Train_on_1_side(front).ipynb

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    "colab": {
      "name": "Train_on_1_side(front).ipynb",
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      "toc_visible": true,
      "mount_file_id": "1zUznO27Hsiuw0C3_29ie2yQGaRpinL8E",
      "authorship_tag": "ABX9TyNtk1VHpKlMjocFtpH7HR3a",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hailuu684/Convolution-net/blob/main/Train_on_1_side(front).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skTrzufiDVy5",
        "outputId": "1a606aaa-546d-47be-bf76-a94e7661ce05"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "print(np.__version__)\n",
        "import pandas as pd\n",
        "from tensorflow.keras.preprocessing.image import load_img, ImageDataGenerator\n",
        "from tensorflow.keras import layers\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "import csv\n",
        "\n",
        "!pip install tensorflow_addons\n",
        "import tensorflow_addons as tfa\n",
        "from tensorflow.keras.layers import Dense, Concatenate, Input, Lambda"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.19.5\n",
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.14.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 5.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.14.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TkPJjYe_D8_i"
      },
      "source": [
        "# Preprocess hungarian"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQA0yotlDzw9"
      },
      "source": [
        "def replacenan(t):\n",
        "    return tf.where(tf.math.is_nan(t), x = tf.zeros_like(t), y = t)\n",
        "\n",
        "def replace_nan_by_10_square_10(t):\n",
        "    return tf.where(tf.math.is_nan(t), x = 10**10, y = t)\n",
        "\n",
        "def replace_nan_by_1000(t):\n",
        "    return tf.where(tf.math.is_nan(t), x = tf.cast(1000,dtype=tf.float32), y = t)\n",
        "\n",
        "def replace_10_square_10_by_zero(t):\n",
        "    return tf.where(tf.math.equal(t, 10**10), x = 0.0, y = t)\n",
        "\n",
        "def replace_zero_by_two(t):\n",
        "    return tf.where(tf.math.equal(t, 0), x = 2.0, y = t)\n",
        "\n",
        "def replace_two_by_zero(t):\n",
        "    return tf.where(tf.math.equal(t, 2), x = 0.0, y = t)\n",
        "\n",
        "def remove_zero(t):\n",
        "    intermediate_tensor = t\n",
        "    # intermediate_tensor = tf.cast(intermediate_tensor,tf.double)\n",
        "    batch = t.shape[0]\n",
        "    zero_vector = tf.zeros(shape=t.shape, dtype=tf.double)\n",
        "    bool_mask = tf.not_equal(intermediate_tensor, zero_vector)\n",
        "    omit_zeros = tf.boolean_mask(intermediate_tensor, bool_mask)\n",
        "    # tf.print('omit zero',omit_zeros)\n",
        "\n",
        "    non_zero_dimension = int(len(omit_zeros) / batch)\n",
        "\n",
        "\n",
        "    omit_zeros = tf.reshape(omit_zeros,shape=(batch,non_zero_dimension))\n",
        "    return omit_zeros"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkYaXsVUEE2j"
      },
      "source": [
        "# y_true and y_pred test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-5NZRKiD-5X"
      },
      "source": [
        "y_true_test_h_l = tf.random.uniform(shape=(16,28,),dtype=tf.double)\n",
        "y_pred_test_h_l = tf.random.uniform(shape=(16,28,),dtype=tf.double)\n",
        "\n",
        "y_true_test_h_l = np.array(y_true_test_h_l)\n",
        "y_pred_test_h_l = np.array(y_pred_test_h_l)\n",
        "\n",
        "y_true_test_h_l[:,8:] = np.nan\n",
        "# y_pred_test_h_l[:,8:] = np.nan\n",
        "\n",
        "y_true_test_h_l[:,11] = 0\n",
        "y_true_test_h_l[:,15] = 0\n",
        "y_true_test_h_l[:,19] = 0\n",
        "y_true_test_h_l[:,23] = 0\n",
        "y_true_test_h_l[:,27] = 0\n",
        "\n",
        "y_pred_test_h_l[:,11] = 0\n",
        "y_pred_test_h_l[:,15] = 0\n",
        "y_pred_test_h_l[:,19] = 0\n",
        "y_pred_test_h_l[:,23] = 0\n",
        "y_pred_test_h_l[:,27] = 0\n",
        "\n",
        "# Test 0 like 0 in relative coordinate\n",
        "# y_true_test_h_l[:,2][0] = 0.5\n",
        "# y_true_test_h_l[:,0][0] = 0\n",
        "\n",
        "\n",
        "\n",
        "for i in range(len(y_true_test_h_l[:,3])):\n",
        "    y_true_test_h_l[:,2][i] = np.random.choice([0,1],p=[0.5, 0.5])\n",
        "    y_pred_test_h_l[:,2][i] = np.random.choice([0,1],p=[0.5, 0.5])\n",
        "\n",
        "    y_true_test_h_l[:,6][i] = np.random.choice([0,1],p=[0.5, 0.5])\n",
        "    y_pred_test_h_l[:,6][i] = np.random.choice([0,1],p=[0.5, 0.5])\n",
        "\n",
        "    # confident values\n",
        "    y_true_test_h_l[:,3][i] = 1.0\n",
        "    y_pred_test_h_l[:,3][i] = 1.0\n",
        "    y_true_test_h_l[:,7][i] = 1.0\n",
        "    y_pred_test_h_l[:,7][i] = 1.0\n",
        "\n",
        "y_true_test_h_l[1,8] = 2.342432\n",
        "y_true_test_h_l[1,9] = 1.534534\n",
        "y_true_test_h_l[1,10] = 0\n",
        "y_true_test_h_l[1,11] = 1.0\n",
        "\n",
        "y_pred_test_h_l[1,8] = 1.3432\n",
        "y_pred_test_h_l[1,9] = 1.7453\n",
        "y_pred_test_h_l[1,10] = 0.2\n",
        "y_pred_test_h_l[1,11] = 0.9\n",
        "\n",
        "y_true_test_h_l = tf.convert_to_tensor(y_true_test_h_l)\n",
        "y_pred_test_h_l = tf.convert_to_tensor(y_pred_test_h_l)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09wWHjIVEKE6"
      },
      "source": [
        "# Hungarian loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfJUolamEHa7"
      },
      "source": [
        "\n",
        "reduce_mean_cls_loss_store = []\n",
        "regression_loss_store = []\n",
        "reduce_confident_loss_store = []\n",
        "# global store_hungarian_row_col\n",
        "def hungarian_loss_fixed(y_true, y_pred): # -----> worked with non-nan values, the trick here is you should not use for loop \n",
        "    from scipy.optimize import linear_sum_assignment\n",
        "\n",
        "    batch_size = tf.shape(y_true)[0]\n",
        "    y_h = int(y_true.shape[1]//4)\n",
        "\n",
        "    y_true_reshape = tf.reshape(y_true,shape=(batch_size,y_h,4))\n",
        "    y_pred_reshape = tf.reshape(y_pred,shape=(batch_size,y_h,4))\n",
        "\n",
        "    # Take only 2 first dimension\n",
        "    y_true_ = y_true_reshape[:,:,:2] # shape = (16,7,2) for example y_true_test_h_l[:,8:] = np.nan\n",
        "    y_pred_ = y_pred_reshape[:,:,:2]\n",
        "\n",
        "\n",
        "    # Replace 0( relative_coordinate) --> 2\n",
        "    y_true_ = replace_zero_by_two(y_true_)\n",
        "    y_pred_ = replace_zero_by_two(y_pred_)\n",
        "\n",
        "\n",
        "    # if change nan --> 10^10\n",
        "    y_true_ = replace_nan_by_10_square_10(y_true_) # shape = (16,7,2)\n",
        "    y_pred_ = replace_nan_by_10_square_10(y_pred_)\n",
        "\n",
        "\n",
        "    # Replace 2 --> 0 of relative coordinate of y_true\n",
        "    y_true_ = replace_two_by_zero(y_true_) # shape = (16,7,2)\n",
        "    y_pred_ = replace_two_by_zero(y_pred_)\n",
        "\n",
        "\n",
        "    y_true_test = y_true_\n",
        "    y_pred_test = y_pred_\n",
        "\n",
        "    # tf.print(y_true_.shape)\n",
        "    y_true_ = tf.cast(y_true_,dtype=tf.double)\n",
        "    y_pred_ = tf.cast(y_pred_,dtype=tf.double)\n",
        "\n",
        "\n",
        "    store_element = [tf.math.reduce_euclidean_norm(tf.constant((y_pred_[:,i,:2] - y_true_[:,j,:2]),dtype=tf.double),axis=-1) for i in range(y_true_.shape[1]) for j in range(y_true_.shape[1])]\n",
        "    store_cost = tf.convert_to_tensor(store_element)\n",
        "    store_cost = tf.transpose(store_cost, perm=[1,0])\n",
        "    \n",
        "\n",
        "    cost_batch, cost_H_W = store_cost.shape\n",
