upd imports and others

intsystems · Nov 30, 2024 · 82c1d48 · 82c1d48
1 parent 7be292f
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diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -28,7 +28,7 @@ jobs:
           
       - name: Generate coverage badge
         run: |
-          python src/badge_generator.py
+          python badge_generator.py
 
       # - name: Commit coverage badge
       #   run: |

diff --git a/README.md b/README.md
@@ -73,6 +73,11 @@ In this project we implement different alternatives to it.
 
 ## 🛠️ Install
 
+### Install using pip
+```bash
+pip install relaxit
+```
+
 ### Install from source
 ```bash
 pip install git+https://github.com/intsystems/discrete-variables-relaxation
@@ -85,10 +90,11 @@ cd discrete-variables-relaxation
 pip install -e .
 ```
 
-## 🚀 Quickstart
+## 🚀 Quickstart 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intsystems/discrete-variables-relaxation/blob/main/demo/quickstart.ipynb)
 ```python
 import torch
-from relaxit.distributions.InvertibleGaussian import InvertibleGaussian
+from relaxit.distributions import InvertibleGaussian
 
 # initialize distribution parameters
 loc = torch.zeros(3, 4, 5, requires_grad=True)
@@ -100,6 +106,8 @@ distribution = InvertibleGaussian(loc, scale, temperature)
 
 # sample with reparameterization
 sample = distribution.rsample()
+print('sample.shape:', sample.shape)
+print('sample.requires_grad:', sample.requires_grad)
 ```
 
 ## 🎮 Demo

diff --git a/README.rst b/README.rst
diff --git a/src/badge_generator.py → badge_generator.py b/src/badge_generator.py → badge_generator.py
diff --git a/basic/basic_code.ipynb b/basic/basic_code.ipynb
@@ -365,12 +365,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "current_dir = os.getcwd()\n",
-    "sys.path.append(os.path.abspath(os.path.join(current_dir, \"..\", \"src\")))\n",
     "from relaxit.distributions import GaussianRelaxedBernoulli"
    ]
   },
@@ -511,12 +509,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "current_dir = os.getcwd()\n",
-    "sys.path.append(os.path.abspath(os.path.join(current_dir, \"..\", \"src\")))\n",
     "from relaxit.distributions import CorrelatedRelaxedBernoulli"
    ]
   },
@@ -581,13 +577,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#  Igor code\n",
-    "current_dir = os.getcwd()\n",
-    "sys.path.append(os.path.abspath(os.path.join(current_dir, \"..\", \"src\")))\n",
     "from relaxit.distributions import StraightThroughBernoulli"
    ]
   },
@@ -692,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -710,8 +703,6 @@
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "current_dir = os.getcwd()\n",
-    "sys.path.append(os.path.abspath(os.path.join(current_dir, \"..\", \"src\")))\n",
     "from relaxit.distributions import HardConcrete\n",
     "\n",
     "%load_ext autoreload\n",

diff --git a/demo/demo.ipynb b/demo/demo.ipynb
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -70,8 +70,6 @@
     "from torch.nn import functional as F\n",
     "from torchvision import datasets, transforms\n",
     "\n",
-    "current_dir = os.getcwd()\n",
-    "sys.path.append(os.path.abspath(os.path.join(current_dir, '..', 'src')))\n",
     "from relaxit.distributions import CorrelatedRelaxedBernoulli\n",
     "\n",
     "%load_ext autoreload\n",

diff --git a/demo/laplace-bridge.ipynb b/demo/laplace-bridge.ipynb
@@ -12,15 +12,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import torch\n",
     "import sys\n",
     "\n",
-    "sys.path.append(\"../src\")\n",
-    "from relaxit.distributions.LogisticNormalSoftmax import LogisticNormalSoftmax\n",
+    "from relaxit.distributions import LogisticNormalSoftmax\n",
     "from relaxit.distributions.approx import (\n",
     "    lognorm_approximation_fn,\n",
     "    dirichlet_approximation_fn,\n",

diff --git a/demo/quickstart.ipynb b/demo/quickstart.ipynb
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quickstart into \"Just Relax It\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from relaxit.distributions import InvertibleGaussian\n",
+    "\n",
+    "# initialize distribution parameters\n",
+    "loc = torch.zeros(3, 4, 5, requires_grad=True)\n",
+    "scale = torch.ones(3, 4, 5, requires_grad=True)\n",
+    "temperature = torch.tensor([1e-0])\n",
+    "\n",
+    "# initialize distribution\n",
+    "distribution = InvertibleGaussian(loc, scale, temperature)\n",
+    "\n",
+    "# sample with reparameterization\n",
+    "sample = distribution.rsample()\n",
+    "print('sample.shape:', sample.shape)\n",
+    "print('sample.requires_grad:', sample.requires_grad)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nkiselev_relaxit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/requirements.txt b/demo/requirements.txt
@@ -7,4 +7,5 @@ torchvision==0.20.1
 matplotlib==3.9.2
 networkx==3.3
 tqdm==4.66.5
-pillow==10.4.0
+pillow==10.4.0
+relaxit==0.1.2
diff --git a/demo/vae_correlated_bernoulli.py b/demo/vae_correlated_bernoulli.py
@@ -9,7 +9,6 @@
 from torchvision import datasets, transforms
 from torchvision.utils import save_image
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
 from relaxit.distributions import CorrelatedRelaxedBernoulli
 
 

diff --git a/demo/vae_gaussian_bernoulli.py b/demo/vae_gaussian_bernoulli.py
@@ -9,7 +9,6 @@
 from torchvision import datasets, transforms
 from torchvision.utils import save_image
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
 from relaxit.distributions import GaussianRelaxedBernoulli
 
 

diff --git a/demo/vae_gumbel_softmax_topk.py b/demo/vae_gumbel_softmax_topk.py
@@ -9,7 +9,6 @@
 from torchvision import datasets, transforms
 from torchvision.utils import save_image
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
 from relaxit.distributions import GumbelSoftmaxTopK
 
 parser = argparse.ArgumentParser(description="VAE MNIST Example")

diff --git a/demo/vae_hard_concrete.py b/demo/vae_hard_concrete.py
@@ -9,7 +9,6 @@
 from torchvision import datasets, transforms
 from torchvision.utils import save_image
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
 from relaxit.distributions import HardConcrete
 
 

diff --git a/demo/vae_invertible_gaussian.py b/demo/vae_invertible_gaussian.py
@@ -9,7 +9,6 @@
 from torchvision import datasets, transforms
 from torchvision.utils import save_image
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
 from relaxit.distributions import InvertibleGaussian
 from relaxit.distributions.kl import kl_divergence