pointnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9acd8ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pickle\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from model import *\n",
    "import os\n",
    "from numpy.linalg import inv\n",
    "from PIL import Image\n",
    "import math\n",
    "import utils\n",
    "\n",
    "def load_pickle(filename):\n",
    "    with open(filename, 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1099f94e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # setup the data\n",
    "# train_dump = load_pickle('train_dump')\n",
    "math.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "adf42399",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for item in train_dump.keys():\n",
    "#     print(type(train_dump[item]['pcd'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "797a36b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def symmetric_orthogonalization(x):\n",
    "    m = x.view(-1, 3, 3)\n",
    "    u, s, v = torch.svd(m)\n",
    "    vt = torch.transpose(v, 1, 2)\n",
    "    det = torch.det(torch.matmul(u, vt))\n",
    "    det = det.view(-1, 1, 1)\n",
    "    vt = torch.cat((vt[:, :2, :], vt[:, -1:, :] * det), 1)\n",
    "    r = torch.matmul(u, vt)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f529be47",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_dir = \"training_data_filtered/training_data/v2.2\"\n",
    "split_dir = \"training_data_filtered/training_data/splits/v2\"\n",
    "objects_csv = 'training_data_filtered/training_data/objects_v1.csv'\n",
    "\n",
    "def get_split_files(split_name):\n",
    "    with open(os.path.join(split_dir, f\"{split_name}.txt\"), 'r') as f:\n",
    "        prefix = [os.path.join(training_data_dir, line.strip()) for line in f if line.strip()]\n",
    "        rgb = [p + \"_color_kinect.png\" for p in prefix]\n",
    "        depth = [p + \"_depth_kinect.png\" for p in prefix]\n",
    "        label = [p + \"_label_kinect.png\" for p in prefix]\n",
    "        meta = [p + \"_meta.pkl\" for p in prefix]\n",
    "    return rgb, depth, label, meta\n",
    "\n",
    "rgb_files, depth_files, label_files, meta_files = get_split_files('train')\n",
    "rgb_files_val, depth_files_val, label_files_val, meta_files_val = get_split_files('val')\n",
    "\n",
    "\n",
    "def read_image(img_path):\n",
    "    '''\n",
    "    inputs:\n",
    "    img_path : the location of the image to be read\n",
    "    outputs:\n",
    "    image converted to torch.tensor\n",
    "    '''\n",
    "    image = np.array(Image.open(img_path))\n",
    "#     print(image)\n",
    "    image = torch.from_numpy(image)\n",
    "    return image\n",
    "\n",
    "class mydataset(Dataset):\n",
    "    # define the init method\n",
    "    def __init__(self,meta_files,depth_files,label_files,transform=None, target_transform = None) -> None:\n",
    "        super().__init__()\n",
    "        self.meta_files = meta_files\n",
    "        self.depth_files = depth_files\n",
    "        self.label_files = label_files\n",
    "        self.pcd = []\n",
    "        self.poses = []\n",
    "        self.transform = transform\n",
    "        self.target_transform = target_transform\n",
    "\n",
    "    # define the len method\n",
    "    def __len__(self):\n",
    "        return len(self.depth_files)\n",
    "\n",
    "    # define the getitem() method\n",
    "    def __getitem__(self,idx):\n",
    "        self.pcd = []\n",
    "        self.poses = []\n",
    "        meta = load_pickle(self.meta_files[idx])\n",
    "        intrinsic = meta['intrinsic']\n",
    "        ext_inv = inv(meta['extrinsic'])\n",
    "        depth = np.array(Image.open(self.depth_files[idx])) / 1000\n",
    "        label = np.array(Image.open(self.label_files[idx]))\n",
    "        z = depth\n",
    "        v, u = np.indices(z.shape)\n",
