test_model_fv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "import tensorflow as tf\n",
    "from glob import glob\n",
    "from typing import List\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import ndarray\n",
    "from typing import Dict, List, Union\n",
    "from rosbag2numpy import config as params\n",
    "from rosbag2numpy.data_processing.data_loader_fv import dataset_loader\n",
    "import time\n",
    "from tqdm import trange,tqdm\n",
    "from rosbag2numpy import SEED\n",
    "from typing import List\n",
    "print(tf.__version__)\n",
    "tf.random.set_seed(SEED)\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenarios:['/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario1', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario2', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario3', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario4', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario5', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario_ph_11', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario_ph_12', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario_ph_13', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario_ph_14', '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario_ph_15']\n",
      "Test scenario:['/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal/scenario_ph_15']\n",
      "Building Dataset.......\n",
      "\n",
      "Number of records, Train files:87, validation files:13, Test Files:16 \n",
      "\n",
      "train,valid,test: (TensorSpec(shape=(None, 25, 1040), dtype=tf.float32, name=None), TensorSpec(shape=(None, 25, 2), dtype=tf.float32, name=None))\n",
      "Test set with all data: ((TensorSpec(shape=(None, 25, 1040), dtype=tf.float32, name=None), TensorSpec(shape=(None, 1536, 1536), dtype=tf.float16, name=None), TensorSpec(shape=(None, 25, 2), dtype=tf.float32, name=None), TensorSpec(shape=(None, 25, 2), dtype=tf.float32, name=None), TensorSpec(shape=(None, None), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.string, name=None)), TensorSpec(shape=(None, 25, 2), dtype=tf.float32, name=None))\n"
     ]
    }
   ],
   "source": [
    "ds_loader = dataset_loader(\n",
    "    tfrec_dir= '/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal',\n",
    "    #'/bigpool/projects/yao_SCANGAN360/New_Folder/tf_records_w_bgm_fv_diff_unequal',#params.get(\"data_dir\"),\n",
    "    batch_size=32,\n",
    "    shuffle_buffer=3200,\n",
    "    normalize_coords=True,\n",
    "    normalize_factor=1536.0\n",
    ")\n",
    "ds_train, ds_valid, ds_test, ds_test_all = ds_loader.build_scenario_dataset(no_train_scene=8,no_valid_scene=1,no_test_scene=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy data for test set and visual inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_test_ds_size(ds_test) -> int:\n",
    "    \"\"\"get the size of test dataset\n",
    "\n",
    "    Args:\n",
    "        ds_test (tf.data.Dataset): [description]\n",
    "\n",
    "    Returns:\n",
    "        [int]: [Number of Samples inside dataset]\n",
    "    \"\"\"\n",
    "    num_of_samples = 0\n",
    "    # Looping through all batches in test dataset\n",
    "    for input_batch, output_batch in ds_test:\n",
    "        # Looping through all samples for a single (current) batch\n",
    "        #print(input_batch[0].numpy().shape)\n",
    "        for _ in range(0, len(input_batch[0].numpy())):\n",
    "            num_of_samples += 1\n",
    "    return num_of_samples\n",
    "\n",
    "def get_np_test_ds(ds_test) -> Dict[str, Union[ndarray, List]]:\n",
    "    \"\"\"Test dataset (in tf.data.Dataset build) to numpy arrays\n",
    "\n",
    "    Args:\n",
    "        ds_test ([type]): tf.data.Dataset\n",
    "\n",
    "    Returns:\n",
    "        Dict[str,Union[ndarray,List]]: dictionary of feature_vector,costmap,init_path,opt_path,car_odo,file_details,unequal_indices, diff_path\n",
    "    \"\"\"\n",
    "    samples = _get_test_ds_size(ds_test)\n",
    "    np_tst_feature_vector = np.zeros(shape=(samples,25,1040))\n",
    "    np_tst_costmap = np.zeros(shape=(samples, 1536, 1536)).astype(np.float16)\n",
    "    \n",
    "    #np_tst_left_bnd = np.zeros(shape=(samples, 25, 2))\n",
    "    #np_tst_right_bnd = np.zeros(shape=(samples, 25, 2))\n",
    "    np_tst_car_odo = np.zeros(shape=(samples, 2))\n",
    "    np_tst_init_path = np.zeros(shape=(samples, 25, 2))\n",
    "    np_tst_opt_path = np.zeros(shape=(samples, 25, 2))\n",
    "    list_tst_file_details = []\n",
    "\n",
    "    #list_unequal_indices = []\n",
    "\n",
    "    #output\n",
    "    np_tst_diff_path = np.zeros(shape=(samples, 25, 2))\n",
    "\n",
    "    j = 0\n",
    "    for input_batch, output_batch in ds_test:\n",
    "\n",
    "        for i in range(len(input_batch[0].numpy())):\n",
    "            np_tst_feature_vector[j] = input_batch[0][i].numpy()\n",
    "            np_tst_costmap[j] = input_batch[1][i].numpy().astype(np.float16)\n",
    "            #np_tst_grid_org_res[j] = input_batch[1][i].numpy()\n",
    "            #np_tst_left_bnd[j] = input_batch[2][i].numpy()\n",
    "            #np_tst_right_bnd[j] = input_batch[3][i].numpy()\n",
    "            np_tst_init_path[j] = input_batch[2][i].numpy()\n",
    "            np_tst_opt_path[j] = input_batch[3][i].numpy()\n",
    "            np_tst_car_odo[j] = input_batch[4][i].numpy()\n",
    "            list_tst_file_details.append(input_batch[5][i])\n",
    "            #list_unequal_indices.append(input_batch[6][i])\n",
    "\n",
    "            #np_tst_opt_path[j] = output_batch[i].numpy()\n",
    "            np_tst_diff_path[j] = output_batch[i].numpy()\n",
    "            j = j + 1\n",
    "\n",
    "    np_ds_test = {\n",
    "        \"feature_vector\": np_tst_feature_vector,\n",
    "        \"cost_map\": np_tst_costmap,\n",
    "        #\"grid_org_res\": np_tst_grid_org_res,\n",
    "        #\"left_bnd\": np_tst_left_bnd,\n",
    "        #\"right_bnd\": np_tst_right_bnd,\n",
    "        \"init_path\": np_tst_init_path,\n",
    "        \"opt_path\": np_tst_opt_path,\n",
    "        \"car_odo\": np_tst_car_odo,\n",
    "        \"file_details\": list_tst_file_details,\n",
    "        #\"unequal_indices\": list_unequal_indices,\n",
    "        \"diff_path\": np_tst_diff_path,\n",
    "\n",
    "    }\n",
    "\n",
    "    # np_tst_gridmap,np_tst_grid_org_res,np_tst_left_bnd,np_tst_right_bnd,np_tst_car_odo,np_tst_init_path,list_tst_file_details,np_tst_opt_path\n",
    "    return np_ds_test\n",
    "\n",
    "np_test_ds_all = get_np_test_ds(ds_test=ds_test_all)\n",
