Code/3dreconstructionVision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pGDC50wOff2l"
   },
   "source": [
    "### Data Download & Extract\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P_RV6r_IKF3p",
    "outputId": "75b4bf4a-7110-4d9c-d093-bb06f9f96b0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: tensorboardX\n",
      "Successfully installed tensorboardX-2.5.1\n",
      "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
      "  warnings.warn(\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1zpk_-nH3o5uaNPSzYcr9YWZgasiVp8JP\n",
      "To: /content/ShapeNet.json\n",
      "100% 1.70M/1.70M [00:00<00:00, 161MB/s]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "if not os.path.exists(\"kaggle/\"):\n",
    "  !pip install gdown\n",
    "  !gdown --id 1LLdQUA-wRLrRCE2Tha9dcHyviy1y_oaP\n",
    "  !mkdir -p /root/.kaggle\n",
    "  !cp kaggle.json /root/.kaggle/kaggle.json\n",
    "  !chmod 600 /root/.kaggle/kaggle.json\n",
    "  !kaggle datasets download -d ronak555/shapenetcorerendering-part1\n",
    "  !kaggle datasets download -d ronak555/shapenetvox32\n",
    "  !unzip shapenetcorerendering-part1.zip\n",
    "  !unzip shapenetvox32.zip\n",
    "  !pip install tensorboardX\n",
    "  !gdown --id 1zpk_-nH3o5uaNPSzYcr9YWZgasiVp8JP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Xoe-P1QfpSo"
   },
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3B6q8oERgaN5"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import json\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "import random\n",
    "import scipy.io\n",
    "import scipy.ndimage\n",
    "import sys\n",
    "import torch\n",
    "import torch.backends.cudnn\n",
    "import torch.utils.data\n",
    "import torch.utils.data.dataset\n",
    "import torchvision.models\n",
    "from datetime import datetime as dt\n",
    "from tensorboardX import SummaryWriter\n",
    "from time import time\n",
    "from enum import Enum, unique\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a9Ti7iwSf8bJ"
   },
   "source": [
    "### Utility Code Section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oVyE93Dcib47"
   },
   "source": [
    "#### Binvox "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2hUm83lPgMxt"
   },
   "source": [
    "##### Voxel Model Class Definition\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HXMNaWlpi9v5"
   },
   "source": [
    "###### Voxel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kGZcerWgjEIX"
   },
   "outputs": [],
   "source": [
    "class Voxels(object):\n",
    "    \"\"\" Holds a binvox model.\n",
    "    data is either a three-dimensional numpy boolean array (dense representation)\n",
    "    or a two-dimensional numpy float array (coordinate representation).\n",
    "    dims, translate and scale are the model metadata.\n",
    "    dims are the voxel dimensions, e.g. [32, 32, 32] for a 32x32x32 model.\n",
    "    scale and translate relate the voxels to the original model coordinates.\n",
    "    To translate voxel coordinates i, j, k to original coordinates x, y, z:\n",
    "    x_n = (i+.5)/dims[0]\n",
    "    y_n = (j+.5)/dims[1]\n",
    "    z_n = (k+.5)/dims[2]\n",
    "    x = scale*x_n + translate[0]\n",
    "    y = scale*y_n + translate[1]\n",
    "    z = scale*z_n + translate[2]\n",
    "    \"\"\"\n",
    "    def __init__(self, data, dims, translate, scale, axis_order):\n",
    "        self.data = data\n",
    "        self.dims = dims\n",
    "        self.translate = translate\n",
    "        self.scale = scale\n",
    "        assert (axis_order in ('xzy', 'xyz'))\n",
    "        self.axis_order = axis_order\n",
    "\n",
    "    def clone(self):\n",
    "        data = self.data.copy()\n",
    "        dims = self.dims[:]\n",
    "        translate = self.translate[:]\n",
    "        return Voxels(data, dims, translate, self.scale, self.axis_order)\n",
    "\n",
    "    def write(self, fp):\n",
    "        write(self, fp)\n",
    "\n",
    "def write(voxel_model, fp):\n",
    "    \"\"\" Write binary binvox format.\n",
    "    Note that when saving a model in sparse (coordinate) format, it is first\n",
    "    converted to dense format.\n",
    "    Doesn't check if the model is 'sane'.\n",
    "    \"\"\"\n",
    "    if voxel_model.data.ndim == 2:\n",
    "        # TODO avoid conversion to dense\n",
    "        dense_voxel_data = sparse_to_dense(voxel_model.data, voxel_model.dims).astype(int)\n",
    "    else:\n",
    "        dense_voxel_data = voxel_model.data.astype(int)\n",
    "\n",
    "    file_header = [\n",
    "        '#binvox 1\\n',\n",
    "        'dim %s\\n' % ' '.join(map(str, voxel_model.dims)),\n",
    "        'translate %s\\n' % ' '.join(map(str, voxel_model.translate)),\n",
    "        'scale %s\\n' % str(voxel_model.scale), 'data\\n'\n",
    "    ]\n",
    "\n",
    "    for fh in file_header:\n",
    "        fp.write(fh.encode('latin-1'))\n",
    "\n",
    "    if voxel_model.axis_order not in ('xzy', 'xyz'):\n",
    "        raise ValueError('[ERROR] Unsupported voxel model axis order')\n",
    "\n",
    "    if voxel_model.axis_order == 'xzy':\n",
    "        voxels_flat = dense_voxel_data.flatten()\n",
    "    elif voxel_model.axis_order == 'xyz':\n",
    "        voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()\n",
    "\n",
    "    # keep a sort of state machine for writing run length encoding\n",
    "    state = voxels_flat[0]\n",
    "    ctr = 0\n",
    "    for c in voxels_flat:\n",
    "        if c == state:\n",
    "            ctr += 1\n",
    "            # if ctr hits max, dump\n",
    "            if ctr == 255:\n",
    "                fp.write(chr(state).encode('latin-1'))\n",
    "                fp.write(chr(ctr).encode('latin-1'))\n",
    "                ctr = 0\n",
    "        else:\n",
    "            # if switch state, dump\n",
    "            fp.write(chr(state).encode('latin-1'))\n",
    "            fp.write(chr(ctr).encode('latin-1'))\n",
    "            state = c\n",
    "            ctr = 1\n",
    "    # flush out remainders\n",
    "    if ctr > 0:\n",
    "        fp.write(chr(state).encode('latin-1'))\n",
    "        fp.write(chr(ctr).encode('latin-1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ab7AxNDbjGrB"
   },
   "source": [
    "###### Write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EeHPPNTti5nq"
   },
   "outputs": [],
   "source": [
    "def write(voxel_model, fp):\n",
    "    \"\"\" Write binary binvox format.\n",
    "    Note that when saving a model in sparse (coordinate) format, it is first\n",
    "    converted to dense format.\n",
    "    Doesn't check if the model is 'sane'.\n",
    "    \"\"\"\n",
    "    if voxel_model.data.ndim == 2:\n",
    "        # TODO avoid conversion to dense\n",
    "        dense_voxel_data = sparse_to_dense(voxel_model.data, voxel_model.dims).astype(int)\n",
    "    else:\n",
    "        dense_voxel_data = voxel_model.data.astype(int)\n",
    "\n",
    "    file_header = [\n",
    "        '#binvox 1\\n',\n",
    "        'dim %s\\n' % ' '.join(map(str, voxel_model.dims)),\n",
    "        'translate %s\\n' % ' '.join(map(str, voxel_model.translate)),\n",
    "        'scale %s\\n' % str(voxel_model.scale), 'data\\n'\n",
    "    ]\n",
    "\n",
    "    for fh in file_header:\n",
    "        fp.write(fh.encode('latin-1'))\n",
    "\n",
    "    if voxel_model.axis_order not in ('xzy', 'xyz'):\n",
    "        raise ValueError('[ERROR] Unsupported voxel model axis order')\n",
    "\n",
    "    if voxel_model.axis_order == 'xzy':\n",
    "        voxels_flat = dense_voxel_data.flatten()\n",
    "    elif voxel_model.axis_order == 'xyz':\n",
    "        voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()\n",
    "\n",
    "    # keep a sort of state machine for writing run length encoding\n",
    "    state = voxels_flat[0]\n",
    "    ctr = 0\n",
    "    for c in voxels_flat:\n",
    "        if c == state:\n",
    "            ctr += 1\n",
    "            # if ctr hits max, dump\n",
    "            if ctr == 255:\n",
    "                fp.write(chr(state).encode('latin-1'))\n",
    "                fp.write(chr(ctr).encode('latin-1'))\n",
    "                ctr = 0\n",
    "        else:\n",
    "            # if switch state, dump\n",
    "            fp.write(chr(state).encode('latin-1'))\n",
    "            fp.write(chr(ctr).encode('latin-1'))\n",
    "            state = c\n",
    "            ctr = 1\n",
    "    # flush out remainders\n",
    "    if ctr > 0:\n",
    "        fp.write(chr(state).encode('latin-1'))\n",
    "        fp.write(chr(ctr).encode('latin-1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7pqa7N5ujskj"
   },
   "source": [
    "##### 3D Model reading utility & Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0_wJTzjdjzXY"
   },
   "source": [
    "###### Reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eSsfukjVirWs"
   },
   "outputs": [],
   "source": [
    "def read_as_3d_array(fp, fix_coords=True):\n",
    "    \"\"\" Read binary binvox format as array.\n",
    "    Returns the model with accompanying metadata.\n",
    "    Voxels are stored in a three-dimensional numpy array, which is simple and\n",
    "    direct, but may use a lot of memory for large models. (Storage requirements\n",
    "    are 8*(d^3) bytes, where d is the dimensions of the binvox model. Numpy\n",
    "    boolean arrays use a byte per element).\n",
    "    Doesn't do any checks on input except for the '#binvox' line.\n",
    "    \"\"\"\n",
    "    dims, translate, scale = read_header(fp)\n",
    "    raw_data = np.frombuffer(fp.read(), dtype=np.uint8)\n",
    "    # if just using reshape() on the raw data:\n",
    "    # indexing the array as array[i,j,k], the indices map into the\n",
    "    # coords as:\n",
    "    # i -> x\n",
    "    # j -> z\n",
    "    # k -> y\n",
    "    # if fix_coords is true, then data is rearranged so that\n",
    "    # mapping is\n",
    "    # i -> x\n",
    "    # j -> y\n",
    "    # k -> z\n",
    "    values, counts = raw_data[::2], raw_data[1::2]\n",
    "    data = np.repeat(values, counts).astype(np.int32)\n",
    "    data = data.reshape(dims)\n",
    "    if fix_coords:\n",
    "        # xzy to xyz TODO the right thing\n",
    "        data = np.transpose(data, (0, 2, 1))\n",
    "        axis_order = 'xyz'\n",
    "    else:\n",
    "        axis_order = 'xzy'\n",
    "    return Voxels(data, dims, translate, scale, axis_order)\n",
    "\n",
    "def read_header(fp):\n",
    "    \"\"\" Read binvox header. Mostly meant for internal use.\n",
    "    \"\"\"\n",
    "    line = fp.readline().strip()\n",
    "    if not line.startswith(b'#binvox'):\n",
    "        raise IOError('[ERROR] Not a binvox file')\n",
    "    dims = list(map(int, fp.readline().strip().split(b' ')[1:]))\n",
    "    translate = list(map(float, fp.readline().strip().split(b' ')[1:]))\n",
    "    scale = list(map(float, fp.readline().strip().split(b' ')[1:]))[0]\n",
    "    fp.readline()\n",
    "    return dims, translate, scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xa9miIwWj4qY"
   },
   "source": [
    "###### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YxwFFd9Oj7_M"
   },
   "outputs": [],
   "source": [
    "def get_volume_views(volume, save_dir, n_itr):\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "\n",
    "    volume = volume.squeeze().__ge__(0.5)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.gca(projection=Axes3D.name)\n",
    "    # ax.set_aspect('equal')\n",
    "    ax.voxels(volume, edgecolor=\"k\")\n",
    "\n",
    "    save_path = os.path.join(save_dir, 'voxels-%06d.png' % n_itr)\n",
    "    plt.savefig(save_path, bbox_inches='tight')\n",
    "    plt.close()\n",
    "    return cv2.imread(save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wfcJYsL8i1EC"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PTTTWQseiJv9"
   },
   "source": [
    "#### Data Loader & Transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0Qb37fM_2DLU"
   },
   "source": [
    "##### Camera methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OfauQkQg2UNx"
   },
   "source": [
    "###### Compute extrinsic matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VmWy3b1f2F2i"
   },
   "outputs": [],
   "source": [
    "MAX_CAMERA_DISTANCE = 1.75\n",
    "\n",
    "def compute_extrinsic_matrix(\n",
    "    azimuth: float, elevation: float, distance: float\n",
    "):  # pragma: no cover\n",
    "    \"\"\"\n",
    "    Copied from meshrcnn codebase:\n",
    "    https://github.com/facebookresearch/meshrcnn/blob/main/shapenet/utils/coords.py#L96\n",
    "    Compute 4x4 extrinsic matrix that converts from homogeneous world coordinates\n",
    "    to homogeneous camera coordinates. We assume that the camera is looking at the\n",
    "    origin.\n",
    "    Used in R2N2 Dataset when computing calibration matrices.\n",
    "    Args:\n",
    "        azimuth: Rotation about the z-axis, in degrees.\n",
    "        elevation: Rotation above the xy-plane, in degrees.\n",
    "        distance: Distance from the origin.\n",
    "    Returns:\n",
    "        FloatTensor of shape (4, 4).\n",
    "    \"\"\"\n",
    "    azimuth, elevation, distance = float(azimuth), float(elevation), float(distance)\n",
    "\n",
    "    az_rad = -math.pi * azimuth / 180.0\n",
    "    el_rad = -math.pi * elevation / 180.0\n",
    "    sa = math.sin(az_rad)\n",
    "    ca = math.cos(az_rad)\n",
    "    se = math.sin(el_rad)\n",
    "    ce = math.cos(el_rad)\n",
    "    R_world2obj = torch.tensor(\n",
    "        [[ca * ce, sa * ce, -se], [-sa, ca, 0], [ca * se, sa * se, ce]]\n",
    "    )\n",
    "    R_obj2cam = torch.tensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])\n",
    "    R_world2cam = R_obj2cam.mm(R_world2obj)\n",
    "    cam_location = torch.tensor([[distance, 0, 0]]).t()\n",
    "    T_world2cam = -(R_obj2cam.mm(cam_location))\n",
    "    RT = torch.cat([R_world2cam, T_world2cam], dim=1)\n",
    "    RT = torch.cat([RT, torch.tensor([[0.0, 0, 0, 1]])])\n",
    "\n",
    "    # Georgia: For some reason I cannot fathom, when Blender loads a .obj file it\n",
    "    # rotates the model 90 degrees about the x axis. To compensate for this quirk we\n",
    "    # roll that rotation into the extrinsic matrix here\n",
    "    rot = torch.tensor([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])\n",
    "    RT = RT.mm(rot.to(RT))\n",
    "\n",
    "    return RT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SqRf-CZc2uCC"
   },
   "source": [
    "###### Compute camera caliberation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j8nAbRqf2yyE"
   },
   "outputs": [],
   "source": [
    "def compute_camera_calibration( RT):\n",
    "    \"\"\"\n",
    "    Helper function for calculating rotation and translation matrices from ShapeNet\n",
    "    to camera transformation and ShapeNet to PyTorch3D transformation.\n",
    "    Args:\n",
    "        RT: Extrinsic matrix that performs ShapeNet world view to camera view\n",
    "            transformation.\n",
    "    Returns:\n",
    "        R: Rotation matrix of shape (3, 3).\n",
    "        T: Translation matrix of shape (3).\n",
    "    \"\"\"\n",
    "    # Transform the mesh vertices from shapenet world to pytorch3d world.\n",
    "    shapenet_to_pytorch3d = torch.tensor(\n",
    "        [\n",
    "            [-1.0, 0.0, 0.0, 0.0],\n",
    "            [0.0, 1.0, 0.0, 0.0],\n",
    "            [0.0, 0.0, -1.0, 0.0],\n",
    "            [0.0, 0.0, 0.0, 1.0],\n",
    "        ],\n",
    "        dtype=torch.float32,\n",
    "    )\n",
    "    RT = torch.transpose(RT, 0, 1).mm(shapenet_to_pytorch3d)  # (4, 4)\n",
    "    # Extract rotation and translation matrices from RT.\n",
    "    R = RT[:3, :3]\n",
    "    T = RT[3, :3]\n",
    "    return R, T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pUyh7XXPg21A"
   },
   "source": [
    "##### ShapeNet Dataset Class Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V9lIORFngbRA"
   },
   "outputs": [],
   "source": [
    "@unique\n",
    "class DatasetType(Enum):\n",
    "    TRAIN = 0\n",
    "    TEST = 1\n",
    "    VAL = 2\n",
    "    \n",
    "class ShapeNetDataset(torch.utils.data.dataset.Dataset):\n",
    "    \"\"\"ShapeNetDataset class used for PyTorch DataLoader\"\"\"\n",
    "    def __init__(self, dataset_type, file_list, n_views_rendering, transforms=None):\n",
    "        self.dataset_type = dataset_type\n",
    "        self.file_list = file_list\n",
    "        self.transforms = transforms\n",
    "        self.n_views_rendering = n_views_rendering\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.file_list)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        taxonomy_name, sample_name, rendering_images, volume = self.get_datum(idx)\n",
    "\n",
    "        if self.transforms:\n",
    "            rendering_images = self.transforms(rendering_images)\n",
    "\n",
    "        return taxonomy_name, sample_name, rendering_images, volume\n",
    "\n",
    "    def set_n_views_rendering(self, n_views_rendering):\n",
    "        self.n_views_rendering = n_views_rendering\n",
    "\n",
    "    def get_datum(self, idx):\n",
    "        taxonomy_name = self.file_list[idx]['taxonomy_name']\n",
    "        sample_name = self.file_list[idx]['sample_name']\n",
    "        rendering_image_paths = self.file_list[idx]['rendering_images']\n",
    "        volume_path = self.file_list[idx]['volume']\n",
    "\n",
    "        # Get data of rendering images\n",
    "        if self.dataset_type == DatasetType.TRAIN:\n",
    "            selected_rendering_image_paths = [\n",
    "                rendering_image_paths[i]\n",
    "                for i in random.sample(range(len(rendering_image_paths)), self.n_views_rendering)\n",
    "            ]\n",
    "        else:\n",
    "            selected_rendering_image_paths = [rendering_image_paths[i] for i in range(self.n_views_rendering)]\n",
    "\n",
    "        rendering_images = []\n",
    "        for image_path in selected_rendering_image_paths:\n",
    "            rendering_image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.\n",
    "            if len(rendering_image.shape) < 3:\n",
    "                print('[FATAL] %s It seems that there is something wrong with the image file %s' %\n",
    "                      (dt.now(), image_path))\n",
    "                sys.exit(2)\n",
    "\n",
    "            rendering_images.append(rendering_image)\n",
    "\n",
    "        # Get data of volume\n",
    "        _, suffix = os.path.splitext(volume_path)\n",
    "\n",
    "        if suffix == '.mat':\n",
    "            volume = scipy.io.loadmat(volume_path)\n",
    "            volume = volume['Volume'].astype(np.float32)\n",
    "        elif suffix == '.binvox':\n",
    "            with open(volume_path, 'rb') as f:\n",
    "                volume = read_as_3d_array(f)\n",
    "                volume = volume.data.astype(np.float32)\n",
    "\n",
    "        return taxonomy_name, sample_name, np.asarray(rendering_images), volume\n",
    "\n",
    "\n",
    "# //////////////////////////////// = End of ShapeNetDataset Class Definition = ///////////////////////////////// #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "euYNBNwkhHFt"
   },
   "source": [
