molecule_decoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "import yaml\n",
    "import pandas as pd\n",
    "from PrepareData import prepare_data\n",
    "\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim, Tensor\n",
    "from torch.nn import functional as F\n",
    "import pickle \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import Draw\n",
    "import seaborn as sns\n",
    "from architecture import CLIP\n",
    "from tqdm import tqdm\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "dtype = torch.float32\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from train_utils import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "config = yaml.safe_load(open('./checkpoints/RUN_unit_norm_fixed_decoder/config.yaml'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CLIP(config)\n",
    "model.to(device)\n",
    "model = torch.nn.parallel.DataParallel(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from train_utils import Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = pickle.load(open(config['data']['vocab_path'], 'rb'))\n",
    "sampler = Sampler(model.module.smiles_decoder,vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.rand(1).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9c39d0c66ec487184c36dbc14bbb4f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "356ddf3d856544fbbb576eabad81bf88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from train_utils import decoder_performance\n",
    "from PrepareData import prepare_data\n",
    "dataloaders, max_charge, num_species = prepare_data(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 10.70it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[']H3H',\n",
       " 'N-(',\n",
       " '#=<mask>4',\n",
       " ']31(+++4+-FO4()=c',\n",
       " 'C)+FHHo4)N#Hn3O[=#FcO1N+H-(o2F++',\n",
       " '[FN',\n",
       " '2==<mask>4(=1NN<mask>o((-FFFF]<mask>H()NcH2=4n',\n",
       " 'oF-o=)No]1#(++4))223+N)H=<mask>[o4FO4+-##',\n",
       " 'F+(+]O+NO=(4+-1())H)H]o-o)3H)oF+<mask>Fnn',\n",
       " '']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sampler.sample_multi(10,torch.rand(512))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, data in enumerate(dataloaders[\"val\"]):\n",
    "    spec_batch = data['IR'].to(device,dtype)\n",
    "    one_hot = data['smiles'].to(device)\n",
    "    spec_latent = model.module.forward_spec(data)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 512])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_latent.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 24.47it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['C4==2o[CO]4=3',\n",
       " '#O]N)+(+FO<mask>=-',\n",
       " '4O[]3oNcc=]4oNNo-N+FF)F(CF=',\n",
       " 'F+FN<mask>oN=)N=NN-N4)',\n",
       " 'Fc[N',\n",
       " '2H+13]C[41H(cnN)4)2+NcH2c',\n",
       " '2O)NO3)2cH+)))-nH[<mask>cHoH)(',\n",
       " '<mask>Occ)N<mask>C-H)=HHo-2c(4)=(H=#(1)2=2+FO+',\n",
       " '1+O])O)-++)]+)4O4)NN4',\n",
       " '-][](]43=(']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler.sample_multi(10,spec_latent[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 17.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage invalid 0.9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 25.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage invalid 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 19.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage invalid 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 30.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage invalid 0.9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:04<00:00, 24.81it/s]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=2200x800>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_performance(config, model ,dataloaders,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Aug 10 05:20:16 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ...  On   | 00000000:31:00.0 Off |                  N/A |\n",
      "| 70%   65C    P2   303W / 350W |  12198MiB / 24576MiB |     86%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  NVIDIA GeForce ...  On   | 00000000:B1:00.0 Off |                  N/A |\n",
      "| 72%   67C    P2   292W / 350W |   9308MiB / 24576MiB |     89%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A     37766      C   python                          12195MiB |\n",
      "|    1   N/A  N/A     37766      C   python                           9305MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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