    "\n",
    "cost_map = np_test_ds_all[\"cost_map\"]\n",
    "feature_v = np_test_ds_all[\"feature_vector\"]\n",
    "init_path = np_test_ds_all[\"init_path\"]\n",
    "opt_path = np_test_ds_all[\"opt_path\"]\n",
    "car_odo = np_test_ds_all[\"car_odo\"]\n",
    "file_details = np_test_ds_all[\"file_details\"]\n",
    "diff_path = np_test_ds_all[\"diff_path\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 25, 1040)]        0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 25, 2)             8344      \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 50)                10600     \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 25, 2)             0         \n",
      "=================================================================\n",
      "Total params: 18,944\n",
      "Trainable params: 18,944\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "wandbmodel_path = '/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210924_031631-lhsjbn8r/files/model-best.h5'\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20211007_142205-4f16v6pz/files/model-best.h5' (GRU-daily-star-19)\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210922_142250-349jb33u/files/model-best.h5' (TDD-4 units - youthful-monkey-60)\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210925_083501-7b6nj1fr/files/model-best.h5' (breezy-sweep-15)\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210925_080546-xoxoxow6/files/model-best.h5' (prime-sweep-5)\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210924_031631-lhsjbn8r/files/model-best.h5' (laced-sweep-28)\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210922_162541-2bwn3ku4/files/model-best.h5'  (iconic-silence- , hybrid (2,2))\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/wandb/run-20210922_010110-1sb0es02/files/model-best.h5' (quiet-meadow-14, LSTM (25,50))\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/wandb/run-20210921_225739-3tq5o6wv/files/model-best.h5' (noble-ebergy-9,bidiLSTM)\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/wandb/run-20210921_223929-2789gz0e/files/model-best.h5' (dark-energy-TDD-64)\n",
    "#'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210922_154305-30js7pw0/files/model-best.h5' (divine-snow-LSTM(2,50))\n",
    "\n",
    "all_model_paths = ['/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210925_083501-7b6nj1fr/files/model-best.h5' , \n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210925_080546-xoxoxow6/files/model-best.h5' ,\n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210924_031631-lhsjbn8r/files/model-best.h5' ,\n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210922_162541-2bwn3ku4/files/model-best.h5'  ,\n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/wandb/run-20210922_010110-1sb0es02/files/model-best.h5' ,\n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/wandb/run-20210921_225739-3tq5o6wv/files/model-best.h5' ,\n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210922_142250-349jb33u/files/model-best.h5',\n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/wandb/run-20210921_223929-2789gz0e/files/model-best.h5' ,\n",
    "'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20210922_154305-30js7pw0/files/model-best.h5' ]\n",
    "\n",
    "#all_model_paths = ['/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/wandb/run-20211007_142205-4f16v6pz/files/model-best.h5']\n",
    "\n",
    "load_wandb  = tf.keras.models.load_model(wandbmodel_path)\n",
    "predictions = load_wandb.predict(feature_v)\n",
    "predicted_paths = init_path - predictions\n",
    "predicted_paths.shape\n",
    "np.shape(np.expand_dims(feature_v[0],axis=0))\n",
    "\n",
    "print(len(all_model_paths))\n",
    "load_wandb.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'run-20210924_031631-lhsjbn8r'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandbmodel_path.split(os.path.sep)[-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'031631-lhsjbn8r/files/model-best.mat'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{wandbmodel_path.split(\"_\")[-1].split(\".\")[0]}.mat'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save .mat for all data including predicted optimized paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210925_083501-7b6nj1fr.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210925_080546-xoxoxow6.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210924_031631-lhsjbn8r.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210922_162541-2bwn3ku4.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210922_010110-1sb0es02.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210921_225739-3tq5o6wv.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210922_142250-349jb33u.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210921_223929-2789gz0e.mat\n",
      "/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles/run-20210922_154305-30js7pw0.mat\n"
     ]
    }
   ],
   "source": [
    "import scipy.io\n",
    "\"\"\"np.savez(\n",
    "\n",
    "    wandbmodel_path.split(\"_\")[-1].split(\".\")[0],\n",
    "    costmap : cost_map,\n",
    "    init_path : init_path,\n",
    "    opt_path : opt_path,\n",
    "    diff_path : diff_path,\n",
    "    car_odo : car_odo, \n",
    "    feature_vector : feature_v,\n",
    "    file_details:file_details, \n",
    "    pred_path : predicted_paths\n",
    "\n",
    "    )\"\"\"\n",
    "\n",
    "\n",
    "for modelpath in all_model_paths:\n",
    "    load_wandb  = tf.keras.models.load_model(modelpath)\n",
    "    predictions = load_wandb.predict(feature_v)\n",
    "    predicted_paths = init_path - predictions\n",
    "\n",
    "    data_np ={\n",
    "        'costmap' : cost_map,\n",
    "        'init_path' : init_path,\n",
    "        'opt_path' : opt_path,\n",
    "        'diff_path' : diff_path,\n",
    "        'car_odo' : car_odo, \n",
    "        'feature_vector' : feature_v,\n",
    "        'file_details':file_details, \n",
    "        'pred_path' : predicted_paths\n",
    "    }\n",
    "#Saving to mat file\n",
    "    mat_root_path = '/projects/malyala/New_folder/rosbag2numpy/test_results/matfiles'\n",
    "    filename = os.path.join(mat_root_path,f'{modelpath.split(os.path.sep)[-3]}.mat')\n",
    "    #f'{wandbmodel_path.split(\"_\")[-1].split(\".\")[0]}.mat'\n",
    "    print(filename)\n",
    "    scipy.io.savemat(file_name=filename,mdict=data_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## profile predict function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timer unit: 1e-06 s\n",
      "\n",
      "Total time: 0.163236 s\n",