    "##### ShapeNet Dataloader Class Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sSBExNRJgwfA"
   },
   "outputs": [],
   "source": [
    "class ShapeNetDataLoader:\n",
    "    def __init__(self, cfg):\n",
    "        self.dataset_taxonomy = None\n",
    "        self.rendering_image_path_template = cfg.DATASETS.SHAPENET.RENDERING_PATH\n",
    "        self.volume_path_template = cfg.DATASETS.SHAPENET.VOXEL_PATH\n",
    "\n",
    "        # Load all taxonomies of the dataset\n",
    "        with open(cfg.DATASETS.SHAPENET.TAXONOMY_FILE_PATH, encoding='utf-8') as file:\n",
    "            self.dataset_taxonomy = json.loads(file.read())\n",
    "\n",
    "    def get_dataset(self, dataset_type, n_views_rendering, transforms=None):\n",
    "        files = []\n",
    "\n",
    "        # Load data for each category\n",
    "        for taxonomy in self.dataset_taxonomy:\n",
    "            taxonomy_folder_name = taxonomy['taxonomy_id']\n",
    "            print('[INFO] %s Collecting files of Taxonomy[ID=%s, Name=%s]' %\n",
    "                  (dt.now(), taxonomy['taxonomy_id'], taxonomy['taxonomy_name']))\n",
    "            samples = []\n",
    "            if dataset_type == DatasetType.TRAIN:\n",
    "                samples = taxonomy['train']\n",
    "            elif dataset_type == DatasetType.TEST:\n",
    "                samples = taxonomy['test']\n",
    "            elif dataset_type == DatasetType.VAL:\n",
    "                samples = taxonomy['val']\n",
    "\n",
    "            files.extend(self.get_files_of_taxonomy(taxonomy_folder_name, samples))\n",
    "\n",
    "        print('[INFO] %s Complete collecting files of the dataset. Total files: %d.' % (dt.now(), len(files)))\n",
    "        return ShapeNetDataset(dataset_type, files, n_views_rendering, transforms)\n",
    "\n",
    "    def get_files_of_taxonomy(self, taxonomy_folder_name, samples):\n",
    "        files_of_taxonomy = []\n",
    "\n",
    "        for sample_idx, sample_name in enumerate(samples):\n",
    "            # Get file path of volumes\n",
    "            volume_file_path = self.volume_path_template % (taxonomy_folder_name, sample_name)\n",
    "            if not os.path.exists(volume_file_path):\n",
    "                print('[WARN] %s Ignore sample %s/%s since volume file not exists.' %\n",
    "                      (dt.now(), taxonomy_folder_name, sample_name))\n",
    "                continue\n",
    "            # Get file list of rendering images\n",
    "            img_file_path = self.rendering_image_path_template % (taxonomy_folder_name, sample_name, 0)\n",
    "            img_folder = os.path.dirname(img_file_path)\n",
    "\n",
    "            metadata_file_path = os.path.join(img_folder,\"rendering_metadata.txt\")\n",
    "            total_views = len(os.listdir(img_folder))\n",
    "            rendering_image_indexes = range(total_views)\n",
    "            rendering_images_file_path = []\n",
    "            for image_idx in rendering_image_indexes:\n",
    "                img_file_path = self.rendering_image_path_template % (taxonomy_folder_name, sample_name, image_idx)\n",
    "                if not os.path.exists(img_file_path):\n",
    "                    continue\n",
    "\n",
    "                rendering_images_file_path.append(img_file_path)\n",
    "\n",
    "            if len(rendering_images_file_path) == 0:\n",
    "                print('[WARN] %s Ignore sample %s/%s since image files not exists.' %\n",
    "                      (dt.now(), taxonomy_folder_name, sample_name))\n",
    "                continue\n",
    "\n",
    "            # Append to the list of rendering images\n",
    "            files_of_taxonomy.append({\n",
    "                'taxonomy_name': taxonomy_folder_name,\n",
    "                'sample_name': sample_name,\n",
    "                'rendering_images': rendering_images_file_path,\n",
    "                'volume': volume_file_path,\n",
    "                'metadata':metadata_file_path,\n",
    "            })\n",
    "\n",
    "            # Report the progress of reading dataset\n",
    "            # if sample_idx % 500 == 499 or sample_idx == n_samples - 1:\n",
    "            #     print('[INFO] %s Collecting %d of %d' % (dt.now(), sample_idx + 1, n_samples))\n",
    "\n",
    "        return files_of_taxonomy\n",
    "\n",
    "\n",
    "# /////////////////////////////// = End of ShapeNetDataLoader Class Definition = /////////////////////////////// #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ADdiR2iktSF"
   },
   "source": [
    "##### Data Transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TxEIx3gUlEb4"
   },
   "source": [
    "###### Compose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-KQUtTA2lHzq"
   },
   "outputs": [],
   "source": [
    "class Compose(object):\n",
    "    \"\"\" Composes several transforms together.\n",
    "    For example:\n",
    "    >>> transforms.Compose([\n",
    "    >>>     transforms.RandomBackground(),\n",
    "    >>>     transforms.CenterCrop(127, 127, 3),\n",
    "    >>>  ])\n",
    "    \"\"\"\n",
    "    def __init__(self, transforms):\n",
    "        self.transforms = transforms\n",
    "\n",
    "    def __call__(self, rendering_images, bounding_box=None):\n",
    "        for t in self.transforms:\n",
    "            if t.__class__.__name__ == 'RandomCrop' or t.__class__.__name__ == 'CenterCrop':\n",
    "                rendering_images = t(rendering_images, bounding_box)\n",
    "            else:\n",
    "                rendering_images = t(rendering_images)\n",
    "\n",
    "        return rendering_images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dEVhZXzslO4_"
   },
   "source": [
    "###### To Tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UA2Ex75IlR0s"
   },
   "outputs": [],
   "source": [
    "class ToTensor(object):\n",
    "    \"\"\"\n",
    "    Convert a PIL Image or numpy.ndarray to tensor.\n",
    "    Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].\n",
    "    \"\"\"\n",
    "    def __call__(self, rendering_images):\n",
    "        assert (isinstance(rendering_images, np.ndarray))\n",
    "        array = np.transpose(rendering_images, (0, 3, 1, 2))\n",
    "        # handle numpy array\n",
    "        tensor = torch.from_numpy(array)\n",
    "\n",
    "        # put it from HWC to CHW format\n",
    "        return tensor.float()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gLB1o9v2le1g"
   },
   "source": [
    "###### Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Zz2P3ff8li3F"
   },
   "outputs": [],
   "source": [
    "class Normalize(object):\n",
    "    def __init__(self, mean, std):\n",
    "        self.mean = mean\n",
    "        self.std = std\n",
    "\n",
    "    def __call__(self, rendering_images):\n",
    "        assert (isinstance(rendering_images, np.ndarray))\n",
    "        rendering_images -= self.mean\n",
    "        rendering_images /= self.std\n",
    "\n",
    "        return rendering_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rjOm8TxPllyy"
   },
   "source": [
    "###### RandomPermuteRGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9F4c7HP0lowj"
   },
   "outputs": [],
   "source": [
    "class RandomPermuteRGB(object):\n",
    "    def __call__(self, rendering_images):\n",
    "        assert (isinstance(rendering_images, np.ndarray))\n",
    "\n",
    "        random_permutation = np.random.permutation(3)\n",
    "        for img_idx, img in enumerate(rendering_images):\n",
    "            rendering_images[img_idx] = img[..., random_permutation]\n",
    "\n",
    "        return rendering_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E9lm_uyzlrhJ"
   },
   "source": [
    "###### Center Crop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "camn6n46lxBS"
   },
   "outputs": [],
   "source": [
    "class CenterCrop(object):\n",
    "    def __init__(self, img_size, crop_size):\n",
    "        \"\"\"Set the height and weight before and after cropping\"\"\"\n",
    "        self.img_size_h = img_size[0]\n",
    "        self.img_size_w = img_size[1]\n",
    "        self.crop_size_h = crop_size[0]\n",
    "        self.crop_size_w = crop_size[1]\n",
    "\n",
    "    def __call__(self, rendering_images, bounding_box=None):\n",
    "        if len(rendering_images) == 0:\n",
    "            return rendering_images\n",
    "\n",
    "        crop_size_c = rendering_images[0].shape[2]\n",
    "        processed_images = np.empty(shape=(0, self.img_size_h, self.img_size_w, crop_size_c))\n",
    "        for img_idx, img in enumerate(rendering_images):\n",
    "            img_height, img_width, _ = img.shape\n",
    "\n",
    "            if bounding_box is not None:\n",
    "                bounding_box = [\n",
    "                    bounding_box[0] * img_width,\n",
    "                    bounding_box[1] * img_height,\n",
    "                    bounding_box[2] * img_width,\n",
    "                    bounding_box[3] * img_height\n",
    "                ]  # yapf: disable\n",
    "\n",
    "                # Calculate the size of bounding boxes\n",
    "                bbox_width = bounding_box[2] - bounding_box[0]\n",
    "                bbox_height = bounding_box[3] - bounding_box[1]\n",
    "                bbox_x_mid = (bounding_box[2] + bounding_box[0]) * .5\n",
    "                bbox_y_mid = (bounding_box[3] + bounding_box[1]) * .5\n",
    "\n",
    "                # Make the crop area as a square\n",
    "                square_object_size = max(bbox_width, bbox_height)\n",
    "                x_left = int(bbox_x_mid - square_object_size * .5)\n",
    "                x_right = int(bbox_x_mid + square_object_size * .5)\n",
    "                y_top = int(bbox_y_mid - square_object_size * .5)\n",
    "                y_bottom = int(bbox_y_mid + square_object_size * .5)\n",
    "\n",
    "                # If the crop position is out of the image, fix it with padding\n",
    "                pad_x_left = 0\n",
    "                if x_left < 0:\n",
    "                    pad_x_left = -x_left\n",
    "                    x_left = 0\n",
    "                pad_x_right = 0\n",
    "                if x_right >= img_width:\n",
    "                    pad_x_right = x_right - img_width + 1\n",
    "                    x_right = img_width - 1\n",
    "                pad_y_top = 0\n",
    "                if y_top < 0:\n",
    "                    pad_y_top = -y_top\n",
    "                    y_top = 0\n",
    "                pad_y_bottom = 0\n",
    "                if y_bottom >= img_height:\n",
    "                    pad_y_bottom = y_bottom - img_height + 1\n",
    "                    y_bottom = img_height - 1\n",
    "\n",
    "                # Padding the image and resize the image\n",
    "                processed_image = np.pad(img[y_top:y_bottom + 1, x_left:x_right + 1],\n",
    "                                         ((pad_y_top, pad_y_bottom), (pad_x_left, pad_x_right), (0, 0)),\n",
    "                                         mode='edge')\n",
    "                processed_image = cv2.resize(processed_image, (self.img_size_w, self.img_size_h))\n",
    "            else:\n",
    "                if img_height > self.crop_size_h and img_width > self.crop_size_w:\n",
    "                    x_left = int(img_width - self.crop_size_w) // 2\n",
    "                    x_right = int(x_left + self.crop_size_w)\n",
    "                    y_top = int(img_height - self.crop_size_h) // 2\n",
    "                    y_bottom = int(y_top + self.crop_size_h)\n",
    "                else:\n",
    "                    x_left = 0\n",
    "                    x_right = img_width\n",
    "                    y_top = 0\n",
    "                    y_bottom = img_height\n",
    "\n",
    "                processed_image = cv2.resize(img[y_top:y_bottom, x_left:x_right], (self.img_size_w, self.img_size_h))\n",
    "\n",
    "            processed_images = np.append(processed_images, [processed_image], axis=0)\n",
    "            # Debug\n",
    "            # fig = plt.figure()\n",
    "            # ax1 = fig.add_subplot(1, 2, 1)\n",
    "            # ax1.imshow(img)\n",
    "            # if not bounding_box is None:\n",
    "            #     rect = patches.Rectangle((bounding_box[0], bounding_box[1]),\n",
    "            #                              bbox_width,\n",
    "            #                              bbox_height,\n",
    "            #                              linewidth=1,\n",
    "            #                              edgecolor='r',\n",
    "            #                              facecolor='none')\n",
    "            #     ax1.add_patch(rect)\n",
    "            # ax2 = fig.add_subplot(1, 2, 2)\n",
    "            # ax2.imshow(processed_image)\n",
    "            # plt.show()\n",
    "        return processed_images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XF8gKUgDl1r-"
   },
   "source": [
    "###### RandomCrop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9TMCzce8l59R"
   },
   "outputs": [],
   "source": [
    "class RandomCrop(object):\n",
    "    def __init__(self, img_size, crop_size):\n",
    "        \"\"\"Set the height and weight before and after cropping\"\"\"\n",
    "        self.img_size_h = img_size[0]\n",
    "        self.img_size_w = img_size[1]\n",
    "        self.crop_size_h = crop_size[0]\n",
    "        self.crop_size_w = crop_size[1]\n",
    "\n",
    "    def __call__(self, rendering_images, bounding_box=None):\n",
    "        if len(rendering_images) == 0:\n",
    "            return rendering_images\n",
    "\n",
    "        crop_size_c = rendering_images[0].shape[2]\n",
    "        processed_images = np.empty(shape=(0, self.img_size_h, self.img_size_w, crop_size_c))\n",
    "        for img_idx, img in enumerate(rendering_images):\n",
    "            img_height, img_width, _ = img.shape\n",
    "\n",
    "            if bounding_box is not None:\n",
    "                bounding_box = [\n",
    "                    bounding_box[0] * img_width,\n",
    "                    bounding_box[1] * img_height,\n",
    "                    bounding_box[2] * img_width,\n",
    "                    bounding_box[3] * img_height\n",
    "                ]  # yapf: disable\n",
    "\n",
    "                # Calculate the size of bounding boxes\n",
    "                bbox_width = bounding_box[2] - bounding_box[0]\n",
    "                bbox_height = bounding_box[3] - bounding_box[1]\n",
    "                bbox_x_mid = (bounding_box[2] + bounding_box[0]) * .5\n",
    "                bbox_y_mid = (bounding_box[3] + bounding_box[1]) * .5\n",
    "\n",
    "                # Make the crop area as a square\n",
    "                square_object_size = max(bbox_width, bbox_height)\n",
    "                square_object_size = square_object_size * random.uniform(0.8, 1.2)\n",
    "\n",
    "                x_left = int(bbox_x_mid - square_object_size * random.uniform(.4, .6))\n",
    "                x_right = int(bbox_x_mid + square_object_size * random.uniform(.4, .6))\n",
    "                y_top = int(bbox_y_mid - square_object_size * random.uniform(.4, .6))\n",
    "                y_bottom = int(bbox_y_mid + square_object_size * random.uniform(.4, .6))\n",
    "\n",
    "                # If the crop position is out of the image, fix it with padding\n",
    "                pad_x_left = 0\n",
    "                if x_left < 0:\n",
    "                    pad_x_left = -x_left\n",
    "                    x_left = 0\n",
    "                pad_x_right = 0\n",
    "                if x_right >= img_width:\n",
    "                    pad_x_right = x_right - img_width + 1\n",
    "                    x_right = img_width - 1\n",
    "                pad_y_top = 0\n",
    "                if y_top < 0:\n",
    "                    pad_y_top = -y_top\n",
    "                    y_top = 0\n",
    "                pad_y_bottom = 0\n",
    "                if y_bottom >= img_height:\n",
    "                    pad_y_bottom = y_bottom - img_height + 1\n",
    "                    y_bottom = img_height - 1\n",
    "\n",
    "                # Padding the image and resize the image\n",
    "                processed_image = np.pad(img[y_top:y_bottom + 1, x_left:x_right + 1],\n",
    "                                         ((pad_y_top, pad_y_bottom), (pad_x_left, pad_x_right), (0, 0)),\n",
    "                                         mode='edge')\n",
    "                processed_image = cv2.resize(processed_image, (self.img_size_w, self.img_size_h))\n",
    "            else:\n",
    "                if img_height > self.crop_size_h and img_width > self.crop_size_w:\n",
    "                    x_left = int(img_width - self.crop_size_w) // 2\n",
    "                    x_right = int(x_left + self.crop_size_w)\n",
    "                    y_top = int(img_height - self.crop_size_h) // 2\n",
    "                    y_bottom = int(y_top + self.crop_size_h)\n",
    "                else:\n",
    "                    x_left = 0\n",
    "                    x_right = img_width\n",
    "                    y_top = 0\n",
    "                    y_bottom = img_height\n",
    "\n",
    "                processed_image = cv2.resize(img[y_top:y_bottom, x_left:x_right], (self.img_size_w, self.img_size_h))\n",
    "\n",
    "            processed_images = np.append(processed_images, [processed_image], axis=0)\n",
    "\n",
    "        return processed_images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vi8_sPrAmBcN"
   },
   "source": [
    "###### Random Flip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HASThCuumGyK"
   },
   "outputs": [],
   "source": [
    "class RandomFlip(object):\n",
    "    def __call__(self, rendering_images):\n",
    "        assert (isinstance(rendering_images, np.ndarray))\n",
    "\n",
    "        for img_idx, img in enumerate(rendering_images):\n",
    "            if random.randint(0, 1):\n",
    "                rendering_images[img_idx] = np.fliplr(img)\n",
    "\n",
    "        return rendering_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vrp28uMqmQeP"
   },
   "source": [
    "###### Color Jitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mGxwWPx5mUHV"
   },
   "outputs": [],
   "source": [
    "class ColorJitter(object):\n",
    "    def __init__(self, brightness, contrast, saturation):\n",
    "        self.brightness = brightness\n",
    "        self.contrast = contrast\n",
    "        self.saturation = saturation\n",
    "\n",
    "    def __call__(self, rendering_images):\n",
    "        if len(rendering_images) == 0:\n",
    "            return rendering_images\n",
    "\n",
    "        # Allocate new space for storing processed images\n",
    "        img_height, img_width, img_channels = rendering_images[0].shape\n",
    "        processed_images = np.empty(shape=(0, img_height, img_width, img_channels))\n",
    "\n",
    "        # Randomize the value of changing brightness, contrast, and saturation\n",
    "        brightness = 1 + np.random.uniform(low=-self.brightness, high=self.brightness)\n",
    "        contrast = 1 + np.random.uniform(low=-self.contrast, high=self.contrast)\n",
    "        saturation = 1 + np.random.uniform(low=-self.saturation, high=self.saturation)\n",
    "\n",