    "        uv1 = np.stack([u + 0.5, v + 0.5, np.ones_like(z)], axis=-1)\n",
    "        points_viewer = uv1 @ np.linalg.inv(intrinsic).T * z[..., None]  # [H, W, 3]\n",
    "        for j,l in enumerate(meta['object_ids']):\n",
    "            crops_pcd = np.array([points_viewer[label==l]])\n",
    "            if crops_pcd.shape[1]==0:\n",
    "                continue\n",
    "            crops_pcd = crops_pcd@ext_inv[:3,:3].T+ext_inv[:3,3]\n",
    "            crops_pcd = crops_pcd.squeeze(0)\n",
    "            if(len(crops_pcd)<800):\n",
    "                continue\n",
    "            crops_pcd = crops_pcd[:800,:]\n",
    "            self.pcd.append(crops_pcd)\n",
    "            self.poses.append(meta['poses_world'][l])\n",
    "        return np.array(self.pcd), np.array(self.poses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d991244d",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = mydataset(meta_files,depth_files,label_files)\n",
    "val_data = mydataset(meta_files_val,depth_files_val,label_files_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5386867e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(training_data, batch_size=1, shuffle=True)\n",
    "val_dataloader = DataLoader(val_data, batch_size=1,shuffle=True)\n",
    "\n",
    "# train_pcd, train_poses = next(iter(train_dataloader))\n",
    "# val_pcd, val_poses = next(iter(val_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e851e212",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82e0bca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STN3d(\n",
       "  (conv1): Conv1d(3, 64, kernel_size=(1,), stride=(1,))\n",
       "  (conv2): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
       "  (conv3): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
       "  (fc1_r): Linear(in_features=1024, out_features=512, bias=True)\n",
       "  (fc1_t): Linear(in_features=1024, out_features=512, bias=True)\n",
       "  (fc2_r): Linear(in_features=512, out_features=256, bias=True)\n",
       "  (fc2_t): Linear(in_features=512, out_features=256, bias=True)\n",
       "  (fc3_r): Linear(in_features=256, out_features=128, bias=True)\n",
       "  (fc3_t): Linear(in_features=256, out_features=128, bias=True)\n",
       "  (fc4_r): Linear(in_features=128, out_features=9, bias=True)\n",
       "  (fc4_t): Linear(in_features=128, out_features=3, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn4_r): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn4_t): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn5_r): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn5_t): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn6_r): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (bn6_t): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defined model\n",
    "device = \"cuda\"\n",
    "\n",
    "net = STN3d()\n",
    "# net = torch.load('poitnet_3.pth')\n",
    "# net = PointNetCls()\n",
    "\n",
    "net.float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fce3c537",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_torch(R_est,t_est,R_gt,t_gt,src_pcd):\n",
    "    with torch.no_grad():\n",
    "        rre = torch.acos(torch.clamp(0.5 * ((torch.bmm(torch.permute(R_est,(0,2,1)), R_gt)).diagonal(offset=0, dim1=-1, dim2=-2).sum(-1) - 1), -1.0, 1.0)).sum()\n",
    "        rte = torch.linalg.norm(t_est - t_gt)\n",
    "    src_pcd = torch.permute(src_pcd,(0,2,1))\n",
    "    tgt_pcd = torch.bmm(src_pcd,torch.permute(R_gt,(0,2,1)))\n",
    "    tgt_pcd = torch.add(tgt_pcd, t_gt.unsqueeze(1))\n",
    "    pred_pcd = torch.bmm(src_pcd,torch.permute(R_est,(0,2,1)))\n",
    "    pred_pcd = torch.add(pred_pcd, t_est.unsqueeze(1))\n",
    "    point_loss = torch.linalg.norm(pred_pcd-tgt_pcd)\n",
    "    final_loss = point_loss#+rre+rte\n",