      "File: <ipython-input-89-bb3b8e5812e9>\n",
      "Function: profile_predict at line 1\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "     1                                           def profile_predict():\n",
      "     2         1     163236.0 163236.0    100.0      load_wandb.predict(feature_v)"
     ]
    }
   ],
   "source": [
    "def profile_predict():\n",
    "    load_wandb.predict(feature_v)\n",
    "\n",
    "%load_ext line_profiler\n",
    "\n",
    "%lprun -f profile_predict profile_predict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving keras model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nopt = tf.keras.optimizers.Adam(learning_rate=0.02)\\npp_model.compile(\\n        optimizer=opt, \\n        loss=[euclidean_distance_loss,endpoint_loss],\\n        loss_weights=params.get(\"loss_weights\"), metrics=params.get(\"metric\")\\n    )#pp_model.summary()\\ntest_loss,test_accuracy = pp_model.evaluate(ds_test)\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving keras models\n",
    "#step 1\n",
    "# serialize model to json(no weights, only structure)\n",
    "\"\"\"\n",
    "with open('best_model.json','w') as f:\n",
    "    f.write(pp_model.to_json())\n",
    "\"\"\"\n",
    "#step 2\n",
    "\"\"\"\n",
    "pp_model.save_weights('best_keras_weights.h5')\n",
    "\"\"\"\n",
    "\n",
    "#step 3\n",
    "# Load serialized model from json(contains no weights only structue)\n",
    "\n",
    "\"\"\"json_file = open('best_model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "pp_model = tf.keras.models.model_from_json(loaded_model_json,custom_objects={'CustomMaskLayer':endpoint_in_model.CustomMaskLayer})\n",
    "\"\"\"\n",
    "#step4\n",
    "# load weights into model created from json\n",
    "\n",
    "\"\"\"pp_model.load_weights('/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/keras_weights.h5')\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "opt = tf.keras.optimizers.Adam(learning_rate=0.02)\n",
    "pp_model.compile(\n",
    "        optimizer=opt, \n",
    "        loss=[euclidean_distance_loss,endpoint_loss],\n",
    "        loss_weights=params.get(\"loss_weights\"), metrics=params.get(\"metric\")\n",
    "    )#pp_model.summary()\n",
    "test_loss,test_accuracy = pp_model.evaluate(ds_test)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting individual sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'scenario_ph_15/2021-07-22_16-12-14/2021-07-22_16-12-15_0_nos156bgm_fv_paths_odo_unequal.npz', shape=(), dtype=string)\n"
     ]
    },
    {
     "data": {
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4yRcHtlyjBsCsfXlgbbXXhSXyGAAGVdUhSV6Za98beE6SF6/TKWUyALBpdLcPHz42+AMOdAbrwPTuN7B2eZL3T1Ksu3cled8qzwMAW8WVA2v7vTmhqm6a5FYDW2dN0cvQsbeuqpus4tihvP6/S/k+ifdneOCe9wUA7FdVfU+SV+TaNxZ+MMmfrsO5ZDIAJD89sPZX3X3ZRjUgkwFYcF8fs37sFDWHjh13niTyGACmIUfX3Esy+6+NTAdg1ia6Lmwv8hgAhv12ktvu9flVSZ7Y3Vev0/lkMgAAAJuWwTowvbsPrH20J3hK0l7OHli72xT1AOCAVVWHJTluYOtrqzh8KIeT4SxdrXHHriaL7zrLXrr7yiQfnbAXAHhOrn0DwlVJnrROF0/IZAAWWlXdLMmJA1tv2eBWZDIAi+zDGb4x7+RJilXVXZIcMrD1gf0cKo8BYHJydLyhr81Xu/v8SfvJ8D/LLavqqClqAsByJwysrea6sO+SxwCwTFXdKcnTly3/UXd/bB1PK5MBAADYtAzWgendcWDtE1PWHDr+qKWbMwBgq7l3ht+XnreKY4dy+DtJPjdFP59NMvT0hDutdFBV3TzJ0C9P1uN9wYq9AEBV3T3Jryxb/oPu/vg6nVImA7Do7jtmffDG+6o6uKpuXVV3q6rvq6pjq6pm0IdMBmBhdfe3k7xrYOvUCXP2cQNrH+vu/eWqPAaAycnR8TbqGrVx5wKANauqbUnuM7C1muvC5DEADKiq7UlemWTHXstfSPI763hOmQwAAMCmZrAOTKGqDkoyNOxmmos5Vjr+llPWBYAD0c8PrO3O8A0Qy91qYO3z3d2TNrN07OcHtvaXw0O9JOvzvuBmVbVjYB0Avvu96iuTbN9r+bwkz1nH08pkABbd0NP5vtDd3/juJ1V1TFX9elV9IMllST6T5ENJ/jXJziTfqap3V9Uzq2pcnu2PTAZg0b1wYO0uSZ62liJV9QNJnjyw9fxVHC6PAWBycnRt/bhGDYAD3Q8nueHA+ttWebw8BoB9/XqSuy1b+4Xu/s46nlMmAwAAsKkZrAPTuWmG/x6dP2XdccefMGVdADigVNUdkvz4wNZZ3X3xKkrcfGBt2hweV+OE/Rwz1Mu4WmsxdPz2jN6HAMCQZ2TfJ+Ws98UTMhmARXe7gbVPJaMn8lbVryX5YpLfT3KPXPvpgd91SJL7JvndJOdW1auqamiw+0pkMgALrbvfm+SlA1t/VFVPr6r9XiNRVf85oxv8Dlq29Ybu/qtVtCGPAWBycnTA0kD9G8+6l+6+IMmVA1snTFMXAJKkqirJswa29iR5yyrLyGMA2EtV3TrJ7yxbfm13r+ZhptOQyQAAAGxqBuvAdK4/Zv3rU9Ydd/xxU9YFgAPG0g0ML83oFyDL/cEqywxl8bQ5PK7G/nJ4qJdO8o116CXxvgCAAVV1YpLfXLb8F9195jqfWiYDsOiGBuB8taoOS3JGRt/nHr6GejuS/HySj1bV/ddwnEwGgOSpSf5m2dq2JC9I8qmq+tWquntVHVNVO6rqiKo6qaoeV1X/kOTNSY5advy7kjxmleeXxwAwOTk67NgMX+s5i6/N0D+PTAdgFp6c5OSB9dd392pvfJfHALBkaWjdK5IcutfyN5P8ygacXiYDAACwqQ09ERVYvWPGrF88TdHu3lNVlye5zrKtY6epCwAHmGcmOWVg/W3dfdYqawxl8VQ5vOSSgbX95fBQL5d195516GU1/QCwYJaG1r0yySF7LV+Y5Fc34PQyGYBFd6OBtQuSvDXJfaeoe3SSd1TVY7v79at4vUwGYOF191VV9cgkT0/yrFz7JoPbJvnDNZT7TpLfS/L8NWSYPAaAycnR1feSzO9rAwArqqrbZvjBapdn3wfFrEQeA8B/+IUk91629mvdfcEGnFsmAwAAsKkNTWgFVu+6Y9a/PYPaQzUOm0FdAJi7qnpgkmcPbF2c5ClrKDWUxfPK4Y3sJfG+AIB9PTXJvZat/Vp379yAc8tkABbW0nC7ofx5XPYdqvP1jL4fvmdGT/U7JMlNkjw4yUuTXDFQZ0eSV1bVnVbRjkwGgCTdfXV3/48kt0jy/CSfWWOJT2U0lOeE7n7uGm8OkMcAMDk5uvpeVqq1Fq5RA2CmqurwJG/McJ48o7u/vIZy8hgAklTVTZL8j2XL7+nuV29QCzIZAACATc1gHZjOQWPWr5pB7d0DawfPoC4AzFVVnZjkrzL8XvTJa7x4YiiL55XDG9lL4n0BAHupqhOS/O6y5bO6+7QNakEmA7DIDh2zfqNln5+e5Dbd/Tvd/cHuvqC7r+zu87v7Xd39i0lOSvKhgVrfk+QNVTXuZ9LfJZMB4No6yeVZ+5Nqb5zkNklOnOCc8hgAJidHV99L4ho1AA4wVbU9yV9m9LPu5f4uyYvXWFIeA8DIS5Mcsdfnu5I8eQPPL5MBAADY1AzWgelsH7O+licWjjNUY8cM6gLA3FTVjZO8PclRA9t/0t1/ucaSQ1k8rxzeyF4S7wsAuLY/z7WfjrMryS9s4PllMgCLbDVZ8PIkp3b3JSu9qLs/n+R+Sc4Z2L5Vksfu5zwyGQCSVNWhVfX7Sb6Y5HlJvn+NJY5O8ugk762qf6iqW63hWHkMAJOTo6vvZaVaa+EaNQBm6SVJHjKw/oUk/6W7e4315DEAC6+qHp3kR5ctP6+7P7uBbchkAAAANjXf3MF0xk00nsXfraEa46YxA8ABr6qul+Tvk9x8YPtNSX59grJXZd8nD8wrh4feF6xXL4n3BQAsqapTkzxo2fJzu/tzG9iGTAZgke0vC85N8rTV3jDQ3ZcuXRz50STXWbb9G1X1qhVqyWQAFl5VHZ/kHUluP7B9eZK3Jfk/Sb6c5KIk101ygySnJHlokhstO+beST5aVY/q7revogV5DACTk6Or72WlWmvhGjUAZqKqXpDkiQNb30jykO7+1gRl5TEAC23p2us/Wbb8qSQv3OBWZDIAAACbmsE6MJ0rxqwvv8BjEgev4XwAcECrqiOTvDPJSQPb70jyM909yZMCrsi+uTuvHB7aX69exp0PgAVTVTdI8kfLlj+Z5Pc3uBWZDMAiu3I/+8/u7l1rKdjdn62qVyf5pWVbt05yu4zyfohMBmChLd1k8J6MMnO5FyV5TnfvHHP4q6vq4CRPyOj76r0H3B2W5G+q6sHd/Q/7aUMeA8Dk5Oja9lyjBsABoap+O8nTB7a+leRB3f3pCUvLYwAW3f9Kcr29Pu8kT+rujR72IpMBAADY1LbNuwHY5C4ds37EDGofPrB2yQzqAsCGqqrDMxqqc9eB7fck+fHu3t9NiOMMZfG8cngje0m8LwBg5E+THL3X51cneeIcLp6QyQAsrKVBsd8es/3NJG+asPTLxqz/pxWOkckALLqXZN+hOp3k57r7aSsM1Rm9sPvK7v6zJPdKctGy7UOS/MXSIPmVyGMAmJwcXX0viWvUADgAVNXTkzx7YOuSJA/u7o9NUV4eA7CwquqhSX5m2fJLu/t9c2hHJgMAALCpGawD07lwzPpR0xStqkMyujBztecDgANSVR2W5O+S3GNg+5+SPKy7vzPFKYaycaocXjJ0Y8T+cnho/9ClXJ91L6vpB4AtrqoekeQnli2/pLs/MId2ZDIAi27cTfrvn2Lg3cez7w39SXLPFY6RyQAsrKr6gSSPHNh6QXefvpZa3f3x7HvDQpLcNMl/28/h8hgAJidH17Y3r68NACRJquq/JnnBwNa3kzyku8+Z8hTyGICFVFVHZDRIfm9fTfKMObSTyGQAAAA2OYN1YDr/Pmb9BlPWvdEazwcAB5yquk6SM5L84MD2B5L8SHdfNuVphrJx2hxOhrN4fznsfQEAG6aqjkryp8uWz0/yzDm0k8hkADh/zPpHJy3Y3Z3kEwNbK2WaTAZgkf3ywNo3kzx/kmLd/Y4kZw5s/WJVrXSthTwGgMnJ0WHfSnLlrHtZek9z3Bp7AYAkSVU9JcmfDGxdnuSh3f2+GZxGHgOwqF6Y5CbL1p7W3ZfMo5nIZAAAADa5HfNuADa5rya5IsnyKcs3m7LuTcesf2HKugCwIarqe5K8Ncl9BrY/lOSHu/vSGZxqKBunzeFkOIv3l8Pj9m+W5Msz7mVX/KIGYNHdJ/teWHBakpOqahb1b1JV9xxY/5cxF2jIZAAW3ReS/MDA+jenrDv09Ltj99PHcjIZgC2vRt8MP3Bg6y1T/iz6tUkesGztBknukORjY46RxwAwOTk6oLuvrqovJbn1QC/TOD7J9oF116gBsKKqenKSFw1s7Ury8O7+hxmdSh4DsKh+atnnH0ty/pjruVYyNCjmkDF1Luju88bUkckAAABsagbrwBS6u6vqvCQnLdu6zZSlxx3/uSnrAsC6q6pDk/xtkvsNbH8kyYO6++IZne6zA2snVNXB3T30NIL9qqqDk9x8ledazf5tkvzTJL3sdfxyn+vunqImAJvf0PScZy59zMLjlz6Wu2+S9w6sy2QAFt25Y9Yvn7LuZQNr113h9TIZgEV1swwPn5smc5Lk/45Zv0vGD9aRxwAwOTm6cj/Lbxpcr2vU9ve1AWCBVdXjk7wk+/7O+ookP9bdZ83wdPIYgEW1PGfvlOT9M6p9wzG1Tkty6phjZDIAsKl0d/z6GjaOv29sBtvm3QBsAR8ZWLvzlDWHjv9Kd++csi4ArKuqOiTJ32T4ycAfT/LA7v7WDE85lMPbk9x+ipp3yPATBz660kHdfUGS8we21uN9wYq9AMAcyGQAFt2Hx6wfMWXdIwfWVvq+WiYDsKiGnrqbJF+fsu64p+Jeb4Vj5DEATE6Ojjf0tbljVQ0N4p+ml6uSfHKKmgBsYVX180lenn1v9r8yyU909ztneT55DAAHBpkMAADAZmewDkzvgwNrJ1XVNDdM3GuV5wGAA8bSk/7elOSHB7Y/leQB3X3hjE/7oSRXD6wPZelqDR27J+NvktzbUF5P3EtVHZnkxFWeBwDmSSYDsOg+kFFOLXf9KesOHb/SAHa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      "text/plain": [
       "<Figure size 6000x6000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx=107\n",
    "normalized_factor=1536\n",
    "\n",
    "b_feature_v = np.zeros_like(feature_v[idx])\n",
    "b_feature_v[np.where(feature_v[idx]>0)]=1\n",
    "b_predictions = load_wandb.predict(np.expand_dims(b_feature_v,axis=0))\n",
    "b_predicted_paths = init_path - b_predictions\n",
    "\n",
    "bgm = np.zeros_like(cost_map[idx])\n",
    "bgm[np.where(cost_map[idx].astype(float)>0)]=1\n",
    "plt.figure(figsize=(15,15),dpi=400)\n",
    "plt.imshow(bgm.astype(float),origin='lower',cmap='gray')\n",
    "plt.colorbar()\n",
    "plt.plot(\n",
    "    init_path[idx][:, 0]*normalized_factor,\n",
    "    init_path[idx][:, 1]*normalized_factor,\n",
    "    \"o-\",\n",
    "    color=\"lawngreen\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "plt.plot(\n",
    "    opt_path[idx][:, 0]*normalized_factor,\n",
    "    opt_path[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"yellow\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "    b_predicted_paths[idx][:, 0]*normalized_factor,\n",
    "    b_predicted_paths[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"orange\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "\n",
    "plt.plot(car_odo[idx][0] *normalized_factor,car_odo[idx][1] *normalized_factor,\n",
    "    \"*\",\n",
    "    color=\"red\",\n",
    "    markersize=9,\n",
    ")\n",
    "plt.legend(['gt_init_path', 'gt_opt_path', 'car_centre'], loc='lower left')\n",
    "print(file_details[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(796.4791564941406, 740.7411651611328, '')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalized_factor=1536\n",
    "idx=48#107\n",
    "plt.figure(figsize=(3,5),dpi=400)\n",
    "plt.imshow(cost_map[idx].astype(float),origin='lower',cmap='gray')\n",
    "\n",
    "#plt.colorbar()\n",
    "plt.plot(\n",
    "    init_path[idx][:, 0]*normalized_factor,\n",
    "    init_path[idx][:, 1]*normalized_factor,\n",
    "    \"-o\",\n",
    "    color=\"lawngreen\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "plt.plot(\n",
    "    opt_path[idx][:, 0]*normalized_factor,\n",
    "    opt_path[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"yellow\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "\n",
    "\"\"\"plt.plot(\n",
    "    predicted_paths[idx][:, 0]*normalized_factor,\n",
    "    predicted_paths[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"orange\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "\"\"\"\n",
    "plt.plot(car_odo[idx][0] *normalized_factor,car_odo[idx][1] *normalized_factor,\n",
    "    \"*\",\n",
    "    color=\"red\",\n",
    "    markersize=10,\n",
    ")\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'car_centre'], loc='lower left')\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'predicted path','car_centre'], loc='lower left',fontsize=2)\n",
    "plt.tight_layout()\n",
    "#plt.title(f\"Test Index: {idx}\",fontsize=3)\n",
    "plt.axis(\"off\")\n",
    "plt.ylim(500,1050)\n",
    "plt.xlim(600,900)\n",
    "plt.xticks(fontsize=4)\n",
    "plt.yticks(fontsize=4)\n",
    "\n",
    "\n",
    "file_name = file_details[idx].numpy().decode('utf-8').replace(\"cm_fv_paths_odo_unequal.npz\",\"\")\n",
    "\n",
    "\n",
    "arrowprops = dict(arrowstyle=\"<->,head_length=0.4,head_width=0.2\",lw=0.6, alpha=0.9, color='w')\n",
    "#bbox=dict(boxstyle=\"round\", alpha=0.7, color='k')\n",
    "\n",
    "ann_x=init_path[idx][24, 0]*normalized_factor\n",
    "ann_y=init_path[idx][24, 1]*normalized_factor\n",
    "plt.annotate(s=\"$P_{dev}$\",xy=(ann_x-15,ann_y+10),size=4, color = 'w',rotation=0)\n",
    "plt.annotate(s=\"\",xy=(ann_x-18,ann_y+5),xytext=(ann_x+8,ann_y+5),size=3, color = 'w',rotation=90,\n",
    "                arrowprops=arrowprops)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking distance cost, and direction cost and feature vector\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.52792019, 0.61546564])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_path[idx][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Direction cost')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calc_direction_cost(car_coords:ndarray,endpoint_unit_normal:ndarray,vector:ndarray):\n",
    "    direction = car_coords-vector\n",
    "    direction_unit = direction/np.linalg.norm(x=direction)\n",
    "\n",
    "    direction_cost = np.dot(direction_unit,endpoint_unit_normal)\n",
    "\n",
    "    return direction_cost\n",
    "\n",
    "def calc_costmap(gridmap:ndarray,car_odo:ndarray,initial_path:ndarray,include_dir_cost:bool=True,distance_mag:float=0.23)->ndarray:\n",
    "    \"\"\"Function to transfor grid map to cost map\n",
    "\n",
    "    Args:\n",
    "        gridmap (ndarray): Binary occupancy grid map\n",
    "        car_odo (ndarray): [description]\n",
    "        initial_path (ndarray): [description]\n",
    "        distance_mag (float, optional): lower it is larger cost considered for the occupied cells. Defaults to 0.40.\n",
    "\n",
    "    Returns:\n",
    "        ndarray: [description]\n",
    "    \"\"\"\n",
    "    \n",
    "    car_coords = car_odo[0:2]\n",
    "    end_coords = initial_path[-1]\n",
    "    \n",
    "    # Normalize coordinates to 0-1536 range\n",
    "    #car_coords = (car_coords-gd_org_res[0:2])/gd_org_res[2]\n",
    "    #end_coords = (end_coords-gd_org_res[0:2])/gd_org_res[2]\n",
    "\n",
    "    # Initialize distance and direction costmap \n",
    "    dist_costmap = np.zeros(shape=(1536,1536))\n",