    "        # Randomize the order of changing brightness, contrast, and saturation\n",
    "        attr_names = ['brightness', 'contrast', 'saturation']\n",
    "        attr_values = [brightness, contrast, saturation]    # The value of changing attrs\n",
    "        attr_indexes = np.array(range(len(attr_names)))    # The order of changing attrs\n",
    "        np.random.shuffle(attr_indexes)\n",
    "\n",
    "        for img_idx, img in enumerate(rendering_images):\n",
    "            processed_image = img\n",
    "            for idx in attr_indexes:\n",
    "                processed_image = self._adjust_image_attr(processed_image, attr_names[idx], attr_values[idx])\n",
    "\n",
    "            processed_images = np.append(processed_images, [processed_image], axis=0)\n",
    "            # print('ColorJitter', np.mean(ori_img), np.mean(processed_image))\n",
    "            # fig = plt.figure(figsize=(8, 4))\n",
    "            # ax1 = fig.add_subplot(1, 2, 1)\n",
    "            # ax1.imshow(ori_img)\n",
    "            # ax2 = fig.add_subplot(1, 2, 2)\n",
    "            # ax2.imshow(processed_image)\n",
    "            # plt.show()\n",
    "        return processed_images\n",
    "\n",
    "    def _adjust_image_attr(self, img, attr_name, attr_value):\n",
    "        \"\"\"\n",
    "        Adjust or randomize the specified attribute of the image\n",
    "\n",
    "        Args:\n",
    "            img: Image in BGR format\n",
    "                Numpy array of shape (h, w, 3)\n",
    "            attr_name: Image attribute to adjust or randomize\n",
    "                       'brightness', 'saturation', or 'contrast'\n",
    "            attr_value: the alpha for blending is randomly drawn from [1 - d, 1 + d]\n",
    "\n",
    "        Returns:\n",
    "            Output image in BGR format\n",
    "            Numpy array of the same shape as input\n",
    "        \"\"\"\n",
    "        gs = self._bgr_to_gray(img)\n",
    "\n",
    "        if attr_name == 'contrast':\n",
    "            img = self._alpha_blend(img, np.mean(gs[:, :, 0]), attr_value)\n",
    "        elif attr_name == 'saturation':\n",
    "            img = self._alpha_blend(img, gs, attr_value)\n",
    "        elif attr_name == 'brightness':\n",
    "            img = self._alpha_blend(img, 0, attr_value)\n",
    "        else:\n",
    "            raise NotImplementedError(attr_name)\n",
    "        return img\n",
    "\n",
    "    def _bgr_to_gray(self, bgr):\n",
    "        \"\"\"\n",
    "        Convert a RGB image to a grayscale image\n",
    "            Differences from cv2.cvtColor():\n",
    "                1. Input image can be float\n",
    "                2. Output image has three repeated channels, other than a single channel\n",
    "\n",
    "        Args:\n",
    "            bgr: Image in BGR format\n",
    "                 Numpy array of shape (h, w, 3)\n",
    "\n",
    "        Returns:\n",
    "            gs: Grayscale image\n",
    "                Numpy array of the same shape as input; the three channels are the same\n",
    "        \"\"\"\n",
    "        ch = 0.114 * bgr[:, :, 0] + 0.587 * bgr[:, :, 1] + 0.299 * bgr[:, :, 2]\n",
    "        gs = np.dstack((ch, ch, ch))\n",
    "        return gs\n",
    "\n",
    "    def _alpha_blend(self, im1, im2, alpha):\n",
    "        \"\"\"\n",
    "        Alpha blending of two images or one image and a scalar\n",
    "\n",
    "        Args:\n",
    "            im1, im2: Image or scalar\n",
    "                Numpy array and a scalar or two numpy arrays of the same shape\n",
    "            alpha: Weight of im1\n",
    "                Float ranging usually from 0 to 1\n",
    "\n",
    "        Returns:\n",
    "            im_blend: Blended image -- alpha * im1 + (1 - alpha) * im2\n",
    "                Numpy array of the same shape as input image\n",
    "        \"\"\"\n",
    "        im_blend = alpha * im1 + (1 - alpha) * im2\n",
    "        return im_blend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HNIFKk1rmclg"
   },
   "source": [
    "###### Random Noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-utD7Yxvmjrq"
   },
   "outputs": [],
   "source": [
    "class RandomNoise(object):\n",
    "    def __init__(self,\n",
    "                 noise_std,\n",
    "                 eigvals=(0.2175, 0.0188, 0.0045),\n",
    "                 eigvecs=((-0.5675, 0.7192, 0.4009), (-0.5808, -0.0045, -0.8140), (-0.5836, -0.6948, 0.4203))):\n",
    "        self.noise_std = noise_std\n",
    "        self.eigvals = np.array(eigvals)\n",
    "        self.eigvecs = np.array(eigvecs)\n",
    "\n",
    "    def __call__(self, rendering_images):\n",
    "        alpha = np.random.normal(loc=0, scale=self.noise_std, size=3)\n",
    "        noise_rgb = \\\n",
    "            np.sum(\n",
    "                np.multiply(\n",
    "                    np.multiply(\n",
    "                        self.eigvecs,\n",
    "                        np.tile(alpha, (3, 1))\n",
    "                    ),\n",
    "                    np.tile(self.eigvals, (3, 1))\n",
    "                ),\n",
    "                axis=1\n",
    "            )\n",
    "\n",
    "        # Allocate new space for storing processed images\n",
    "        img_height, img_width, img_channels = rendering_images[0].shape\n",
    "        assert (img_channels == 3), \"Please use RandomBackground to normalize image channels\"\n",
    "        processed_images = np.empty(shape=(0, img_height, img_width, img_channels))\n",
    "\n",
    "        for img_idx, img in enumerate(rendering_images):\n",
    "            processed_image = img[:, :, ::-1]    # BGR -> RGB\n",
    "            for i in range(img_channels):\n",
    "                processed_image[:, :, i] += noise_rgb[i]\n",
    "\n",
    "            processed_image = processed_image[:, :, ::-1]    # RGB -> BGR\n",
    "            processed_images = np.append(processed_images, [processed_image], axis=0)\n",
    "            # from copy import deepcopy\n",
    "            # ori_img = deepcopy(img)\n",
    "            # print(noise_rgb, np.mean(processed_image), np.mean(ori_img))\n",
    "            # print('RandomNoise', np.mean(ori_img), np.mean(processed_image))\n",
    "            # fig = plt.figure(figsize=(8, 4))\n",
    "            # ax1 = fig.add_subplot(1, 2, 1)\n",
    "            # ax1.imshow(ori_img)\n",
    "            # ax2 = fig.add_subplot(1, 2, 2)\n",
    "            # ax2.imshow(processed_image)\n",
    "            # plt.show()\n",
    "        return processed_images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bc96f8yfmrdC"
   },
   "source": [
    "###### Random Background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T_ODhDYZmv7U"
   },
   "outputs": [],
   "source": [
    "class RandomBackground(object):\n",
    "    def __init__(self, random_bg_color_range, random_bg_folder_path=None):\n",
    "        self.random_bg_color_range = random_bg_color_range\n",
    "        self.random_bg_files = []\n",
    "        if random_bg_folder_path is not None:\n",
    "            self.random_bg_files = os.listdir(random_bg_folder_path)\n",
    "            self.random_bg_files = [os.path.join(random_bg_folder_path, rbf) for rbf in self.random_bg_files]\n",
    "\n",
    "    def __call__(self, rendering_images):\n",
    "        if len(rendering_images) == 0:\n",
    "            return rendering_images\n",
    "\n",
    "        img_height, img_width, img_channels = rendering_images[0].shape\n",
    "        # If the image has the alpha channel, add the background\n",
    "        if not img_channels == 4:\n",
    "            return rendering_images\n",
    "\n",
    "        # Generate random background\n",
    "        r, g, b = np.array([\n",
    "            np.random.randint(self.random_bg_color_range[i][0], self.random_bg_color_range[i][1] + 1) for i in range(3)\n",
    "        ]) / 255.\n",
    "\n",
    "        random_bg = None\n",
    "        if len(self.random_bg_files) > 0:\n",
    "            random_bg_file_path = random.choice(self.random_bg_files)\n",
    "            random_bg = cv2.imread(random_bg_file_path).astype(np.float32) / 255.\n",
    "\n",
    "        # Apply random background\n",
    "        processed_images = np.empty(shape=(0, img_height, img_width, img_channels - 1))\n",
    "        for img_idx, img in enumerate(rendering_images):\n",
    "            alpha = (np.expand_dims(img[:, :, 3], axis=2) == 0).astype(np.float32)\n",
    "            img = img[:, :, :3]\n",
    "            bg_color = random_bg if random.randint(0, 1) and random_bg is not None else np.array([[[r, g, b]]])\n",
    "            img = alpha * bg_color + (1 - alpha) * img\n",
    "\n",
    "            processed_images = np.append(processed_images, [img], axis=0)\n",
    "\n",
    "        return processed_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9fZM9mJSoSVT"
   },
   "source": [
    "#### Training Utility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gx8vKLOUoah9"
   },
   "source": [
    "###### Save Checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gDFJsydzooq_"
   },
   "outputs": [],
   "source": [
    "def save_checkpoints(cfg, file_path, epoch_idx, encoder, encoder_solver, decoder, decoder_solver, refiner,\n",
    "                     refiner_solver, merger, merger_solver, best_iou, best_epoch):\n",
    "    print('[INFO] %s Saving checkpoint to %s ...' % (dt.now(), file_path))\n",
    "    checkpoint = {\n",
    "        'epoch_idx': epoch_idx,\n",
    "        'best_iou': best_iou,\n",
    "        'best_epoch': best_epoch,\n",
    "        'encoder_state_dict': encoder.state_dict(),\n",
    "        'encoder_solver_state_dict': encoder_solver.state_dict(),\n",
    "        'decoder_state_dict': decoder.state_dict(),\n",
    "        'decoder_solver_state_dict': decoder_solver.state_dict()\n",
    "    }\n",
    "\n",
    "    if cfg.NETWORK.USE_REFINER:\n",
    "        checkpoint['refiner_state_dict'] = refiner.state_dict()\n",
    "        checkpoint['refiner_solver_state_dict'] = refiner_solver.state_dict()\n",
    "    if cfg.NETWORK.USE_MERGER:\n",
    "        checkpoint['merger_state_dict'] = merger.state_dict()\n",
    "        checkpoint['merger_solver_state_dict'] = merger_solver.state_dict()\n",
    "\n",
    "    torch.save(checkpoint, file_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wrCSXKU6ow_F"
   },
   "source": [
    "##### Average Meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "huVlQztDo1Wd"
   },
   "outputs": [],
   "source": [
    "class AverageMeter(object):\n",
    "    \"\"\"Computes and stores the average and current value\"\"\"\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.count = 0\n",
    "\n",
    "    def update(self, val, n=1):\n",
    "        self.val = val\n",
    "        self.sum += val * n\n",
    "        self.count += n\n",
    "        self.avg = self.sum / self.count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IRNbbvFipM9N"
   },
   "source": [
    "##### Init weights, count, cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "u6lKUt4ipv0n"
   },
   "outputs": [],
   "source": [
    "def var_or_cuda(x):\n",
    "    if torch.cuda.is_available():\n",
    "        x = x.cuda(non_blocking=True)\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oEm7aIjBp1qC"
   },
   "outputs": [],
   "source": [
    "def init_weights(m):\n",
    "    if type(m) == torch.nn.Conv2d or type(m) == torch.nn.Conv3d or type(m) == torch.nn.ConvTranspose3d:\n",
    "        torch.nn.init.kaiming_normal_(m.weight)\n",
    "        if m.bias is not None:\n",
    "            torch.nn.init.constant_(m.bias, 0)\n",
    "    elif type(m) == torch.nn.BatchNorm2d or type(m) == torch.nn.BatchNorm3d:\n",
    "        torch.nn.init.constant_(m.weight, 1)\n",
    "        torch.nn.init.constant_(m.bias, 0)\n",
    "    elif type(m) == torch.nn.Linear:\n",
    "        torch.nn.init.normal_(m.weight, 0, 0.01)\n",
    "        torch.nn.init.constant_(m.bias, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d6tFag-Ep33x"
   },
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5hKQUz8lqQWB"
   },
   "source": [
    "### Model Declaration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2mPOBPETqZEv"
   },
   "source": [
    "#### Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W1oKnCYAqvwZ"
   },
   "outputs": [],
   "source": [
    "class Decoder(torch.nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.cfg = cfg\n",
    "\n",
    "        # Layer Definition\n",
    "        self.layer1 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(2048, 512, kernel_size=4, stride=2, bias=cfg.NETWORK.TCONV_USE_BIAS, padding=1),\n",
    "            torch.nn.BatchNorm3d(512),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer2 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(512, 128, kernel_size=4, stride=2, bias=cfg.NETWORK.TCONV_USE_BIAS, padding=1),\n",
    "            torch.nn.BatchNorm3d(128),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer3 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(128, 32, kernel_size=4, stride=2, bias=cfg.NETWORK.TCONV_USE_BIAS, padding=1),\n",
    "            torch.nn.BatchNorm3d(32),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer4 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(32, 8, kernel_size=4, stride=2, bias=cfg.NETWORK.TCONV_USE_BIAS, padding=1),\n",
    "            torch.nn.BatchNorm3d(8),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer5 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(8, 1, kernel_size=1, bias=cfg.NETWORK.TCONV_USE_BIAS),\n",
    "            torch.nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, image_features):\n",
    "        image_features = image_features.permute(1, 0, 2, 3, 4).contiguous()\n",
    "        image_features = torch.split(image_features, 1, dim=0)\n",
    "        gen_volumes = []\n",
    "        raw_features = []\n",
    "\n",
    "        for features in image_features:\n",
    "            gen_volume = features.view(-1, 2048, 2, 2, 2)\n",
    "            # print(gen_volume.size())   # torch.Size([batch_size, 2048, 2, 2, 2])\n",
    "            gen_volume = self.layer1(gen_volume)\n",
    "            # print(gen_volume.size())   # torch.Size([batch_size, 512, 4, 4, 4])\n",
    "            gen_volume = self.layer2(gen_volume)\n",
    "            # print(gen_volume.size())   # torch.Size([batch_size, 128, 8, 8, 8])\n",
    "            gen_volume = self.layer3(gen_volume)\n",
    "            # print(gen_volume.size())   # torch.Size([batch_size, 32, 16, 16, 16])\n",
    "            gen_volume = self.layer4(gen_volume)\n",
    "            raw_feature = gen_volume\n",
    "            # print(gen_volume.size())   # torch.Size([batch_size, 8, 32, 32, 32])\n",
    "            gen_volume = self.layer5(gen_volume)\n",
    "            # print(gen_volume.size())   # torch.Size([batch_size, 1, 32, 32, 32])\n",
    "            raw_feature = torch.cat((raw_feature, gen_volume), dim=1)\n",
    "            # print(raw_feature.size())  # torch.Size([batch_size, 9, 32, 32, 32])\n",
    "\n",
    "            gen_volumes.append(torch.squeeze(gen_volume, dim=1))\n",
    "            raw_features.append(raw_feature)\n",
    "\n",
    "        gen_volumes = torch.stack(gen_volumes).permute(1, 0, 2, 3, 4).contiguous()\n",
    "        raw_features = torch.stack(raw_features).permute(1, 0, 2, 3, 4, 5).contiguous()\n",
    "        # print(gen_volumes.size())      # torch.Size([batch_size, n_views, 32, 32, 32])\n",
    "        # print(raw_features.size())     # torch.Size([batch_size, n_views, 9, 32, 32, 32])\n",
    "        return raw_features, gen_volumes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NL1Wb1MCqpPo"
   },
   "source": [
    "#### Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vRrdXVvhrA8E"
   },
   "outputs": [],
   "source": [
    "class Encoder(torch.nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.cfg = cfg\n",
    "\n",
    "        # Layer Definition\n",
    "        vgg16_bn = torchvision.models.vgg16_bn(pretrained=True)\n",
    "        self.vgg = torch.nn.Sequential(*list(vgg16_bn.features.children()))[:27]\n",
    "        self.layer1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(512, 512, kernel_size=3),\n",
    "            torch.nn.BatchNorm2d(512),\n",
    "            torch.nn.ELU(),\n",
    "        )\n",
    "        self.layer2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(512, 512, kernel_size=3),\n",
    "            torch.nn.BatchNorm2d(512),\n",
    "            torch.nn.ELU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=3)\n",
    "        )\n",
    "        self.layer3 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(512, 256, kernel_size=1),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ELU()\n",
    "        )\n",
    "\n",
    "        # Don't update params in VGG16\n",
    "        for param in vgg16_bn.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "    def forward(self, rendering_images):\n",
    "        # print(rendering_images.size())  # torch.Size([batch_size, n_views, img_c, img_h, img_w])\n",
    "        rendering_images = rendering_images.permute(1, 0, 2, 3, 4).contiguous()\n",
    "        rendering_images = torch.split(rendering_images, 1, dim=0)\n",
    "        image_features = []\n",
    "\n",
    "        for img in rendering_images:\n",
    "            features = self.vgg(img.squeeze(dim=0))\n",
    "            # print(features.size())    # torch.Size([batch_size, 512, 28, 28])\n",
    "            features = self.layer1(features)\n",
    "            # print(features.size())    # torch.Size([batch_size, 512, 26, 26])\n",
    "            features = self.layer2(features)\n",
    "            # print(features.size())    # torch.Size([batch_size, 512, 24, 24])\n",
    "            features = self.layer3(features)\n",
    "            # print(features.size())    # torch.Size([batch_size, 256, 8, 8])\n",
    "            image_features.append(features)\n",
    "\n",
    "        image_features = torch.stack(image_features).permute(1, 0, 2, 3, 4).contiguous()\n",
    "        # print(image_features.size())  # torch.Size([batch_size, n_views, 256, 8, 8])\n",
    "        return image_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6n7j0EtZqpDI"
   },
   "source": [
    "#### Merger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Eh3jIAQRrIl1"
   },
   "outputs": [],
   "source": [
    "class Merger(torch.nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super(Merger, self).__init__()\n",
    "        self.cfg = cfg\n",
    "\n",
    "        # Layer Definition\n",
    "        self.layer1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(9, 16, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm3d(16),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE)\n",
    "        )\n",
    "        self.layer2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(16, 8, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm3d(8),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE)\n",
    "        )\n",
    "        self.layer3 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(8, 4, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm3d(4),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE)\n",
    "        )\n",