        "    cost_reshape = tf.reshape(store_cost,shape=(cost_batch,int(np.sqrt(cost_H_W)), int(np.sqrt(cost_H_W)) ))\n",
        "    # print(cost_reshape.shape) # shape = (16,7,7)\n",
        "\n",
        "    final_cost = []\n",
        "    pair_row_col = []\n",
        "\n",
        "    y_true_cls_conf = y_true_reshape[:,:,2:4] \n",
        "    y_pred_cls_conf = tf.sigmoid(y_pred_reshape[:,:,2:4])\n",
        "\n",
        "    # store_hungarian_row_col = []\n",
        "    for i in range(cost_reshape.shape[0]):\n",
        "        row_ind, col_ind = linear_sum_assignment(cost_reshape[i,:]) # find the row_ind and col_ind for each batch\n",
        "        chosen_elements = zip(row_ind.tolist(), col_ind.tolist())\n",
        "        # print(row_ind,col_ind)\n",
        "        for row,col in zip(row_ind,col_ind): \n",
        "            if cost_reshape[i,row,col] < 10**10:\n",
        "                final_cost.append(cost_reshape[i,row,col])  \n",
        "\n",
        "        pair_row_col.append(chosen_elements)\n",
        "\n",
        "\n",
        "    final_cost = tf.convert_to_tensor(final_cost) \n",
        "    # print('final cost shape',final_cost.shape) # shape = ( 112,) = (16,7)\n",
        "    \n",
        "\n",
        "    cost = tf.cast(final_cost,dtype=tf.float32)\n",
        "    #-----------------------------\n",
        "    #--------Hungarian loss-------\n",
        "    #-----------------------------\n",
        "\n",
        "    final_cost = tf.reduce_mean(final_cost) # shape = ()\n",
        "    # tf.print('\\nDone hungarian loss part')\n",
        "    #----------------\n",
        "    #---class loss--- note that 16 is the batch\n",
        "    #----------------\n",
        "    # print(y_true_reshape)\n",
        "\n",
        "    y_true_cls = y_true_reshape[:,:,2] # shape = (16,7,1) for example y_true_test_h_l[:,8:] = np.nan\n",
        "    y_pred_cls = tf.sigmoid(y_pred_reshape[:,:,2])\n",
        "    \n",
        "    # Confident scores\n",
        "    y_true_conf = y_true_reshape[:,:,3] # shape = (16,7,1) for example y_true_test_h_l[:,8:] = np.nan\n",
        "    y_pred_conf = tf.sigmoid(y_pred_reshape[:,:,3])\n",
        "\n",
        "    bce = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n",
        "    \n",
        "    y_true_cls_tf = []\n",
        "    y_pred_cls_tf = []\n",
        "    cls_loss = []\n",
        "    store_confident_loss = []\n",
        "\n",
        "    y_true_stack_pair = []\n",
        "    y_pred_stack_pair = []\n",
        "\n",
        "    for i,batch_pair in enumerate(pair_row_col):\n",
        "        for row,col in batch_pair: # y_true is row and y_pred is col ( just changed)\n",
        "            # tf.print(row,col)\n",
        "            \n",
        "            y_true_paired = y_true_cls_conf[i,row,:]\n",
        "            y_pred_paired = y_pred_cls_conf[i,col,:]\n",
        "\n",
        "\n",
        "            y_true_stack_pair.append(y_true_paired)\n",
        "            y_pred_stack_pair.append(y_pred_paired)    \n",
        "            \n",
        "    batch_size_stack_pair = int(len(y_true_stack_pair) / 7)\n",
        "    # tf.print(batch_size_stack_pair)\n",
        "    y_true_stack_pair = tf.reshape(y_true_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "    y_pred_stack_pair = tf.reshape(y_pred_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "\n",
        "    for i in range(len(y_true_stack_pair)):\n",
        "        num_true_object = np.count_nonzero(~np.isnan(np.array(y_true_stack_pair[i,:,0])))\n",
        "\n",
        "        y_true_cls_true_obj = y_true_stack_pair[i,:num_true_object,0]\n",
        "        y_pred_cls_true_obj = y_pred_stack_pair[i,:num_true_object,0]\n",
        "\n",
        "        y_true_conf_true_obj = y_true_stack_pair[i,:num_true_object,1]\n",
        "        y_pred_conf_true_obj = y_pred_stack_pair[i,:num_true_object,1]\n",
        "\n",
        "        # y_true_true_obj = y_true_stack_pair[i,:num_true_object,:]\n",
        "        # y_pred_true_obj = y_pred_stack_pair[i,:num_true_object,:]\n",
        "\n",
        "        # # get the cls and confident scores if confident scores > 0.5\n",
        "        # y_true_true_obj_filtered = y_true_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "        # y_pred_true_obj_filtered = y_pred_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "\n",
        "        cls_bce_loss = bce(y_true_cls_true_obj, y_pred_cls_true_obj)\n",
        "        cls_loss.append(cls_bce_loss)\n",
        "\n",
        "        confident_loss = bce(y_true_conf_true_obj, y_pred_conf_true_obj)\n",
        "        store_confident_loss.append(confident_loss)\n",
        "\n",
        "\n",
        "\n",
        "    reduce_mean_cls_loss = tf.reduce_mean(cls_loss)\n",
        "    # tf.print('\\nDone class loss part')\n",
        "    #------------------------\n",
        "    #-----Confident Loss-----\n",
        "    #------------------------\n",
        "    reduce_confident_loss = tf.reduce_mean(store_confident_loss)\n",
        "    # print('confident loss', store_confident_loss)\n",
        "\n",
        "    #----------------------------\n",
        "    #------ SUM OF LOSSES--------\n",
        "    #----------------------------\n",
        "    regression_loss = final_cost\n",
        "\n",
        "    # Training\n",
        "    regression_loss = tf.cast(regression_loss,dtype=tf.float32)\n",
        "\n",
        "    # Testing\n",
        "    # regression_loss = tf.cast(regression_loss,dtype=tf.double)\n",
        "\n",
        "    # tf.print('\\nreduce_confident_loss = ', reduce_confident_loss)\n",
        "    # tf.print('\\nreduce_mean_cls_loss = ', reduce_mean_cls_loss)\n",
        "    # tf.print('\\nregression loss = ', regression_loss)\n",
        "    sum_loss = reduce_mean_cls_loss + regression_loss + reduce_confident_loss\n",
        "\n",
        "    reduce_mean_cls_loss_store.append(reduce_mean_cls_loss)\n",
        "    regression_loss_store.append(regression_loss)\n",
        "    reduce_confident_loss_store.append(reduce_confident_loss)\n",
        "    return sum_loss\n",
        "\n",
        "# Use with model.fit() \n",
        "def hungarian_loss_fit_fixed(y_true, y_pred):\n",
        "    loss = tf.py_function(func=hungarian_loss_fixed, inp=[y_true,y_pred], Tout=tf.float32,)\n",
        "    loss_tensor = tf.convert_to_tensor(loss)\n",
        "    # print('hungarian loss shape',cost_tensor.shape)\n",
        "\n",
        "    return loss_tensor"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVPTsfynS-2z"
      },
      "source": [
        "# Get hungarian col and row"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTXgX-CVTBj1"
      },
      "source": [
        "\n",
        "def get_hungarian_row_col(y_true, y_pred):\n",
        "    from scipy.optimize import linear_sum_assignment\n",
        "\n",
        "    batch_size = tf.shape(y_true)[0]\n",
        "    y_h = int(y_true.shape[1]//4)\n",
        "\n",
        "    y_true_reshape = tf.reshape(y_true,shape=(batch_size,y_h,4))\n",
        "    y_pred_reshape = tf.reshape(y_pred,shape=(batch_size,y_h,4))\n",
        "\n",
        "    # Take only 2 first dimension\n",
        "    y_true_ = y_true_reshape[:,:,:2] # shape = (16,7,2) for example y_true_test_h_l[:,8:] = np.nan\n",
        "    y_pred_ = y_pred_reshape[:,:,:2]\n",
        "\n",
        "    y_true_ = tf.cast(y_true_,dtype=tf.float32)\n",
        "    y_pred_ = tf.cast(y_pred_,dtype=tf.float32)\n",
        "\n",
        "    # Replace 0( relative_coordinate) --> 2\n",
        "    y_true_ = replace_zero_by_two(y_true_)\n",
        "    y_pred_ = replace_zero_by_two(y_pred_)\n",
        "\n",
        "\n",