    "    dir_costmap =  np.zeros(shape=(1536,1536))\n",
    "\n",
    "    ################# calculate distance costmap ################\n",
    "    \n",
    "    # get occuppied cell indices\n",
    "    (y,x) = np.where(gridmap==np.max(gridmap))\n",
    "    # create coordinate pairs of location of occupied cells\n",
    "    coords = np.array((x,y)).T\n",
    "\n",
    "    # eucledian difference\n",
    "    distance = np.square(coords-car_coords)\n",
    "    distance = np.sum(distance, axis=1)\n",
    "    distance = 1/(distance**distance_mag)\n",
    "    dist_costmap[(y,x)]=distance\n",
    "\n",
    "    ################# Direction cost map ######################\n",
    "    \n",
    "    if include_dir_cost:\n",
    "\n",
    "        # end point direction\n",
    "        direction_vector = car_coords-end_coords\n",
    "\n",
    "        # unit vector between, car location and end point\n",
    "        end_dir_unit = direction_vector/np.linalg.norm(x=direction_vector)\n",
    "\n",
    "        # calculate dir cost for all occupied cell locations\n",
    "        direction_cost= np.asarray([calc_direction_cost(car_coords=car_coords,endpoint_unit_normal=end_dir_unit,vector=coord) for coord in coords])\n",
    "\n",
    "        # zero down all opposite directions to end point\n",
    "        direction_cost[np.where(direction_cost<0)]=0.0\n",
    "\n",
    "        dir_costmap[(y,x)] = direction_cost\n",
    "    else:\n",
    "        dir_costmap = np.ones(shape=(1536,1536))\n",
    "\n",
    "    # pointwise multiply distance cost and direction cost\n",
    "    costmap = dist_costmap*dir_costmap\n",
    "\n",
    "    # normalize cost map between [0,1]\n",
    "    costmap = costmap/np.max(costmap)\n",
    "    #cp=r'C:\\Users\\Teja\\Documents\\_INFOTECH\\Thesis\\sample_Ros_bag\\npzcostmaps'\n",
    "    #np.savez_compressed(os.path.join(cp,file_details.decode(\"utf-8\").split('/')[-1]),grid_data=costmap.astype(np.float16))\n",
    "    return costmap.astype(float)\n",
    "\n",
    "\"\"\"final_total_cost_map = np.zeros_like(cost_map[idx])\n",
    "for pnt in range(20):\n",
    "    total_cost_map = calc_costmap(gridmap=cost_map[idx].astype(float),\n",
    "                                    car_odo=init_path[idx][pnt]*normalized_factor,initial_path=init_path[idx]*normalized_factor,\n",
    "                                    #car_odo=car_odo[idx]*normalized_factor,initial_path=init_path[idx]*normalized_factor,\n",
    "                                    include_dir_cost=True)\n",
    "    final_total_cost_map+=total_cost_map\"\"\"\n",
    "\n",
    "total_cost_map = calc_costmap(gridmap=cost_map[idx].astype(float),\n",
    "                                #car_odo=init_path[idx][pnt]*normalized_factor,initial_path=init_path[idx]*normalized_factor,\n",
    "                                car_odo=car_odo[idx]*normalized_factor,initial_path=init_path[idx]*normalized_factor,\n",
    "                                include_dir_cost=True)\n",
    "total_cost_map = cost_map[idx]\n",
    "plt.figure(figsize=(3,5),dpi=400)\n",
    "plt.imshow((total_cost_map).astype(float),origin='lower',cmap='jet')\n",
    "\n",
    "#plt.colorbar(fraction=0.05)\n",
    "plt.plot(\n",
    "    init_path[idx][:, 0]*normalized_factor,\n",
    "    init_path[idx][:, 1]*normalized_factor,\n",
    "    \"o-\",\n",
    "    color=\"lawngreen\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "plt.plot(\n",
    "    opt_path[idx][:, 0]*normalized_factor,\n",
    "    opt_path[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"yellow\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "    predicted_paths[idx][:, 0]*normalized_factor,\n",
    "    predicted_paths[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"orange\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "\n",
    "plt.plot(car_odo[idx][0] *normalized_factor,car_odo[idx][1] *normalized_factor,\n",
    "    \"*\",\n",
    "    color=\"red\",\n",
    "    markersize=9,\n",
    ")\n",
    "plt.axis(\"off\")\n",
    "plt.ylim(500,1050)\n",
    "plt.xlim(600,900)\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'car_centre'], loc='lower left')\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'predicted path','car_centre'], loc='lower left')\n",
    "\n",
    "plt.title(\"Direction cost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature vector illustration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(773.4791564941406, 770.7411651611328, '$\\\\}$')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1250x1750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,7),dpi=250)\n",
    "plt.imshow(total_cost_map.astype(float),origin='lower',cmap='gray')\n",
    "\n",
    "#plt.colorbar(fraction=0.05)\n",
    "plt.plot(\n",
    "    init_path[idx][:, 0]*normalized_factor,\n",
    "    init_path[idx][:, 1]*normalized_factor,\n",
    "    \"o-\",\n",
    "    color=\"lawngreen\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\n",
    "'''plt.plot(\n",
    "    opt_path[idx][:, 0]*normalized_factor,\n",
    "    opt_path[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"yellow\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")'''\n",
    "\n",
    "\n",
    "\"\"\"plt.plot(\n",
    "    predicted_paths[idx][:, 0]*normalized_factor,\n",
    "    predicted_paths[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"orange\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\"\"\"\n",
    "\n",
    "plt.plot(car_odo[idx][0] *normalized_factor,car_odo[idx][1] *normalized_factor,\n",
    "    \"*\",\n",
    "    color=\"red\",\n",
    "    markersize=7,\n",
    ")\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'car_centre'], loc='lower left')\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'predicted path','car_centre'], loc='lower left')\n",
    "plt.axis(\"off\")\n",
    "plt.ylim(500,1100)\n",
    "plt.xlim(600,900)\n",
    "\n",
    "\n",
    "xoff = 13\n",
    "yoff = 20\n",
    "\n",
    "point_idx = 3\n",
    "for point_idx in range(25):\n",
    "    pth_x = init_path[idx][point_idx,0]*normalized_factor\n",
    "    pth_y = init_path[idx][point_idx,1]*normalized_factor\n",
    "    box_x = [pth_x-xoff, pth_x-xoff, pth_x+xoff, pth_x+xoff, pth_x-xoff]\n",
    "    box_y = [pth_y-yoff, pth_y+yoff, pth_y+yoff, pth_y-yoff, pth_y-yoff] \n",
    "    plt.plot(box_x,box_y,color='orange',linewidth=0.8)\n",
    "\n",
    "plt.annotate(r\"$25 x 1040$\",fontsize=6,\n",
    "            xy=(pth_x-50, pth_y-50), \n",
    "            xytext=(pth_x-20, pth_y+50),\n",
    "            rotation=0, color='orange'\n",
    "            #xycoords='figure fraction'\n",