    "        self.layer4 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(4, 2, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm3d(2),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE)\n",
    "        )\n",
    "        self.layer5 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(2, 1, kernel_size=3, padding=1),\n",
    "            torch.nn.BatchNorm3d(1),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE)\n",
    "        )\n",
    "\n",
    "    def forward(self, raw_features, coarse_volumes):\n",
    "        n_views_rendering = coarse_volumes.size(1)\n",
    "        raw_features = torch.split(raw_features, 1, dim=1)\n",
    "        volume_weights = []\n",
    "\n",
    "        for i in range(n_views_rendering):\n",
    "            raw_feature = torch.squeeze(raw_features[i], dim=1)\n",
    "            # print(raw_feature.size())       # torch.Size([batch_size, 9, 32, 32, 32])\n",
    "\n",
    "            volume_weight = self.layer1(raw_feature)\n",
    "            # print(volume_weight.size())     # torch.Size([batch_size, 16, 32, 32, 32])\n",
    "            volume_weight = self.layer2(volume_weight)\n",
    "            # print(volume_weight.size())     # torch.Size([batch_size, 8, 32, 32, 32])\n",
    "            volume_weight = self.layer3(volume_weight)\n",
    "            # print(volume_weight.size())     # torch.Size([batch_size, 4, 32, 32, 32])\n",
    "            volume_weight = self.layer4(volume_weight)\n",
    "            # print(volume_weight.size())     # torch.Size([batch_size, 2, 32, 32, 32])\n",
    "            volume_weight = self.layer5(volume_weight)\n",
    "            # print(volume_weight.size())     # torch.Size([batch_size, 1, 32, 32, 32])\n",
    "\n",
    "            volume_weight = torch.squeeze(volume_weight, dim=1)\n",
    "            # print(volume_weight.size())     # torch.Size([batch_size, 32, 32, 32])\n",
    "            volume_weights.append(volume_weight)\n",
    "\n",
    "        volume_weights = torch.stack(volume_weights).permute(1, 0, 2, 3, 4).contiguous()\n",
    "        volume_weights = torch.softmax(volume_weights, dim=1)\n",
    "        # print(volume_weights.size())        # torch.Size([batch_size, n_views, 32, 32, 32])\n",
    "        # print(coarse_volumes.size())        # torch.Size([batch_size, n_views, 32, 32, 32])\n",
    "        coarse_volumes = coarse_volumes * volume_weights\n",
    "        coarse_volumes = torch.sum(coarse_volumes, dim=1)\n",
    "\n",
    "        return torch.clamp(coarse_volumes, min=0, max=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "URmcWcqRqo05"
   },
   "source": [
    "#### Refiner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wiMkm_uArQEb"
   },
   "outputs": [],
   "source": [
    "class Refiner(torch.nn.Module):\n",
    "    def __init__(self, cfg):\n",
    "        super(Refiner, self).__init__()\n",
    "        self.cfg = cfg\n",
    "\n",
    "        # Layer Definition\n",
    "        self.layer1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(1, 32, kernel_size=4, padding=2),\n",
    "            torch.nn.BatchNorm3d(32),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE),\n",
    "            torch.nn.MaxPool3d(kernel_size=2)\n",
    "        )\n",
    "        self.layer2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(32, 64, kernel_size=4, padding=2),\n",
    "            torch.nn.BatchNorm3d(64),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE),\n",
    "            torch.nn.MaxPool3d(kernel_size=2)\n",
    "        )\n",
    "        self.layer3 = torch.nn.Sequential(\n",
    "            torch.nn.Conv3d(64, 128, kernel_size=4, padding=2),\n",
    "            torch.nn.BatchNorm3d(128),\n",
    "            torch.nn.LeakyReLU(cfg.NETWORK.LEAKY_VALUE),\n",
    "            torch.nn.MaxPool3d(kernel_size=2)\n",
    "        )\n",
    "        self.layer4 = torch.nn.Sequential(\n",
    "            torch.nn.Linear(8192, 2048),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer5 = torch.nn.Sequential(\n",
    "            torch.nn.Linear(2048, 8192),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer6 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(128, 64, kernel_size=4, stride=2, bias=cfg.NETWORK.TCONV_USE_BIAS, padding=1),\n",
    "            torch.nn.BatchNorm3d(64),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer7 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(64, 32, kernel_size=4, stride=2, bias=cfg.NETWORK.TCONV_USE_BIAS, padding=1),\n",
    "            torch.nn.BatchNorm3d(32),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "        self.layer8 = torch.nn.Sequential(\n",
    "            torch.nn.ConvTranspose3d(32, 1, kernel_size=4, stride=2, bias=cfg.NETWORK.TCONV_USE_BIAS, padding=1),\n",
    "            torch.nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, coarse_volumes):\n",
    "        volumes_32_l = coarse_volumes.view((-1, 1, self.cfg.CONST.N_VOX, self.cfg.CONST.N_VOX, self.cfg.CONST.N_VOX))\n",
    "        # print(volumes_32_l.size())       # torch.Size([batch_size, 1, 32, 32, 32])\n",
    "        volumes_16_l = self.layer1(volumes_32_l)\n",
    "        # print(volumes_16_l.size())       # torch.Size([batch_size, 32, 16, 16, 16])\n",
    "        volumes_8_l = self.layer2(volumes_16_l)\n",
    "        # print(volumes_8_l.size())        # torch.Size([batch_size, 64, 8, 8, 8])\n",
    "        volumes_4_l = self.layer3(volumes_8_l)\n",
    "        # print(volumes_4_l.size())        # torch.Size([batch_size, 128, 4, 4, 4])\n",
    "        flatten_features = self.layer4(volumes_4_l.view(-1, 8192))\n",
    "        # print(flatten_features.size())   # torch.Size([batch_size, 2048])\n",
    "        flatten_features = self.layer5(flatten_features)\n",
    "        # print(flatten_features.size())   # torch.Size([batch_size, 8192])\n",
    "        volumes_4_r = volumes_4_l + flatten_features.view(-1, 128, 4, 4, 4)\n",
    "        # print(volumes_4_r.size())        # torch.Size([batch_size, 128, 4, 4, 4])\n",
    "        volumes_8_r = volumes_8_l + self.layer6(volumes_4_r)\n",
    "        # print(volumes_8_r.size())        # torch.Size([batch_size, 64, 8, 8, 8])\n",
    "        volumes_16_r = volumes_16_l + self.layer7(volumes_8_r)\n",
    "        # print(volumes_16_r.size())       # torch.Size([batch_size, 32, 16, 16, 16])\n",
    "        volumes_32_r = (volumes_32_l + self.layer8(volumes_16_r)) * 0.5\n",
    "        # print(volumes_32_r.size())       # torch.Size([batch_size, 1, 32, 32, 32])\n",
    "\n",
    "        return volumes_32_r.view((-1, self.cfg.CONST.N_VOX, self.cfg.CONST.N_VOX, self.cfg.CONST.N_VOX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SqeVd2eug0KX"
   },
   "source": [
    "### Global Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DM_MYMpRQIkU"
   },
   "outputs": [],
   "source": [
    "def globalConfig():\n",
    "  from easydict import EasyDict as edict\n",
    "  config = edict()\n",
    "  config.DATASETS                                = edict()\n",
    "  config.DATASETS.SHAPENET                       = edict()\n",
    "  config.DATASETS.SHAPENET.TAXONOMY_FILE_PATH    = './datasets/ShapeNet.json'\n",
    "  config.DATASETS.SHAPENET.RENDERING_PATH        = '/home/hzxie/Datasets/ShapeNet/ShapeNetRendering/%s/%s/rendering/%02d.png'\n",
    "  config.DATASETS.SHAPENET.VOXEL_PATH            = '/home/hzxie/Datasets/ShapeNet/ShapeNetVox32/%s/%s/model.binvox'\n",
    "\n",
    "  config.DATASET                                 = edict()\n",
    "  config.DATASET.MEAN                            = [0.5, 0.5, 0.5]\n",
    "  config.DATASET.STD                             = [0.5, 0.5, 0.5]\n",
    "  config.DATASET.TRAIN_DATASET                   = 'ShapeNet'\n",
    "  config.DATASET.TEST_DATASET                    = 'ShapeNet'\n",
    "\n",
    "  # Common\n",
    "  #\n",
    "  config.CONST                                   = edict()\n",
    "  config.CONST.DEVICE                            = '0'\n",
    "  config.CONST.RNG_SEED                          = 0\n",
    "  config.CONST.IMG_W                             = 224       # Image width for input\n",
    "  config.CONST.IMG_H                             = 224       # Image height for input\n",
    "  config.CONST.N_VOX                             = 32\n",
    "  config.CONST.BATCH_SIZE                        = 64\n",
    "  config.CONST.N_VIEWS_RENDERING                 = 1         # Dummy property for Pascal 3D\n",
    "  config.CONST.CROP_IMG_W                        = 128       # Dummy property for Pascal 3D\n",
    "  config.CONST.CROP_IMG_H                        = 128       # Dummy property for Pascal 3D\n",
    "\n",
    "  #\n",
    "  # Directories\n",
    "  config.DIR                                     = edict()\n",
    "  config.DIR.OUT_PATH                            = './output'\n",
    "  # config.DIR.RANDOM_BG_PATH                      = '/home/hzxie/Datasets/SUN2012/JPEGImages'\n",
    "\n",
    "  #\n",
    "  # Network\n",
    "  #\n",
    "  config.NETWORK                                 = edict()\n",
    "  config.NETWORK.LEAKY_VALUE                     = .2\n",
    "  config.NETWORK.TCONV_USE_BIAS                  = False\n",
    "  config.NETWORK.USE_REFINER                     = True\n",
    "  config.NETWORK.USE_MERGER                      = True\n",
    "\n",
    "  #\n",
    "  # Training\n",
    "  #\n",
    "  config.TRAIN                                   = edict()\n",
    "  config.TRAIN.RESUME_TRAIN                      = False\n",
    "  config.TRAIN.NUM_WORKER                        = 4             # number of data workers\n",
    "  config.TRAIN.NUM_EPOCHES                       = 250\n",
    "  config.TRAIN.BRIGHTNESS                        = .4\n",
    "  config.TRAIN.CONTRAST                          = .4\n",
    "  config.TRAIN.SATURATION                        = .4\n",
    "  config.TRAIN.NOISE_STD                         = .1\n",
    "  config.TRAIN.RANDOM_BG_COLOR_RANGE             = [[225, 255], [225, 255], [225, 255]]\n",
    "  config.TRAIN.POLICY                            = 'adam'        # available options: sgd, adam\n",
    "  config.TRAIN.EPOCH_START_USE_REFINER           = 0\n",
    "  config.TRAIN.EPOCH_START_USE_MERGER            = 0\n",
    "  config.TRAIN.ENCODER_LEARNING_RATE             = 1e-3\n",
    "  config.TRAIN.DECODER_LEARNING_RATE             = 1e-3\n",
    "  config.TRAIN.REFINER_LEARNING_RATE             = 1e-3\n",
    "  config.TRAIN.MERGER_LEARNING_RATE              = 1e-4\n",
    "  config.TRAIN.ENCODER_LR_MILESTONES             = [150]\n",
    "  config.TRAIN.DECODER_LR_MILESTONES             = [150]\n",
    "  config.TRAIN.REFINER_LR_MILESTONES             = [150]\n",
    "  config.TRAIN.MERGER_LR_MILESTONES              = [150]\n",
    "  config.TRAIN.BETAS                             = (.9, .999)\n",
    "  config.TRAIN.MOMENTUM                          = .9\n",
    "  config.TRAIN.GAMMA                             = .5\n",
    "  config.TRAIN.SAVE_FREQ                         = 10            # weights will be overwritten every save_freq epoch\n",
    "  config.TRAIN.UPDATE_N_VIEWS_RENDERING          = False\n",
    "\n",
    "  #\n",
    "  # Testing options\n",
    "  #\n",
    "  config.TEST                                    = edict()\n",
    "  config.TEST.RANDOM_BG_COLOR_RANGE              = [[240, 240], [240, 240], [240, 240]]\n",
    "  config.TEST.VOXEL_THRESH                       = [.2, .3, .4, .5]\n",
    "  return config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6_jNJc-VPTUb"
   },
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cQge3ukAPXN4"
   },
   "outputs": [],
   "source": [
    "def test_net(cfg,\n",
    "             epoch_idx=-1,\n",
    "             output_dir=None,\n",
    "             test_data_loader=None,\n",
    "             test_writer=None,\n",
    "             encoder=None,\n",
    "             decoder=None,\n",
    "             refiner=None,\n",
    "             merger=None):\n",
    "    # Enable the inbuilt cudnn auto-tuner to find the best algorithm to use\n",
    "    torch.backends.cudnn.benchmark = True\n",
    "\n",
    "    # Load taxonomies of dataset\n",
    "    taxonomies = []\n",
    "    with open(cfg.DATASETS[cfg.DATASET.TEST_DATASET.upper()].TAXONOMY_FILE_PATH, encoding='utf-8') as file:\n",
    "        taxonomies = json.loads(file.read())\n",
    "    taxonomies = {t['taxonomy_id']: t for t in taxonomies}\n",
    "\n",
    "    # Set up data loader\n",
    "    if test_data_loader is None:\n",
    "        # Set up data augmentation\n",
    "        IMG_SIZE = cfg.CONST.IMG_H, cfg.CONST.IMG_W\n",
    "        CROP_SIZE = cfg.CONST.CROP_IMG_H, cfg.CONST.CROP_IMG_W\n",
    "        test_transforms = Compose([\n",
    "            CenterCrop(IMG_SIZE, CROP_SIZE),\n",
    "            RandomBackground(cfg.TEST.RANDOM_BG_COLOR_RANGE),\n",
    "            Normalize(mean=cfg.DATASET.MEAN, std=cfg.DATASET.STD),\n",
    "            ToTensor(),\n",
    "        ])\n",
    "\n",
    "        dataset_loader = ShapeNetDataLoader(cfg)\n",
    "        test_data_loader = torch.utils.data.DataLoader(dataset=dataset_loader.get_dataset(\n",
    "                            DatasetType.TEST, cfg.CONST.N_VIEWS_RENDERING, test_transforms),\n",
    "                                                       batch_size=1,\n",
    "                                                       num_workers=1,\n",
    "                                                       pin_memory=True,\n",
    "                                                       shuffle=False)\n",
    "\n",
    "    # Set up networks\n",
    "    if decoder is None or encoder is None:\n",
    "        encoder = Encoder(cfg)\n",
    "        decoder = Decoder(cfg)\n",
    "        refiner = Refiner(cfg)\n",
    "        merger = Merger(cfg)\n",
    "\n",
    "        if torch.cuda.is_available():\n",
    "            encoder = torch.nn.DataParallel(encoder).cuda()\n",
    "            decoder = torch.nn.DataParallel(decoder).cuda()\n",
    "            refiner = torch.nn.DataParallel(refiner).cuda()\n",
    "            merger = torch.nn.DataParallel(merger).cuda()\n",
    "\n",
    "        print('[INFO] %s Loading weights from %s ...' % (dt.now(), cfg.CONST.WEIGHTS))\n",
    "        checkpoint = torch.load(cfg.CONST.WEIGHTS)\n",
    "        epoch_idx = checkpoint['epoch_idx']\n",
    "        encoder.load_state_dict(checkpoint['encoder_state_dict'])\n",
    "        decoder.load_state_dict(checkpoint['decoder_state_dict'])\n",
    "\n",
    "        if cfg.NETWORK.USE_REFINER:\n",
    "            refiner.load_state_dict(checkpoint['refiner_state_dict'])\n",
    "        if cfg.NETWORK.USE_MERGER:\n",
    "            merger.load_state_dict(checkpoint['merger_state_dict'])\n",
    "\n",
    "    # Set up loss functions\n",
    "    bce_loss = torch.nn.BCELoss()\n",
    "    mse_loss = torch.nn.MSELoss()\n",
    "\n",
    "    # Testing loop\n",
    "    n_samples = len(test_data_loader)\n",
    "    test_iou = dict()\n",
    "    encoder_losses = AverageMeter()\n",
    "    refiner_losses = AverageMeter()\n",
    "\n",
    "    # Switch models to evaluation mode\n",
    "    encoder.eval()\n",
    "    decoder.eval()\n",
    "    refiner.eval()\n",
    "    merger.eval()\n",
    "\n",
    "    for sample_idx, (taxonomy_id, sample_name, rendering_images, ground_truth_volume) in enumerate(test_data_loader):\n",
    "        taxonomy_id = taxonomy_id[0] if isinstance(taxonomy_id[0], str) else taxonomy_id[0].item()\n",
    "        sample_name = sample_name[0]\n",
    "\n",
    "        with torch.no_grad():\n",
    "            # Get data from data loader\n",
    "            rendering_images = var_or_cuda(rendering_images)\n",
    "            ground_truth_volume = var_or_cuda(ground_truth_volume)\n",
    "\n",
    "            # Test the encoder, decoder, refiner and merger\n",
    "            image_features = encoder(rendering_images)\n",
    "            raw_features, generated_volume = decoder(image_features)\n",
    "\n",
    "            if cfg.NETWORK.USE_MERGER and epoch_idx >= cfg.TRAIN.EPOCH_START_USE_MERGER:\n",
    "                generated_volume = merger(raw_features, generated_volume)\n",
    "            else:\n",
    "                generated_volume = torch.mean(generated_volume, dim=1)\n",
    "            encoder_loss = mse_loss(generated_volume, ground_truth_volume) * 10\n",
    "\n",
    "            if cfg.NETWORK.USE_REFINER and epoch_idx >= cfg.TRAIN.EPOCH_START_USE_REFINER:\n",
    "                generated_volume = refiner(generated_volume)\n",
    "                refiner_loss = mse_loss(generated_volume, ground_truth_volume) * 10\n",
    "            else:\n",
    "                refiner_loss = encoder_loss\n",
    "\n",
    "            # Append loss and accuracy to average metrics\n",
    "            encoder_losses.update(encoder_loss.item())\n",
    "            refiner_losses.update(refiner_loss.item())\n",
    "\n",
    "            # IoU per sample\n",
    "            sample_iou = []\n",
    "            for th in cfg.TEST.VOXEL_THRESH:\n",
    "                _volume = torch.ge(generated_volume, th).float()\n",
    "                intersection = torch.sum(_volume.mul(ground_truth_volume)).float()\n",
    "                union = torch.sum(torch.ge(_volume.add(ground_truth_volume), 1)).float()\n",
    "                sample_iou.append((intersection / union).item())\n",
    "\n",
    "            # IoU per taxonomy\n",
    "            if taxonomy_id not in test_iou:\n",
    "                test_iou[taxonomy_id] = {'n_samples': 0, 'iou': []}\n",
    "            test_iou[taxonomy_id]['n_samples'] += 1\n",
    "            test_iou[taxonomy_id]['iou'].append(sample_iou)\n",
    "\n",
    "            # Append generated volumes to TensorBoard\n",
    "            if output_dir and sample_idx < 3:\n",
    "                img_dir = output_dir % 'images'\n",
    "                # Volume Visualization\n",
    "                gv = generated_volume.cpu().numpy()\n",
    "                rendering_views = get_volume_views(gv, os.path.join(img_dir, 'test'),\n",
    "                                                                              epoch_idx)\n",
    "                test_writer.add_image('Test Sample#%02d/Volume Reconstructed' % sample_idx, np.transpose(rendering_views,(2,0,1)), epoch_idx)\n",
    "                gtv = ground_truth_volume.cpu().numpy()\n",
    "                rendering_views = get_volume_views(gtv, os.path.join(img_dir, 'test'),\n",
    "                                                                              epoch_idx)\n",
    "                test_writer.add_image('Test Sample#%02d/Volume GroundTruth' % sample_idx, np.transpose(rendering_views,(2,0,1)), epoch_idx)\n",
    "\n",
    "            # Print sample loss and IoU\n",
    "            print('[INFO] %s Test[%d/%d] Taxonomy = %s Sample = %s EDLoss = %.4f RLoss = %.4f IoU = %s' %\n",
    "                  (dt.now(), sample_idx + 1, n_samples, taxonomy_id, sample_name, encoder_loss.item(),\n",
    "                   refiner_loss.item(), ['%.4f' % si for si in sample_iou]))\n",
    "\n",
    "    # Output testing results\n",
    "    mean_iou = []\n",
    "    for taxonomy_id in test_iou:\n",
    "        test_iou[taxonomy_id]['iou'] = np.mean(test_iou[taxonomy_id]['iou'], axis=0)\n",
    "        mean_iou.append(test_iou[taxonomy_id]['iou'] * test_iou[taxonomy_id]['n_samples'])\n",