        "    # if change nan --> 10^10\n",
        "    y_true_ = replace_nan_by_1000(y_true_) # shape = (16,7,2)\n",
        "    y_pred_ = replace_nan_by_1000(y_pred_)\n",
        "\n",
        "\n",
        "    # Replace 2 --> 0 of relative coordinate of y_true\n",
        "    y_true_ = replace_two_by_zero(y_true_) # shape = (16,7,2)\n",
        "    y_pred_ = replace_two_by_zero(y_pred_)\n",
        "\n",
        "\n",
        "    y_true_test = y_true_\n",
        "    y_pred_test = y_pred_\n",
        "\n",
        "\n",
        "    # tf.print(y_true_.shape)\n",
        "    \n",
        "\n",
        "    store_element = [tf.math.reduce_euclidean_norm(tf.constant((y_pred_[:,i,:2] - y_true_[:,j,:2]),dtype=tf.float32),axis=-1) for i in range(y_true_.shape[1]) for j in range(y_true_.shape[1])]\n",
        "    store_cost = tf.convert_to_tensor(store_element)\n",
        "    store_cost = tf.transpose(store_cost, perm=[1,0])\n",
        "    \n",
        "\n",
        "    cost_batch, cost_H_W = store_cost.shape\n",
        "    cost_reshape = tf.reshape(store_cost,shape=(cost_batch,int(np.sqrt(cost_H_W)), int(np.sqrt(cost_H_W)) ))\n",
        "    # print(cost_reshape.shape) # shape = (16,7,7)\n",
        "    # print(cost_reshape)\n",
        "\n",
        "    pair_row_col = []\n",
        "\n",
        "    for i in range(cost_reshape.shape[0]):\n",
        "        row_ind, col_ind = linear_sum_assignment(cost_reshape[i,:]) # find the row_ind and col_ind for each batch\n",
        "        chosen_elements = zip(row_ind.tolist(), col_ind.tolist())   \n",
        "        pair_row_col.append(chosen_elements)\n",
        "    return pair_row_col\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "juLv6t-DEPQC"
      },
      "source": [
        "# Custom mse metric using hungarian algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uREuYOfELvH"
      },
      "source": [
        "import tensorflow.keras.backend as K\n",
        "def custom_mse_hungarian(y_true, y_pred):\n",
        "    y_true = tf.convert_to_tensor(y_true)\n",
        "    y_pred = tf.convert_to_tensor(y_pred)\n",
        "    \n",
        "    # Get indices from hungarian algorithm\n",
        "    pair_row_col = get_hungarian_row_col(y_true, y_pred)\n",
        "    \n",
        "    batch_size = tf.shape(y_true)[0]\n",
        "    y_h = int(y_true.shape[1]//4)\n",
        "    \n",
        "    y_true_reshape = tf.reshape(y_true,shape=(batch_size,y_h,4))\n",
        "    y_pred_reshape = tf.reshape(y_pred,shape=(batch_size,y_h,4))\n",
        "    \n",
        "    store_loss = []\n",
        "    for i,batch_pair in enumerate(pair_row_col):\n",
        "        for row,col in batch_pair: # y_true is row and y_pred is col ( just changed)\n",
        "            # tf.print(row,col)\n",
        "            if y_pred_reshape[i,col,3] > 0.5:\n",
        "                loss = K.square(y_true_reshape[i,row,:2] - y_pred_reshape[i,col,:2])  \n",
        "                loss = tf.reduce_mean(loss)\n",
        "\n",
        "                # Need some modify here y_true_reshape[i,row,:2] shape = (1,1,2) --> not correct, or not, need to test again\n",
        "\n",
        "                if not tf.math.is_nan(loss):\n",
        "                    store_loss.append(loss)\n",
        "                else:\n",
        "                    pass\n",
        "            else:\n",
        "                pass\n",
        "\n",
        "    loss = tf.reduce_mean(store_loss)\n",
        "    loss = tf.cast(loss,dtype=tf.float32)\n",
        "\n",
        "    return loss\n",
        "\n",
        "def custom_mse_hungarian_py_func(y_true, y_pred):\n",
        "    loss = tf.py_function(func=custom_mse_hungarian, inp=[y_true,y_pred], Tout=tf.float32,name='custom_mse')\n",
        "    loss_tensor = tf.convert_to_tensor(loss)\n",
        "    # print('hungarian loss shape',cost_tensor.shape)\n",
        "\n",
        "    return loss_tensor"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbh3GbaLEWRj"
      },
      "source": [
        "# Precision and recall at callback"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hT46pGrdETR0"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "from tensorflow.keras.callbacks import Callback\n",
        "\n",
        "save_precision_05 = []\n",
        "save_precision_075 = []\n",
        "save_recall_05 = []\n",
        "save_recall_075 = []\n",
        "\n",
        "class Evaluation_callback(Callback):\n",
        "    def __init__(self, validation_data, interval=1):\n",
        "        super(Evaluation_callback, self).__init__()\n",
        "\n",
        "        self.interval = interval\n",
        "        self.y_val = np.concatenate([y for x, y in validation_data], axis=0)\n",
        "        self.X_val = np.concatenate([x for x, y in validation_data], axis=0)\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs={}):\n",
        "        if epoch % self.interval == 0:\n",
        "            \n",
        "            y_pred = self.model.predict(self.X_val, verbose=0)\n",
        "            \n",
        "            batch_size = tf.shape(self.y_val)[0]\n",
        "            y_h = int(self.y_val.shape[1]//4)\n",
        "\n",
        "            y_true_reshape = tf.reshape(self.y_val,shape=(batch_size,y_h,4))\n",
        "            y_pred_reshape = tf.reshape(y_pred,shape=(batch_size,y_h,4))\n",
        "\n",
        "            # Get class   \n",
        "            y_true_cls = y_true_reshape[:,:,2] # shape = (16,7,1) for example y_true_test_h_l[:,8:] = np.nan\n",
        "            y_pred_cls = tf.sigmoid(y_pred_reshape[:,:,2])\n",
        "\n",
        "            # Get class and confident scores  \n",
        "            y_true_cls_conf = y_true_reshape[:,:,2:4] \n",
        "            y_pred_cls_conf = tf.sigmoid(y_pred_reshape[:,:,2:4])\n",
        "\n",
        "            store_precision_skl = []\n",
        "            store_recall_skl = []\n",
        "\n",
        "            # Get indices from hungarian algorithm\n",
        "            pair_row_col = get_hungarian_row_col(self.y_val, y_pred)\n",
        "\n",
        "            y_true_stack_pair = []\n",
        "            y_pred_stack_pair = []\n",
        "\n",
        "            # recall from tensorflow\n",
        "            recall_tf = tf.keras.metrics.Recall()   \n",
        "            # precision from tensorflow\n",
        "            precision_tf = tf.keras.metrics.Precision()\n",
        "            \n",
        "            # Self implemented precision and recall\n",
        "            false_negative = 0\n",
        "            true_positive = 0\n",
        "            false_positive = 0\n",
        "            self_imp_recall_store = []\n",
        "            self_imp_precision_store = []\n",
        "\n",
        "            for i,batch_pair in enumerate(pair_row_col):\n",
        "                for row,col in batch_pair: # y_true is row and y_pred is col ( just changed)\n",
        "                    # tf.print(row,col)\n",
        "                    \n",
        "                    y_true_paired = y_true_cls_conf[i,row,:]\n",
        "                    y_pred_paired = y_pred_cls_conf[i,col,:]\n",
        "\n",
        "                    # print('y-true-paired',y_true_paired)\n",
        "                    # print('y-pred-paired',y_pred_paired)\n",
        "\n",
        "                    y_true_stack_pair.append(y_true_paired)\n",
        "                    y_pred_stack_pair.append(y_pred_paired)\n",
        "            \n",
        "            batch_size_stack_pair = int(len(y_true_stack_pair) / 7)\n",
        "            # tf.print(batch_size_stack_pair)\n",
