plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.axis as axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['pytorch',  'cuda', 'pytorch cuda*']\n",
    "values = [0.0969,  0.0050, 0.013605]\n",
    "\n",
    "plt.figure(figsize=(30, 6))\n",
    "\n",
    "#plt.yscale(\"log\")\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.bar(names, values)\n",
    "\n",
    "plt.ylabel(\"duration [ms]\")\n",
    "\n",
    "plt.title('matmul (1, 265) x (265, 265)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['pytorch',  'cuda', 'pytorch cuda*']\n",
    "values = [0.8053,  0.0050, 0.042301]\n",
    "\n",
    "plt.figure(figsize=(30, 6))\n",
    "\n",
    "#plt.yscale(\"log\")\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.bar(names, values)\n",
    "\n",
    "plt.ylabel(\"duration [ms]\")\n",
    "\n",
    "plt.title('conv2d inp : (1, 1, 32, 32), filter : (1, 1, 5, 5)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['pytorch',  'cuda', 'pytorch cuda*']\n",
    "values = [0.2687,  0.0090, 0.042301]\n",
    "\n",
    "plt.figure(figsize=(30, 6))\n",
    "\n",
    "#plt.yscale(\"log\")\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.bar(names, values)\n",
    "\n",
    "plt.ylabel(\"duration [ms]\")\n",
    "\n",
    "plt.title('avgpool inp : (1, 1, 32, 32), kernel : (2,2)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12 (main, Apr  5 2022, 01:52:34) \n[Clang 12.0.0 ]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "79314514d26de768ca61e694a41cb6473546bc8da7ab820605403cff1edf8265"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}