    "    mean_iou = np.sum(mean_iou, axis=0) / n_samples\n",
    "\n",
    "    # Print header\n",
    "    print('============================ TEST RESULTS ============================')\n",
    "    print('Taxonomy', end='\\t')\n",
    "    print('#Sample', end='\\t')\n",
    "    print('Baseline', end='\\t')\n",
    "    for th in cfg.TEST.VOXEL_THRESH:\n",
    "        print('t=%.2f' % th, end='\\t')\n",
    "    print()\n",
    "    # Print body\n",
    "    for taxonomy_id in test_iou:\n",
    "        print('%s' % taxonomies[taxonomy_id]['taxonomy_name'].ljust(8), end='\\t')\n",
    "        print('%d' % test_iou[taxonomy_id]['n_samples'], end='\\t')\n",
    "        if 'baseline' in taxonomies[taxonomy_id]:\n",
    "            print('%.4f' % taxonomies[taxonomy_id]['baseline']['%d-view' % cfg.CONST.N_VIEWS_RENDERING], end='\\t\\t')\n",
    "        else:\n",
    "            print('N/a', end='\\t\\t')\n",
    "\n",
    "        for ti in test_iou[taxonomy_id]['iou']:\n",
    "            print('%.4f' % ti, end='\\t')\n",
    "        print()\n",
    "    # Print mean IoU for each threshold\n",
    "    print('Overall ', end='\\t\\t\\t\\t')\n",
    "    for mi in mean_iou:\n",
    "        print('%.4f' % mi, end='\\t')\n",
    "    print('\\n')\n",
    "\n",
    "    # Add testing results to TensorBoard\n",
    "    max_iou = np.max(mean_iou)\n",
    "    if test_writer is not None:\n",
    "        test_writer.add_scalar('EncoderDecoder/EpochLoss', encoder_losses.avg, epoch_idx)\n",
    "        test_writer.add_scalar('Refiner/EpochLoss', refiner_losses.avg, epoch_idx)\n",
    "        test_writer.add_scalar('Refiner/IoU', max_iou, epoch_idx)\n",
    "\n",
    "    return max_iou"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JzVOP6far3V6"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "23WFns-W25t7"
   },
   "source": [
    "##### Connect to drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lAq_RtNM2-j3",
    "outputId": "1377a3e5-db3a-43e6-a00d-cc1f209f8fb1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DiTYG880zs3-"
   },
   "source": [
    "##### Intializing New Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7aBN1R-0z3FD"
   },
   "outputs": [],
   "source": [
    "cfg = globalConfig()\n",
    "cfg['DATASETS'][\"SHAPENET\"][\"TAXONOMY_FILE_PATH\"] = \"./ShapeNet.json\"\n",
    "cfg['DATASETS'][\"SHAPENET\"][\"RENDERING_PATH\"] = \"./kaggle/tmp/ShapeNetRendering/%s/%s/rendering/%02d.png\"\n",
    "cfg['DATASETS'][\"SHAPENET\"][\"VOXEL_PATH\"] = \"./kaggle/tmp/ShapeNetVox32/%s/%s/model.binvox\"\n",
    "cfg['CONST'][\"IMG_W\"] = 224\n",
    "cfg['CONST'][\"IMG_H\"] = 224\n",
    "cfg['CONST'][\"BATCH_SIZE\"] = 64\n",
    "cfg['CONST'][\"N_VIEWS_RENDERING\"] = 1\n",
    "cfg[\"TRAIN\"][\"RESUME_TRAIN\"] = True\n",
    "cfg[\"TRAIN\"]['NUM_EPOCHES']=  31\n",
    "cfg[\"TRAIN\"]['POLICY'] = 'adam'\n",
    "cfg[\"TRAIN\"]['ENCODER_LEARNING_RATE'] = 0.001\n",
    "cfg[\"TRAIN\"]['DECODER_LEARNING_RATE'] = 0.001\n",
    "cfg[\"TRAIN\"]['REFINER_LEARNING_RATE'] = 0.001\n",
    "cfg[\"TRAIN\"]['MERGER_LEARNING_RATE'] = 0.0001\n",
    "cfg[\"TRAIN\"]['SAVE_FREQ'] = 10\n",
    "cfg[\"TEST\"] ['VOXEL_THRESH'] = [0.2, 0.3, 0.4, 0.5]\n",
    "cfg[\"EXPT_NAME\"] = \"exp0\"\n",
    "cfg[\"DIR\"][\"OUT_PATH\"] = \"./gdrive/MyDrive/Vision/Code/\" + cfg[\"EXPT_NAME\"] + \"/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4kPnClF-97kO"
   },
   "source": [
    "##### Loading Old Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "id": "-CuwmcUp96pB",
    "outputId": "dea100a9-3556-4e0c-9848-8868940af50b"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-d89a4e9e5f84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"TRAIN\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'NUM_EPOCHES'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m  \u001b[0;36m31\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"EXPT_NAME\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"exp0\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DIR\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"OUT_PATH\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0;34m+\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"EXPT_NAME\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary +: 'str'"
     ]
    }
   ],
   "source": [
    "exptname = \"exp0\"\n",
    "path = \"/content/gdrive/MyDrive/Vision/Code/\" + exptname + \"/sample.json\"\n",
    "with open(path,\"r\") as openfile:\n",
    "  cfg = json.load(openfile)\n",
    "\n",
    "cfg[\"TRAIN\"][\"RESUME_TRAIN\"] = True\n",
    "cfg[\"TRAIN\"]['NUM_EPOCHES']=  31\n",
    "cfg[\"EXPT_NAME\"] = \"exp0\"\n",
    "# cfg[\"DIR\"][\"OUT_PATH\"] =  + cfg[\"EXPT_NAME\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xm4S1GIZuKBv"
   },
   "source": [
    "##### Defining transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TfY6NHZBupOY"
   },
   "outputs": [],
   "source": [
    "train_transforms = Compose([\n",
    "        RandomCrop((cfg.CONST.IMG_H,cfg.CONST.IMG_W), (cfg.CONST.CROP_IMG_H, cfg.CONST.CROP_IMG_W)),\n",
    "        RandomBackground(cfg.TRAIN.RANDOM_BG_COLOR_RANGE),\n",
    "        ColorJitter(cfg.TRAIN.BRIGHTNESS, cfg.TRAIN.CONTRAST, cfg.TRAIN.SATURATION),\n",
    "        RandomNoise(cfg.TRAIN.NOISE_STD),\n",
    "        Normalize(mean=cfg.DATASET.MEAN, std=cfg.DATASET.STD),\n",
    "        RandomFlip(),\n",
    "        RandomPermuteRGB(),\n",
    "        ToTensor(),\n",
    "    ])\n",
    "\n",
    "val_transforms = Compose([\n",
    "        CenterCrop((cfg.CONST.IMG_H,cfg.CONST.IMG_W), (cfg.CONST.CROP_IMG_H, cfg.CONST.CROP_IMG_W)),\n",
    "        RandomBackground(cfg.TEST.RANDOM_BG_COLOR_RANGE),\n",
    "        Normalize(mean=cfg.DATASET.MEAN, std=cfg.DATASET.STD),\n",
    "        ToTensor(),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LM0e48_j_irP"
   },
   "source": [
    "##### Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NcWXk8rQ_qiX",
    "outputId": "5f796c88-cb28-4b0a-bd81-c68c1a302475"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 2022-12-12 02:27:35.697315 Collecting files of Taxonomy[ID=02691156, Name=aeroplane]\n",
      "[INFO] 2022-12-12 02:27:37.071673 Collecting files of Taxonomy[ID=02828884, Name=bench]\n",
      "[INFO] 2022-12-12 02:27:37.669600 Collecting files of Taxonomy[ID=02933112, Name=cabinet]\n",
      "[INFO] 2022-12-12 02:27:38.068842 Collecting files of Taxonomy[ID=02958343, Name=car]\n",
      "[WARN] 2022-12-12 02:27:38.351183 Ignore sample 02958343/f9c1d7748c15499c6f2bd1c4e9adb41 since volume file not exists.\n",
      "[INFO] 2022-12-12 02:27:39.757437 Collecting files of Taxonomy[ID=03001627, Name=chair]\n",
      "[INFO] 2022-12-12 02:27:41.458416 Collecting files of Taxonomy[ID=03211117, Name=display]\n",
      "[INFO] 2022-12-12 02:27:41.708627 Collecting files of Taxonomy[ID=03636649, Name=lamp]\n",
      "[INFO] 2022-12-12 02:27:42.242991 Collecting files of Taxonomy[ID=03691459, Name=speaker]\n",
      "[INFO] 2022-12-12 02:27:42.634842 Collecting files of Taxonomy[ID=04090263, Name=rifle]\n",
      "[INFO] 2022-12-12 02:27:43.561167 Collecting files of Taxonomy[ID=04256520, Name=sofa]\n",
      "[INFO] 2022-12-12 02:27:44.278944 Collecting files of Taxonomy[ID=04379243, Name=table]\n",
      "[INFO] 2022-12-12 02:27:46.402264 Collecting files of Taxonomy[ID=04401088, Name=telephone]\n",
      "[INFO] 2022-12-12 02:27:46.654177 Collecting files of Taxonomy[ID=04530566, Name=watercraft]\n",
      "[INFO] 2022-12-12 02:27:47.108455 Complete collecting files of the dataset. Total files: 30642.\n",
      "[INFO] 2022-12-12 02:27:47.110389 Collecting files of Taxonomy[ID=02691156, Name=aeroplane]\n",
      "[INFO] 2022-12-12 02:27:47.249149 Collecting files of Taxonomy[ID=02828884, Name=bench]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 2022-12-12 02:27:47.319659 Collecting files of Taxonomy[ID=02933112, Name=cabinet]\n",
      "[INFO] 2022-12-12 02:27:47.373112 Collecting files of Taxonomy[ID=02958343, Name=car]\n",
      "[INFO] 2022-12-12 02:27:47.673904 Collecting files of Taxonomy[ID=03001627, Name=chair]\n",
      "[INFO] 2022-12-12 02:27:47.915157 Collecting files of Taxonomy[ID=03211117, Name=display]\n",
      "[INFO] 2022-12-12 02:27:47.955390 Collecting files of Taxonomy[ID=03636649, Name=lamp]\n",
      "[INFO] 2022-12-12 02:27:48.035441 Collecting files of Taxonomy[ID=03691459, Name=speaker]\n",
      "[INFO] 2022-12-12 02:27:48.089063 Collecting files of Taxonomy[ID=04090263, Name=rifle]\n",
      "[INFO] 2022-12-12 02:27:48.179137 Collecting files of Taxonomy[ID=04256520, Name=sofa]\n",
      "[INFO] 2022-12-12 02:27:48.291649 Collecting files of Taxonomy[ID=04379243, Name=table]\n",
      "[INFO] 2022-12-12 02:27:48.603713 Collecting files of Taxonomy[ID=04401088, Name=telephone]\n",
      "[INFO] 2022-12-12 02:27:48.640972 Collecting files of Taxonomy[ID=04530566, Name=watercraft]\n",
      "[INFO] 2022-12-12 02:27:48.710864 Complete collecting files of the dataset. Total files: 4371.\n"
     ]
    }
   ],
   "source": [
    "train_dataset_loader = ShapeNetDataLoader(cfg)\n",
    "val_dataset_loader = ShapeNetDataLoader(cfg)\n",
    "train_data_loader = torch.utils.data.DataLoader(dataset=train_dataset_loader.get_dataset(\n",
    "                    DatasetType.TRAIN, cfg.CONST.N_VIEWS_RENDERING, train_transforms),\n",
    "                                                    batch_size=cfg.CONST.BATCH_SIZE,\n",
    "                                                    num_workers=cfg.TRAIN.NUM_WORKER,\n",
    "                                                    pin_memory=True,\n",
    "                                                    shuffle=True,\n",
    "                                                    drop_last=True)\n",
    "val_data_loader = torch.utils.data.DataLoader(dataset=val_dataset_loader.get_dataset(\n",
    "                  DatasetType.VAL, cfg.CONST.N_VIEWS_RENDERING, val_transforms),\n",
    "                                                  batch_size=1,\n",
    "                                                  num_workers=1,\n",
    "                                                  pin_memory=True,\n",
    "                                                  shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ldloFD2jA3K2"
   },
   "source": [
    "##### Initializing Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jKmfBwd0A9T7",
    "outputId": "31cb9a75-3d9c-4ea6-fa67-8f1bfde41ac0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_BN_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_BN_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DEBUG] 2022-12-12 02:27:51.292713 Parameters in Encoder: 7772480.\n",
      "[DEBUG] 2022-12-12 02:27:51.293096 Parameters in Decoder: 71583064.\n",
      "[DEBUG] 2022-12-12 02:27:51.293275 Parameters in Refiner: 34880352.\n",
      "[DEBUG] 2022-12-12 02:27:51.293483 Parameters in Merger: 8571.\n"
     ]
    }
   ],
   "source": [
    "encoder = Encoder(cfg)\n",
    "decoder = Decoder(cfg)\n",
    "refiner = Refiner(cfg)\n",
    "merger = Merger(cfg)\n",
    "print('[DEBUG] %s Parameters in Encoder: %d.' % (dt.now(), count_parameters(encoder)))\n",
    "print('[DEBUG] %s Parameters in Decoder: %d.' % (dt.now(), count_parameters(decoder)))\n",
    "print('[DEBUG] %s Parameters in Refiner: %d.' % (dt.now(), count_parameters(refiner)))\n",
    "print('[DEBUG] %s Parameters in Merger: %d.' % (dt.now(), count_parameters(merger)))\n",
    "\n",
    "# Initialize weights of networks\n",
    "encoder.apply(init_weights)\n",
    "decoder.apply(init_weights)\n",
    "refiner.apply(init_weights)\n",
    "merger.apply(init_weights)\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "  encoder = torch.nn.DataParallel(encoder).cuda()\n",
    "  decoder = torch.nn.DataParallel(decoder).cuda()\n",
    "  refiner = torch.nn.DataParallel(refiner).cuda()\n",
    "  merger = torch.nn.DataParallel(merger).cuda()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lyX-SFsuBqni"
   },
   "source": [
    "##### Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EBz-6Z2oBKF9"
   },
   "outputs": [],
   "source": [
    "# Set up solver\n",
    "if cfg.TRAIN.POLICY == 'adam':\n",
    "    encoder_solver = torch.optim.Adam(filter(lambda p: p.requires_grad, encoder.parameters()),\n",
    "                                      lr=cfg.TRAIN.ENCODER_LEARNING_RATE,\n",
    "                                      betas=cfg.TRAIN.BETAS)\n",
    "    decoder_solver = torch.optim.Adam(decoder.parameters(),\n",
    "                                      lr=cfg.TRAIN.DECODER_LEARNING_RATE,\n",
    "                                      betas=cfg.TRAIN.BETAS)\n",
    "    refiner_solver = torch.optim.Adam(refiner.parameters(),\n",
    "                                      lr=cfg.TRAIN.REFINER_LEARNING_RATE,\n",
    "                                      betas=cfg.TRAIN.BETAS)\n",
    "    merger_solver = torch.optim.Adam(merger.parameters(), lr=cfg.TRAIN.MERGER_LEARNING_RATE, betas=cfg.TRAIN.BETAS)\n",
    "elif cfg.TRAIN.POLICY == 'sgd':\n",
    "        encoder_solver = torch.optim.SGD(filter(lambda p: p.requires_grad, encoder.parameters()),\n",
    "                                         lr=cfg.TRAIN.ENCODER_LEARNING_RATE,\n",
    "                                         momentum=cfg.TRAIN.MOMENTUM)\n",
    "        decoder_solver = torch.optim.SGD(decoder.parameters(),\n",
    "                                         lr=cfg.TRAIN.DECODER_LEARNING_RATE,\n",
    "                                         momentum=cfg.TRAIN.MOMENTUM)\n",
    "        refiner_solver = torch.optim.SGD(refiner.parameters(),\n",
    "                                         lr=cfg.TRAIN.REFINER_LEARNING_RATE,\n",
    "                                         momentum=cfg.TRAIN.MOMENTUM)\n",
    "        merger_solver = torch.optim.SGD(merger.parameters(),\n",
    "                                        lr=cfg.TRAIN.MERGER_LEARNING_RATE,\n",
    "                                        momentum=cfg.TRAIN.MOMENTUM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l4wGHQipBm5Q"
   },
   "source": [
    "##### Scheduler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_xzsXYinBvtD"
   },
   "outputs": [],
   "source": [
    "# Set up learning rate scheduler to decay learning rates dynamically\n",
    "encoder_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(encoder_solver,\n",
    "                                                            milestones=cfg.TRAIN.ENCODER_LR_MILESTONES,\n",
    "                                                            gamma=cfg.TRAIN.GAMMA)\n",
    "decoder_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(decoder_solver,\n",
    "                                                            milestones=cfg.TRAIN.DECODER_LR_MILESTONES,\n",
    "                                                            gamma=cfg.TRAIN.GAMMA)\n",
    "refiner_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(refiner_solver,\n",
    "                                                            milestones=cfg.TRAIN.REFINER_LR_MILESTONES,\n",
    "                                                            gamma=cfg.TRAIN.GAMMA)\n",
    "merger_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(merger_solver,\n",
    "                                                            milestones=cfg.TRAIN.MERGER_LR_MILESTONES,\n",
    "                                                            gamma=cfg.TRAIN.GAMMA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IbqjfcEUB1Yi"
   },
   "source": [
    "##### Loss function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "k1iiCWSBCDzk"
   },
   "outputs": [],
   "source": [
    "# Set up loss functions\n",
    "bce_loss = torch.nn.BCELoss()\n",
    "mse_loss = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "waGdI7RMCFJr"
   },
   "source": [
    "##### Load pretrained if exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3axce28tCV1h"
   },
   "outputs": [],
   "source": [
    "# Load pretrained model if exists\n",
    "init_epoch = 0\n",
    "best_iou = -1\n",
    "best_epoch = -1\n",
    "if 'WEIGHTS' in cfg.CONST and cfg.TRAIN.RESUME_TRAIN:\n",
    "    print('[INFO] %s Recovering from %s ...' % (dt.now(), cfg.CONST.WEIGHTS))\n",
    "    checkpoint = torch.load(cfg.CONST.WEIGHTS)\n",
    "    init_epoch = checkpoint['epoch_idx']\n",
    "    best_iou = checkpoint['best_iou']\n",
    "    best_epoch = checkpoint['best_epoch']\n",
    "\n",
    "    encoder.load_state_dict(checkpoint['encoder_state_dict'])\n",
    "    decoder.load_state_dict(checkpoint['decoder_state_dict'])\n",
    "    if cfg.NETWORK.USE_REFINER:\n",
    "        refiner.load_state_dict(checkpoint['refiner_state_dict'])\n",
    "    if cfg.NETWORK.USE_MERGER:\n",
    "        merger.load_state_dict(checkpoint['merger_state_dict'])\n",
    "\n",
    "    print('[INFO] %s Recover complete. Current epoch #%d, Best IoU = %.4f at epoch #%d.' %\n",
    "          (dt.now(), init_epoch, best_iou, best_epoch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fkgLuwbMCdGe"
   },
   "source": [
    "##### Summary writer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N0nzWNGtChev",
    "outputId": "2a19c50c-b3f6-4c6d-f610-5cbc189f4bea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
      "  warnings.warn(\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1P_rcNSYNBYwZp-lbS2AiMDMv1ZCBFExK\n",
      "To: /content/gdrive/MyDrive/Vision/Code/exp0/PTN\n",
      "100% 596k/596k [00:00<00:00, 112MB/s]\n"
     ]
    }
   ],
   "source": [
    "output_dir = os.path.join(cfg.DIR.OUT_PATH, '%s', dt.now().isoformat())\n",
    "log_dir = output_dir % 'logs'\n",
    "ckpt_dir = output_dir % 'checkpoints'\n",
    "train_writer = SummaryWriter(os.path.join(log_dir, 'train'))\n",
    "val_writer = SummaryWriter(os.path.join(log_dir, 'test'))\n",
    "with open(cfg.DIR.OUT_PATH + \"sample.json\",\"w\") as outfile:\n",
    "  json.dump(cfg,outfile)\n",
    "  \n",
    "!mkdir -p $cfg.DIR.OUT_PATH\n",
    "!gdown --id 1P_rcNSYNBYwZp-lbS2AiMDMv1ZCBFExK -O $cfg.DIR.OUT_PATH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rqFGppcU-yjL"
   },
   "source": [
    "##### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "inveUthcDHYG",
    "outputId": "cb84858f-7102-481b-d814-d784121fc89e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 2022-12-12 02:28:17.149140 [Epoch 1/31][Batch 1/478] BatchTime = 16.903 (s) DataTime = 4.508 (s) EDLoss = 4.7985 RLoss = 3.4295\n",
      "[INFO] 2022-12-12 02:28:18.945793 [Epoch 1/31][Batch 2/478] BatchTime = 1.797 (s) DataTime = 0.003 (s) EDLoss = 2.9865 RLoss = 2.4391\n",
      "[INFO] 2022-12-12 02:28:20.744182 [Epoch 1/31][Batch 3/478] BatchTime = 1.798 (s) DataTime = 0.002 (s) EDLoss = 2.6992 RLoss = 2.1082\n",
      "[INFO] 2022-12-12 02:28:22.544312 [Epoch 1/31][Batch 4/478] BatchTime = 1.800 (s) DataTime = 0.002 (s) EDLoss = 2.4298 RLoss = 1.8748\n",
      "[INFO] 2022-12-12 02:28:24.332349 [Epoch 1/31][Batch 5/478] BatchTime = 1.788 (s) DataTime = 0.002 (s) EDLoss = 2.4179 RLoss = 1.7704\n",
      "[INFO] 2022-12-12 02:28:26.124426 [Epoch 1/31][Batch 6/478] BatchTime = 1.792 (s) DataTime = 0.002 (s) EDLoss = 2.3024 RLoss = 1.6189\n",
      "[INFO] 2022-12-12 02:28:27.927495 [Epoch 1/31][Batch 7/478] BatchTime = 1.803 (s) DataTime = 0.002 (s) EDLoss = 2.1352 RLoss = 1.5101\n",
      "[INFO] 2022-12-12 02:28:29.751749 [Epoch 1/31][Batch 8/478] BatchTime = 1.824 (s) DataTime = 0.002 (s) EDLoss = 2.2755 RLoss = 1.5390\n",