        "            y_true_stack_pair = tf.reshape(y_true_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "            y_pred_stack_pair = tf.reshape(y_pred_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "\n",
        "            for i in range(len(y_true_stack_pair)):\n",
        "                num_true_object = np.count_nonzero(~np.isnan(np.array(y_true_stack_pair[i,:,0])))\n",
        "\n",
        "                y_true_true_obj = y_true_stack_pair[i,:num_true_object,:]\n",
        "                y_pred_true_obj = y_pred_stack_pair[i,:num_true_object,:]\n",
        "\n",
        "                # get the cls and confident scores if confident scores > 0.5\n",
        "                y_true_true_obj_filtered = y_true_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "                y_pred_true_obj_filtered = y_pred_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "\n",
        "\n",
        "                if len(y_true_true_obj_filtered) != 0 and len (y_pred_true_obj_filtered) != 0:\n",
        "                    precision = precision_tf(y_true_true_obj_filtered[:,0], y_pred_true_obj_filtered[:,0])\n",
        "                    recall = recall_tf(y_true_true_obj_filtered[:,0], y_pred_true_obj_filtered[:,0])\n",
        "                    \n",
        "                    store_precision_skl.append(precision)\n",
        "                    store_recall_skl.append(recall)\n",
        "\n",
        "\n",
        "                    # # ------------------------self - implemented------------------------------\n",
        "                    # # true_positive = num_true_object\n",
        "                    # # True positive  calculations\n",
        "                    # y_true_test_self_imp = K.cast(y_true_true_obj_filtered[:,0], 'int32')\n",
        "                    # y_pred_test_self_imp = K.cast(K.round(y_pred_true_obj_filtered[:,0]), 'int32')\n",
        "\n",
        "                    # correct_preds = K.cast(K.equal(y_pred_test_self_imp, y_true_test_self_imp) , 'int32')\n",
        "                    # # tf.print('correct prediction ', correct_preds)\n",
        "                    # true_pos = K.cast(K.sum(correct_preds * y_true_test_self_imp) , 'int32')\n",
        "                    \n",
        "                    # true_positive += true_pos\n",
        "                    # false_negative += K.cast(K.sum(K.cast(K.greater(y_pred_test_self_imp, y_true_test_self_imp), 'int32')), 'int32') \n",
        "\n",
        "                    # false_positive += K.sum(K.round(K.clip(y_pred_test_self_imp, 0, 1)))\n",
        "                    y_true_converted_double = tf.cast(y_true_true_obj_filtered[:,0],dtype=tf.double)\n",
        "                    y_pred_converted_double = tf.cast(y_pred_true_obj_filtered[:,0],dtype=tf.double)\n",
        "\n",
        "                    true_positives = K.sum(K.round(K.clip(y_true_converted_double * y_pred_converted_double, 0, 1)))\n",
        "                    possible_positives = K.sum(K.round(K.clip(y_true_converted_double, 0, 1)))\n",
        "                    self_imp_recall = true_positives / (possible_positives + K.epsilon())\n",
        "                    \n",
        "                    predicted_positives = K.sum(K.round(K.clip(y_pred_converted_double, 0, 1)))\n",
        "                    self_imp_precision = true_positives / (predicted_positives + K.epsilon())\n",
        "\n",
        "                    self_imp_precision_store.append(self_imp_precision)\n",
        "                    self_imp_recall_store.append(self_imp_recall)\n",
        "            \n",
        "            if len(store_precision_skl) == 0 and len(store_recall_skl) == 0:\n",
        "                precision = 0\n",
        "                recall = 0\n",
        "                self_implemented_recall = 0\n",
        "                self_implemented_precision = 0\n",
        "            else:\n",
        "                precision = tf.reduce_mean(store_precision_skl)\n",
        "                recall = tf.reduce_mean(store_recall_skl)\n",
        "                # self_implemented_recall = float(true_positive/(true_positive+false_negative))\n",
        "                # self_implemented_precision = float(true_positive/(true_positive+false_positive))\n",
        "                self_implemented_recall = tf.reduce_mean(self_imp_recall_store)\n",
        "                self_implemented_precision = tf.reduce_mean(self_imp_precision_store)\n",
        "            tf.print('Threshold = 0.5')\n",
        "            tf.print(\"interval precision evaluation - epoch: {:d} - score: {:.6f}\".format(epoch, precision))\n",
        "            tf.print(\"interval recall evaluation - epoch: {:d} - score: {:.6f}\".format(epoch, recall))\n",
        "            tf.print('')\n",
        "            tf.print('Threshold = 0.75')\n",
        "            tf.print(\"interval self-implemented precision evaluation - epoch: {:d} - score: {:.6f}\".format(epoch, self_implemented_precision))\n",
        "            tf.print(\"interval self-implemented recall evaluation - epoch: {:d} - score: {:.6f}\".format(epoch, self_implemented_recall))\n",
        "\n",
        "            save_precision_05.append(precision)\n",
        "            save_precision_075.append(self_implemented_precision)\n",
        "            save_recall_05.append(recall)\n",
        "            save_recall_075.append(self_implemented_recall)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bW20XCrWWjk8"
      },
      "source": [
        "# Precision as metric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1lFnaJ6WmIM"
      },
      "source": [
        "class Precision(tf.keras.metrics.Metric): # with the tf.keras.metrics.Metric, should add sample_weight, otherwise it will get error\n",
        "    \"\"\"Stateful Metric to count the total recall over all batches.\n",
        "\n",
        "    Assumes predictions and targets of shape `(samples, 1)`.\n",
        "\n",
        "    # Arguments\n",
        "        name: String, name for the metric.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, name='precision', **kwargs):\n",
        "        super(Precision, self).__init__(name=name, **kwargs)\n",
        "        # self.stateful = True\n",
        "        self.precision = self.add_weight(name='precision', initializer='zeros')\n",
        "\n",
        "    # def reset_states(self):\n",
        "    #     K.set_value(self.recall, 0.0)\n",
        "\n",
        "    def update_state(self, y_true, y_pred,sample_weight=None):\n",
        "        batch_size = tf.shape(y_true)[0]\n",
        "        y_h = int(y_true.shape[1]//4)\n",
        "        \n",
        "        y_true_reshape = tf.reshape(y_true,shape=(batch_size,y_h,4))\n",
        "        y_pred_reshape = tf.reshape(y_pred,shape=(batch_size,y_h,4))\n",
        "\n",
        "        # Get class and confident scores  \n",
        "        y_true_cls_conf = y_true_reshape[:,:,2:4] \n",
        "        y_pred_cls_conf = tf.sigmoid(y_pred_reshape[:,:,2:4])\n",
        "        \n",
        "\n",
        "\n",
        "        # Get class   \n",
        "        y_true_cls = y_true_reshape[:,:,2] # shape = (16,7,1) for example y_true_test_h_l[:,8:] = np.nan\n",
        "        y_pred_cls = tf.sigmoid(y_pred_reshape[:,:,2])\n",
        "        \n",
        "        # precision from tensorflow\n",
        "        precision_tf = tf.keras.metrics.Precision()\n",
        "        # precision_km_0 = km.binary_precision(label=0)\n",
        "        # precision_km_1 = km.binary_precision(label=1)\n",
        "        store_precision_tf = []\n",
        "        \n",
        "\n",
        "        pair_row_col = get_hungarian_row_col(y_true, y_pred)\n",
        "\n",
        "        y_true_stack_pair = []\n",
        "        y_pred_stack_pair = []\n",
