Merge pull request #158 from masa-su/develop/v0.3.1

Develop/v0.3.1

.travis.yml

@@ -2,10 +2,6 @@ language: python
 python:
     - 3.6.5
 install:
-  - pip install pytest-cov
-  - pip install pytest-flake8
-  - pip install sphinx
-  - pip install sphinx_rtd_theme
-  - python setup.py install
+  - pip install -e "."[test]
 script:
   - pytest

README.md

@@ -7,8 +7,9 @@
 [![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20-blue)](https://github.com/masa-su/pixyz)
 [![Pytorch Version](https://img.shields.io/badge/pytorch-1.0-yellow.svg)](https://github.com/masa-su/pixyz)
 [![Read the Docs](https://readthedocs.org/projects/pixyz/badge/?version=latest)](http://docs.pixyz.io)
-[![TravisCI](https://travis-ci.org/masa-su/pixyz.svg?branch=master)](https://github.com/masa-su/pixyz)
+[![TravisCI](https://travis-ci.org/masa-su/pixyz.svg?branch=master)](https://travis-ci.org/github/masa-su/pixyz)
 [![Downloads](https://pepy.tech/badge/pixyz)](https://pepy.tech/project/pixyz)
+[![Docker Pulls](https://img.shields.io/docker/pulls/kenoharada/pixyz.svg)](https://hub.docker.com/r/kenoharada/pixyz)
 
 [Docs](https://docs.pixyz.io) | [Examples](https://github.com/masa-su/pixyz/tree/master/examples) | [Pixyzoo](https://github.com/masa-su/pixyzoo)
 
@@ -52,6 +53,16 @@ $ git clone https://github.com/masa-su/pixyz.git
 $ pip install -e pixyz
 ```
 
+
+You can also install pixyz and PyTorch environment through Docker Hub
+```
+# pull docker image from https://hub.docker.com/r/kenoharada/pixyz
+$ docker pull kenoharada/pixyz:v0.3.0_python_3.7.7_pytorch_1.6.0_cuda_10.1
+
+# Run pixyz environment
+$ docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 --rm -it kenoharada/pixyz:v0.3.0_python_3.7.7_pytorch_1.6.0_cuda_10.1
+```
+
 ## Quick Start
 
 Here, we consider to implement a variational auto-encoder (VAE) which is one of the most well-known deep generative models. VAE is composed of a inference model

examples/cvae.ipynb

@@ -7,6 +7,15 @@
     "# Conditional variational autoencoder (using the VAE class)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -740,9 +749,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/distributions.ipynb

@@ -7,6 +7,15 @@
     "# Examples of creating and operating distributions in Pixyz"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -874,9 +883,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/gan.ipynb

@@ -7,6 +7,15 @@
     "# Generative adversarial network (using the GAN class)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -1842,9 +1851,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/glow.ipynb

@@ -7,6 +7,15 @@
     "# Glow （CIFAR10）"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -866,9 +875,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/gmm.ipynb

@@ -7,6 +7,15 @@
     "# Gaussian Mixture Model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -821,9 +830,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/hierarchical_variational_inference.ipynb

@@ -7,6 +7,15 @@
     "# Variational inference on a hierarchical latent model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -736,9 +745,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/jmvae.ipynb

@@ -8,6 +8,15 @@
     "Original paper: Joint Multimodal Learning with Deep Generative Models (https://arxiv.org/abs/1611.01891 )"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -722,18 +731,18 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.3"
   },
   "pycharm": {
    "stem_cell": {
     "cell_type": "raw",
-    "source": [],
     "metadata": {
      "collapsed": false
-    }
+    },
+    "source": []
    }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}

examples/jmvae_poe.ipynb

@@ -9,6 +9,15 @@
     "* The PoE encoder is originally proposed in \"Multimodal Generative Models for Scalable Weakly-Supervised Learning\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -757,18 +766,18 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   },
   "pycharm": {
    "stem_cell": {
     "cell_type": "raw",
-    "source": [],
     "metadata": {
      "collapsed": false
-    }
+    },
+    "source": []
    }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}

examples/m2.ipynb

@@ -7,6 +7,15 @@
     "# Semi-supervised learning with M2 model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -860,9 +869,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/maximum_likelihood.ipynb

@@ -7,6 +7,15 @@
     "# Maximum likelihood estimation (using the ML class)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -553,9 +562,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/mmd_vae.ipynb

@@ -7,6 +7,15 @@
     "# MMD-VAE (using the Model class)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tqdm torchvision tensorboardX"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -683,18 +692,18 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.3"
   },
   "pycharm": {
    "stem_cell": {
     "cell_type": "raw",
-    "source": [],
     "metadata": {
      "collapsed": false
-    }
+    },
+    "source": []
    }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}