    "    return final_loss,rre,rte,point_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c86b454f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_rre_symmetry(R_est: np.ndarray, R_gt: np.ndarray,\n",
    "                         sym_rots: np.ndarray, rot_axis=None):\n",
    "    assert R_est.shape == (3, 3), 'R_est: expected shape (3, 3), received shape {}.'.format(R_est.shape)\n",
    "    assert R_gt.shape == (3, 3), 'R_gt: expected shape (3, 3), received shape {}.'.format(R_gt.shape)\n",
    "\n",
    "    if rot_axis is None:\n",
    "        R_gt_sym = R_gt @ sym_rots\n",
    "        rre_sym_all = np.arccos(np.clip(0.5 * (np.trace(R_est.T @ R_gt_sym, axis1=-2, axis2=-1) - 1), -1.0, 1.0))\n",
    "        rre_best = np.min(rre_sym_all)\n",
    "    else:\n",
    "        R_gt_sym = R_gt @ sym_rots\n",
    "        rot_axis_gt = R_gt_sym @ rot_axis  # [?, 3]\n",
    "        rot_axis_est = R_est @ rot_axis  # [3]\n",
    "        rre_sym = np.arccos(np.clip(np.dot(rot_axis_gt, rot_axis_est), -1.0, 1.0))  # [?]\n",
    "        rre_best = np.min(rre_sym)\n",
    "    return rre_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f9f13c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "writer = SummaryWriter('runs/exp_normals')\n",
    "\n",
    "data_val = next(iter(val_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "428c8ee8",
   "metadata": {},
   "outputs": [
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      "\r",
      "  0%|          | 0/25 [00:00<?, ?it/s]"
     ]
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     "text": [
      "/tmp/ipykernel_8085/2995440779.py:67: RuntimeWarning: invalid value encountered in true_divide\n",
      "  crops_pcd /= np.max(np.linalg.norm(crops_pcd, axis=1))\n"
     ]
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      "loss_train: 42.942496871948244\n",
      "rre_train: 10.152966809272765\n",
      "rte_train: 0.8781071603298187\n",
      "ptloss_train: 42.942496871948244\n",
      "[3/25][2860/3964]\n",
      "loss_train: 43.89529390335083\n",
      "rre_train: 9.867034685611724\n",
      "rte_train: 0.9161214619874954\n",
      "ptloss_train: 43.89529390335083\n",
      "[3/25][2880/3964]\n",
      "loss_train: 43.59363079071045\n",
      "rre_train: 8.688474082946778\n",
      "rte_train: 0.9030913531780242\n",
      "ptloss_train: 43.59363079071045\n",
      "[3/25][2900/3964]\n",
      "loss_train: 43.37990016937256\n",
      "rre_train: 9.489988768100739\n",
      "rte_train: 0.9262374371290207\n",
      "ptloss_train: 43.37990016937256\n",
      "[3/25][2900/3964]\n",
      "loss_val: 48.336612701416016\n",
      "rre_val: 8.137679100036621\n",
      "rte_val: 0.6549288034439087\n",
      "[3/25][2920/3964]\n",
      "loss_train: 44.05183334350586\n",
      "rre_train: 9.099649143218993\n",
      "rte_train: 0.8713203728199005\n",
      "ptloss_train: 44.05183334350586\n",
      "[3/25][2940/3964]\n",
      "loss_train: 49.60046033859253\n",
      "rre_train: 11.398163437843323\n",
      "rte_train: 1.0843536317348481\n",
      "ptloss_train: 49.60046033859253\n",
      "[3/25][2960/3964]\n",
      "loss_train: 43.19715223312378\n",
      "rre_train: 9.409719347953796\n",
      "rte_train: 1.0879924923181534\n",
      "ptloss_train: 43.19715223312378\n",
      "[3/25][2980/3964]\n",
      "loss_train: 46.064295864105226\n",
      "rre_train: 10.995204305648803\n",
      "rte_train: 0.9894351810216904\n",
      "ptloss_train: 46.064295864105226\n",
      "[3/25][3000/3964]\n",
      "loss_train: 41.74473266601562\n",
      "rre_train: 7.9709625244140625\n",
      "rte_train: 0.9363941431045533\n",
      "ptloss_train: 41.74473266601562\n",
      "[3/25][3000/3964]\n",
      "loss_val: 39.86687088012695\n",
      "rre_val: 6.212984085083008\n",
      "rte_val: 0.7678095102310181\n",
      "[3/25][3020/3964]\n",
      "loss_train: 47.44801015853882\n",
      "rre_train: 11.110191881656647\n",
      "rte_train: 1.0569457978010177\n",