      "[INFO] 2022-12-12 02:28:31.553326 [Epoch 1/31][Batch 9/478] BatchTime = 1.802 (s) DataTime = 0.002 (s) EDLoss = 2.2369 RLoss = 1.4058\n",
      "[INFO] 2022-12-12 02:28:33.365962 [Epoch 1/31][Batch 10/478] BatchTime = 1.813 (s) DataTime = 0.002 (s) EDLoss = 1.9997 RLoss = 1.3113\n",
      "[INFO] 2022-12-12 02:28:35.172378 [Epoch 1/31][Batch 11/478] BatchTime = 1.806 (s) DataTime = 0.002 (s) EDLoss = 2.0327 RLoss = 1.2404\n",
      "[INFO] 2022-12-12 02:28:36.981268 [Epoch 1/31][Batch 12/478] BatchTime = 1.809 (s) DataTime = 0.002 (s) EDLoss = 2.1797 RLoss = 1.3694\n",
      "[INFO] 2022-12-12 02:28:38.822186 [Epoch 1/31][Batch 13/478] BatchTime = 1.841 (s) DataTime = 0.002 (s) EDLoss = 2.0186 RLoss = 1.1783\n",
      "[INFO] 2022-12-12 02:28:40.672158 [Epoch 1/31][Batch 14/478] BatchTime = 1.850 (s) DataTime = 0.002 (s) EDLoss = 1.9971 RLoss = 1.1683\n",
      "[INFO] 2022-12-12 02:28:42.526594 [Epoch 1/31][Batch 15/478] BatchTime = 1.854 (s) DataTime = 0.002 (s) EDLoss = 2.0168 RLoss = 1.1347\n",
      "[INFO] 2022-12-12 02:28:44.375367 [Epoch 1/31][Batch 16/478] BatchTime = 1.849 (s) DataTime = 0.002 (s) EDLoss = 2.0410 RLoss = 1.2396\n",
      "[INFO] 2022-12-12 02:28:46.216453 [Epoch 1/31][Batch 17/478] BatchTime = 1.841 (s) DataTime = 0.002 (s) EDLoss = 2.0423 RLoss = 1.2503\n",
      "[INFO] 2022-12-12 02:28:48.064374 [Epoch 1/31][Batch 18/478] BatchTime = 1.848 (s) DataTime = 0.002 (s) EDLoss = 1.8871 RLoss = 1.1127\n",
      "[INFO] 2022-12-12 02:28:49.900012 [Epoch 1/31][Batch 19/478] BatchTime = 1.836 (s) DataTime = 0.002 (s) EDLoss = 1.9793 RLoss = 1.1447\n",
      "[INFO] 2022-12-12 02:28:51.748744 [Epoch 1/31][Batch 20/478] BatchTime = 1.849 (s) DataTime = 0.002 (s) EDLoss = 1.8720 RLoss = 1.1205\n",
      "[INFO] 2022-12-12 02:28:53.608166 [Epoch 1/31][Batch 21/478] BatchTime = 1.859 (s) DataTime = 0.002 (s) EDLoss = 1.9075 RLoss = 1.1030\n",
      "[INFO] 2022-12-12 02:28:55.464637 [Epoch 1/31][Batch 22/478] BatchTime = 1.856 (s) DataTime = 0.002 (s) EDLoss = 1.8278 RLoss = 1.0077\n",
      "[INFO] 2022-12-12 02:28:57.326100 [Epoch 1/31][Batch 23/478] BatchTime = 1.861 (s) DataTime = 0.002 (s) EDLoss = 1.8130 RLoss = 1.0389\n",
      "[INFO] 2022-12-12 02:28:59.199562 [Epoch 1/31][Batch 24/478] BatchTime = 1.873 (s) DataTime = 0.002 (s) EDLoss = 1.7653 RLoss = 1.0659\n",
      "[INFO] 2022-12-12 02:29:01.068590 [Epoch 1/31][Batch 25/478] BatchTime = 1.869 (s) DataTime = 0.002 (s) EDLoss = 1.7794 RLoss = 0.9876\n",
      "[INFO] 2022-12-12 02:29:02.951449 [Epoch 1/31][Batch 26/478] BatchTime = 1.883 (s) DataTime = 0.003 (s) EDLoss = 1.7969 RLoss = 0.9853\n",
      "[INFO] 2022-12-12 02:29:04.823457 [Epoch 1/31][Batch 27/478] BatchTime = 1.872 (s) DataTime = 0.002 (s) EDLoss = 1.7843 RLoss = 1.0242\n",
      "[INFO] 2022-12-12 02:29:06.717965 [Epoch 1/31][Batch 28/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 1.7523 RLoss = 1.0896\n",
      "[INFO] 2022-12-12 02:29:08.609835 [Epoch 1/31][Batch 29/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 1.7348 RLoss = 0.9466\n",
      "[INFO] 2022-12-12 02:29:10.505939 [Epoch 1/31][Batch 30/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 1.7896 RLoss = 1.0369\n",
      "[INFO] 2022-12-12 02:29:12.432090 [Epoch 1/31][Batch 31/478] BatchTime = 1.926 (s) DataTime = 0.002 (s) EDLoss = 1.7182 RLoss = 0.8526\n",
      "[INFO] 2022-12-12 02:29:14.330321 [Epoch 1/31][Batch 32/478] BatchTime = 1.898 (s) DataTime = 0.002 (s) EDLoss = 1.6647 RLoss = 0.8635\n",
      "[INFO] 2022-12-12 02:29:16.268348 [Epoch 1/31][Batch 33/478] BatchTime = 1.938 (s) DataTime = 0.002 (s) EDLoss = 1.7235 RLoss = 0.9697\n",
      "[INFO] 2022-12-12 02:29:18.186273 [Epoch 1/31][Batch 34/478] BatchTime = 1.918 (s) DataTime = 0.002 (s) EDLoss = 1.7073 RLoss = 1.0232\n",
      "[INFO] 2022-12-12 02:29:20.107828 [Epoch 1/31][Batch 35/478] BatchTime = 1.922 (s) DataTime = 0.003 (s) EDLoss = 1.6694 RLoss = 0.8468\n",
      "[INFO] 2022-12-12 02:29:22.034251 [Epoch 1/31][Batch 36/478] BatchTime = 1.926 (s) DataTime = 0.002 (s) EDLoss = 1.7133 RLoss = 0.8901\n",
      "[INFO] 2022-12-12 02:29:23.964213 [Epoch 1/31][Batch 37/478] BatchTime = 1.930 (s) DataTime = 0.002 (s) EDLoss = 1.6269 RLoss = 0.8645\n",
      "[INFO] 2022-12-12 02:29:25.895064 [Epoch 1/31][Batch 38/478] BatchTime = 1.931 (s) DataTime = 0.002 (s) EDLoss = 1.6517 RLoss = 0.9056\n",
      "[INFO] 2022-12-12 02:29:27.822559 [Epoch 1/31][Batch 39/478] BatchTime = 1.927 (s) DataTime = 0.002 (s) EDLoss = 1.6623 RLoss = 0.9169\n",
      "[INFO] 2022-12-12 02:29:29.749176 [Epoch 1/31][Batch 40/478] BatchTime = 1.927 (s) DataTime = 0.003 (s) EDLoss = 1.6561 RLoss = 0.8527\n",
      "[INFO] 2022-12-12 02:29:31.682858 [Epoch 1/31][Batch 41/478] BatchTime = 1.934 (s) DataTime = 0.002 (s) EDLoss = 1.6669 RLoss = 0.8862\n",
      "[INFO] 2022-12-12 02:29:33.578129 [Epoch 1/31][Batch 42/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 1.6294 RLoss = 0.8250\n",
      "[INFO] 2022-12-12 02:29:35.480671 [Epoch 1/31][Batch 43/478] BatchTime = 1.903 (s) DataTime = 0.002 (s) EDLoss = 1.5929 RLoss = 0.9497\n",
      "[INFO] 2022-12-12 02:29:37.373047 [Epoch 1/31][Batch 44/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 1.6317 RLoss = 0.8213\n",
      "[INFO] 2022-12-12 02:29:39.261075 [Epoch 1/31][Batch 45/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 1.5885 RLoss = 0.8613\n",
      "[INFO] 2022-12-12 02:29:41.154249 [Epoch 1/31][Batch 46/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 1.5885 RLoss = 0.7816\n",
      "[INFO] 2022-12-12 02:29:43.032331 [Epoch 1/31][Batch 47/478] BatchTime = 1.878 (s) DataTime = 0.002 (s) EDLoss = 1.5694 RLoss = 0.7925\n",
      "[INFO] 2022-12-12 02:29:44.911806 [Epoch 1/31][Batch 48/478] BatchTime = 1.879 (s) DataTime = 0.002 (s) EDLoss = 1.5368 RLoss = 0.8105\n",
      "[INFO] 2022-12-12 02:29:46.796539 [Epoch 1/31][Batch 49/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 1.5205 RLoss = 0.9443\n",
      "[INFO] 2022-12-12 02:29:48.684781 [Epoch 1/31][Batch 50/478] BatchTime = 1.888 (s) DataTime = 0.003 (s) EDLoss = 1.5067 RLoss = 0.7933\n",
      "[INFO] 2022-12-12 02:29:50.553296 [Epoch 1/31][Batch 51/478] BatchTime = 1.869 (s) DataTime = 0.002 (s) EDLoss = 1.5061 RLoss = 0.7851\n",
      "[INFO] 2022-12-12 02:29:52.436583 [Epoch 1/31][Batch 52/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 1.5160 RLoss = 0.7503\n",
      "[INFO] 2022-12-12 02:29:54.302762 [Epoch 1/31][Batch 53/478] BatchTime = 1.866 (s) DataTime = 0.002 (s) EDLoss = 1.4912 RLoss = 0.9217\n",
      "[INFO] 2022-12-12 02:29:56.173888 [Epoch 1/31][Batch 54/478] BatchTime = 1.871 (s) DataTime = 0.002 (s) EDLoss = 1.4676 RLoss = 0.7944\n",
      "[INFO] 2022-12-12 02:29:58.038436 [Epoch 1/31][Batch 55/478] BatchTime = 1.865 (s) DataTime = 0.002 (s) EDLoss = 1.4664 RLoss = 0.8725\n",
      "[INFO] 2022-12-12 02:29:59.943090 [Epoch 1/31][Batch 56/478] BatchTime = 1.905 (s) DataTime = 0.002 (s) EDLoss = 1.4495 RLoss = 0.7589\n",
      "[INFO] 2022-12-12 02:30:01.811432 [Epoch 1/31][Batch 57/478] BatchTime = 1.868 (s) DataTime = 0.002 (s) EDLoss = 1.4981 RLoss = 0.7257\n",
      "[INFO] 2022-12-12 02:30:03.691714 [Epoch 1/31][Batch 58/478] BatchTime = 1.880 (s) DataTime = 0.002 (s) EDLoss = 1.4799 RLoss = 0.7554\n",
      "[INFO] 2022-12-12 02:30:05.574155 [Epoch 1/31][Batch 59/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 1.4335 RLoss = 0.7879\n",
      "[INFO] 2022-12-12 02:30:07.458949 [Epoch 1/31][Batch 60/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 1.4605 RLoss = 0.7939\n",
      "[INFO] 2022-12-12 02:30:09.324415 [Epoch 1/31][Batch 61/478] BatchTime = 1.865 (s) DataTime = 0.002 (s) EDLoss = 1.4314 RLoss = 0.8189\n",
      "[INFO] 2022-12-12 02:30:11.204423 [Epoch 1/31][Batch 62/478] BatchTime = 1.880 (s) DataTime = 0.001 (s) EDLoss = 1.4236 RLoss = 0.8406\n",
      "[INFO] 2022-12-12 02:30:13.093204 [Epoch 1/31][Batch 63/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 1.4683 RLoss = 0.7804\n",
      "[INFO] 2022-12-12 02:30:14.982088 [Epoch 1/31][Batch 64/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 1.3769 RLoss = 0.8908\n",
      "[INFO] 2022-12-12 02:30:16.870960 [Epoch 1/31][Batch 65/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 1.4002 RLoss = 0.7488\n",
      "[INFO] 2022-12-12 02:30:18.771423 [Epoch 1/31][Batch 66/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 1.3803 RLoss = 0.8167\n",
      "[INFO] 2022-12-12 02:30:20.667758 [Epoch 1/31][Batch 67/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 1.3736 RLoss = 0.8096\n",
      "[INFO] 2022-12-12 02:30:22.547512 [Epoch 1/31][Batch 68/478] BatchTime = 1.880 (s) DataTime = 0.002 (s) EDLoss = 1.3653 RLoss = 0.7599\n",
      "[INFO] 2022-12-12 02:30:24.447995 [Epoch 1/31][Batch 69/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 1.3635 RLoss = 0.8425\n",
      "[INFO] 2022-12-12 02:30:26.335034 [Epoch 1/31][Batch 70/478] BatchTime = 1.887 (s) DataTime = 0.002 (s) EDLoss = 1.4135 RLoss = 0.7314\n",
      "[INFO] 2022-12-12 02:30:28.227624 [Epoch 1/31][Batch 71/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 1.3726 RLoss = 0.7823\n",
      "[INFO] 2022-12-12 02:30:30.123006 [Epoch 1/31][Batch 72/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 1.3699 RLoss = 0.7641\n",
      "[INFO] 2022-12-12 02:30:32.016833 [Epoch 1/31][Batch 73/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 1.3603 RLoss = 0.7472\n",
      "[INFO] 2022-12-12 02:30:33.930625 [Epoch 1/31][Batch 74/478] BatchTime = 1.914 (s) DataTime = 0.002 (s) EDLoss = 1.3533 RLoss = 0.8120\n",
      "[INFO] 2022-12-12 02:30:35.826347 [Epoch 1/31][Batch 75/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 1.3117 RLoss = 0.7101\n",
      "[INFO] 2022-12-12 02:30:37.718055 [Epoch 1/31][Batch 76/478] BatchTime = 1.892 (s) DataTime = 0.001 (s) EDLoss = 1.3303 RLoss = 0.7461\n",
      "[INFO] 2022-12-12 02:30:39.618479 [Epoch 1/31][Batch 77/478] BatchTime = 1.900 (s) DataTime = 0.008 (s) EDLoss = 1.2981 RLoss = 0.7895\n",
      "[INFO] 2022-12-12 02:30:41.516710 [Epoch 1/31][Batch 78/478] BatchTime = 1.898 (s) DataTime = 0.010 (s) EDLoss = 1.3310 RLoss = 0.7738\n",
      "[INFO] 2022-12-12 02:30:43.419809 [Epoch 1/31][Batch 79/478] BatchTime = 1.903 (s) DataTime = 0.003 (s) EDLoss = 1.3193 RLoss = 0.7059\n",
      "[INFO] 2022-12-12 02:30:45.338598 [Epoch 1/31][Batch 80/478] BatchTime = 1.919 (s) DataTime = 0.002 (s) EDLoss = 1.2864 RLoss = 0.7679\n",
      "[INFO] 2022-12-12 02:30:47.228308 [Epoch 1/31][Batch 81/478] BatchTime = 1.890 (s) DataTime = 0.002 (s) EDLoss = 1.2895 RLoss = 0.7199\n",
      "[INFO] 2022-12-12 02:30:49.123648 [Epoch 1/31][Batch 82/478] BatchTime = 1.895 (s) DataTime = 0.003 (s) EDLoss = 1.2640 RLoss = 0.7210\n",
      "[INFO] 2022-12-12 02:30:51.016360 [Epoch 1/31][Batch 83/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 1.2375 RLoss = 0.7546\n",
      "[INFO] 2022-12-12 02:30:52.942538 [Epoch 1/31][Batch 84/478] BatchTime = 1.926 (s) DataTime = 0.002 (s) EDLoss = 1.2508 RLoss = 0.6762\n",
      "[INFO] 2022-12-12 02:30:54.837693 [Epoch 1/31][Batch 85/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 1.2448 RLoss = 0.7337\n",
      "[INFO] 2022-12-12 02:30:56.728554 [Epoch 1/31][Batch 86/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 1.2385 RLoss = 0.7464\n",
      "[INFO] 2022-12-12 02:30:58.632691 [Epoch 1/31][Batch 87/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 1.2211 RLoss = 0.6302\n",
      "[INFO] 2022-12-12 02:31:00.526550 [Epoch 1/31][Batch 88/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 1.2506 RLoss = 0.7150\n",
      "[INFO] 2022-12-12 02:31:02.431418 [Epoch 1/31][Batch 89/478] BatchTime = 1.905 (s) DataTime = 0.002 (s) EDLoss = 1.2156 RLoss = 0.6116\n",
      "[INFO] 2022-12-12 02:31:04.314509 [Epoch 1/31][Batch 90/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 1.2034 RLoss = 0.6114\n",
      "[INFO] 2022-12-12 02:31:06.210493 [Epoch 1/31][Batch 91/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 1.1955 RLoss = 0.8529\n",
      "[INFO] 2022-12-12 02:31:08.085335 [Epoch 1/31][Batch 92/478] BatchTime = 1.875 (s) DataTime = 0.002 (s) EDLoss = 1.1858 RLoss = 0.6876\n",
      "[INFO] 2022-12-12 02:31:09.967535 [Epoch 1/31][Batch 93/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 1.2324 RLoss = 0.7935\n",
      "[INFO] 2022-12-12 02:31:11.852629 [Epoch 1/31][Batch 94/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 1.2224 RLoss = 0.6864\n",
      "[INFO] 2022-12-12 02:31:13.756049 [Epoch 1/31][Batch 95/478] BatchTime = 1.903 (s) DataTime = 0.002 (s) EDLoss = 1.1954 RLoss = 0.6654\n",
      "[INFO] 2022-12-12 02:31:15.642618 [Epoch 1/31][Batch 96/478] BatchTime = 1.887 (s) DataTime = 0.002 (s) EDLoss = 1.2056 RLoss = 0.7588\n",
      "[INFO] 2022-12-12 02:31:17.548039 [Epoch 1/31][Batch 97/478] BatchTime = 1.905 (s) DataTime = 0.002 (s) EDLoss = 1.1835 RLoss = 0.6647\n",
      "[INFO] 2022-12-12 02:31:19.440624 [Epoch 1/31][Batch 98/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 1.2190 RLoss = 0.7495\n",
      "[INFO] 2022-12-12 02:31:21.330925 [Epoch 1/31][Batch 99/478] BatchTime = 1.890 (s) DataTime = 0.003 (s) EDLoss = 1.1253 RLoss = 0.7977\n",
      "[INFO] 2022-12-12 02:31:23.222884 [Epoch 1/31][Batch 100/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 1.2057 RLoss = 0.6801\n",
      "[INFO] 2022-12-12 02:31:25.110862 [Epoch 1/31][Batch 101/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 1.1456 RLoss = 0.6979\n",
      "[INFO] 2022-12-12 02:31:27.003238 [Epoch 1/31][Batch 102/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 1.1348 RLoss = 0.6094\n",
      "[INFO] 2022-12-12 02:31:28.890474 [Epoch 1/31][Batch 103/478] BatchTime = 1.887 (s) DataTime = 0.002 (s) EDLoss = 1.1614 RLoss = 0.7837\n",
      "[INFO] 2022-12-12 02:31:30.785457 [Epoch 1/31][Batch 104/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 1.1700 RLoss = 0.6645\n",
      "[INFO] 2022-12-12 02:31:32.694574 [Epoch 1/31][Batch 105/478] BatchTime = 1.909 (s) DataTime = 0.002 (s) EDLoss = 1.1596 RLoss = 0.6432\n",
      "[INFO] 2022-12-12 02:31:34.573784 [Epoch 1/31][Batch 106/478] BatchTime = 1.879 (s) DataTime = 0.002 (s) EDLoss = 1.1139 RLoss = 0.7960\n",
      "[INFO] 2022-12-12 02:31:36.477580 [Epoch 1/31][Batch 107/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 1.1500 RLoss = 0.6350\n",
      "[INFO] 2022-12-12 02:31:38.368347 [Epoch 1/31][Batch 108/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 1.1843 RLoss = 0.7542\n",
      "[INFO] 2022-12-12 02:31:40.266920 [Epoch 1/31][Batch 109/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 1.1265 RLoss = 0.6725\n",
      "[INFO] 2022-12-12 02:31:42.172876 [Epoch 1/31][Batch 110/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 1.1938 RLoss = 0.7389\n",
      "[INFO] 2022-12-12 02:31:44.063086 [Epoch 1/31][Batch 111/478] BatchTime = 1.890 (s) DataTime = 0.002 (s) EDLoss = 1.1010 RLoss = 0.6544\n",
      "[INFO] 2022-12-12 02:31:45.948075 [Epoch 1/31][Batch 112/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 1.1077 RLoss = 0.6874\n",
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      "[INFO] 2022-12-12 02:32:29.435555 [Epoch 1/31][Batch 135/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 1.0225 RLoss = 0.6031\n",
      "[INFO] 2022-12-12 02:32:31.328613 [Epoch 1/31][Batch 136/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 1.1147 RLoss = 0.7368\n",
      "[INFO] 2022-12-12 02:32:33.225883 [Epoch 1/31][Batch 137/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 1.0463 RLoss = 0.7839\n",
      "[INFO] 2022-12-12 02:32:35.106843 [Epoch 1/31][Batch 138/478] BatchTime = 1.881 (s) DataTime = 0.001 (s) EDLoss = 1.0630 RLoss = 0.7157\n",
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      "[INFO] 2022-12-12 02:32:38.900288 [Epoch 1/31][Batch 140/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 1.0415 RLoss = 0.6067\n",
      "[INFO] 2022-12-12 02:32:40.800523 [Epoch 1/31][Batch 141/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 1.0240 RLoss = 0.6947\n",
      "[INFO] 2022-12-12 02:32:42.673824 [Epoch 1/31][Batch 142/478] BatchTime = 1.873 (s) DataTime = 0.002 (s) EDLoss = 1.0309 RLoss = 0.7117\n",
      "[INFO] 2022-12-12 02:32:44.571903 [Epoch 1/31][Batch 143/478] BatchTime = 1.898 (s) DataTime = 0.002 (s) EDLoss = 1.0163 RLoss = 0.6414\n",
      "[INFO] 2022-12-12 02:32:46.448934 [Epoch 1/31][Batch 144/478] BatchTime = 1.877 (s) DataTime = 0.002 (s) EDLoss = 0.9882 RLoss = 0.5926\n",
      "[INFO] 2022-12-12 02:32:48.343743 [Epoch 1/31][Batch 145/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 1.0419 RLoss = 0.7200\n",
      "[INFO] 2022-12-12 02:32:50.232085 [Epoch 1/31][Batch 146/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 1.0211 RLoss = 0.5961\n",
      "[INFO] 2022-12-12 02:32:52.127751 [Epoch 1/31][Batch 147/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 1.0179 RLoss = 0.5797\n",
      "[INFO] 2022-12-12 02:32:54.022264 [Epoch 1/31][Batch 148/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.9948 RLoss = 0.6105\n",
      "[INFO] 2022-12-12 02:32:55.919598 [Epoch 1/31][Batch 149/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.9963 RLoss = 0.6643\n",
      "[INFO] 2022-12-12 02:32:57.816769 [Epoch 1/31][Batch 150/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.9744 RLoss = 0.6159\n",
      "[INFO] 2022-12-12 02:32:59.721605 [Epoch 1/31][Batch 151/478] BatchTime = 1.905 (s) DataTime = 0.002 (s) EDLoss = 0.9987 RLoss = 0.6634\n",
      "[INFO] 2022-12-12 02:33:01.613877 [Epoch 1/31][Batch 152/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.9891 RLoss = 0.5990\n",
      "[INFO] 2022-12-12 02:33:03.496708 [Epoch 1/31][Batch 153/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.9884 RLoss = 0.6320\n",
      "[INFO] 2022-12-12 02:33:05.392231 [Epoch 1/31][Batch 154/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.9446 RLoss = 0.6071\n",
      "[INFO] 2022-12-12 02:33:07.296510 [Epoch 1/31][Batch 155/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.9441 RLoss = 0.6581\n",
      "[INFO] 2022-12-12 02:33:09.199393 [Epoch 1/31][Batch 156/478] BatchTime = 1.903 (s) DataTime = 0.002 (s) EDLoss = 0.9422 RLoss = 0.5563\n",
      "[INFO] 2022-12-12 02:33:11.092096 [Epoch 1/31][Batch 157/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.9663 RLoss = 0.6040\n",
      "[INFO] 2022-12-12 02:33:12.986220 [Epoch 1/31][Batch 158/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.9373 RLoss = 0.6152\n",
      "[INFO] 2022-12-12 02:33:14.893956 [Epoch 1/31][Batch 159/478] BatchTime = 1.908 (s) DataTime = 0.002 (s) EDLoss = 0.9502 RLoss = 0.5882\n",
      "[INFO] 2022-12-12 02:33:16.800035 [Epoch 1/31][Batch 160/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.9333 RLoss = 0.5412\n",
      "[INFO] 2022-12-12 02:33:18.685215 [Epoch 1/31][Batch 161/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 0.9373 RLoss = 0.6894\n",
      "[INFO] 2022-12-12 02:33:20.591442 [Epoch 1/31][Batch 162/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.9422 RLoss = 0.6692\n",
      "[INFO] 2022-12-12 02:33:22.493215 [Epoch 1/31][Batch 163/478] BatchTime = 1.902 (s) DataTime = 0.002 (s) EDLoss = 0.9544 RLoss = 0.6592\n",
      "[INFO] 2022-12-12 02:33:24.388206 [Epoch 1/31][Batch 164/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.9296 RLoss = 0.5063\n",
      "[INFO] 2022-12-12 02:33:26.285511 [Epoch 1/31][Batch 165/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.9274 RLoss = 0.6839\n",
