From cbaf11f1514e2c9aa1539b5d8c31122272c5dcf2 Mon Sep 17 00:00:00 2001
From: Piotr Migdal <piotr.migdal@deepsense.io>
Date: Fri, 7 Apr 2017 14:49:36 +0200
Subject: [PATCH] roi pooling minimal example in Jupyter Notebook

---
 README.md                                  |   3 +
 examples/roi_pooling_minimal_example.ipynb | 148 +++++++++++++++++++++
 2 files changed, 151 insertions(+)
 create mode 100644 examples/roi_pooling_minimal_example.ipynb

diff --git a/README.md b/README.md
index 685288e..63e20a2 100644
--- a/README.md
+++ b/README.md
@@ -41,3 +41,6 @@ from roi_pooling.roi_pooling_ops import roi_pooling
 rpooling = roi_pooling(feature_map, rois, 7, 7)
 # continue the model
 ```
+
+Working example in Jupyter Notebook: [examples/roi_pooling_minimal_example.ipynb](https://github.com/deepsense-io/roi-pooling/blob/master/examples/roi_pooling_minimal_example.ipynb)
+
diff --git a/examples/roi_pooling_minimal_example.ipynb b/examples/roi_pooling_minimal_example.ipynb
new file mode 100644
index 0000000..c1edc35
--- /dev/null
+++ b/examples/roi_pooling_minimal_example.ipynb
@@ -0,0 +1,148 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* blog post: [Region of interest pooling explained - deepsense.io](https://deepsense.io/region-of-interest-pooling-explained/)\n",
+    "* repository: [deepsense-io/roi-pooling](https://github.com/deepsense-io/roi-pooling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "\n",
+    "from roi_pooling.roi_pooling_ops import roi_pooling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# 4x4 feature map with only 1 channel\n",
+    "input_value = [[\n",
+    "    [[1], [2], [4], [4]],\n",
+    "    [[3], [4], [1], [2]],\n",
+    "    [[6], [2], [1], [7]],\n",
+    "    [[1], [3], [2], [8]]\n",
+    "]]\n",
+    "input_value = np.asarray(input_value, dtype='float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# regions of interest as lists of:\n",
+    "# feature map index, upper left, bottom right coordinates\n",
+    "rois_value = [\n",
+    "    [0, 0, 0, 1, 3],\n",
+    "    [0, 2, 2, 3, 3],\n",
+    "    [0, 1, 0, 3, 2]\n",
+    "]\n",
+    "rois_value = np.asarray(rois_value, dtype='int32')\n",
+    "\n",
+    "# in this case we have 3 RoI pooling operations:\n",
+    "# * channel 0, rectangular region (0, 0) to (1, 3)\n",
+    "#              xx..\n",
+    "#              xx..\n",
+    "#              xx..\n",
+    "#              xx..\n",
+    "#\n",
+    "# * channel 0, rectangular region (2, 2) to (3, 3)\n",
+    "#              ....\n",
+    "#              ....\n",
+    "#              ..xx\n",
+    "#              ..xx\n",
+    "# * channel 0, rectangular region (1, 0) to (3, 2)\n",
+    "#              ....\n",
+    "#              xxx.\n",
+    "#              xxx.\n",
+    "#              xxx."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[[ 3.  4.]\n",
+      "   [ 6.  3.]]]\n",
+      "\n",
+      "\n",
+      " [[[ 1.  7.]\n",
+      "   [ 2.  8.]]]\n",
+      "\n",
+      "\n",
+      " [[[ 4.  4.]\n",
+      "   [ 4.  7.]]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_featuremap = tf.placeholder(tf.float32)\n",
+    "rois = tf.placeholder(tf.int32)\n",
+    "input_const = tf.constant(input_value, tf.float32)\n",
+    "rois_const = tf.constant(rois_value, tf.int32)\n",
+    "y = roi_pooling(input_const, rois_const, pool_height=2, pool_width=2)\n",
+    "\n",
+    "with tf.Session('') as sess:\n",
+    "    y_output = sess.run(y, feed_dict={input_featuremap: input_value, rois: rois_value})\n",
+    "    print(y_output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}