      "ptloss_train: 47.44801015853882\n",
      "[3/25][3040/3964]\n",
      "loss_train: 43.69290208816528\n",
      "rre_train: 10.521667957305908\n",
      "rte_train: 0.9708476603031159\n",
      "ptloss_train: 43.69290208816528\n",
      "[3/25][3060/3964]\n",
      "loss_train: 43.174605751037596\n",
      "rre_train: 9.879358959197997\n",
      "rte_train: 0.9121585294604302\n",
      "ptloss_train: 43.174605751037596\n",
      "[3/25][3080/3964]\n",
      "loss_train: 43.29069890975952\n",
      "rre_train: 9.377338624000549\n",
      "rte_train: 0.898872746527195\n",
      "ptloss_train: 43.29069890975952\n",
      "[3/25][3100/3964]\n",
      "loss_train: 41.93804693222046\n",
      "rre_train: 9.72524288892746\n",
      "rte_train: 0.8634551122784615\n",
      "ptloss_train: 41.93804693222046\n",
      "[3/25][3100/3964]\n",
      "loss_val: 39.03451919555664\n",
      "rre_val: 6.270152568817139\n",
      "rte_val: 0.7163915634155273\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3/25][3120/3964]\n",
      "loss_train: 42.648281288146975\n",
      "rre_train: 9.019437885284423\n",
      "rte_train: 0.9032479494810104\n",
      "ptloss_train: 42.648281288146975\n",
      "[3/25][3140/3964]\n",
      "loss_train: 49.02192163467407\n",
      "rre_train: 11.175740838050842\n",
      "rte_train: 0.9198022902011871\n",
      "ptloss_train: 49.02192163467407\n",
      "[3/25][3160/3964]\n",
      "loss_train: 44.38603038787842\n",
      "rre_train: 9.408256363868713\n",
      "rte_train: 0.9626613855361938\n",
      "ptloss_train: 44.38603038787842\n",
      "[3/25][3180/3964]\n",
      "loss_train: 44.94854335784912\n",
      "rre_train: 9.755979776382446\n",
      "rte_train: 0.9361491844058036\n",
      "ptloss_train: 44.94854335784912\n",
      "[3/25][3200/3964]\n",
      "loss_train: 43.89461278915405\n",
      "rre_train: 9.985981225967407\n",
      "rte_train: 0.9364152699708939\n",
      "ptloss_train: 43.89461278915405\n",
      "[3/25][3200/3964]\n",
      "loss_val: 41.63776397705078\n",
      "rre_val: 6.9435553550720215\n",
      "rte_val: 0.7019118070602417\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 2/25 [21:40<4:09:11, 650.08s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_8085/2208117106.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;31m#         r_pred = r_pred.squeeze(2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;31m#         t_pred = t_pred.squeeze(2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mr_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msymmetric_orthogonalization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr_pred\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     31\u001b[0m \u001b[0;31m#         r_pred = torch.permute(r_pred,(0,2,1))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mtrain_poses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_8085/2758805168.py\u001b[0m in \u001b[0;36msymmetric_orthogonalization\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msvd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mvt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mdet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvt\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      6\u001b[0m     \u001b[0mdet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mvt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\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[0mdet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\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": [
    "from tqdm import tqdm,trange\n",
    "import matplotlib.pyplot as plt\n",
    "from gradient_flow import *\n",
    "\n",
    "\n",
    "batch_size = 1\n",
    "epochs = 25\n",
    "print_freq = 20\n",
    "print_freq_val = 100\n",
    "epoch_save = 100\n",
    "batch_loss = []\n",
    "batch_loss_val = []\n",
    "optim = torch.optim.Adam(net.parameters(), lr=0.0001)\n",
