notebook/predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bd99715",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "import cv2\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import rdkit.Chem as Chem\n",
    "from rdkit.Chem import Draw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc5a932",
   "metadata": {},
   "source": [
    "### Specify the model checkpoint and image file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2e09c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_PATH = '../ckpts/swin_base_char_aux_200k.pth'\n",
    "IMAGE_PATH = '../assets/example.png'\n",
    "MORE_IMAGES = ['../assets/example.png', '../assets/example1.png', '../assets/example2.png']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ec92c8",
   "metadata": {},
   "source": [
    "### Load the model and run inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "988348ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/rsg/nlp/yujieq/anaconda3/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "/data/rsg/nlp/yujieq/anaconda3/lib/python3.7/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
     ]
    }
   ],
   "source": [
    "from molscribe import MolScribe\n",
    "\n",
    "device = torch.device('cuda')\n",
    "model = MolScribe(MODEL_PATH, device)\n",
    "smiles, molblock = model.predict_image_file(IMAGE_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d94cb60d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted SMILES:\n",
      "Fc1ccc(-c2cc(-c3ccccc3)n(-c3ccccc3)c2)cc1\n",
      "\n",
      "Predicted MolBlock:\n",
      "\n",
      "     RDKit          2D\n",
      "\n",
      " 14 15  0  0  0  0  0  0  0  0999 V2000\n",
      "    4.8691   -8.5238    0.0000 R   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.8691   -6.9365    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.5166   -5.9841    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.0576   -4.5556    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.1108   -3.2857    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.4878   -3.4444    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.6763   -2.1746    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.2173   -0.7460    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.2705    0.5238    0.0000 F   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.8403   -0.5873    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.7871   -1.8571    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.5454   -4.5556    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    6.0864   -5.9841    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    7.5742   -6.4603    0.0000 R   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  1  0\n",
      "  2  3  1  0\n",