    "            )\n",
    "plt.annotate(r\"$\\}$\",fontsize=22,\n",
    "            xy=(pth_x-50, pth_y-50), \n",
    "            xytext=(pth_x-15, pth_y+35),\n",
    "            rotation=90, color='orange'\n",
    "            #xycoords='figure fraction'\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature for single way point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,5),dpi=400)\n",
    "plt.imshow(total_cost_map.astype(float),origin='lower',cmap='gray')\n",
    "\n",
    "#plt.colorbar(fraction=0.05)\n",
    "plt.plot(\n",
    "    init_path[idx][:, 0]*normalized_factor,\n",
    "    init_path[idx][:, 1]*normalized_factor,\n",
    "    \"o-\",\n",
    "    color=\"lawngreen\",\n",
    "    markersize=1,\n",
    "    linewidth=1,alpha=0.6\n",
    ")\n",
    "'''plt.plot(\n",
    "    opt_path[idx][:, 0]*normalized_factor,\n",
    "    opt_path[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"yellow\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")'''\n",
    "\n",
    "\n",
    "\"\"\"plt.plot(\n",
    "    predicted_paths[idx][:, 0]*normalized_factor,\n",
    "    predicted_paths[idx][:, 1]*normalized_factor,\n",
    "    \"--\",\n",
    "    color=\"orange\",\n",
    "    markersize=1,\n",
    "    linewidth=1,\n",
    ")\"\"\"\n",
    "\n",
    "\"\"\"plt.plot(car_odo[idx][0] *normalized_factor,car_odo[idx][1] *normalized_factor,\n",
    "    \"*\",\n",
    "    color=\"red\",\n",
    "    markersize=3,\n",
    ")\"\"\"\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'car_centre'], loc='lower left')\n",
    "#plt.legend(['gt_init_path', 'gt_opt_path', 'predicted path','car_centre'], loc='lower left')\n",
    "plt.axis(\"off\")\n",
    "plt.ylim(500,800)\n",
    "plt.xlim(650,850)\n",
    "\n",
    "\n",
    "xoff = 13\n",
    "yoff = 20\n",
    "\n",
    "#point_idx = 3\n",
    "for point_idx in range(1):\n",
    "    pth_x = init_path[idx][point_idx,0]*normalized_factor\n",
    "    pth_y = init_path[idx][point_idx,1]*normalized_factor\n",
    "    box_x = [pth_x-xoff, pth_x-xoff, pth_x+xoff, pth_x+xoff, pth_x-xoff]\n",
    "    box_y = [pth_y-yoff, pth_y+yoff, pth_y+yoff, pth_y-yoff, pth_y-yoff] \n",
    "    plt.plot(box_x,box_y,color='orange',linewidth=0.8)\n",
    "\n",
    "    # arrow and box for annotations\n",
    "    plt.plot(pth_x,pth_y,\"o-\",color=\"orange\",\n",
    "            markersize=2,linewidth=1)\n",
    "    plt.annotate(s=\"$P^{{1}}_{{init}}$\",xy=(pth_x,pth_y),xytext=(pth_x-6,pth_y-10),size=6, color = 'w',)\n",
    "    arrowprops = dict(arrowstyle=\"<->,head_length=0.4,head_width=0.2\",lw=0.6, alpha=0.9, color='w')\n",
    "\n",
    "    #lowerleft\n",
    "    plt.plot(box_x[0],box_y[0],\"o-\",color=\"magenta\",\n",
    "            markersize=2,linewidth=1)\n",
    "    plt.annotate(s=\"$x_l^{i},y_l^{i}$\",xy=(box_x[0],box_y[0]),xytext=(box_x[0]-12,box_y[0]-5),size=3.5, color = 'w')\n",
    "\n",
    "    #upperright\n",
    "    plt.plot(box_x[2],box_y[2],\"o-\",color=\"magenta\",\n",
    "            markersize=2,linewidth=1)\n",
    "    plt.annotate(s=\"$x_h^{i},y_h^{i}$\",xy=(box_x[2],box_y[2]),xytext=(box_x[2]+2,box_y[2]+2),size=3.5, color = 'w')\n",
    "\n",
    "\n",
    "    #bbox=dict(boxstyle=\"round\", alpha=0.7, color='k')\n",
    "    plt.annotate(s=\"40 cells\",xy=(pth_x-1.6*xoff,pth_y-yoff/2),size=5, color = 'w',rotation=90)\n",
    "    plt.annotate(s=\"\",xy=(pth_x-1.15*xoff,pth_y-1.1*yoff),xytext=(pth_x-1.15*xoff,pth_y+1.1*yoff),size=3, color = 'w',rotation=90,\n",
    "                    arrowprops=arrowprops)\n",
    "    #plt.arrow(x=pth_x-1.15*xoff,y=pth_y-yoff,dx=0,dy=40,color='w')\n",
    "\n",
    "    plt.annotate(s=\"26 cells\",xy=(pth_x-xoff/1.1,pth_y-1.5*yoff),size=5, color = 'w',rotation=0)\n",
    "    plt.annotate(s=\"\",xy=(pth_x-1.2*xoff,pth_y-1.15*yoff),xytext=(pth_x+1.2*xoff,pth_y-1.15*yoff),size=3, color = 'w',rotation=0,\n",
    "                    arrowprops=arrowprops)\n",
    "\n",
    "\n",
    "    # annotation arrow\n",
    "    arrowprops = dict(arrowstyle=\"->,head_length=0.2,head_width=0.2\",lw=0.9, connectionstyle=\"angle3,angleA=90,angleB=0\",color='w')\n",
    "    plt.annotate('1 x 1040', \n",
    "                xy=(pth_x-xoff-10,pth_y-yoff-10), \n",
    "                xytext=(pth_x-xoff-35,pth_y-yoff-35), color='w',\n",
    "                arrowprops=arrowprops,\n",
    "                size = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics and evaluation\n",
    " * DTW (Dynamic time warp)\n",
    " * ADE (Average Displacement error)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 144/144 [00:00<00:00, 15325.94it/s]\n",
      "1it [00:00,  1.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8863525772036971\n",
      "\n",
      " Model: run-20211007_142205-4f16v6pz, DTW: 38000.478, avg_DTW:263.892, ADE: 0.8863525772036971\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from pydtw import dtw2d\n",
    "from tqdm import tqdm\n",
    "from datetime import datetime\n",
    "\n",
    "def calc_dtw(optimized_path_gt:ndarray, predicted_paths_m:ndarray)->List:\n",
    "    all_cost = []\n",
    "    for index in trange(optimized_path_gt.shape[0]):\n",
    "        cost_matrix, cost, _,_ = dtw2d(optimized_path_gt[index],predicted_paths_m[index])\n",
    "        all_cost.append(cost)\n",
    "    return all_cost\n",
    "\n",
    "def ADE(optimized_path_gt:ndarray, predicted_paths_m:ndarray):\n",
    "    # batchsize,25,2\n",
    "    dist = np.mean(np.sqrt(np.sum((optimized_path_gt - predicted_paths_m)**2,axis=2)),axis=1)\n",
    "    #np.linalg.norm(optimized_path_gt - predicted_paths_m)\n",
    "    print(sum(dist)/len(dist))\n",
    "    return sum(dist)/len(dist)\n",
    "\n",
    "def eval_models(model_paths:List[str],opt_paths,save_results:bool=False):\n",
    "\n",
    "    for i,m_path in tqdm(enumerate(model_paths)):\n",
    "\n",
    "        load_wandb  = tf.keras.models.load_model(m_path)\n",
    "        predictions = load_wandb.predict(feature_v)\n",
    "        predicted_paths = init_path - predictions\n",
    "\n",
    "        scaled_opt_paths = opt_paths*1536\n",
    "        scaled_predicted_paths = predicted_paths*1536\n",
    "\n",
    "        dtw = calc_dtw(optimized_path_gt=scaled_opt_paths,predicted_paths_m=scaled_predicted_paths)\n",
    "        avg_de = ADE(optimized_path_gt=scaled_opt_paths,predicted_paths_m=scaled_predicted_paths)\n",
    "\n",
    "        print(f\"\\n Model: {m_path.split('/')[-3]}, DTW: {sum(dtw):.3f}, avg_DTW:{np.mean(dtw):.3f}, ADE: {avg_de}\")\n",
    "\n",
    "        if save_results:\n",
