gen_for_eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set CUDA:2 as current device.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import torch\n",
    "\n",
    "sys.path.append(os.path.abspath(sys.path[0] + '/../'))\n",
    "torch.cuda.set_device(2)\n",
    "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
    "torch.autograd.set_grad_enabled(False)\n",
    "\n",
    "from data.spherical_view_syn import *\n",
    "from configs.spherical_view_syn import SphericalViewSynConfig\n",
    "from utils import netio\n",
    "from utils import img\n",
    "from utils import misc\n",
    "from utils import device\n",
    "from utils import view\n",
    "from components.gen_final import GenFinal\n",
    "\n",
    "\n",
    "def load_net(path):\n",
    "    config = SphericalViewSynConfig()\n",
    "    config.from_id(path[:-4])\n",
    "    config.SAMPLE_PARAMS['perturb_sample'] = False\n",
    "    #config.print()\n",
    "    net = config.create_net().to(device.default())\n",
    "    netio.load(path, net)\n",
    "    return net\n",
    "\n",
    "\n",
    "def find_file(prefix):\n",
    "    for path in os.listdir():\n",
    "        if path.startswith(prefix + '@'):\n",
    "            return path\n",
    "    return None\n",
    "\n",
    "\n",
    "def load_views(data_desc_file) -> view.Trans:\n",
    "    with open(data_desc_file, 'r', encoding='utf-8') as file:\n",
    "        data_desc = json.loads(file.read())\n",
    "        view_centers = torch.tensor(\n",
    "            data_desc['view_centers'], device=device.default()).view(-1, 3)\n",
    "        view_rots = torch.tensor(\n",
    "            data_desc['view_rots'], device=device.default()).view(-1, 3, 3)\n",
    "        return view.Trans(view_centers, view_rots)\n",
    "\n",
    "scenes = {\n",
    "    'gas': '__0_user_study/us_gas_all_in_one',\n",
    "    'mc': '__0_user_study/us_mc_all_in_one',\n",
    "    'bedroom': 'bedroom_all_in_one',\n",
    "    'gallery': 'gallery_all_in_one',\n",
    "    'lobby': 'lobby_all_in_one'\n",
    "}\n",
    "\n",
    "fov_list = [20, 45, 110]\n",
    "res_list = [(128, 128), (256, 256), (256, 230)]\n",
    "res_full = (1600, 1440)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change working directory to  /home/dengnc/deep_view_syn/data/__0_user_study/us_mc_all_in_one\n",
      "Load net from fovea@nmsl-rgb_e10_fc128x4_d1-50_s32.pth ...\n",
      "Load net from periph@nnmsl-rgb_e10_fc64x4_d1-50_s16.pth ...\n",
      "Dataset loaded.\n",
      "views: [110]\n"
     ]
    }
   ],
   "source": [
    "scene = 'mc'\n",
    "os.chdir(sys.path[0] + '/../data/' + scenes[scene])\n",
    "print('Change working directory to ', os.getcwd())\n",
    "\n",
    "fovea_net = load_net(find_file('fovea'))\n",
    "periph_net = load_net(find_file('periph'))\n",
    "\n",
    "# Load Dataset\n",
    "views = load_views('nerf_views.json')\n",
    "print('Dataset loaded.')\n",
    "print('views:', views.size())\n",
    "gen = GenFinal(fov_list, res_list, res_full, fovea_net, periph_net,\n",
    "               device=device.default())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for view_idx in range(20):\n",
    "    center = (0, 0)\n",
    "    test_view = views.get(view_idx)\n",
    "    images = gen.gen(center, test_view,\n",
    "                     mono_trans=view.Trans(test_view.trans_point(\n",
    "                         torch.tensor([-0.03, 0, 0], device=device.default())\n",
    "                     ), test_view.r))\n",
    "    #left_images = gen(center, view.Trans(\n",
    "    #    test_view.trans_point(\n",
    "    #        torch.tensor([-0.03, 0, 0], device=device.default())\n",
    "    #    ), test_view.r), mono_trans=test_view, ret_raw=True)\n",
    "    #right_images = gen(center, view.Trans(\n",
    "    #    test_view.trans_point(\n",
    "    #        torch.tensor([0.03, 0, 0], device=device.default())\n",
    "    #    ), test_view.r), mono_trans=test_view, ret_raw=True)\n",
    "    #plot_figures(images, center)\n",
    "\n",
    "    outputdir = '/home/dengnc/deep_view_syn/data/__1_eval/output_mono_periph/ref_as_right_eye/%s/' % scene\n",
    "    misc.create_dir(outputdir)\n",
    "    #for key in images:\n",
    "    key = 'blended'\n",
    "    img.save(images[key], outputdir + 'view%04d_%s.png' % (view_idx, key))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "gen() takes 3 positional arguments but 4 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-245bea67ea44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mcenter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mtest_view\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mviews\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_view\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0;31m#plot_figures(images, center)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: gen() takes 3 positional arguments but 4 were given"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "gaze_idx = 0\n",
    "for y in np.linspace(-200, 200, 11):\n",
    "    for x in np.linspace(-200, 200, 11):\n",
    "        center = (int(x), int(y))\n",
    "        test_view = views.get(0)\n",
    "        images = gen.gen(center, test_view, True)\n",
    "        #plot_figures(images, center)\n",
    "\n",
    "        misc.create_dir('output/eval_gaze')\n",
    "        out_path = 'output/eval_gaze/gaze%03d_%d,%d.png' % (gaze_idx, x, y)\n",
    "        img.save(images['blended'], out_path)\n",
    "        print('Output ' + out_path)\n",
    "        gaze_idx += 1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.9 64-bit ('pytorch': conda)",
   "name": "python379jvsc74a57bd0660ca2a75467d3af74a68fcc6f40bc78ab96b99ff17d2f100b5ca821fbb183f2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}