        "\n",
        "        store_true_object = []\n",
        "        false_negative = 0\n",
        "        true_positive = 0\n",
        "        for i,batch_pair in enumerate(pair_row_col):\n",
        "            for row,col in batch_pair: # y_true is row and y_pred is col ( just changed)\n",
        "                # tf.print(row,col)\n",
        "                \n",
        "                y_true_paired = y_true_cls_conf[i,row,:]\n",
        "                y_pred_paired = y_pred_cls_conf[i,col,:]\n",
        "\n",
        "                # print('y-true-paired',y_true_paired)\n",
        "                # print('y-pred-paired',y_pred_paired)\n",
        "\n",
        "                y_true_stack_pair.append(y_true_paired)\n",
        "                y_pred_stack_pair.append(y_pred_paired)\n",
        "\n",
        "\n",
        "        batch_size_stack_pair = int(len(y_true_stack_pair) / 7)\n",
        "        # tf.print(batch_size_stack_pair)\n",
        "        y_true_stack_pair = tf.reshape(y_true_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "        y_pred_stack_pair = tf.reshape(y_pred_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "\n",
        "        for i in range(len(y_true_stack_pair)):\n",
        "            num_true_object = np.count_nonzero(~np.isnan(np.array(y_true_stack_pair[i,:,0])))\n",
        "            # y_true_cls_true_obj = y_true_stack_pair[i,:num_true_object,0]\n",
        "            # y_pred_cls_true_obj = y_pred_stack_pair[i,:num_true_object,0]\n",
        "\n",
        "            # y_true_conf_true_obj = y_true_stack_pair[i,:num_true_object,1]\n",
        "            # y_pred_conf_true_obj = y_pred_stack_pair[i,:num_true_object,1]\n",
        "\n",
        "            y_true_true_obj = y_true_stack_pair[i,:num_true_object,:]\n",
        "            y_pred_true_obj = y_pred_stack_pair[i,:num_true_object,:]\n",
        "\n",
        "            # get the cls and confident scores if confident scores > 0.5\n",
        "            y_true_true_obj_filtered = y_true_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "            y_pred_true_obj_filtered = y_pred_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "\n",
        "\n",
        "            if len(y_true_true_obj_filtered) != 0 and len (y_pred_true_obj_filtered) != 0:\n",
        "                # print('y_true',y_true_true_obj_filtered)\n",
        "                # print('y_pred',y_pred_true_obj_filtered)\n",
        "\n",
        "                # -----------------------using library tensorflow-------------------------\n",
        "                precision = precision_tf(y_true_true_obj_filtered[:,0], y_pred_true_obj_filtered[:,0])\n",
        "                # precision_0 = precision_km_0(y_true_true_obj_filtered[:,0], y_pred_true_obj_filtered[:,0])\n",
        "                # precision_1 = precision_km_1(y_true_true_obj_filtered[:,0], y_pred_true_obj_filtered[:,0])\n",
        "                # tf.print('recall = ', recall)\n",
        "                store_precision_tf.append(precision)\n",
        "                # store_precision_tf.append(float((precision_0+precision_1)/2))\n",
        "\n",
        "            if any(tf.math.is_nan(y_true_true_obj_filtered[:,0])) or any(tf.math.is_nan(y_pred_true_obj_filtered[:,0])):\n",
        "                tf.print('y true class = ', y_true_true_obj_filtered[:,0])\n",
        "                tf.print('y pred class = ', y_pred_true_obj_filtered[:,0])\n",
        "                tf.print('num_true_object = ', num_true_object)\n",
        "            # else:\n",
        "                # tf.print('y true class = ', y_true_true_obj_filtered[:,0])\n",
        "                # tf.print('y pred class = ', y_pred_true_obj_filtered[:,0])\n",
        "                # tf.print('num_true_object = ', num_true_object)\n",
        "\n",
        "                # # ------------------------self - implemented------------------------------\n",
        "                # # true_positive = num_true_object\n",
        "                # # True positive  calculations\n",
        "                # y_true_test_self_imp = K.cast(y_true_true_obj_filtered[:,0], 'int32')\n",
        "                # y_pred_test_self_imp = K.cast(K.round(y_pred_true_obj_filtered[:,0]), 'int32')\n",
        "\n",
        "                # correct_preds = K.cast(K.equal(y_pred_test_self_imp, y_true_test_self_imp) , 'int32')\n",
        "                # # tf.print('correct prediction ', correct_preds)\n",
        "                # true_pos = K.cast(K.sum(correct_preds * y_true_test_self_imp) , 'int32')\n",
        "                \n",
        "                # true_positive += true_pos\n",
        "                # false_negative += K.cast(K.sum(K.cast(K.greater(y_pred_test_self_imp, y_true_test_self_imp), 'int32')), 'int32') \n",
        "\n",
        "        # tf.print('false negative',false_negative)\n",
        "        # tf.print('true_positive',true_positive)\n",
        "    \n",
        "\n",
        "        # recall_self_implemented = (K.cast(true_positive, 'float32') / (K.cast(true_positive, 'float32') + K.cast(false_negative, 'float32') + K.cast(K.epsilon(), 'float32')))\n",
        "        # tf.print(store_recall_tf)\n",
        "        if len(store_precision_tf) == 0:\n",
        "            precision = 0\n",
        "        else:\n",
        "            precision = tf.reduce_mean(store_precision_tf)\n",
        "        # tf.print('recall = ', recall)\n",
        "        # self.add_update(K.update(self.precision,\n",
        "        #                             precision))\n",
        "        if sample_weight is not None:\n",
        "            sample_weight = tf.cast(sample_weight, self.dtype)\n",
        "            precision = tf.multiply(precision, sample_weight)\n",
        "\n",
        "        self.precision.assign(precision)\n",
        "\n",
        "    def result(self):\n",
        "        return self.precision\n",
        "    \n",
        "    def reset_state(self):\n",
        "        self.precision.assign(0)\n",
        "\n",
        "# class Precision(tf.keras.layers.Layer):\n",
        "#     \"\"\"Stateful Metric to count the total recall over all batches.\n",
        "\n",
        "#     Assumes predictions and targets of shape `(samples, 1)`.\n",
        "\n",
        "#     # Arguments\n",
        "#         name: String, name for the metric.\n",
        "#     \"\"\"\n",
        "\n",
        "#     def __init__(self, name='precision', **kwargs):\n",
        "#         super(Precision, self).__init__(name=name, **kwargs)\n",
        "#         self.stateful = True\n",
        "#         self.precision = K.variable(value=0.0, dtype='float32')\n",
        "\n",
        "#     def reset_states(self):\n",
        "#         K.set_value(self.recall, 0.0)\n",
        "\n",
        "#     def __call__(self, y_true, y_pred):\n",
        "#         batch_size = tf.shape(y_true)[0]\n",
        "#         y_h = int(y_true.shape[1]//4)\n",
        "        \n",
        "#         y_true_reshape = tf.reshape(y_true,shape=(batch_size,y_h,4))\n",
        "#         y_pred_reshape = tf.reshape(y_pred,shape=(batch_size,y_h,4))\n",
        "\n",
        "#         # Get class and confident scores  \n",
        "#         y_true_cls_conf = y_true_reshape[:,:,2:4] \n",
        "#         y_pred_cls_conf = tf.sigmoid(y_pred_reshape[:,:,2:4])\n",
        "        \n",
        "\n",
        "\n",
        "#         # Get class   \n",
        "#         y_true_cls = y_true_reshape[:,:,2] # shape = (16,7,1) for example y_true_test_h_l[:,8:] = np.nan\n",
        "#         y_pred_cls = tf.sigmoid(y_pred_reshape[:,:,2])\n",
        "        \n",
        "#         # precision from tensorflow\n",
