{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/e/dengnc')\n",
    "\n",
    "from typing import List\n",
    "import torch\n",
    "from torch import nn\n",
    "import matplotlib.pyplot as plt\n",
    "from deep_view_syn.data.lf_syn import LightFieldSynDataset\n",
    "from deep_view_syn.my import util\n",
    "from deep_view_syn.trans_unet import LatentSpaceTransformer\n",
    "\n",
    "device = torch.device(\"cuda:2\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = '../data/lf_syn_2020.12.23'\n",
    "TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
    "\n",
    "train_dataset = LightFieldSynDataset(TRAIN_DATA_DESC_FILE)\n",
    "train_data_loader = torch.utils.data.DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=3,\n",
    "    num_workers=8,\n",
    "    pin_memory=True,\n",
    "    shuffle=True,\n",
    "    drop_last=False)\n",
    "print(len(train_data_loader))\n",
    "\n",
    "print(train_dataset.cam_params)\n",
    "print(train_dataset.sparse_view_positions)\n",
    "print(train_dataset.diopter_of_layers)\n",
    "plt.figure()\n",
    "util.PlotImageTensor(train_dataset.sparse_view_images[0])\n",
    "plt.figure()\n",
    "util.PlotImageTensor(train_dataset.sparse_view_depths[0] / 255 * 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test disparity wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "transformer = LatentSpaceTransformer(train_dataset.sparse_view_images.size()[2],\n",
    "                                     train_dataset.cam_params,\n",
    "                                     train_dataset.diopter_of_layers,\n",
    "                                     train_dataset.sparse_view_positions)\n",
    "novel_views = torch.stack([\n",
    "    train_dataset.view_positions[13],\n",
    "    train_dataset.view_positions[30],\n",
    "    train_dataset.view_positions[57],\n",
    "], dim=0)\n",
    "trans_images = transformer(train_dataset.sparse_view_images.to(device),\n",
    "                           train_dataset.sparse_view_depths.to(device),\n",
    "                           novel_views)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "mask = (torch.sum(trans_images[0], 1) > 1e-5).to(dtype=torch.float)\n",
    "blended = torch.sum(trans_images[0], 0)\n",
    "weight = torch.sum(mask, 0)\n",
    "blended = blended / weight.unsqueeze(0)\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "util.PlotImageTensor(train_dataset.view_images[13])\n",
    "plt.figure(figsize=(6, 6))\n",
    "util.PlotImageTensor(blended)\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(2, 4, 1)\n",
    "util.PlotImageTensor(train_dataset.sparse_view_images[0])\n",
    "plt.subplot(2, 4, 2)\n",
    "util.PlotImageTensor(train_dataset.sparse_view_images[1])\n",
    "plt.subplot(2, 4, 3)\n",
    "util.PlotImageTensor(train_dataset.sparse_view_images[2])\n",
    "plt.subplot(2, 4, 4)\n",
    "util.PlotImageTensor(train_dataset.sparse_view_images[3])\n",
    "\n",
    "plt.subplot(2, 4, 5)\n",
    "util.PlotImageTensor(trans_images[0, 0])\n",
    "plt.subplot(2, 4, 6)\n",
    "util.PlotImageTensor(trans_images[0, 1])\n",
    "plt.subplot(2, 4, 7)\n",
    "util.PlotImageTensor(trans_images[0, 2])\n",
    "plt.subplot(2, 4, 8)\n",
    "util.PlotImageTensor(trans_images[0, 3])\n"
   ]
  }
 ],
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