    "            file_path = '/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/test_results/metrics_results'\n",
    "            file_path = os.path.join(file_path,'all_results.txt')\n",
    "            with open(file_path, 'a') as f:\n",
    "                if i==0:\n",
    "                    f.write(f\"INFO: {datetime.now()}\\n\")\n",
    "                f.write(f\"Model: {m_path.split('/')[-3]}, DTW: {sum(dtw):.3f}, avg_DTW:{np.mean(dtw):.3f}, ADE: {avg_de:.3f} \\n\")\n",
    "                f.close()\n",
    "\n",
    "eval_models(model_paths=all_model_paths,opt_paths=opt_path,save_results=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save all plots from test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'run-20210924_031631-lhsjbn8r'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandbmodel_path.split(\"/\")[-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 144/144 [01:41<00:00,  1.42it/s]\n",
      "100%|██████████| 144/144 [01:31<00:00,  1.57it/s]\n",
      "100%|██████████| 144/144 [01:36<00:00,  1.49it/s]\n",
      "100%|██████████| 144/144 [01:52<00:00,  1.28it/s]\n",
      "100%|██████████| 144/144 [01:46<00:00,  1.35it/s]\n",
      "100%|██████████| 144/144 [01:46<00:00,  1.35it/s]\n",
      "100%|██████████| 144/144 [01:51<00:00,  1.30it/s]\n",
      "100%|██████████| 144/144 [01:47<00:00,  1.34it/s]\n",
      "100%|██████████| 144/144 [01:55<00:00,  1.24it/s]\n",
      "100%|██████████| 9/9 [15:55<00:00, 106.22s/it]\n"
     ]
    }
   ],
   "source": [
    "for wandbmodel_path in tqdm(all_model_paths):\n",
    "    load_wandb  = tf.keras.models.load_model(wandbmodel_path)\n",
    "    predictions = load_wandb.predict(feature_v)\n",
    "    predicted_paths = init_path - predictions\n",
    "\n",
    "\n",
    "    SAVE_ALL = False\n",
    "\n",
    "    if SAVE_ALL:\n",
    "        model_name = wandbmodel_path.split(\"/\")[-3]#'bgm_LSTM_2_50'#wandbmodel_path.split(\"/\")[-3]\n",
    "        #'TDD_64_first'#wandbmodel_path.split(\"/\")[-3]#'TDD_4_2'\n",
    "        save_dir_root = '/projects/malyala/New_folder/rosbag2numpy/test_results/feature_vec_res/crop/binary'\n",
    "        #'/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/test_results/feature_vec_res/crop/binary'\n",
    "\n",
    "        #%matplotlib agg\n",
    "        #idx= 23#35#23#54#50#25#122#82#42#48\n",
    "        normalized_factor=1536\n",
    "\n",
    "        for idx in trange(predicted_paths.shape[0]):\n",
    "\n",
    "            #plt.figure(figsize=(15,15),dpi=400)\n",
    "            plt.figure(figsize=(3,5),dpi=500)\n",
    "\n",
    "            plt.imshow(cost_map[idx].astype(float),origin='lower',cmap='gray')\n",
    "            #plt.colorbar()\n",
    "            plt.plot(\n",
    "                init_path[idx][:, 0]*normalized_factor,\n",
    "                init_path[idx][:, 1]*normalized_factor,\n",
    "                \"o-\",\n",
    "                color=\"lawngreen\",\n",
    "                markersize=1.5,\n",
    "                linewidth=1,\n",
    "            )\n",
    "            plt.plot(\n",
    "                opt_path[idx][:, 0]*normalized_factor,\n",
    "                opt_path[idx][:, 1]*normalized_factor,\n",
    "                \"--\",\n",
    "                color=\"yellow\",\n",
    "                markersize=1,\n",
    "                linewidth=1,\n",
    "            )\n",
    "            plt.plot(\n",
    "                predicted_paths[idx][:, 0]*normalized_factor,\n",
    "                predicted_paths[idx][:, 1]*normalized_factor,\n",
    "                \"--\",\n",
    "                color=\"orange\",\n",
    "                markersize=1,\n",
    "                linewidth=1,\n",
    "            )\n",
    "            plt.plot(car_odo[idx][0] *normalized_factor,car_odo[idx][1] *normalized_factor,\n",
    "                \"r*\",\n",
    "                color=\"red\",\n",
    "                markersize=10,\n",
    "            )\n",
    "\n",
    "            plt.tight_layout()\n",
    "            #plt.title(f\"Test Index: {idx}\",fontsize=3)\n",
    "            plt.axis(\"off\")\n",
    "            plt.ylim(500,1050)\n",
    "            plt.xlim(600,900)\n",
    "            plt.xticks(fontsize=4)\n",
    "            plt.yticks(fontsize=4)\n",
    "            \n",
    "            #plt.legend(['gt_init_path', 'gt_opt_path', 'predicted path','car_centre'], loc='lower left')\n",
    "\n",
    "            file_name = file_details[idx].numpy().decode('utf-8').replace(\"cm_fv_paths_odo_unequal.npz\",\"\")\n",
    "            #plt.title(f\"{file_name}\\nTest Index: {idx}\")\n",
    "\n",
    "            split_details = os.path.split(file_name)[0]\n",
    "            scene_name = os.path.split(split_details)[0]\n",
    "            \n",
    "            # directory to save \n",
    "            save_dir = os.path.join(save_dir_root,model_name,scene_name)\n",
    "\n",
    "            if not os.path.exists(save_dir):\n",
    "                os.makedirs(save_dir)\n",
    "            plt.savefig(f\"{save_dir}/Test_index_{idx}.png\",dpi=500,figsize=(3,5),bbox_inches='tight')\n",
    "            plt.close()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iteration robustness check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_feature_vector_old(gridmap:ndarray,init_path:ndarray,offset)->ndarray:\n",
    "    feature_vector = np.zeros(shape=(init_path.shape[0],4*offset[0]*offset[1]))\n",
    "    # np.array(init_path.shape[0],4*offset[0]*offset[1])\n",
    "    for i in range(init_path.shape[0]):\n",
    "        x_l,y_l = (init_path[i].astype(int)) - offset\n",
    "        x_h,y_h = (init_path[i].astype(int)) + offset\n",
    "        cell = 0\n",
    "        #print(\"lower:\",(x_l,y_l))\n",
    "        #print(\"higher:\",(x_h,y_h))\n",
    "\n",
    "        for x in range(x_l,x_h):\n",
    "            for y in range(y_l,y_h):\n",
    "                #print(x,y)\n",
    "                #if cell<feature_vector.shape[1]:\n",
    "                    #gridmap[row][column]\n",
    "                feature_vector[i][cell] = gridmap[y][x]\n",
    "                cell+=1\n",
    "    return feature_vector\n",
    "\n",
    "# optimized version (matrix extraction)\n",
    "def get_feature_vector(gridmap:ndarray,init_path:ndarray,offset)->ndarray:\n",
    "    feature_vector = np.zeros(shape=(init_path.shape[0],4*offset[0]*offset[1]))\n",
    "    for i in range(init_path.shape[0]):\n",
    "        x_l,y_l = (init_path[i].astype(int)) - offset\n",
    "        x_h,y_h = (init_path[i].astype(int)) + offset\n",
    "        \n",