    "# define the train routine\n",
    "for epoch in trange(epochs):\n",
    "    print_count = 0\n",
    "    print_loss = 0\n",
    "    print_rre = 0\n",
    "    print_rte = 0\n",
    "    print_ptloss = 0\n",
    "    epoch_step = 0\n",
    "    for data in train_dataloader:\n",
    "        epoch_step += 1\n",
    "        print_count += 1\n",
    "        optim.zero_grad()\n",
    "        pcd = data[0].float().squeeze(0).permute(0,2,1).to(device)\n",
    "        r_pred,t_pred = net(pcd)\n",
    "#         r_pred = r_pred.squeeze(2)\n",
    "#         t_pred = t_pred.squeeze(2)\n",
    "        r_pred = symmetric_orthogonalization(r_pred)\n",
    "#         r_pred = torch.permute(r_pred,(0,2,1))\n",
    "        train_poses = data[1].squeeze(0).to(device)\n",
    "        R_gt = train_poses[:,:3,:3]\n",
    "        t_gt = train_poses[:,:3,3]\n",
    "        \n",
    "        loss,rre,rte,tr_ptloss = loss_torch(r_pred,t_pred,R_gt,t_gt,pcd)\n",
    "        print_loss += loss.item()\n",
    "        print_rre +=rre.item()\n",
    "        print_rte += rte.item()\n",
    "        print_ptloss += tr_ptloss.item()\n",
    "        loss.backward()\n",
    "        optim.step()\n",
    "        # print(print_loss)\n",
    "        if(print_count % print_freq == 0): \n",
    "            print(f\"[{epoch+1}/{epochs}][{epoch_step}/{len(train_dataloader)}]\") \n",
    "            print(f\"loss_train: {print_loss / print_freq}\")\n",
    "            print(f\"rre_train: {print_rre / print_freq}\")\n",
    "            print(f\"rte_train: {print_rte / print_freq}\")\n",
    "            print(f\"ptloss_train: {print_ptloss / print_freq}\")\n",
    "            writer.add_scalar('loss',print_loss / print_freq,print_count)\n",
    "            batch_loss.append(print_loss / print_freq)\n",
    "            print_loss = 0\n",
    "            print_rre = 0\n",
    "            print_rte = 0\n",
    "            print_ptloss = 0\n",
    "        if(print_count % print_freq_val == 0):\n",
    "            with torch.no_grad():\n",
    "                r_pred,t_pred = net(data_val[0].float().squeeze(0).permute(0,2,1).to(device))\n",
    "                r_pred = symmetric_orthogonalization(r_pred)\n",
    "                r_pred = torch.permute(r_pred,(0,2,1))\n",
    "                train_poses = data_val[1].squeeze(0).to(device)\n",
    "                R_gt = train_poses[:,:3,:3]\n",
    "                t_gt = train_poses[:,:3,3]\n",
    "                val_loss,rre_val,rte_val,val_ptloss = loss_torch(r_pred,t_pred,R_gt,t_gt,data_val[0].float().squeeze(0).permute(0,2,1).to(device))\n",
    "                print(f\"[{epoch+1}/{epochs}][{epoch_step}/{len(train_dataloader)}]\") \n",
    "                print(f\"loss_val: {val_loss.item()}\")\n",
    "                print(f\"rre_val: {rre_val.item()}\")\n",
    "                print(f\"rte_val: {rte_val.item()}\")\n",
    "                writer.add_scalar('val_loss',val_loss,print_count)\n",
    "                batch_loss_val.append(val_loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47bd225a",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(net,'poitnet_4.pth')\n",
    "# t_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b53b681d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sample(idx):\n",
    "    pcd = []\n",
    "    poses = []\n",
    "    meta = load_pickle(meta_files[idx])\n",
    "    intrinsic = meta['intrinsic']\n",
    "    ext_inv = inv(meta['extrinsic'])\n",
    "    depth = np.array(Image.open(depth_files[idx])) / 1000\n",
    "    label = np.array(Image.open(label_files[idx]))\n",
    "    z = depth\n",
    "    v, u = np.indices(z.shape)\n",
    "    uv1 = np.stack([u + 0.5, v + 0.5, np.ones_like(z)], axis=-1)\n",
    "    points_viewer = uv1 @ np.linalg.inv(intrinsic).T * z[..., None]  # [H, W, 3]\n",