      "  2 13  1  0\n",
      "  3  4  2  0\n",
      "  4  5  1  0\n",
      "  4 12  1  0\n",
      "  5  6  2  0\n",
      "  5 11  1  0\n",
      "  6  7  1  0\n",
      "  7  8  2  0\n",
      "  8  9  1  0\n",
      "  8 10  1  0\n",
      " 10 11  2  0\n",
      " 12 13  2  0\n",
      " 13 14  1  0\n",
      "A    1\n",
      "Ph\n",
      "A   14\n",
      "Ph\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "image = cv2.imread(IMAGE_PATH)\n",
    "plt.imshow(image)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "print('\\nPredicted SMILES:')\n",
    "print(smiles)\n",
    "\n",
    "print('\\nPredicted MolBlock:')\n",
    "print(molblock)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faaa4a5d",
   "metadata": {},
   "source": [
    "### Predicted SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "046b5817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mol = Chem.MolFromSmiles(smiles)\n",
    "svg = Draw.MolsToGridImage([mol])\n",
    "display(svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c620c9df",
   "metadata": {},
   "source": [
    "### Predicted MolBlock\n",
    "RDKit does not display superatoms. To view the full prediction, copy-paste the molblock\n",
    "to ChemDraw (note the beginning blank line must be included)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50dd2e42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QiHbv3v3++++7ubnduXPH3t7+GZ9kHhlt/aSo7iFS3SOjbRKJRGKx+Pbt27a2tvr+EgAA0DEYYxBqNBp/f//Lly8vWbJkz5499fX1MpmsrKyMeUy0RWNXWVn5jKl4PJ5YLNY9Ndq5c2cXFxfmp1gsnj179r/+9a/AwMDTp0/jxAkAANNkjEFIRLdu3Ro4cKBWq7WwsHj2+7gFAoFYLNY9ROPq6so8Msr8KhaLBYKnPhBUXFzcr1+/ysrKbdu2rVixgoXvAQAAxs5Ig5CIDh48eO3atX379rXYHeHg4ND812fsjmiP5OTkyZMnCwSCS5cuDR06VF/FAwBAR2G8QUhEVVVVarWa7a3uy5cv37lzp4+PT1ZWlp2dHatrAQCAsTHqIDQMpVI5YsSIrKysadOmSaVSrssBAACDMoqXbnNLJBIlJCTY2dklJSXt2bOH63IAAMCg0BH+m1QqDQ0NFYlEV65c6d+/P9flAACAgaAj/Lfp06cvWbJEqVSGhYW1PqQCAAD+qNAR/odSqRw2bFhOTk5oaGhCQgLX5QAAgCGgI/wPkUiUmJhoa2ubmJj4j3/8g+tyAADAENARtpSYmBgWFmZhYZGZmenn58d1OQAAwC50hC2FhoYuXLiwoaEhNDS0traW63IAAIBd6Ajb0NDQMHTo0Nzc3BkzZhw7dozrcgAAgEXoCNtgYWGRmJjYqVOn+Pj4mJgYrssBAAAWIQjb1r179x07dhBRZGTknTt3uC4HAADYgiB8qrlz586dO/fx48ehoaH19fVclwMAAKxAED7Lrl273njjjby8vOXLl3NdCwAAsAIPyzxHXl7e4MGD6+vrDx06FBERwXU5AACgZ+gIn6NXr17btm0jomXLluXn53NdDgAA6Bk6wnaJiIiIjY3t3bv3tWvXXvEoYAAAMCroCNtl9+7dvr6+P/3006pVq7iuBQAA9AkdYXvdvn17yJAhT548iYuLmz17NtflAACAfiAIX8DevXuXLl1qY2Nz/fr1Hj166H1+Ho+n9zkBAODZEIQvQKvVTpky5cyZMxqNho35L168OHjwYGtrazYmBwCANuEe4Qvg8XiTJk1iKQWJaOzYsYmJiSxNDgAAbUIQvhjmHdz79u3T6ltMTIxWq42Ojub6KwIAmBZcGn0BBQUFr7/+uqWlZWlpqa2trX4nf/z4sZubW21tbX5+vq+vr34nBwCAp0FH+AJiY2O1Wu3UqVP1noJEZG1tHRoaSkSHDh3S++QAAPA06AjbS6PReHt7FxUVXbhw4e2332ZjiYyMjLfeeksikRQXF/P5fDaWAACAFtARtldaWlpRUZG3t/eYMWNYWmLEiBG+vr5yufz8+fMsLQEAAC0gCNvr4MGDRBQeHq7b7Xfq1Ck/P7+jR4++9JyJiYkDBgxISEjQjTDv9cZpwAAABoMgbJeqqqrjx4+bmZnNmzdPNxgdHZ2bm1tVVfXS09bW1mZnZzd/UjQiIoLP5586daqiouJVCgYAgHZCELaLVCp98uSJv7+/l5cXMyKXy8+dO2dpafkqr1sLCwuzsbG5ePFiUVERM+Lu7v7OO+80NjY2bxMBAIA9CMJ2Ya5Vzp8/XzcSGxvb1NQ0efJkOzu7l57WxsZm6tSpGo3m8OHDukFmFeZKLAAAsA1PjT7f3bt3e/bsaW9vL5fLmTOYtFqtr6/vvXv3fvjhh/Hjx7/K5KmpqW+//XbXrl0LCgqYu4+NjY3u7u4VFRU3b97s27evfr4DAAA8BTrC52M29k2fPl13EuHVq1fv3bvXpUuXgICAV5x89OjRPj4+v/76a3p6OjNibm4eFhZG2FAIAGAQCMLnUKvVzHXL5tdFmeuWc+bMMTN71f+APB4vPDyc/vtaKLPWkSNHVCrVK84PAADPhkujz/HDDz9MmDChZ8+ed+7cYUbq6+uZd6H9/PPPr7/++qsvwWxPtLS0lMvlunfW+Pn55ebmnjx5Migo6NWXAACAp0FH+BxMozZ37lzdyIkTJ2pqaoYPH66XFCQiT0/P0aNHP378WCqV6gaZfRrYUAgAwDYE4bNUVFR89913AoGgeRAy0dj8SumrY2Kv+dXR8PBwkUh05syZsrIyPS4EAAAtIAifJT4+vrGxcdy4ca6ursxIQUFBamqqjY0N8zyLvkybNs3e3j4jI+Pu3bvMiJOT04QJE5qaml7lzTUAAPBcCMJnad38MQdQBAcH29jY6HEhS0vLadOmEVHrDYUHDhzQ40IAANACHpZ5qtzcXD8/P2dn5wcPHpibm1OzAyguXryo91dvM0dPuLu7FxUVMUdPNDU1eXh4lJaWXr9+feDAgfpdDgAAGOgIn4p5UGXmzJlMCtL/HUDh4+MzevRovS/HHD0hk8lSUlKYEYFAwLy/DW+ZAQBgD4KwbSqV6siRI9TW9sHmB1DoF3P0RJsbCp88ecLGigAAgEujbTt16lRwcLCfn19OTg4zUlVVJZFIlEplYWGhp6cnG4vKZDJPT08+ny+TyV577TVmcMiQIdeuXYuPj9fv4zkAAMBAR9i21o/J6A6gYCkFqdnRE/Hx8bpBpgZsKAQAYAmCsA1lZWVnz54ViUTNj1hiY/tga603FM6cOdPKyur8+fMlJSWsLg0AYJoQhG04cuRIU1PTpEmTnJycmJH8/PzMzEx7e3tmkwN7pkyZ4uDgkJ2dnZuby4zY2dkFBQVpNJq4uDhWlwYAME0Iwja0bv7u3btnZ2cXHBysO4CCJRYWFqGhoba2tvfv39cN6k4oxA1dAAC9w8MyLV2/fn3w4MESiaS4uJjZz8eor6+vra0Vi8VsF6BQKKytra2trXUjuv2Lly9fHjFiBNsFAACYFHSELWVkZPB4vEGDBjVPQSKysrIyQAoSkYuLS/MUJCIzM7OhQ4fyeLyIiIhvvvmmqKjIAGUAAJgIdIQtlZeX9+jRo7q6euHChd98841+X6X2ErRa7eeff75x40aBQNDY2MgM9u/fPzg4ODg4uFevXpxWBwDQ4SEI2yCVShcvXvzo0SMPD4+YmBi9v02t/RQKRVhYWFpamlAojIqKGjhwYFJSklQqLS0tZT7QpUuXd999NzAwcPz48UKhkKs6AQA6LgRh24qLi8PDwy9dusTj8ZYvX/7ll1+KRCID13Dt2rVp06aVlJS4urrGx8f7+/sz4xqN5sqVK8nJycePH//ll1+YQUdHx4kTJ06aNGnChAktrqwCAMAzIAifSqPR7NixY+3atY2Njb17946Li+vbt6/BVt+/f39kZGRDQ8OwYcOkUqm7u3vrz6jV6oyMjBMnTpw4cUK3y9DR0XHp0qWjRo3y9/c3fHgDAHQ4CMLnuH79+pw5c+7du2dhYbFx48aPPvrIzIzdJ4waGxuXL1/+7bffEtGKFSva2Yzm5eUlJyd///33xcXFarVaq9WKRKLBgwe/8847QUFBuhe2AQBACwjC53vy5Mm6deu2b99ORH/6059iYmLa7M/0ori4OCQk5MaNG1ZWVvv27ZszZ86LzpCfn3/27NmzZ8/m5+czIyKRaNSoURMnTgwICLC3t2/x+YSEhNdee23s2LF6qB4AoANCELbXyZMn//znP1dUVNjb2+/atWvWrFl6X+LixYszZsz4/ffffXx8kpKS/Pz8XmU2uVx+8eLFlJSU1NTUpqYmIjIzM+vdu/c777wzderUrl27EtG8efPc3d3Nzc3r6uo2b96sn68BANChIAhfQFFRUURExKVLlzw8vCdPvhMVJbKy0tvk+/aRVLruwoWosWPHHjt2zNnZWV8zy+Vypke8fv26Wq0mIjMzs0GDBq1bt65Pnz5r1qwRiURff/0125d8AQCME4LwxajV6qioqOPHR2Znj+zRg+Li6NWPjn/yhJYto5gYEgjUmzYdXLt2fou9/PpSU1OTnp6ekpLyww8/PH78+MyZM+Xl5UqlsqioaOzYsdiSCACmCUH4MvLyaM4cunmT+Hz68EPatIn+7xD7F3bnDk2dSj//TI6OFBdH776r10Kforq6+uLFi1OmTEEXCACAIHxJSiVt2ECbN5NGQwMHUlwc9ejxwpMkJdGCBVRbS4MHU1ISdenCQqEAAPBMaAhekkhEf/87paRQ58504wb160fbtlH7/6dCpaIPPqDQUKqtpffeo/R0pCAAADfQEb6q6mqKjCTmrMBx4+jAAZJInvMnZWUUFkaXLpGlJe3dSxERBigTAADahiDUD6mUFi+mR4/I2Zmio2ny5Kd+MjOTpk8nmYy6dqWkJOrf34BVAgBAKwhCvSkupogISk8nIgoPp9Wr6dw5WrKE7OyIiOrqaNcukkho8WJ68oRGjqSEBHJz47ZkAADAPUL98fCgH3+kTz4hgYASEig1ldato7Vr//2vNTW0bh1ZWVFjI23YQKmpSEEAAKOAjlD/MjPpp5/IyYk2bqSyMjpzhgYNIrmc3N1Jo6GcHFwOBQAwIugI9W/YMFq0iIjI3p42baIFC0il+s+/IgUBAIwKgpBd771HAgHt2sV1HQAA8BQCrgv4gxMIKDqaxo6lkSO5LgUAANqCjpB1AwZQaCh99hnXdQAAQFvQERrCV1+Rry/XRQAAQFvQEbLF2vo/r5ixtaW//528vTktCAAA2oLtE+wqLKTvv6fkZEpJ4boUAABoCzpCdjU20sqVVFLCdR0AAPAUCEIAADBpCEIAADBpCEIAADBpCEIAADBpCEIAADBpCEIAADBpCEIAADBpCEIAADBpCEIAADBpCEJ2lZRkjRixNjAwj+tCAACgbTh9gl1du9oVFsbxeJlE/+S6FgAAaAM6QgAAMGkIQgAAMGkIQgAAMGkIQgAAMGkIQgAAMGkIQgAAMGkIQgAAMGkIQgAAMGkIQnY9ePDA1dU1KCiI60IAAKBtPK1Wy3UNf3A//fSTq6vra6+9xnUhAADQBnSE7MrKyjp//rxCobh9+zbXtQAAQBsQhCxSKpVpaWmpqan79u0rKCjguhwAAGgDXrrNIpFI9OabbxLRrVu3/P39uS4HAADagCBkV5cuXYYMGfLbb79ZWVlxXQsAALQBD8sAAIBJwz1CAAAwaQhCAAAwaQhCAAAwaQhCAAAwaQhCAAAwaQhCAAAwaQhCAAAwaQhCAAAwaf8LnfI7LRxDAvsAAAEuelRYdHJka2l0UEtMIHJka2l0IDIwMjEuMDMuMgAAeJx7v2/tPQYg4AFiRgYI4ANifiBuYGQw0AAJMEpyAEnHkvxcv9KcHA0ZBm12B5AEMzObQwaYZmSDCLAgaLgEmgpOBgWQkdgUoKnEaTs30KmMTAmMPAlMzBlMzCwKzNwJLKwZTCxcCaxsCWzsGUzsHAzsnAmcXBlM3DwZTDy8DCJMrIxMzNw8bKxs7JxcLOIwvzLwrT4/26EmkeMgiANin/9+9wCI/ZM/0WF/7X4w+871Roeq4xPB7Ehxd4eXHkFgtnXBPnvewhgw+7+irv1SHW4wu/bCbPvbP+32g9i/6rrsDomz2YPYVcdNHbwjxcDiN+OLHEq2vgWzXxdvhJt/6vAhuL034z85HN91DswWAwAxl1VE06EKhgAAAT56VFh0TU9MIHJka2l0IDIwMjEuMDMuMgAAeJyllM9uwyAMxu88hc+Vatlg8+dYLdtlWjXt0GfY7nt/zZCUomqTMopQ8n0m/OIEg4PaPpbXr2/ozS/OAQuwAtCvvZQCF09Erj4vmGNhE8eM6kOuMUIbJTjAX4ix31EilhB1oJz3UwIqx1gpiiULD5Sn/+RCmhpFUFXjHCUgM9W/cQzos6Y5CqPktFHE2hyFMKYYKsUjJ5n8IkbPqVEIDUKzufhE2/re1cvLforHLLTlorllNbVGKadWdYy2RJP1oqii8mi9RKQc5dHaTahJ/LqPbInG/7J7N9qrfbtWY8ogcjV8M6vajN20z7HDw/cRC8fBMHdj4dSNqdyNqdJpGZiuI6ZuAB7TMWWH1jrn0qoVDk1Y9OBOLeLeP5tiqeoN4Pm8uB+2WcazuGKWLwAAANd6VFh0U01JTEVTIHJka2l0IDIwMjEuMDMuMgAAeJxdjruOwkAMRX9lS4I8lj3jV5iKhppfQCMkkMJmtbslH88kFJFwYx1f6+juG7e2Sy23vk5Da3lo37z/eu4c1SVDMhSjAtWQwgSS4hjCUBVVtLOgqhpUQVK3jQsyU0AqmEMdKqOEryx9oBKaW4GUkV1syTN7Z8IVCbOTAqHmElAzhtCaavSvbvdwhsTY5bywstnWTjBs5KX9WEw3jrdvgMv//Dj/zj8Hwsc8ne7T9TjdL394vh24fJ6eL61cQ5qVTfbiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mol = Chem.MolFromMolBlock(molblock)\n",
    "svg = Draw.MolsToGridImage([mol])\n",
    "display(svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5cd7775",
   "metadata": {},
   "source": [
    "### Run inference with multiple images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "730ed692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Fc1ccc(-c2cc(-c3ccccc3)n(-c3ccccc3)c2)cc1', 'COc1ccc(-c2cc(-c3ccccc3)n(-c3ccccc3)c2)cc1', 'Cc1ccc(-n2cc(C)cc2-c2ccccc2)cc1')\n"
     ]
    }
   ],
   "source": [
    "smiles, molblock = model.predict_image_files(MORE_IMAGES)\n",
    "print(smiles)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}