        "#         precision_tf = tf.keras.metrics.Precision()\n",
        "      \n",
        "#         store_precision_tf = []\n",
        "        \n",
        "\n",
        "#         pair_row_col = get_hungarian_row_col(y_true, y_pred)\n",
        "\n",
        "#         y_true_stack_pair = []\n",
        "#         y_pred_stack_pair = []\n",
        "\n",
        "#         store_true_object = []\n",
        "#         false_negative = 0\n",
        "#         true_positive = 0\n",
        "#         for i,batch_pair in enumerate(pair_row_col):\n",
        "#             for row,col in batch_pair: # y_true is row and y_pred is col ( just changed)\n",
        "#                 # tf.print(row,col)\n",
        "                \n",
        "#                 y_true_paired = y_true_cls_conf[i,row,:]\n",
        "#                 y_pred_paired = y_pred_cls_conf[i,col,:]\n",
        "\n",
        "           \n",
        "\n",
        "#                 y_true_stack_pair.append(y_true_paired)\n",
        "#                 y_pred_stack_pair.append(y_pred_paired)\n",
        "\n",
        "\n",
        "#         batch_size_stack_pair = int(len(y_true_stack_pair) / 7)\n",
        "#         # tf.print(batch_size_stack_pair)\n",
        "#         y_true_stack_pair = tf.reshape(y_true_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "#         y_pred_stack_pair = tf.reshape(y_pred_stack_pair,shape=(batch_size_stack_pair,7,2))\n",
        "\n",
        "#         for i in range(len(y_true_stack_pair)):\n",
        "#             num_true_object = np.count_nonzero(~np.isnan(np.array(y_true_stack_pair[i,:,0])))\n",
        "           \n",
        "\n",
        "#             y_true_true_obj = y_true_stack_pair[i,:num_true_object,:]\n",
        "#             y_pred_true_obj = y_pred_stack_pair[i,:num_true_object,:]\n",
        "\n",
        "#             # get the cls and confident scores if confident scores > 0.5\n",
        "#             y_true_true_obj_filtered = y_true_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "#             y_pred_true_obj_filtered = y_pred_true_obj[y_pred_true_obj[:,1]>0.5]\n",
        "\n",
        "\n",
        "#             if len(y_true_true_obj_filtered) != 0 and len (y_pred_true_obj_filtered) != 0:\n",
        "#                 # print('y_true',y_true_true_obj_filtered)\n",
        "#                 # print('y_pred',y_pred_true_obj_filtered)\n",
        "\n",
        "#                 # -----------------------using library tensorflow-------------------------\n",
        "#                 precision = precision_tf(y_true_true_obj_filtered[:,0], y_pred_true_obj_filtered[:,0])\n",
        "                \n",
        "#                 store_precision_tf.append(precision)\n",
        "#                 # store_precision_tf.append(float((precision_0+precision_1)/2))\n",
        "\n",
        "#             if any(tf.math.is_nan(y_true_true_obj_filtered[:,0])) or any(tf.math.is_nan(y_pred_true_obj_filtered[:,0])):\n",
        "#                 tf.print('y true class = ', y_true_true_obj_filtered[:,0])\n",
        "#                 tf.print('y pred class = ', y_pred_true_obj_filtered[:,0])\n",
        "#                 tf.print('num_true_object = ', num_true_object)\n",
        "            \n",
        "#         if len(store_precision_tf) == 0:\n",
        "#             precision = 0\n",
        "#         else:\n",
        "#             precision = tf.reduce_mean(store_precision_tf)\n",
        "#         # tf.print('recall = ', recall)\n",
        "#         self.add_update(K.update(self.precision,\n",
        "#                                     precision))\n",
        "#         return precision\n",
        "\n",
        "#     def get_config(self):\n",
        "#         config = super().get_config().copy()\n",
        "#         config.update({\n",
        "#             'precision': self.precision  \n",
        "#             })\n",
        "#         return config\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdFOfytpEl9q"
      },
      "source": [
        "# Preprocess data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_M6Tc9-EZif",
        "outputId": "0a6c1712-e6b3-4fd3-c603-c395253af496"
      },
      "source": [
        "def add_confident_value(label):\n",
        "    reshape = tf.reshape(label,shape = (7,3))\n",
        "    store_new_values = np.empty(shape = (7,4))\n",
        "\n",
        "    for i in range(reshape.shape[0]):\n",
        "        for j in range(reshape.shape[1]):\n",
        "            store_new_values[i,j] = reshape[i,j]\n",
        "        \n",
        "        if np.isnan(reshape[i,2]):\n",
        "            # print('in if statement',reshape_test_[i,2])\n",
        "            store_new_values[i,3] = 0\n",
        "        else:\n",
        "            # print('in elif statement',reshape_test_[i,2])\n",
        "            store_new_values[i,3] = 1\n",
        "    store_new_values = tf.reshape(store_new_values,shape=(28,))\n",
        "    return store_new_values\n",
        "\n",
        "def plot_images(images):\n",
        "    assert isinstance(images, (list, tuple, np.ndarray))    \n",
        "    cols = min(6, len(images))\n",
        "    rows = 1 + (len(images)-1)//cols\n",
        "    plt.figure(figsize=(3.2*cols, 1.2*rows))\n",
        "    for n, image in enumerate(images):\n",
        "        plt.subplot(rows, cols, n+1)\n",
        "        plt.xticks([], [])\n",
        "        plt.yticks([], [])\n",
        "        # plt.xlabel(f'{pred_titles[n]}', size=14, c='blue')\n",
        "        plt.imshow(image)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "def get_dir_name(filename):\n",
        "    pos1 = filename.rfind('_')\n",
        "    pos2 = filename.find('.')\n",
        "    return filename[pos1+1:pos2], filename[0:pos1]\n",
        "\n",
        "TRAIN_SET_PATH = '/content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test'\n",
        "town_listdir = os.listdir(TRAIN_SET_PATH)\n",
        "print(town_listdir)\n",
        "\n",
        "\n",
        "resize_H = 50\n",
        "resize_W = 100\n",
        "def read_rgb_image(rgb_path):\n",
        "    image = cv2.imread(rgb_path)\n",
        "    image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n",
        "    image = cv2.resize(image,(resize_W,resize_H))\n",
        "\n",
        "    image = tf.keras.preprocessing.image.img_to_array(image)\n",
        "    image = image[0:,:,:]\n",
        "    image = image/255.0\n",
        "    return image\n",
        "\n",
        "def read_depth_image(depth_path):\n",
        "    depth_img = cv2.imread(depth_path)\n",
        "    depth_img = cv2.cvtColor(depth_img,cv2.COLOR_BGR2RGB)\n",
        "    depth_img = cv2.resize(depth_img,(resize_W,resize_H))\n",
        "\n",
        "    R = depth_img[:, :, 0].astype('float32')\n",
        "    G = depth_img[:, :, 1].astype('float32')\n",
        "    B = depth_img[:, :, 2].astype('float32')\n",
        "    normalized = (R + G * 256 + B * 256 * 256) / (256 * 256 * 256 - 1)\n",
        "    in_meters = 1000 * normalized\n",
        "    in_meters[in_meters >= 1000] = 0\n",
        "    in_meters = tf.expand_dims(in_meters,axis=-1)\n",
        "    return in_meters\n",
        "\n",
        "def normalize_on_rgb_depth(concat_img):\n",
        "    # mean = [103.939, 116.779, 123.68, 1]\n",
        "    channel_avg = np.array([0.485, 0.456, 0.406])\n",
        "    channel_std = np.array([0.229, 0.224, 0.225])\n",
        "    # concat_img[:,:,0:3] = (concat_img[:,:,0:3] - channel_avg) / channel_std\n",
        "    \n",
        "    \n",
        "    concat_img_list = concat_img.numpy()\n",
        "    # print(concat_img_list.shape)\n",
        "    concat_img_list[:,:,0:3] = (concat_img_list[:,:,0:3] - channel_avg) / channel_std\n",
