sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import glob\n",
    "from PIL import Image,ImageOps,ImageEnhance\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import matplotlib.animation\n",
    "from collections import OrderedDict\n",
    "\n",
    "from models import generators,discriminators\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "\n",
    "netG = generators.Res_Generator(z_dim = 128, n_classes = 4,base_ch = 52\n",
    "                                ,att = True,img_ch = 1,leak = 0\n",
    "                                ,cond_method='conv1x1',SN=False).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading checkpoint for the Artifical dataset provided by TotalEnergies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = torch.load('trained_models/AR_model.pth',map_location=device)\n",
    "_ = netG.load_state_dict(checkpoint['netG_state_dict'])\n",
    "_=netG.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = torch.tensor([0.16])\n",
    "y = y.repeat(4,4)\n",
    "y[0,0] = 0.5\n",
    "y[-1,-1] = 0.5\n",
    "\n",
    "y = y.unsqueeze(0).unsqueeze(0)\n",
    "\n",
    "\n",
    "plt.imshow(y[0,0])\n",
    "plt.colorbar()\n",
    "plt.axis('off')\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "N = 2\n",
    "with torch.no_grad():\n",
    "    z = torch.randn(N**2, 128).to(device=device)\n",
    "    imgs = netG(z,y.to(device=device)).cpu().squeeze(1)\n",
    "    \n",
    "fig2, axes = plt.subplots(N,N,figsize=(10,10))\n",
    "for i,iax in enumerate( axes.flatten() ):\n",
    "\n",
    "    _ = iax.imshow(imgs[i].numpy())\n",
    "\n",
    "        \n",
    "    iax.set_xticks([])\n",
    "    iax.set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading checkpoint for the Real masks of the Brahmaputra river"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = torch.load('trained_models/BR_model.pth',map_location=device) \n",
    "_ = netG.load_state_dict(checkpoint['netG_state_dict'])\n",
    "_=netG.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = torch.tensor([[0, 0, 0.1479, 0.1560],\n",
    "          [0.4206, 0.2695, 0.1770, 0.1895],\n",
    "          [0.8, 0.2667, 0.1723, 0.1851],\n",
    "          [0.4061, 0.2424, 0,0]]).unsqueeze(0).unsqueeze(0)\n",
    "\n",
    "plt.imshow(y[0,0])\n",
    "plt.colorbar()\n",
    "plt.axis('off')\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "N = 2\n",
    "with torch.no_grad():\n",
    "    z = torch.randn(N**2, 128).to(device=device)\n",
    "    imgs = netG(z,y.to(device=device)).cpu().squeeze(1)\n",
    "    \n",
    "fig2, axes = plt.subplots(N,N,figsize=(10,10))\n",
    "for i,iax in enumerate( axes.flatten() ):\n",
    "\n",
    "    _ = iax.imshow(np.round(imgs[i].numpy()*0.5+0.5))\n",
    "\n",
    "        \n",
    "    iax.set_xticks([])\n",
    "    iax.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit ('base': conda)",
   "language": "python",
   "name": "python3710jvsc74a57bd05cc64d85353abfa0cbce42cd46ee6c1cdbe4858630bbd2b24dba579fcc5a2329"
  },
  "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": 2
}