unify naming: tensorboard-pytorch -> tensorboardX

HISTORY.rst

@@ -26,9 +26,6 @@ History
 * Multiple Embeddings in One Experiment 
 * Supports Chainer and mxnet
 
-* fixed incorrect image<->label pairing in embedding function (#12)
-* unifies API call and adds docstring. Documentation is available at: http://tensorboard-pytorch.readthedocs.io/
-
 0.7 (2017-08-22)
 -----------------
 * remove tensorflow dependency for embedding function

README.md

@@ -1,9 +1,9 @@
 # tensorboardX
 
-[![Build Status](https://travis-ci.org/lanpa/tensorboard-pytorch.svg?branch=master)](https://travis-ci.org/lanpa/tensorboard-pytorch)
+[![Build Status](https://travis-ci.org/lanpa/tensorboardX.svg?branch=master)](https://travis-ci.org/lanpa/tensorboardX)
 [![PyPI version](https://badge.fury.io/py/tensorboardX.svg)](https://badge.fury.io/py/tensorboardX)
 [![Downloads](https://img.shields.io/badge/pip--downloads-5K+-brightgreen.svg)](https://bigquery.cloud.google.com/savedquery/966219917372:edb59a0d70c54eb687ab2a9417a778ee)
-[![Documentation Status](https://readthedocs.org/projects/tensorboard-pytorch/badge/?version=latest)](https://tensorboard-pytorch.readthedocs.io/en/latest/?badge=latest)
+[![Documentation Status](https://readthedocs.org/projects/tensorboardX/badge/?version=latest)](https://tensorboardX.readthedocs.io/en/latest/?badge=latest)
 
 Write TensorBoard events with simple function call.
 
@@ -15,7 +15,7 @@ Write TensorBoard events with simple function call.
 
 * requirement for `demo_graph.py` is tensorboardX>=1.2 and pytorch>=0.4
 
-* [FAQ](https://github.com/lanpa/tensorboard-pytorch/wiki)
+* [FAQ](https://github.com/lanpa/tensorboardX/wiki)
 
 ## Install
 
@@ -25,7 +25,7 @@ Tested on anaconda2 / anaconda3, with PyTorch 0.4 / torchvision 0.2 / tensorboar
 
 or build from source:
 
-`pip install git+https://github.com/lanpa/tensorboard-pytorch`
+`pip install git+https://github.com/lanpa/tensorboardX`
 
 
 ## Example
@@ -97,9 +97,7 @@ writer.close()
 
 ## Tweaks
 
-To add more ticks for the slider (show more image history), you can modify the hardcoded `event_accumulator` in `~/anaconda3/lib/python3.6/site-packages/tensorflow/tensorboard/backend/application.py` as you wish.
-
-For `tensorflow-tensorboard` > 0.17 see https://github.com/lanpa/tensorboard-pytorch/issues/44
+To add more ticks for the slider (show more image history), check https://github.com/lanpa/tensorboardX/issues/44
 
 ## Reference
 

docs/Makefile

@@ -4,7 +4,7 @@
 # You can set these variables from the command line.
 SPHINXOPTS    =
 SPHINXBUILD   = sphinx-build
-SPHINXPROJ    = tensorboard-pytorch
+SPHINXPROJ    = tensorboardX
 SOURCEDIR     = .
 BUILDDIR      = _build
 

docs/conf.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 #
-# tensorboard-pytorch documentation build configuration file, created by
+# tensorboardX documentation build configuration file, created by
 # sphinx-quickstart on Wed Aug  9 01:38:01 2017.
 #
 # This file is execfile()d with the current directory set to its

docs/tutorial.rst

@@ -113,7 +113,7 @@ that have correct size for ``m``. Let ``r = m(t)``, then please invoke
 require gradient, therefore it will be omitted when back tracing. To draw the
 input node, pass an additional parameter ``requires_grad=True`` when creating the
 input tensor. See
-`The graph demo <https://github.com/lanpa/tensorboard-pytorch/blob/master/demo_graph.py>`_ for
+`The graph demo <https://github.com/lanpa/tensorboardX/blob/master/demo_graph.py>`_ for
 complete example.
 
 
@@ -147,7 +147,7 @@ distant points have very different label (semantically or visually). Here the
 metadata is a list of labels, and the length of the list should equal to n, the
 number of the points. The label_imgs is a 4D tensor of size ``NCHW``. ``N`` should equal
 to ``n`` as well. See
-`The embedding demo <https://github.com/lanpa/tensorboard-pytorch/blob/master/demo_embedding.py>`_ for
+`The embedding demo <https://github.com/lanpa/tensorboardX/blob/master/demo_embedding.py>`_ for
 complete example.
 