        "\n",
        "    concat_img_list[:,:,3][concat_img_list[:,:,3] == 0.0] = 1 # log10(1) = 0 thats what we want, not -inf\n",
        "\n",
        "    concat_img_list[:,:,3] = np.log10(concat_img_list[:,:,3])\n",
        "    concat_img_tensor = tf.convert_to_tensor(concat_img_list)\n",
        "    # concat_img_tensor.astype(np.float32)\n",
        "    # print(concat_img_tensor)\n",
        "    concat_img_tensor = tf.cast(concat_img_tensor,tf.float32)\n",
        "    return concat_img_tensor"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Town04', 'Town01']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBhmITV5bjdi"
      },
      "source": [
        "# Normalize input label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7SSrkn3bmOS"
      },
      "source": [
        "save_mean_std_relative = {}\n",
        "def normalize_relative_coord(path,type_object):\n",
        "    csv_df = pd.read_csv(path)\n",
        "    csv_df['relative_x'] = (csv_df['relative_x'] - csv_df['relative_x'].mean() ) / csv_df['relative_x'].std()\n",
        "    csv_df['relative_y'] = (csv_df['relative_y'] - csv_df['relative_y'].mean() ) / csv_df['relative_y'].std()\n",
        "    save_mean_std_relative[f'mean_relative_x_{type_object}'] = csv_df['relative_x'].mean()\n",
        "    save_mean_std_relative[f'std_relative_x_{type_object}'] = csv_df['relative_x'].std()\n",
        "\n",
        "    save_mean_std_relative[f'mean_relative_y_{type_object}'] = csv_df['relative_y'].mean()\n",
        "    save_mean_std_relative[f'std_relative_y_{type_object}'] = csv_df['relative_y'].std()\n",
        "    return csv_df"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTdzFvsDE5ES"
      },
      "source": [
        "# Load data train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbzORWxFEoTe"
      },
      "source": [
        "def load_4_side_data_train():\n",
        "    def fetch_data_pair():\n",
        "        for i,(town) in enumerate(town_listdir): # use enumerate for debugging to check the loops\n",
        "            town_path = os.path.join(TRAIN_SET_PATH,town)\n",
        "\n",
        "            csv_ego_path = os.path.join(town_path,'location.csv')\n",
        "\n",
        "            csv_agent_path = os.path.join(town_path,'agent_vehicle_information.csv')\n",
        "            csv_walker_path = os.path.join(town_path,'walkers_information.csv')\n",
        "            # print(csv_ego_path,csv_agent_path)\n",
        "            rgb_path = os.path.join(town_path,'rgb_image')\n",
        "\n",
        "            front_rgb_path = os.path.join(rgb_path,'front_image')\n",
        "            \n",
        "            round_1_4 = os.listdir(front_rgb_path)\n",
        "            round_1_4.sort()\n",
        "\n",
        "            SET_TRAIN_PATH = [os.path.join(front_rgb_path,round_1_4[i]) for i in range(0,4)]\n",
        "            \n",
        "\n",
        "            # # Not normalize input data\n",
        "            # df = pd.read_csv(csv_ego_path)\n",
        "            # df_agent = pd.read_csv(csv_agent_path)\n",
        "            # df_walker = pd.read_csv(csv_walker_path)\n",
        "\n",
        "\n",
        "            # ----------------------------------------------\n",
        "            #----------NORMALIZE INPUT DATA-----------------\n",
        "            #-----------------------------------------------\n",
        "            df = normalize_relative_coord(csv_ego_path,type_object='ego_vehicle_train')\n",
        "            # df_agent = normalize_relative_coord(csv_agent_path, type_object='agent_vehicle_train')\n",
        "            df_walker = normalize_relative_coord(csv_walker_path, type_object='walker_train')\n",
        "\n",
        "            #------------------------------------------------\n",
        "            #--------FILL THE MISSING VALUES-----------------\n",
        "            #------------------------------------------------\n",
        "            df['relative_x'] = df['relative_x'].fillna(0)\n",
        "            df['relative_y'] = df['relative_y'].fillna(0)\n",
        "            df['throttle'] = df['throttle'].fillna(0)\n",
        "            df['steer'] = df['steer'].fillna(0)\n",
        "\n",
        "            df_walker['relative_x'] = df_walker['relative_x'].fillna(0)\n",
        "            df_walker['relative_y'] = df_walker['relative_y'].fillna(0)\n",
        "            # df_walker['throttle'] = df_walker['throttle'].fillna(0)\n",
        "            # df_walker['steer'] = df_walker['steer'].fillna(0)\n",
        "            #-------------------------------------------------\n",
        "\n",
        "            \n",
        "            # print('in train',os.path.isfile(csv_agent_path))\n",
        "            for round in SET_TRAIN_PATH:  \n",
        "                for subdir,_,name_images in os.walk(round):\n",
        "                    name_images.sort()\n",
        "                    # print(subdir)\n",
        "                    '''\n",
        "                    subdir: /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town02/rgb_image/front_image/round1\n",
        "                    '''\n",
        "\n",
        "                    # split the directory to get the depth directory\n",
        "                    '''\n",
        "                    head1: /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town02/rgb_image/front_image\n",
        "                    split_round: round1 -> round 4\n",
        "                    '''\n",
        "                    head1, split_round = os.path.split(subdir)\n",
        "                    \n",
        "                    '''\n",
        "                    rgb_img_path_split: /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town02/rgb_image\n",
        "                    '''\n",
        "                    rgb_img_path_split, front_img_split = os.path.split(head1)\n",
        "                    path_to_town = head1[:97]\n",
        "\n",
        "                    # print(path_to_town)\n",
        "                    left_path = 'left_image'\n",
        "                    right_path = 'right_image'\n",
        "                    back_path = 'back_image'\n",
        "                    front_path = 'front_image'\n",
        "\n",
        "\n",
        "                    path_to_depth = os.path.join(path_to_town,'depth_image')\n",
        "                    subdir_depth_front = os.path.join(path_to_depth, front_img_split)\n",
        "                    subdir_depth_round = os.path.join(subdir_depth_front, split_round)\n",
        "                    # print(subdir_depth_front)\n",
        "\n",
        "                    for name in name_images:\n",
        "                        # print(name)\n",
        "                        path_to_rgb_front_img = os.path.join(subdir,name)\n",
        "                        # print(subdir)\n",
        "\n",
        "                        # # path to rgb left image\n",
        "                        # path_to_rgb_left = os.path.join(rgb_img_path_split,left_path)\n",
        "                        # path_to_rgb_left_img = os.path.join(path_to_rgb_left,split_round)\n",
        "                        # path_to_rgb_left_img = os.path.join(path_to_rgb_left_img,name)\n",
        "                        # # print(path_to_rgb_left_img)\n",
        "                        # # print(os.path.isfile(path_to_rgb_left_img))\n",
        "                        # # print(path_to_rgb_left_img)\n",
        "\n",
        "                        # # path to rgb right image\n",
        "                        # path_to_rgb_right = os.path.join(rgb_img_path_split,right_path)\n",
