 20 CPU cores.
-See the [tutorial](http://tensorpack.readthedocs.io/en/latest/tutorial/efficient-dataflow.html) on other options to speed up your data.
+See the [tutorial](http://tensorpack.readthedocs.io/tutorial/efficient-dataflow.html) on other options to speed up your data.
 
 ![imagenet](imagenet-resnet.png)
 

tensorpack/callbacks/base.py

@@ -14,7 +14,7 @@ __all__ = ['Callback', 'ProxyCallback', 'CallbackFactory']
 class Callback(object):
     """ Base class for all callbacks. See
     `Write a Callback
-    <http://tensorpack.readthedocs.io/en/latest/tutorial/extend/callback.html>`_
+    <http://tensorpack.readthedocs.io/tutorial/extend/callback.html>`_
     for more detailed explanation of the callback methods.
 
     Attributes:

tensorpack/dataflow/remote.py

@@ -90,7 +90,7 @@ class RemoteDataZMQ(DataFlow):
     Produce data from ZMQ PULL socket(s).
     It is the receiver-side counterpart of :func:`send_dataflow_zmq`, which uses :mod:`tensorpack.utils.serialize`
     for serialization.
-    See http://tensorpack.readthedocs.io/en/latest/tutorial/efficient-dataflow.html#distributed-dataflow
+    See http://tensorpack.readthedocs.io/tutorial/efficient-dataflow.html#distributed-dataflow
 
     Attributes:
         cnt1, cnt2 (int): number of data points received from addr1 and addr2

tensorpack/train/tower.py

@@ -193,7 +193,7 @@ class SingleCostTrainer(TowerTrainer):
         Note:
             `get_cost_fn` will be part of the tower function.
             It must follows the `rules of tower function.
-            <http://tensorpack.readthedocs.io/en/latest/tutorial/trainer.html#tower-trainer>`_.
+            <http://tensorpack.readthedocs.io/tutorial/trainer.html#tower-trainer>`_.
         """
         get_cost_fn = TowerFuncWrapper(get_cost_fn, inputs_desc)
         get_opt_fn = memoized(get_opt_fn)