 
@@ -156,9 +156,9 @@ useful commands
 install
 =======
 
-Simply type ``pip install tensorboard-pytorch`` in Bash to install this package.
+Simply type ``pip install tensorboardX`` in Bash to install this package.
 To use the newest version, you might need to build from source or ``pip install
-tensorboard-pytorch —-no-cache-dir`` .  To run tensorboard web server, you need
+tensorboardX —-no-cache-dir`` .  To run tensorboard web server, you need
 to install tensorflow by ``pip install tensorflow`` or ``pip install tensorflow-gpu``.
 After that, type ``tensorboard --logdir=<yourlogdir>`` to start the server, where
 ``yourlogdir`` is the parameter of the object constructor. I think this command is

docs/tutorial_zh.rst

@@ -9,7 +9,7 @@ Google TensorFlow 附加的工具 Tensorboard 是一個很好用的視覺化工
 安裝
 -------
 在命令列輸入pip install tensorboardX即可
-或是最新版源碼安裝pip install git+https://github.com/lanpa/tensorboard-pytorch 
+或是最新版源碼安裝pip install git+https://github.com/lanpa/tensorboardX 
 
 使用
 -------
@@ -75,7 +75,7 @@ forward propagate 一次 m(x) 或是多個輸入時：m((x, y, z))
 
 高維度資料視覺化／降維 (embedding)
 ---------------------------------------------------
-因為人類對物體的了解程度只有三維，所以當資料的維度超過三的時候我們沒辦法將他視覺化。這時候就需要降維來讓資料的維度小於等於三。降維運算由 tensorboard 以 Javascript 執行，演算法有 PCA 及 t-sne 兩種可選。這邊我們只需要負責提供每個點的高維度特徵即可。提供的格式是一個矩陣，一個 n x d 的矩陣 n 點的數量， d 是維度的多寡。 高維度特徵可以是原始資料。比如說影像，或是網路學到的壓縮結果。這原始資料決定了資料的分佈情形。如果要看得更清楚一點，你可以再傳 metadata / label_imgs 的參數進去（metadata是一個 python list 長度為 n, label_imgs 是一個 4 維矩陣，大小是 nCHW。這樣每個點就會有他對應的文字或圖在旁邊。不懂的話就看範例吧：https://github.com/lanpa/tensorboard-pytorch/blob/master/demo_embedding.py
+因為人類對物體的了解程度只有三維，所以當資料的維度超過三的時候我們沒辦法將他視覺化。這時候就需要降維來讓資料的維度小於等於三。降維運算由 tensorboard 以 Javascript 執行，演算法有 PCA 及 t-sne 兩種可選。這邊我們只需要負責提供每個點的高維度特徵即可。提供的格式是一個矩陣，一個 n x d 的矩陣 n 點的數量， d 是維度的多寡。 高維度特徵可以是原始資料。比如說影像，或是網路學到的壓縮結果。這原始資料決定了資料的分佈情形。如果要看得更清楚一點，你可以再傳 metadata / label_imgs 的參數進去（metadata是一個 python list 長度為 n, label_imgs 是一個 4 維矩陣，大小是 nCHW。這樣每個點就會有他對應的文字或圖在旁邊。不懂的話就看範例吧：https://github.com/lanpa/tensorboardX/blob/master/demo_embedding.py
 
 紀錄短片
 ---------------

setup.py

@@ -25,7 +25,7 @@
     long_description= history,
     author='Tzu-Wei Huang',
     author_email='[email protected]',
-    url='https://github.com/lanpa/tensorboard-pytorch',
+    url='https://github.com/lanpa/tensorboardX',
     packages=find_packages(exclude=['docs', 'tests']),
     include_package_data=True,
     install_requires=requirements,

tensorboardX/visdom_writer.py

@@ -178,12 +178,12 @@ def add_video(self, tag, vid_tensor, global_step=None, fps=4):
             global_step (int): Global step value to record
             fps (float or int): Frames per second
         Shape:
-            vid_tensor: :math:`(B, C, T, H, W)`. (if following tensorboard-pytorch format)
+            vid_tensor: :math:`(B, C, T, H, W)`. (if following tensorboardX format)
             vid_tensor: :math:`(T, H, W, C)`. (if following visdom format)
             B = batches, C = colors (1, 3, or 4), T = time frames, H = height, W = width
         """
         shape = vid_tensor.shape
-        # A batch of videos (tensorboard-pytorch format) is a 5D tensor
+        # A batch of videos (tensorboardX format) is a 5D tensor
         if len(shape) > 4:
             for i in range(shape[0]):
                 # Reshape each video to Visdom's (T x H x W x C) and write each video