cifar_spike_encoding_pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Poisson Rate Coding using Pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<torch._C.Generator at 0x7f8c766ad030>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computation\n",
    "import torch\n",
    "\n",
    "# Dataset\n",
    "from torchvision.datasets import CIFAR10\n",
    "from torchvision.transforms import transforms\n",
    "\n",
    "# Visualization\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Matplotlib params\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['axes.grid'] = False\n",
    "\n",
    "# Fixing seed\n",
    "torch.manual_seed(42)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Prepration"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "batch_size_train = 64\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "testset = CIFAR10('.pytorch/CIFAR10', train=False, transform=transform_test, download=True)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size_train, shuffle=False, num_workers=2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_batch, labels_batch = next(iter(testloader))\n",
    "\n",
    "image = image_batch[11]\n",
    "\n",
    "# Normalizing the image\n",
    "image = (image - image.min())/(image.max()-image.min())\n",
    "plt.imshow(image.permute([1, 2, 0]))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Rate Coding"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Creating Sample"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "time_steps = 100\n",
    "\n",
    "# Repeating by the number of time_steps\n",
    "raw_vector = torch.cat([\n",
    "    torch.ones(1, 3, 32, 32),\n",
    "    image.repeat(time_steps, 1, 1, 1)\n",
    "])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Encoding"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "rate_coded_vector = torch.poisson(raw_vector)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Visualizing rate-coded sample"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 4 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplot_mosaic(\n",
    "    \"\"\"\n",
    "    A.BCD\n",
    "    \"\"\",\n",
    "    figsize=(15, 5),\n",
    "    gridspec_kw={'width_ratios':[1,0.3,1,1,1]}\n",
    ")\n",
    "fig.suptitle('Poisson Rate Coding', fontsize=20)\n",
    "\n",
    "ax['A'].imshow(image.permute([1, 2, 0]))\n",
    "ax['A'].set_xlabel('Original image', fontsize=18)\n",
    "\n",
    "ax['B'].imshow(rate_coded_vector[0].permute([1, 2, 0]))\n",
    "ax['B'].set_xlabel('t = 0', fontsize=18)\n",
    "\n",
    "ax['C'].imshow(rate_coded_vector[:time_steps//3].mean(0).permute([1, 2, 0]))\n",
    "ax['C'].set_xlabel(f't = {time_steps//3}', fontsize=18)\n",
    "\n",
    "ax['D'].imshow(rate_coded_vector.mean(0).permute([1, 2, 0]))\n",
    "ax['D'].grid(None)\n",
    "ax['D'].set_xlabel(f't = {time_steps}', fontsize=18)\n",
    "\n",
    "plt.grid(None)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}