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Nianchen Deng's avatar
tog'21 baseline    
Nianchen Deng committed
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set CUDA:0 as current device.\n",
      "Change working directory to  /home/dengnc/dvs/data/__new/barbershop_all\n",
      "Load net from fovea200@snerffast4-rgb_e6_fc512x4_d1.20-6.00_s64_~p.pth ...\n",
      "Load net from periph200@snerffast2-rgb_e6_fc256x4_d1.20-6.00_s32_~p.pth ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dengnc/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py:1709: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
    "sys.path.append(rootdir)\n",
    "torch.cuda.set_device(0)\n",
    "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
    "torch.autograd.set_grad_enabled(False)\n",
    "\n",
    "from configs.spherical_view_syn import SphericalViewSynConfig\n",
    "from utils import netio\n",
    "from utils import img\n",
    "from utils import device\n",
    "from utils.view import *\n",
    "from components.fnr import FoveatedNeuralRenderer\n",
    "\n",
    "\n",
    "def load_net(path):\n",
    "    config = SphericalViewSynConfig()\n",
    "    config.from_id(os.path.splitext(path)[0])\n",
    "    config.sa['perturb_sample'] = False\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",
    "scenes = {\n",
    "    'classroom': 'classroom_all',\n",
    "    'stones': 'stones_all',\n",
    "    'barbershop': 'barbershop_all',\n",
    "    'lobby': 'lobby_all'\n",
    "}\n",
    "\n",
    "# origin\n",
    "fov_list = [20, 45, 110]\n",
    "res_list = [(256, 256), (256, 256), (400, 360)]\n",
    "\n",
    "# only fovea & mid\n",
    "fov_list = [20, 45, 110]\n",
    "res_list = [(256, 256), (256, 256), (900, 810)]\n",
    "\n",
    "# only fovea & mid, expand fovea to 30\n",
    "fov_list = [30, 45, 110]\n",
    "res_list = [(400, 400), (256, 256), (900, 810)]\n",
    "\n",
    "# only fovea & mid, expand fovea to 40\n",
    "fov_list = [60, 110, 110]\n",
    "res_list = [(800, 800), (900, 900), (900, 810)]\n",
    "\n",
    "res_full = (1600, 1440)\n",
    "\n",
    "scene = 'barbershop'\n",
    "os.chdir(f'{rootdir}/data/__new/{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",
    "renderer = FoveatedNeuralRenderer(fov_list, res_list, nn.ModuleList([fovea_net, periph_net, periph_net]),\n",
    "                                  res_full, device=device.default())\n",
    "\n",
    "view = Trans(torch.tensor([-0.03081111, 0.0020451, -0.01802763], device=device.default()),\n",
    "             torch.tensor([0.998645, 0.002576269, -0.05197617,\n",
    "                           -0.001313272, 0.9997034, 0.02431908,\n",
    "                           0.0520234, -0.02421787, 0.9983522], device=device.default()).view(3, 3))\n",
    "gaze = [37.55656052, 20.7297554]\n",
    "images = renderer(view, gaze, using_mask=False, ret_raw=True)\n",
    "outputdir = '../__demo/mono_f60&m110/'\n",
    "misc.create_dir(outputdir)\n",
    "img.save(images['layers_img'][0], f'{outputdir}{scene}_fovea.png')\n",
    "img.save(images['blended'], f'{outputdir}{scene}.png')\n",
    "img.save(images['blended_raw'], f'{outputdir}{scene}_noCE.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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