      "[INFO] 2022-12-12 02:33:28.188613 [Epoch 1/31][Batch 166/478] BatchTime = 1.903 (s) DataTime = 0.002 (s) EDLoss = 0.9570 RLoss = 0.5375\n",
      "[INFO] 2022-12-12 02:33:30.085918 [Epoch 1/31][Batch 167/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.9195 RLoss = 0.5737\n",
      "[INFO] 2022-12-12 02:33:31.979899 [Epoch 1/31][Batch 168/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.9149 RLoss = 0.5873\n",
      "[INFO] 2022-12-12 02:33:33.876299 [Epoch 1/31][Batch 169/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.9163 RLoss = 0.6265\n",
      "[INFO] 2022-12-12 02:33:35.777001 [Epoch 1/31][Batch 170/478] BatchTime = 1.901 (s) DataTime = 0.001 (s) EDLoss = 0.9180 RLoss = 0.5835\n",
      "[INFO] 2022-12-12 02:33:37.693926 [Epoch 1/31][Batch 171/478] BatchTime = 1.917 (s) DataTime = 0.003 (s) EDLoss = 0.9542 RLoss = 0.6224\n",
      "[INFO] 2022-12-12 02:33:39.593054 [Epoch 1/31][Batch 172/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.9817 RLoss = 0.6872\n",
      "[INFO] 2022-12-12 02:33:41.490921 [Epoch 1/31][Batch 173/478] BatchTime = 1.898 (s) DataTime = 0.001 (s) EDLoss = 0.9266 RLoss = 0.7298\n",
      "[INFO] 2022-12-12 02:33:43.393030 [Epoch 1/31][Batch 174/478] BatchTime = 1.902 (s) DataTime = 0.001 (s) EDLoss = 0.8947 RLoss = 0.5460\n",
      "[INFO] 2022-12-12 02:33:45.301883 [Epoch 1/31][Batch 175/478] BatchTime = 1.909 (s) DataTime = 0.001 (s) EDLoss = 0.9157 RLoss = 0.6713\n",
      "[INFO] 2022-12-12 02:33:47.204562 [Epoch 1/31][Batch 176/478] BatchTime = 1.903 (s) DataTime = 0.005 (s) EDLoss = 0.9015 RLoss = 0.6075\n",
      "[INFO] 2022-12-12 02:33:49.113655 [Epoch 1/31][Batch 177/478] BatchTime = 1.909 (s) DataTime = 0.003 (s) EDLoss = 0.8795 RLoss = 0.5770\n",
      "[INFO] 2022-12-12 02:33:51.013791 [Epoch 1/31][Batch 178/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.8914 RLoss = 0.5436\n",
      "[INFO] 2022-12-12 02:33:52.910876 [Epoch 1/31][Batch 179/478] BatchTime = 1.897 (s) DataTime = 0.003 (s) EDLoss = 0.8582 RLoss = 0.5571\n",
      "[INFO] 2022-12-12 02:33:54.796526 [Epoch 1/31][Batch 180/478] BatchTime = 1.886 (s) DataTime = 0.002 (s) EDLoss = 0.8963 RLoss = 0.6807\n",
      "[INFO] 2022-12-12 02:33:56.692888 [Epoch 1/31][Batch 181/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.8575 RLoss = 0.6119\n",
      "[INFO] 2022-12-12 02:33:58.589786 [Epoch 1/31][Batch 182/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.9239 RLoss = 0.7521\n",
      "[INFO] 2022-12-12 02:34:00.481664 [Epoch 1/31][Batch 183/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.9075 RLoss = 0.6607\n",
      "[INFO] 2022-12-12 02:34:02.381378 [Epoch 1/31][Batch 184/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.8726 RLoss = 0.5956\n",
      "[INFO] 2022-12-12 02:34:04.274308 [Epoch 1/31][Batch 185/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.8634 RLoss = 0.5390\n",
      "[INFO] 2022-12-12 02:34:06.180247 [Epoch 1/31][Batch 186/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.8748 RLoss = 0.5914\n",
      "[INFO] 2022-12-12 02:34:08.090768 [Epoch 1/31][Batch 187/478] BatchTime = 1.911 (s) DataTime = 0.002 (s) EDLoss = 0.8778 RLoss = 0.6723\n",
      "[INFO] 2022-12-12 02:34:09.979610 [Epoch 1/31][Batch 188/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 0.8497 RLoss = 0.5794\n",
      "[INFO] 2022-12-12 02:34:11.877401 [Epoch 1/31][Batch 189/478] BatchTime = 1.898 (s) DataTime = 0.002 (s) EDLoss = 0.8061 RLoss = 0.5572\n",
      "[INFO] 2022-12-12 02:34:13.757036 [Epoch 1/31][Batch 190/478] BatchTime = 1.880 (s) DataTime = 0.002 (s) EDLoss = 0.8642 RLoss = 0.6488\n",
      "[INFO] 2022-12-12 02:34:15.663499 [Epoch 1/31][Batch 191/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.8413 RLoss = 0.5221\n",
      "[INFO] 2022-12-12 02:34:17.557235 [Epoch 1/31][Batch 192/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.8486 RLoss = 0.6800\n",
      "[INFO] 2022-12-12 02:34:19.444769 [Epoch 1/31][Batch 193/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.8405 RLoss = 0.6362\n",
      "[INFO] 2022-12-12 02:34:21.346071 [Epoch 1/31][Batch 194/478] BatchTime = 1.901 (s) DataTime = 0.002 (s) EDLoss = 0.8625 RLoss = 0.6785\n",
      "[INFO] 2022-12-12 02:34:23.237335 [Epoch 1/31][Batch 195/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 0.8189 RLoss = 0.5319\n",
      "[INFO] 2022-12-12 02:34:25.112911 [Epoch 1/31][Batch 196/478] BatchTime = 1.876 (s) DataTime = 0.002 (s) EDLoss = 0.8486 RLoss = 0.5712\n",
      "[INFO] 2022-12-12 02:34:27.022830 [Epoch 1/31][Batch 197/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.8340 RLoss = 0.6143\n",
      "[INFO] 2022-12-12 02:34:28.918219 [Epoch 1/31][Batch 198/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.8363 RLoss = 0.6315\n",
      "[INFO] 2022-12-12 02:34:30.821929 [Epoch 1/31][Batch 199/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.8105 RLoss = 0.6237\n",
      "[INFO] 2022-12-12 02:34:32.704537 [Epoch 1/31][Batch 200/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.8404 RLoss = 0.6035\n",
      "[INFO] 2022-12-12 02:34:34.610786 [Epoch 1/31][Batch 201/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.7574 RLoss = 0.5573\n",
      "[INFO] 2022-12-12 02:34:36.517601 [Epoch 1/31][Batch 202/478] BatchTime = 1.907 (s) DataTime = 0.002 (s) EDLoss = 0.7635 RLoss = 0.5239\n",
      "[INFO] 2022-12-12 02:34:38.412116 [Epoch 1/31][Batch 203/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.8047 RLoss = 0.5900\n",
      "[INFO] 2022-12-12 02:34:40.297167 [Epoch 1/31][Batch 204/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 0.7713 RLoss = 0.5055\n",
      "[INFO] 2022-12-12 02:34:42.199176 [Epoch 1/31][Batch 205/478] BatchTime = 1.902 (s) DataTime = 0.002 (s) EDLoss = 0.7237 RLoss = 0.4478\n",
      "[INFO] 2022-12-12 02:34:44.086775 [Epoch 1/31][Batch 206/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.7467 RLoss = 0.5277\n",
      "[INFO] 2022-12-12 02:34:45.974794 [Epoch 1/31][Batch 207/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.7828 RLoss = 0.5527\n",
      "[INFO] 2022-12-12 02:34:47.868939 [Epoch 1/31][Batch 208/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.7189 RLoss = 0.4960\n",
      "[INFO] 2022-12-12 02:34:49.777336 [Epoch 1/31][Batch 209/478] BatchTime = 1.908 (s) DataTime = 0.002 (s) EDLoss = 0.8014 RLoss = 0.5915\n",
      "[INFO] 2022-12-12 02:34:51.679492 [Epoch 1/31][Batch 210/478] BatchTime = 1.902 (s) DataTime = 0.002 (s) EDLoss = 0.8442 RLoss = 0.7407\n",
      "[INFO] 2022-12-12 02:34:53.579899 [Epoch 1/31][Batch 211/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.7450 RLoss = 0.5054\n",
      "[INFO] 2022-12-12 02:34:55.461164 [Epoch 1/31][Batch 212/478] BatchTime = 1.881 (s) DataTime = 0.002 (s) EDLoss = 0.7670 RLoss = 0.5965\n",
      "[INFO] 2022-12-12 02:34:57.346183 [Epoch 1/31][Batch 213/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 0.7380 RLoss = 0.5354\n",
      "[INFO] 2022-12-12 02:34:59.254656 [Epoch 1/31][Batch 214/478] BatchTime = 1.908 (s) DataTime = 0.002 (s) EDLoss = 0.7500 RLoss = 0.5888\n",
      "[INFO] 2022-12-12 02:35:01.163604 [Epoch 1/31][Batch 215/478] BatchTime = 1.909 (s) DataTime = 0.002 (s) EDLoss = 0.7638 RLoss = 0.5432\n",
      "[INFO] 2022-12-12 02:35:03.072414 [Epoch 1/31][Batch 216/478] BatchTime = 1.909 (s) DataTime = 0.003 (s) EDLoss = 0.7740 RLoss = 0.5899\n",
      "[INFO] 2022-12-12 02:35:04.967997 [Epoch 1/31][Batch 217/478] BatchTime = 1.896 (s) DataTime = 0.003 (s) EDLoss = 0.7911 RLoss = 0.6304\n",
      "[INFO] 2022-12-12 02:35:06.878527 [Epoch 1/31][Batch 218/478] BatchTime = 1.911 (s) DataTime = 0.002 (s) EDLoss = 0.7541 RLoss = 0.6164\n",
      "[INFO] 2022-12-12 02:35:08.780978 [Epoch 1/31][Batch 219/478] BatchTime = 1.902 (s) DataTime = 0.002 (s) EDLoss = 0.7061 RLoss = 0.5310\n",
      "[INFO] 2022-12-12 02:35:10.662286 [Epoch 1/31][Batch 220/478] BatchTime = 1.881 (s) DataTime = 0.002 (s) EDLoss = 0.7667 RLoss = 0.6107\n",
      "[INFO] 2022-12-12 02:35:12.549080 [Epoch 1/31][Batch 221/478] BatchTime = 1.887 (s) DataTime = 0.002 (s) EDLoss = 0.7409 RLoss = 0.5583\n",
      "[INFO] 2022-12-12 02:35:14.438445 [Epoch 1/31][Batch 222/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 0.7506 RLoss = 0.5583\n",
      "[INFO] 2022-12-12 02:35:16.326491 [Epoch 1/31][Batch 223/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.7300 RLoss = 0.6153\n",
      "[INFO] 2022-12-12 02:35:18.223940 [Epoch 1/31][Batch 224/478] BatchTime = 1.897 (s) DataTime = 0.003 (s) EDLoss = 0.6971 RLoss = 0.5253\n",
      "[INFO] 2022-12-12 02:35:20.112299 [Epoch 1/31][Batch 225/478] BatchTime = 1.888 (s) DataTime = 0.003 (s) EDLoss = 0.7432 RLoss = 0.5552\n",
      "[INFO] 2022-12-12 02:35:22.003714 [Epoch 1/31][Batch 226/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 0.6761 RLoss = 0.4615\n",
      "[INFO] 2022-12-12 02:35:23.903757 [Epoch 1/31][Batch 227/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.6383 RLoss = 0.4542\n",
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      "[INFO] 2022-12-12 02:36:32.016830 [Epoch 1/31][Batch 263/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 0.6369 RLoss = 0.5091\n",
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      "[INFO] 2022-12-12 02:36:41.515322 [Epoch 1/31][Batch 268/478] BatchTime = 1.886 (s) DataTime = 0.002 (s) EDLoss = 0.6601 RLoss = 0.5135\n",
      "[INFO] 2022-12-12 02:36:43.423600 [Epoch 1/31][Batch 269/478] BatchTime = 1.908 (s) DataTime = 0.002 (s) EDLoss = 0.6535 RLoss = 0.5603\n",
      "[INFO] 2022-12-12 02:36:45.318015 [Epoch 1/31][Batch 270/478] BatchTime = 1.894 (s) DataTime = 0.005 (s) EDLoss = 0.5623 RLoss = 0.4340\n",
      "[INFO] 2022-12-12 02:36:47.216761 [Epoch 1/31][Batch 271/478] BatchTime = 1.899 (s) DataTime = 0.003 (s) EDLoss = 0.6659 RLoss = 0.5980\n",
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      "[INFO] 2022-12-12 02:36:51.014883 [Epoch 1/31][Batch 273/478] BatchTime = 1.911 (s) DataTime = 0.002 (s) EDLoss = 0.6739 RLoss = 0.5725\n",
      "[INFO] 2022-12-12 02:36:52.901958 [Epoch 1/31][Batch 274/478] BatchTime = 1.887 (s) DataTime = 0.002 (s) EDLoss = 0.7247 RLoss = 0.6510\n",
      "[INFO] 2022-12-12 02:36:54.788496 [Epoch 1/31][Batch 275/478] BatchTime = 1.887 (s) DataTime = 0.002 (s) EDLoss = 0.5761 RLoss = 0.4799\n",
      "[INFO] 2022-12-12 02:36:56.682255 [Epoch 1/31][Batch 276/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.6686 RLoss = 0.5533\n",
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      "[INFO] 2022-12-12 02:37:06.132524 [Epoch 1/31][Batch 281/478] BatchTime = 1.874 (s) DataTime = 0.002 (s) EDLoss = 0.6100 RLoss = 0.5328\n",
      "[INFO] 2022-12-12 02:37:08.027092 [Epoch 1/31][Batch 282/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.6794 RLoss = 0.6324\n",
      "[INFO] 2022-12-12 02:37:09.931378 [Epoch 1/31][Batch 283/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.6183 RLoss = 0.5355\n",
      "[INFO] 2022-12-12 02:37:11.810012 [Epoch 1/31][Batch 284/478] BatchTime = 1.879 (s) DataTime = 0.002 (s) EDLoss = 0.6182 RLoss = 0.5424\n",
      "[INFO] 2022-12-12 02:37:13.707037 [Epoch 1/31][Batch 285/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.6693 RLoss = 0.5730\n",
      "[INFO] 2022-12-12 02:37:15.598661 [Epoch 1/31][Batch 286/478] BatchTime = 1.892 (s) DataTime = 0.003 (s) EDLoss = 0.6260 RLoss = 0.5542\n",
      "[INFO] 2022-12-12 02:37:17.504574 [Epoch 1/31][Batch 287/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.7075 RLoss = 0.6351\n",
      "[INFO] 2022-12-12 02:37:19.400399 [Epoch 1/31][Batch 288/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.6418 RLoss = 0.5374\n",
      "[INFO] 2022-12-12 02:37:21.295811 [Epoch 1/31][Batch 289/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.6058 RLoss = 0.5221\n",
      "[INFO] 2022-12-12 02:37:23.186342 [Epoch 1/31][Batch 290/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 0.6196 RLoss = 0.5153\n",
      "[INFO] 2022-12-12 02:37:25.081210 [Epoch 1/31][Batch 291/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.5952 RLoss = 0.5026\n",
      "[INFO] 2022-12-12 02:37:26.990757 [Epoch 1/31][Batch 292/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.5775 RLoss = 0.4693\n",
      "[INFO] 2022-12-12 02:37:28.900543 [Epoch 1/31][Batch 293/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.5909 RLoss = 0.5127\n",
      "[INFO] 2022-12-12 02:37:30.788943 [Epoch 1/31][Batch 294/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.5581 RLoss = 0.4725\n",
      "[INFO] 2022-12-12 02:37:32.698522 [Epoch 1/31][Batch 295/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.5413 RLoss = 0.4467\n",
      "[INFO] 2022-12-12 02:37:34.583607 [Epoch 1/31][Batch 296/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 0.5714 RLoss = 0.4834\n",
      "[INFO] 2022-12-12 02:37:36.492368 [Epoch 1/31][Batch 297/478] BatchTime = 1.909 (s) DataTime = 0.002 (s) EDLoss = 0.5445 RLoss = 0.4591\n",
      "[INFO] 2022-12-12 02:37:38.384793 [Epoch 1/31][Batch 298/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.5382 RLoss = 0.4299\n",
      "[INFO] 2022-12-12 02:37:40.285220 [Epoch 1/31][Batch 299/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.6120 RLoss = 0.5515\n",
      "[INFO] 2022-12-12 02:37:42.167519 [Epoch 1/31][Batch 300/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 0.6010 RLoss = 0.5503\n",
      "[INFO] 2022-12-12 02:37:44.054332 [Epoch 1/31][Batch 301/478] BatchTime = 1.887 (s) DataTime = 0.002 (s) EDLoss = 0.5934 RLoss = 0.5006\n",
      "[INFO] 2022-12-12 02:37:45.943603 [Epoch 1/31][Batch 302/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 0.5181 RLoss = 0.4078\n",
      "[INFO] 2022-12-12 02:37:47.867442 [Epoch 1/31][Batch 303/478] BatchTime = 1.924 (s) DataTime = 0.002 (s) EDLoss = 0.5311 RLoss = 0.4231\n",
      "[INFO] 2022-12-12 02:37:49.777934 [Epoch 1/31][Batch 304/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.6124 RLoss = 0.5379\n",
      "[INFO] 2022-12-12 02:37:51.670832 [Epoch 1/31][Batch 305/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.6117 RLoss = 0.5270\n",
      "[INFO] 2022-12-12 02:37:53.561991 [Epoch 1/31][Batch 306/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 0.5484 RLoss = 0.4822\n",
      "[INFO] 2022-12-12 02:37:55.456795 [Epoch 1/31][Batch 307/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.6961 RLoss = 0.6600\n",
      "[INFO] 2022-12-12 02:37:57.344846 [Epoch 1/31][Batch 308/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.5712 RLoss = 0.5344\n",
      "[INFO] 2022-12-12 02:37:59.244760 [Epoch 1/31][Batch 309/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.5610 RLoss = 0.4749\n",
      "[INFO] 2022-12-12 02:38:01.137428 [Epoch 1/31][Batch 310/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.5657 RLoss = 0.4629\n",
      "[INFO] 2022-12-12 02:38:03.036602 [Epoch 1/31][Batch 311/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.5224 RLoss = 0.4243\n",
      "[INFO] 2022-12-12 02:38:04.920838 [Epoch 1/31][Batch 312/478] BatchTime = 1.884 (s) DataTime = 0.002 (s) EDLoss = 0.6705 RLoss = 0.6113\n",
      "[INFO] 2022-12-12 02:38:06.830978 [Epoch 1/31][Batch 313/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.6510 RLoss = 0.6085\n",
      "[INFO] 2022-12-12 02:38:08.735853 [Epoch 1/31][Batch 314/478] BatchTime = 1.905 (s) DataTime = 0.002 (s) EDLoss = 0.6019 RLoss = 0.5160\n",
      "[INFO] 2022-12-12 02:38:10.641589 [Epoch 1/31][Batch 315/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.4882 RLoss = 0.3768\n",
      "[INFO] 2022-12-12 02:38:12.532505 [Epoch 1/31][Batch 316/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 0.5525 RLoss = 0.4870\n",
      "[INFO] 2022-12-12 02:38:14.437554 [Epoch 1/31][Batch 317/478] BatchTime = 1.905 (s) DataTime = 0.002 (s) EDLoss = 0.6438 RLoss = 0.5551\n",
      "[INFO] 2022-12-12 02:38:16.330681 [Epoch 1/31][Batch 318/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.5377 RLoss = 0.4719\n",
      "[INFO] 2022-12-12 02:38:18.238044 [Epoch 1/31][Batch 319/478] BatchTime = 1.907 (s) DataTime = 0.002 (s) EDLoss = 0.5997 RLoss = 0.5484\n",
      "[INFO] 2022-12-12 02:38:20.134048 [Epoch 1/31][Batch 320/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.5691 RLoss = 0.5208\n",
      "[INFO] 2022-12-12 02:38:22.025979 [Epoch 1/31][Batch 321/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.5749 RLoss = 0.5252\n",
      "[INFO] 2022-12-12 02:38:23.933063 [Epoch 1/31][Batch 322/478] BatchTime = 1.907 (s) DataTime = 0.002 (s) EDLoss = 0.5864 RLoss = 0.5177\n",
      "[INFO] 2022-12-12 02:38:25.818746 [Epoch 1/31][Batch 323/478] BatchTime = 1.886 (s) DataTime = 0.004 (s) EDLoss = 0.5326 RLoss = 0.4679\n",
      "[INFO] 2022-12-12 02:38:27.706777 [Epoch 1/31][Batch 324/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.5172 RLoss = 0.4170\n",
      "[INFO] 2022-12-12 02:38:29.593160 [Epoch 1/31][Batch 325/478] BatchTime = 1.886 (s) DataTime = 0.002 (s) EDLoss = 0.5173 RLoss = 0.4473\n",
      "[INFO] 2022-12-12 02:38:31.492531 [Epoch 1/31][Batch 326/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.5995 RLoss = 0.5453\n",
      "[INFO] 2022-12-12 02:38:33.384185 [Epoch 1/31][Batch 327/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.6421 RLoss = 0.6126\n",
      "[INFO] 2022-12-12 02:38:35.273333 [Epoch 1/31][Batch 328/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 0.5198 RLoss = 0.4350\n",
      "[INFO] 2022-12-12 02:38:37.179615 [Epoch 1/31][Batch 329/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.5415 RLoss = 0.4498\n",
      "[INFO] 2022-12-12 02:38:39.085977 [Epoch 1/31][Batch 330/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.5824 RLoss = 0.5447\n",
      "[INFO] 2022-12-12 02:38:40.972281 [Epoch 1/31][Batch 331/478] BatchTime = 1.886 (s) DataTime = 0.002 (s) EDLoss = 0.6005 RLoss = 0.5394\n",
      "[INFO] 2022-12-12 02:38:42.859410 [Epoch 1/31][Batch 332/478] BatchTime = 1.887 (s) DataTime = 0.003 (s) EDLoss = 0.5719 RLoss = 0.5234\n",
      "[INFO] 2022-12-12 02:38:44.747668 [Epoch 1/31][Batch 333/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.5736 RLoss = 0.4921\n",
      "[INFO] 2022-12-12 02:38:46.643508 [Epoch 1/31][Batch 334/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.6275 RLoss = 0.5644\n",
      "[INFO] 2022-12-12 02:38:48.525426 [Epoch 1/31][Batch 335/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 0.5664 RLoss = 0.5304\n",
      "[INFO] 2022-12-12 02:38:50.419230 [Epoch 1/31][Batch 336/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.5282 RLoss = 0.4589\n",
      "[INFO] 2022-12-12 02:38:52.303814 [Epoch 1/31][Batch 337/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 0.6110 RLoss = 0.5670\n",
      "[INFO] 2022-12-12 02:38:54.197683 [Epoch 1/31][Batch 338/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.7347 RLoss = 0.7632\n",
      "[INFO] 2022-12-12 02:38:56.091565 [Epoch 1/31][Batch 339/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.6091 RLoss = 0.5721\n",
      "[INFO] 2022-12-12 02:38:57.985100 [Epoch 1/31][Batch 340/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.5823 RLoss = 0.5067\n",
      "[INFO] 2022-12-12 02:38:59.875017 [Epoch 1/31][Batch 341/478] BatchTime = 1.890 (s) DataTime = 0.002 (s) EDLoss = 0.6035 RLoss = 0.5342\n",
      "[INFO] 2022-12-12 02:39:01.784966 [Epoch 1/31][Batch 342/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.5839 RLoss = 0.5115\n",
      "[INFO] 2022-12-12 02:39:03.683784 [Epoch 1/31][Batch 343/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.5704 RLoss = 0.5092\n",