    "    for j,l in enumerate(meta['object_ids']):\n",
    "        crops_pcd = np.array([points_viewer[label==l]])\n",
    "        crops_pcd = crops_pcd@ext_inv[:3,:3].T+ext_inv[:3,3]\n",
    "        crops_pcd = crops_pcd.squeeze(0)\n",
    "        if(len(crops_pcd)<1000):\n",
    "            continue\n",
    "        crops_pcd = crops_pcd[:1000,:]\n",
    "        pcd.append(crops_pcd)\n",
    "        poses.append(meta['poses_world'][l])\n",
    "    return np.array(pcd), np.array(poses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a7ce241",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_dir = \"training_data_filtered/training_data/v2.2\"\n",
    "split_dir = \"training_data_filtered/training_data/splits/v2\"\n",
    "objects_csv = 'training_data_filtered/training_data/objects_v1.csv'\n",
    "\n",
    "def get_split_files(split_name):\n",
    "    with open(os.path.join(split_dir, f\"{split_name}.txt\"), 'r') as f:\n",
    "        prefix = [os.path.join(training_data_dir, line.strip()) for line in f if line.strip()]\n",
    "        rgb = [p + \"_color_kinect.png\" for p in prefix]\n",
    "        depth = [p + \"_depth_kinect.png\" for p in prefix]\n",
    "        label = [p + \"_label_kinect.png\" for p in prefix]\n",
    "        meta = [p + \"_meta.pkl\" for p in prefix]\n",
    "    return rgb, depth, label, meta\n",
    "\n",
    "rgb_files, depth_files, label_files, meta_files = get_split_files('train')\n",
    "rgb_files_val, depth_files_val, label_files_val, meta_files_val = get_split_files('val')\n",
    "\n",
    "def get_sample(idx):\n",
    "    pcd = []\n",
    "    poses = []\n",
    "    meta = load_pickle(meta_files[idx])\n",
    "    intrinsic = meta['intrinsic']\n",
    "    ext_inv = inv(meta['extrinsic'])\n",
    "    depth = np.array(Image.open(depth_files[idx])) / 1000\n",
    "    label = np.array(Image.open(label_files[idx]))\n",
    "    z = depth\n",
    "    v, u = np.indices(z.shape)\n",
    "    uv1 = np.stack([u + 0.5, v + 0.5, np.ones_like(z)], axis=-1)\n",
    "    points_viewer = uv1 @ np.linalg.inv(intrinsic).T * z[..., None]  # [H, W, 3]\n",
    "    for j,l in enumerate(meta['object_ids']):\n",
    "        crops_pcd = np.array([points_viewer[label==l]])\n",
    "        crops_pcd = crops_pcd@ext_inv[:3,:3].T+ext_inv[:3,3]\n",
    "        crops_pcd = crops_pcd.squeeze(0)\n",
    "        if(len(crops_pcd)<1000):\n",
    "            continue\n",
    "        crops_pcd = crops_pcd[:1000,:]\n",
    "        pcd.append(crops_pcd)\n",
    "        poses.append(meta['poses_world'][l])\n",
    "    return np.array(pcd), np.array(poses)\n",
    "\n",
    "\n",
    "s =1\n",
    "# scene = scenes[s]\n",
    "boxed_image = np.array(Image.open(rgb_files[s])) / 255 \n",
    "meta = load_pickle(meta_files[s])\n",
    "pcd,gt_pose = get_sample(s)\n",
    "pcd = torch.from_numpy(pcd)\n",
    "print(gt_pose)\n",
    "# poses_world = np.array([test_dump[scene]['poses_world'][idx] for idx in meta['object_ids']])\n",
    "with torch.no_grad():\n",
    "    poses_world = net(pcd.float().permute(0,2,1).to(device))\n",
    "    print(poses_world[0].size())\n",
    "    poses_world_r = symmetric_orthogonalization(poses_world[0]).detach().cpu().numpy()\n",
    "    poses_world_t = poses_world[1].detach().cpu().numpy()\n",
    "    print(poses_world_r)\n",
    "box_sizes = np.array([meta['extents'][idx] * meta['scales'][idx] for idx in meta['object_ids']])\n",
    "for i in range(len(poses_world_r)):\n",
    "    print(i)\n",
    "    utils.draw_projected_box3d(\n",
    "        boxed_image, poses_world_t[i], box_sizes[i], poses_world_r[i], meta['extrinsic'], meta['intrinsic'],\n",
    "        thickness=2)\n",
    "\n",
    "Image.fromarray((boxed_image * 255).astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8963e9b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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