    "        # boundary box snip from costmap\n",
    "        feature_vector[i] = gridmap[y_l:y_h,x_l:x_h].flatten()\n",
    "        \n",
    "    return feature_vector\n",
    "\n",
    "\n",
    "idx=48#48\n",
    "sample_init = np_test_ds_all[\"init_path\"][idx]\n",
    "sample_opt = np_test_ds_all[\"opt_path\"][idx]\n",
    "sample_gm = np_test_ds_all[\"cost_map\"][idx]\n",
    "sample_odo = np_test_ds_all[\"car_odo\"][idx]\n",
    "sample_fv = np_test_ds_all[\"feature_vector\"][idx]\n",
    "sample_gt_diff = np_test_ds_all[\"diff_path\"][idx]\n",
    "sample_file_details = np_test_ds_all[\"file_details\"][idx]\n",
    "\n",
    "def iter_plot_sample(save,cost_map:ndarray,init_path:ndarray,opt_path:ndarray,predicted_path:ndarray,car_odo:ndarray,file_details,itn,idx):\n",
    "    norm_fac = 1536\n",
    "    plt.figure(figsize=(3,5),dpi=500)\n",
    "    \n",
    "    plt.imshow(cost_map.astype(float),origin='lower',cmap='gray')\n",
    "    #plt.colorbar()\n",
    "   \n",
    "    plt.plot(\n",
    "        init_path[:, 0]*norm_fac,\n",
    "        init_path[:, 1]*norm_fac,\n",
    "        \"o-\",\n",
    "        color=\"lawngreen\",\n",
    "        markersize=1,\n",
    "        linewidth=1,\n",
    "    )\n",
    "    plt.plot(\n",
    "        opt_path[:, 0]*norm_fac,\n",
    "        opt_path[:, 1]*norm_fac,\n",
    "        \"--\",\n",
    "        color=\"yellow\",\n",
    "        markersize=1,\n",
    "        linewidth=1,\n",
    "    )\n",
    "    plt.plot(\n",
    "        predicted_path[:, 0]*norm_fac,\n",
    "        predicted_path[:, 1]*norm_fac,\n",
    "        \"--\",\n",
    "        color=\"orange\",\n",
    "        markersize=1,\n",
    "        linewidth=1,\n",
    "    )\n",
    "    plt.plot(car_odo[0] *norm_fac,car_odo[1] *norm_fac,\n",
    "        \"r*\",\n",
    "        color=\"red\",\n",
    "        markersize=8,\n",
    "    )\n",
    "\n",
    "    file_name = file_details.numpy().decode('utf-8').replace(\"cm_fv_paths_odo_unequal.npz\",\"\")\n",
    "    #plt.title(f\"{file_name}\\nTest_sample:{idx}\\nTest iteration: {itn}\")\n",
    "    plt.title(f\"Test iteration: {itn}\",fontsize=4)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    #plt.title(f\"Test Index: {idx}\",fontsize=3)\n",
    "    plt.axis(\"off\")\n",
    "    plt.ylim(500,1050)\n",
    "    plt.xlim(600,900)\n",
    "    plt.xticks(fontsize=4)\n",
    "    plt.yticks(fontsize=4)\n",
    "\n",
    "    #plt.legend(['gt_init_path', 'gt_opt_path', 'predicted_path','car_centre'], loc='lower left')\n",
    "    \n",
    "    split_details = os.path.split(file_name)[0]\n",
    "    scene_name = os.path.split(split_details)[0]\n",
    "    save_dir_root='/netpool/work/gpu-3/users/malyalasa/New_folder/rosbag2numpy/test_results/iter_results'\n",
    "    model_name = wandbmodel_path.split(\"/\")[-3]\n",
    "    # directory t save \n",
    "    save_dir = os.path.join(save_dir_root,model_name,scene_name)\n",
    "\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "    if save:\n",
    "        plt.savefig(f\"{save_dir}/Test_index_{idx}_iter_{itn}.png\",dpi=500,bbox_inches='tight',figsize=(3,5))\n",
    "        plt.close()\n",
    "    #plt.show()\n",
    "\n",
    "\n",
    "save_iter_init=[]\n",
    "save_iter_pred=[]\n",
    "for itn in trange(3):\n",
    "    model_output = load_wandb.predict(np.expand_dims(sample_fv,axis=0))\n",
    "    pred_path = (sample_init -model_output)[0]\n",
    "\n",
    "    save_iter_init.append(sample_init)\n",
    "    save_iter_pred.append(pred_path)\n",
    "    iter_plot_sample(save=False,cost_map=sample_gm,init_path=sample_init,opt_path=sample_opt, predicted_path=pred_path, car_odo=sample_odo, file_details = sample_file_details,itn=itn,idx=idx)\n",
    "\n",
    "    sample_init = pred_path\n",
    "    sample_fv = get_feature_vector(gridmap=sample_gm,init_path=sample_init*1536,offset=[13,20])\n",
    "\n",
    "\n",
    "\n",
    "import scipy.io\n",
    "iter_data_np ={\n",
    "    'init_iter' : np.asarray(save_iter_init),\n",
    "    'pred_iter' : np.asarray(save_iter_pred),\n",
    "}\n",
    "#Saving to mat file\n",
    "mat_root_path = '/projects/malyala/New_folder/rosbag2numpy/test_results/iter_results/matfiles'\n",
    "filename = os.path.join(mat_root_path,f'{idx}_{wandbmodel_path.split(os.path.sep)[-3]}.mat')\n",
    "#os.makedirs('/projects/malyala/New_folder/rosbag2numpy/test_results/iter_results/matfiles')\n",
    "#f'{wandbmodel_path.split(\"_\")[-1].split(\".\")[0]}.mat'\n",
    "print(filename)\n",
    "print(np.shape(np.asarray(save_iter_pred)))\n",
    "scipy.io.savemat(file_name=filename,mdict=iter_data_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line profile of feature vector function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timer unit: 1e-06 s\n",
      "\n",
      "Total time: 0.001845 s\n",
      "File: <ipython-input-21-c68dd37fe6a3>\n",
      "Function: get_feature_vector_new at line 20\n",
      "\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
      "==============================================================\n",
      "    20                                           def get_feature_vector_new(gridmap:ndarray,init_path:ndarray,offset)->ndarray:\n",
      "    21         1         49.0     49.0      2.7      feature_vector = np.zeros(shape=(init_path.shape[0],4*offset[0]*offset[1]))\n",
      "    22        26         32.0      1.2      1.7      for i in range(init_path.shape[0]):\n",
      "    23        25        248.0      9.9     13.4          x_l,y_l = (init_path[i].astype(int)) - offset\n",
      "    24        25        247.0      9.9     13.4          x_h,y_h = (init_path[i].astype(int)) + offset\n",
      "    25                                                   \n",
      "    26                                                   # boundary box snip from costmap\n",
      "    27        25       1268.0     50.7     68.7          feature_vector[i] = gridmap[y_l:y_h,x_l:x_h].flatten()\n",
      "    28                                                   \n",
      "    29         1          1.0      1.0      0.1      return feature_vector"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "#%lprun -f get_feature_vector_new get_feature_vector_new(gridmap=sample_gm,init_path=sample_init*1536,offset=[13,20])\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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  "kernelspec": {
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  "language_info": {
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   "file_extension": ".py",
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