      "[INFO] 2022-12-12 02:39:05.574666 [Epoch 1/31][Batch 344/478] BatchTime = 1.891 (s) DataTime = 0.002 (s) EDLoss = 0.5498 RLoss = 0.4706\n",
      "[INFO] 2022-12-12 02:39:07.473397 [Epoch 1/31][Batch 345/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.5547 RLoss = 0.4873\n",
      "[INFO] 2022-12-12 02:39:09.356539 [Epoch 1/31][Batch 346/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.5881 RLoss = 0.5393\n",
      "[INFO] 2022-12-12 02:39:11.234861 [Epoch 1/31][Batch 347/478] BatchTime = 1.878 (s) DataTime = 0.002 (s) EDLoss = 0.6027 RLoss = 0.5439\n",
      "[INFO] 2022-12-12 02:39:13.137097 [Epoch 1/31][Batch 348/478] BatchTime = 1.902 (s) DataTime = 0.002 (s) EDLoss = 0.6008 RLoss = 0.5960\n",
      "[INFO] 2022-12-12 02:39:15.031549 [Epoch 1/31][Batch 349/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.6452 RLoss = 0.6720\n",
      "[INFO] 2022-12-12 02:39:16.924510 [Epoch 1/31][Batch 350/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.5342 RLoss = 0.4564\n",
      "[INFO] 2022-12-12 02:39:18.830984 [Epoch 1/31][Batch 351/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.5135 RLoss = 0.4472\n",
      "[INFO] 2022-12-12 02:39:20.714209 [Epoch 1/31][Batch 352/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.5864 RLoss = 0.5415\n",
      "[INFO] 2022-12-12 02:39:22.616216 [Epoch 1/31][Batch 353/478] BatchTime = 1.902 (s) DataTime = 0.003 (s) EDLoss = 0.5649 RLoss = 0.5072\n",
      "[INFO] 2022-12-12 02:39:24.499678 [Epoch 1/31][Batch 354/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.5651 RLoss = 0.5016\n",
      "[INFO] 2022-12-12 02:39:26.381331 [Epoch 1/31][Batch 355/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 0.6089 RLoss = 0.5547\n",
      "[INFO] 2022-12-12 02:39:28.262425 [Epoch 1/31][Batch 356/478] BatchTime = 1.881 (s) DataTime = 0.003 (s) EDLoss = 0.5195 RLoss = 0.4365\n",
      "[INFO] 2022-12-12 02:39:30.147977 [Epoch 1/31][Batch 357/478] BatchTime = 1.886 (s) DataTime = 0.002 (s) EDLoss = 0.5412 RLoss = 0.4711\n",
      "[INFO] 2022-12-12 02:39:32.046752 [Epoch 1/31][Batch 358/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.5656 RLoss = 0.4992\n",
      "[INFO] 2022-12-12 02:39:33.951050 [Epoch 1/31][Batch 359/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.5674 RLoss = 0.5292\n",
      "[INFO] 2022-12-12 02:39:35.851457 [Epoch 1/31][Batch 360/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.5509 RLoss = 0.4809\n",
      "[INFO] 2022-12-12 02:39:37.763188 [Epoch 1/31][Batch 361/478] BatchTime = 1.912 (s) DataTime = 0.012 (s) EDLoss = 0.4760 RLoss = 0.4159\n",
      "[INFO] 2022-12-12 02:39:39.669457 [Epoch 1/31][Batch 362/478] BatchTime = 1.906 (s) DataTime = 0.002 (s) EDLoss = 0.5835 RLoss = 0.5577\n",
      "[INFO] 2022-12-12 02:39:41.573793 [Epoch 1/31][Batch 363/478] BatchTime = 1.904 (s) DataTime = 0.005 (s) EDLoss = 0.5321 RLoss = 0.4974\n",
      "[INFO] 2022-12-12 02:39:43.454795 [Epoch 1/31][Batch 364/478] BatchTime = 1.881 (s) DataTime = 0.005 (s) EDLoss = 0.4685 RLoss = 0.4244\n",
      "[INFO] 2022-12-12 02:39:45.361944 [Epoch 1/31][Batch 365/478] BatchTime = 1.907 (s) DataTime = 0.003 (s) EDLoss = 0.5568 RLoss = 0.5266\n",
      "[INFO] 2022-12-12 02:39:47.254075 [Epoch 1/31][Batch 366/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.5442 RLoss = 0.5038\n",
      "[INFO] 2022-12-12 02:39:49.162650 [Epoch 1/31][Batch 367/478] BatchTime = 1.909 (s) DataTime = 0.002 (s) EDLoss = 0.5816 RLoss = 0.5361\n",
      "[INFO] 2022-12-12 02:39:51.056240 [Epoch 1/31][Batch 368/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.4795 RLoss = 0.4144\n",
      "[INFO] 2022-12-12 02:39:52.931623 [Epoch 1/31][Batch 369/478] BatchTime = 1.875 (s) DataTime = 0.002 (s) EDLoss = 0.6424 RLoss = 0.6314\n",
      "[INFO] 2022-12-12 02:39:54.827438 [Epoch 1/31][Batch 370/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.6113 RLoss = 0.5897\n",
      "[INFO] 2022-12-12 02:39:56.717300 [Epoch 1/31][Batch 371/478] BatchTime = 1.890 (s) DataTime = 0.002 (s) EDLoss = 0.5350 RLoss = 0.4951\n",
      "[INFO] 2022-12-12 02:39:58.605363 [Epoch 1/31][Batch 372/478] BatchTime = 1.888 (s) DataTime = 0.002 (s) EDLoss = 0.6291 RLoss = 0.5904\n",
      "[INFO] 2022-12-12 02:40:00.488796 [Epoch 1/31][Batch 373/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.5616 RLoss = 0.5053\n",
      "[INFO] 2022-12-12 02:40:02.381656 [Epoch 1/31][Batch 374/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.5566 RLoss = 0.5014\n",
      "[INFO] 2022-12-12 02:40:04.275183 [Epoch 1/31][Batch 375/478] BatchTime = 1.894 (s) DataTime = 0.002 (s) EDLoss = 0.6223 RLoss = 0.5885\n",
      "[INFO] 2022-12-12 02:40:06.185877 [Epoch 1/31][Batch 376/478] BatchTime = 1.911 (s) DataTime = 0.002 (s) EDLoss = 0.6244 RLoss = 0.6062\n",
      "[INFO] 2022-12-12 02:40:08.097873 [Epoch 1/31][Batch 377/478] BatchTime = 1.912 (s) DataTime = 0.002 (s) EDLoss = 0.5755 RLoss = 0.5539\n",
      "[INFO] 2022-12-12 02:40:09.989473 [Epoch 1/31][Batch 378/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.5105 RLoss = 0.4232\n",
      "[INFO] 2022-12-12 02:40:11.886095 [Epoch 1/31][Batch 379/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.5596 RLoss = 0.4984\n",
      "[INFO] 2022-12-12 02:40:13.785832 [Epoch 1/31][Batch 380/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.6162 RLoss = 0.6005\n",
      "[INFO] 2022-12-12 02:40:15.689255 [Epoch 1/31][Batch 381/478] BatchTime = 1.903 (s) DataTime = 0.002 (s) EDLoss = 0.4989 RLoss = 0.4372\n",
      "[INFO] 2022-12-12 02:40:17.572535 [Epoch 1/31][Batch 382/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.5190 RLoss = 0.4612\n",
      "[INFO] 2022-12-12 02:40:19.451543 [Epoch 1/31][Batch 383/478] BatchTime = 1.879 (s) DataTime = 0.002 (s) EDLoss = 0.6361 RLoss = 0.5911\n",
      "[INFO] 2022-12-12 02:40:21.355625 [Epoch 1/31][Batch 384/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.5815 RLoss = 0.5429\n",
      "[INFO] 2022-12-12 02:40:23.266931 [Epoch 1/31][Batch 385/478] BatchTime = 1.911 (s) DataTime = 0.002 (s) EDLoss = 0.5450 RLoss = 0.5155\n",
      "[INFO] 2022-12-12 02:40:25.162054 [Epoch 1/31][Batch 386/478] BatchTime = 1.895 (s) DataTime = 0.002 (s) EDLoss = 0.5105 RLoss = 0.4669\n",
      "[INFO] 2022-12-12 02:40:27.046737 [Epoch 1/31][Batch 387/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 0.4780 RLoss = 0.4021\n",
      "[INFO] 2022-12-12 02:40:28.939926 [Epoch 1/31][Batch 388/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.5551 RLoss = 0.5172\n",
      "[INFO] 2022-12-12 02:40:30.824588 [Epoch 1/31][Batch 389/478] BatchTime = 1.885 (s) DataTime = 0.002 (s) EDLoss = 0.5374 RLoss = 0.5040\n",
      "[INFO] 2022-12-12 02:40:32.724704 [Epoch 1/31][Batch 390/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.5452 RLoss = 0.5188\n",
      "[INFO] 2022-12-12 02:40:34.610737 [Epoch 1/31][Batch 391/478] BatchTime = 1.886 (s) DataTime = 0.002 (s) EDLoss = 0.5578 RLoss = 0.5298\n",
      "[INFO] 2022-12-12 02:40:36.509878 [Epoch 1/31][Batch 392/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.6100 RLoss = 0.5786\n",
      "[INFO] 2022-12-12 02:40:38.395956 [Epoch 1/31][Batch 393/478] BatchTime = 1.886 (s) DataTime = 0.002 (s) EDLoss = 0.4979 RLoss = 0.4469\n",
      "[INFO] 2022-12-12 02:40:40.285416 [Epoch 1/31][Batch 394/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 0.5724 RLoss = 0.5334\n",
      "[INFO] 2022-12-12 02:40:42.169654 [Epoch 1/31][Batch 395/478] BatchTime = 1.884 (s) DataTime = 0.002 (s) EDLoss = 0.5215 RLoss = 0.4697\n",
      "[INFO] 2022-12-12 02:40:44.080097 [Epoch 1/31][Batch 396/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.5201 RLoss = 0.4660\n",
      "[INFO] 2022-12-12 02:40:45.983709 [Epoch 1/31][Batch 397/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.5129 RLoss = 0.4665\n",
      "[INFO] 2022-12-12 02:40:47.887530 [Epoch 1/31][Batch 398/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.5353 RLoss = 0.4659\n",
      "[INFO] 2022-12-12 02:40:49.767832 [Epoch 1/31][Batch 399/478] BatchTime = 1.880 (s) DataTime = 0.002 (s) EDLoss = 0.5528 RLoss = 0.5227\n",
      "[INFO] 2022-12-12 02:40:51.664174 [Epoch 1/31][Batch 400/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.5926 RLoss = 0.5854\n",
      "[INFO] 2022-12-12 02:40:53.560293 [Epoch 1/31][Batch 401/478] BatchTime = 1.896 (s) DataTime = 0.002 (s) EDLoss = 0.5303 RLoss = 0.4941\n",
      "[INFO] 2022-12-12 02:40:55.447506 [Epoch 1/31][Batch 402/478] BatchTime = 1.887 (s) DataTime = 0.003 (s) EDLoss = 0.4918 RLoss = 0.4510\n",
      "[INFO] 2022-12-12 02:40:57.334531 [Epoch 1/31][Batch 403/478] BatchTime = 1.887 (s) DataTime = 0.003 (s) EDLoss = 0.5257 RLoss = 0.4982\n",
      "[INFO] 2022-12-12 02:40:59.234684 [Epoch 1/31][Batch 404/478] BatchTime = 1.900 (s) DataTime = 0.002 (s) EDLoss = 0.5187 RLoss = 0.4774\n",
      "[INFO] 2022-12-12 02:41:01.144642 [Epoch 1/31][Batch 405/478] BatchTime = 1.910 (s) DataTime = 0.002 (s) EDLoss = 0.5351 RLoss = 0.5094\n",
      "[INFO] 2022-12-12 02:41:03.043778 [Epoch 1/31][Batch 406/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.5378 RLoss = 0.4945\n",
      "[INFO] 2022-12-12 02:41:04.928270 [Epoch 1/31][Batch 407/478] BatchTime = 1.884 (s) DataTime = 0.002 (s) EDLoss = 0.5564 RLoss = 0.5418\n",
      "[INFO] 2022-12-12 02:41:06.818065 [Epoch 1/31][Batch 408/478] BatchTime = 1.890 (s) DataTime = 0.002 (s) EDLoss = 0.5581 RLoss = 0.5147\n",
      "[INFO] 2022-12-12 02:41:08.711315 [Epoch 1/31][Batch 409/478] BatchTime = 1.893 (s) DataTime = 0.002 (s) EDLoss = 0.4721 RLoss = 0.4245\n",
      "[INFO] 2022-12-12 02:41:10.602923 [Epoch 1/31][Batch 410/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.4916 RLoss = 0.4442\n",
      "[INFO] 2022-12-12 02:41:12.479374 [Epoch 1/31][Batch 411/478] BatchTime = 1.876 (s) DataTime = 0.002 (s) EDLoss = 0.5591 RLoss = 0.5374\n",
      "[INFO] 2022-12-12 02:41:14.378033 [Epoch 1/31][Batch 412/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.4486 RLoss = 0.3855\n",
      "[INFO] 2022-12-12 02:41:16.286950 [Epoch 1/31][Batch 413/478] BatchTime = 1.909 (s) DataTime = 0.002 (s) EDLoss = 0.5248 RLoss = 0.4730\n",
      "[INFO] 2022-12-12 02:41:18.179304 [Epoch 1/31][Batch 414/478] BatchTime = 1.892 (s) DataTime = 0.002 (s) EDLoss = 0.4998 RLoss = 0.4724\n",
      "[INFO] 2022-12-12 02:41:20.082869 [Epoch 1/31][Batch 415/478] BatchTime = 1.904 (s) DataTime = 0.002 (s) EDLoss = 0.4889 RLoss = 0.4499\n",
      "[INFO] 2022-12-12 02:41:21.966445 [Epoch 1/31][Batch 416/478] BatchTime = 1.884 (s) DataTime = 0.002 (s) EDLoss = 0.4736 RLoss = 0.4326\n",
      "[INFO] 2022-12-12 02:41:23.863614 [Epoch 1/31][Batch 417/478] BatchTime = 1.897 (s) DataTime = 0.002 (s) EDLoss = 0.4955 RLoss = 0.4613\n",
      "[INFO] 2022-12-12 02:41:25.764298 [Epoch 1/31][Batch 418/478] BatchTime = 1.901 (s) DataTime = 0.002 (s) EDLoss = 0.4561 RLoss = 0.4079\n",
      "[INFO] 2022-12-12 02:41:27.653520 [Epoch 1/31][Batch 419/478] BatchTime = 1.889 (s) DataTime = 0.002 (s) EDLoss = 0.4465 RLoss = 0.3979\n",
      "[INFO] 2022-12-12 02:41:29.525775 [Epoch 1/31][Batch 420/478] BatchTime = 1.872 (s) DataTime = 0.002 (s) EDLoss = 0.5380 RLoss = 0.5157\n",
      "[INFO] 2022-12-12 02:41:31.439087 [Epoch 1/31][Batch 421/478] BatchTime = 1.913 (s) DataTime = 0.002 (s) EDLoss = 0.5507 RLoss = 0.5321\n",
      "[INFO] 2022-12-12 02:41:33.319434 [Epoch 1/31][Batch 422/478] BatchTime = 1.880 (s) DataTime = 0.002 (s) EDLoss = 0.5369 RLoss = 0.4976\n",
      "[INFO] 2022-12-12 02:41:35.209534 [Epoch 1/31][Batch 423/478] BatchTime = 1.890 (s) DataTime = 0.002 (s) EDLoss = 0.4915 RLoss = 0.4295\n",
      "[INFO] 2022-12-12 02:41:37.121888 [Epoch 1/31][Batch 424/478] BatchTime = 1.912 (s) DataTime = 0.002 (s) EDLoss = 0.4536 RLoss = 0.3879\n",
      "[INFO] 2022-12-12 02:41:39.005219 [Epoch 1/31][Batch 425/478] BatchTime = 1.883 (s) DataTime = 0.002 (s) EDLoss = 0.4747 RLoss = 0.4332\n",
      "[INFO] 2022-12-12 02:41:40.903842 [Epoch 1/31][Batch 426/478] BatchTime = 1.899 (s) DataTime = 0.002 (s) EDLoss = 0.4774 RLoss = 0.4303\n",
      "[INFO] 2022-12-12 02:41:42.785537 [Epoch 1/31][Batch 427/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 0.5018 RLoss = 0.4664\n",
      "[INFO] 2022-12-12 02:41:44.667236 [Epoch 1/31][Batch 428/478] BatchTime = 1.882 (s) DataTime = 0.002 (s) EDLoss = 0.4053 RLoss = 0.3538\n",
      "[INFO] 2022-12-12 02:41:46.594249 [Epoch 1/31][Batch 429/478] BatchTime = 1.927 (s) DataTime = 0.002 (s) EDLoss = 0.5316 RLoss = 0.5215\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-3f8315284d8b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;31m# Append loss to average metrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mencoder_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoder_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m     63\u001b[0m         \u001b[0mrefiner_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrefiner_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\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     64\u001b[0m         \u001b[0;31m# Append loss to TensorBoard\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch_idx in range(init_epoch, cfg.TRAIN.NUM_EPOCHES):\n",
    "    # Tick / tock\n",
    "    epoch_start_time = time()\n",
    "\n",
    "    # Batch average meterics\n",
    "    batch_time = AverageMeter()\n",
    "    data_time = AverageMeter()\n",
    "    encoder_losses = AverageMeter()\n",
    "    refiner_losses = AverageMeter()\n",
    "\n",
    "    # switch models to training mode\n",
    "    encoder.train()\n",
    "    decoder.train()\n",
    "    merger.train()\n",
    "    refiner.train()\n",
    "\n",
    "    batch_end_time = time()\n",
    "    n_batches = len(train_data_loader)\n",
    "    for batch_idx, (taxonomy_names, sample_names, rendering_images,\n",
    "                    ground_truth_volumes) in enumerate(train_data_loader):\n",
    "        # Measure data time\n",
    "        data_time.update(time() - batch_end_time)\n",
    "\n",
    "        # Get data from data loader\n",
    "        rendering_images = var_or_cuda(rendering_images)\n",
    "        ground_truth_volumes = var_or_cuda(ground_truth_volumes)\n",
    "\n",
    "        # Train the encoder, decoder, refiner, and merger\n",
    "        image_features = encoder(rendering_images)\n",
    "        raw_features, generated_volumes = decoder(image_features)\n",
    "\n",
    "        if cfg.NETWORK.USE_MERGER and epoch_idx >= cfg.TRAIN.EPOCH_START_USE_MERGER:\n",
    "            generated_volumes = merger(raw_features, generated_volumes)\n",
    "        else:\n",
    "            generated_volumes = torch.mean(generated_volumes, dim=1)\n",
    "        encoder_loss = mse_loss(generated_volumes, ground_truth_volumes) * 10\n",
    "\n",
    "        if cfg.NETWORK.USE_REFINER and epoch_idx >= cfg.TRAIN.EPOCH_START_USE_REFINER:\n",
    "            generated_volumes = refiner(generated_volumes)\n",
    "            refiner_loss = mse_loss(generated_volumes, ground_truth_volumes) * 10\n",
    "        else:\n",
    "            refiner_loss = encoder_loss\n",
    "\n",
    "        # Gradient decent\n",
    "        encoder.zero_grad()\n",
    "        decoder.zero_grad()\n",
    "        refiner.zero_grad()\n",
    "        merger.zero_grad()\n",
    "\n",
    "        if cfg.NETWORK.USE_REFINER and epoch_idx >= cfg.TRAIN.EPOCH_START_USE_REFINER:\n",
    "            encoder_loss.backward(retain_graph=True)\n",
    "            refiner_loss.backward()\n",
    "        else:\n",
    "            encoder_loss.backward()\n",
    "\n",
    "        encoder_solver.step()\n",
    "        decoder_solver.step()\n",
    "        refiner_solver.step()\n",
    "        merger_solver.step()\n",
    "\n",
    "        # Append loss to average metrics\n",
    "        encoder_losses.update(encoder_loss.item())\n",
    "        refiner_losses.update(refiner_loss.item())\n",
    "        # Append loss to TensorBoard\n",
    "        n_itr = epoch_idx * n_batches + batch_idx\n",
    "        train_writer.add_scalar('EncoderDecoder/BatchLoss', encoder_loss.item(), n_itr)\n",
    "        train_writer.add_scalar('Refiner/BatchLoss', refiner_loss.item(), n_itr)\n",
    "\n",
    "        # Tick / tock\n",
    "        batch_time.update(time() - batch_end_time)\n",
    "        batch_end_time = time()\n",
    "        print(\n",
    "            '[INFO] %s [Epoch %d/%d][Batch %d/%d] BatchTime = %.3f (s) DataTime = %.3f (s) EDLoss = %.4f RLoss = %.4f'\n",
    "            % (dt.now(), epoch_idx + 1, cfg.TRAIN.NUM_EPOCHES, batch_idx + 1, n_batches, batch_time.val,\n",
    "                data_time.val, encoder_loss.item(), refiner_loss.item()))\n",
    "\n",
    "    # Append epoch loss to TensorBoard\n",
    "    train_writer.add_scalar('EncoderDecoder/EpochLoss', encoder_losses.avg, epoch_idx + 1)\n",
    "    train_writer.add_scalar('Refiner/EpochLoss', refiner_losses.avg, epoch_idx + 1)\n",
    "\n",
    "    # Adjust learning rate\n",
    "    encoder_lr_scheduler.step()\n",
    "    decoder_lr_scheduler.step()\n",
    "    refiner_lr_scheduler.step()\n",
    "    merger_lr_scheduler.step()\n",
    "\n",
    "    # Tick / tock\n",
    "    epoch_end_time = time()\n",
    "    print('[INFO] %s Epoch [%d/%d] EpochTime = %.3f (s) EDLoss = %.4f RLoss = %.4f' %\n",
    "          (dt.now(), epoch_idx + 1, cfg.TRAIN.NUM_EPOCHES, epoch_end_time - epoch_start_time, encoder_losses.avg,\n",
    "            refiner_losses.avg))\n",
    "\n",
    "    # Update Rendering Views\n",
    "    if cfg.TRAIN.UPDATE_N_VIEWS_RENDERING:\n",
    "        n_views_rendering = random.randint(1, cfg.CONST.N_VIEWS_RENDERING)\n",
    "        train_data_loader.dataset.set_n_views_rendering(n_views_rendering)\n",
    "        print('[INFO] %s Epoch [%d/%d] Update #RenderingViews to %d' %\n",
    "              (dt.now(), epoch_idx + 2, cfg.TRAIN.NUM_EPOCHES, n_views_rendering))\n",
    "\n",
    "    # Validate the training models\n",
    "    iou = test_net(cfg, epoch_idx + 1, output_dir, val_data_loader, val_writer, encoder, decoder, refiner, merger)\n",
    "\n",
    "    # Save weights to file\n",
    "    if (epoch_idx + 1) % cfg.TRAIN.SAVE_FREQ == 0:\n",
    "        if not os.path.exists(ckpt_dir):\n",
    "            os.makedirs(ckpt_dir)\n",
    "\n",
    "        save_checkpoints(cfg, os.path.join(ckpt_dir, 'ckpt-epoch-%04d.pth' % (epoch_idx + 1)),\n",
    "                                              epoch_idx + 1, encoder, encoder_solver, decoder, decoder_solver,\n",
    "                                              refiner, refiner_solver, merger, merger_solver, best_iou, best_epoch)\n",
    "    if iou > best_iou:\n",
    "        if not os.path.exists(ckpt_dir):\n",
    "            os.makedirs(ckpt_dir)\n",
    "\n",
    "        best_iou = iou\n",
    "        best_epoch = epoch_idx + 1\n",
    "        save_checkpoints(cfg, os.path.join(ckpt_dir, 'best-ckpt.pth'), epoch_idx + 1, encoder,\n",
    "                                              encoder_solver, decoder, decoder_solver, refiner, refiner_solver,\n",
    "                                              merger, merger_solver, best_iou, best_epoch)\n",
    "\n",
    "# Close SummaryWriter for TensorBoard\n",
    "train_writer.close()\n",
    "val_writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4PZ33ic2KPIN"
   },
   "outputs": [],
   "source": []
  }
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