        "                        # path_to_rgb_right_img = os.path.join(path_to_rgb_right,split_round)\n",
        "                        # path_to_rgb_right_img = os.path.join(path_to_rgb_right_img,name)\n",
        "                        # # print(os.path.isfile(path_to_rgb_right_img))\n",
        "\n",
        "                        # # path to rgb back image\n",
        "                        # path_to_rgb_back = os.path.join(rgb_img_path_split,back_path)\n",
        "                        # path_to_rgb_back_img = os.path.join(path_to_rgb_back,split_round)\n",
        "                        # path_to_rgb_back_img = os.path.join(path_to_rgb_back_img,name)\n",
        "                        # # print(os.path.isfile(path_to_rgb_back_img))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "                        # --------------------Split the path to get the path depth---------------------------\n",
        "\n",
        "                       \n",
        "                        path_to_depth_front = os.path.join(subdir_depth_round,name)\n",
        "                        # path_to_depth_front_img = os.path.join(subdir_depth_round,name)\n",
        "                        # print(os.path.isfile(path_to_depth_front))\n",
        "\n",
        "                        # split the depth path \n",
        "                        path_to_depth_split = path_to_depth_front[:109]\n",
        "                        path_from_depth_round_to_img = path_to_depth_front[122:] # round4/round4_024192.png\n",
        "                        # ----> works when rgb image has .png extension\n",
        "                        \n",
        "                        test = path_to_depth_front[122:142]\n",
        "                        path_from_depth_round_to_img = test + '.png'\n",
        "                        # print(path_from_depth_round_to_img)\n",
        "\n",
        "                        # path to depth front image\n",
        "                        path_to_depth_front = os.path.join(path_to_depth_split,front_path)\n",
        "                        path_to_depth_front_img = os.path.join(path_to_depth_front,path_from_depth_round_to_img)\n",
        "\n",
        "                        # # path to depth right image\n",
        "                        # path_to_depth_right = os.path.join(path_to_depth_split,right_path)\n",
        "                        # path_to_depth_right_img = os.path.join(path_to_depth_right,path_from_depth_round_to_img)\n",
        "                        # # print(os.path.isfile(path_to_depth_right))\n",
        "\n",
        "                        # # path to depth left image\n",
        "                        # path_to_depth_left = os.path.join(path_to_depth_split,left_path)\n",
        "                        # path_to_depth_left_img = os.path.join(path_to_depth_left,path_from_depth_round_to_img)\n",
        "                        # # print(os.path.isfile(path_to_depth_left))\n",
        "                        # # print(path_to_depth_left_img)\n",
        "\n",
        "                        # # path to depth back image\n",
        "                        # path_to_depth_back = os.path.join(path_to_depth_split,back_path)\n",
        "                        # path_to_depth_back_img = os.path.join(path_to_depth_back,path_from_depth_round_to_img)\n",
        "                        # # print(os.path.isfile(path_to_depth_back))\n",
        "\n",
        "                        frame,round = get_dir_name(path_to_rgb_front_img)\n",
        "                        try:\n",
        "                            frame = int(frame)\n",
        "                        except:\n",
        "                            # print('Can not convert', str ,\"to int\")\n",
        "                            # print(frame, \"   \", path_to_rgb_front_img)\n",
        "                            pass\n",
        "\n",
        "                        # label_steer = 0\n",
        "                        # label_throttle = 0\n",
        "                        # label_velocity = 0\n",
        "\n",
        "                        #------------------------------\n",
        "                        #------create nan array--------\n",
        "                        #------------------------------\n",
        "                        label_array = np.empty((21,))\n",
        "                        label_array[:] = np.NaN\n",
        "                        label_array = np.array(label_array)\n",
        "                        #-------------------------------\n",
        "\n",
        "                        label = []\n",
        "\n",
        "                        for index,row in df.iterrows():\n",
        "                            if row['frame'] == frame:\n",
        "                                cls = 1\n",
        "                                relative_x = row['relative_x']\n",
        "                                relative_y = row['relative_y']\n",
        "                                label.append([float(relative_x), float(relative_y),int(cls)])\n",
        "                                # print('im okay')\n",
        "\n",
        "                        label_walker = []\n",
        "                    \n",
        "                        for index, row in df_walker.iterrows():\n",
        "                            if row['frame'] == frame:\n",
        "                                cls = 0\n",
        "                                relative_x = row['relative_x']\n",
        "                                relative_y = row['relative_y']\n",
        "                                label_walker.append([float(relative_x),float(relative_y),int(cls)])\n",
        "                            else:\n",
        "                                # print('searching for the correct frame of walker')\n",
        "                                # print('frame of walker',row['frame'])\n",
        "                                # print('frame of images',frame)\n",
        "                                pass\n",
        "                                # label_walker.append([float(np.nan),float(np.nan),int(np.nan)])\n",
        "\n",
        "                        #-------------------------------------------\n",
        "                        #----------TODO---------------------------\n",
        "                        #---------------------------------------\n",
        "                        # Generate again with the relative to the walkers, cannot do it in here because we do not calculate the\n",
        "                        # relative in the same loop\n",
        "\n",
        "                        # After having it, it needs simply add to the let say label_walker, the concatenate label and label_walker\n",
        "                        # then using for loop as below to add value to the NaN arrays\n",
        "                        \n",
        "\n",
        "                        label = np.array(label)\n",
        "                        label_walker = np.array(label_walker)\n",
        "                        \n",
        "                        # print(label.shape)\n",
        "                        \n",
        "\n",
        "                        # if label.shape[0] == 1 and label_agent.shape[0] == 1: # label shape from the df is (1,3). so I need to squeeze it to (3,)\n",
        "                            # print('OKE') \n",
        "                        if label.shape[0] != 0 or label_walker.shape[0] != 0:\n",
        "                            # label = tf.squeeze(label,axis=0)\n",
        "                            # label_agent = tf.squeeze(label_agent,axis=0)\n",
        "                            if label.shape[0] != 1 and len(label) != 0:\n",
        "                                # print(label)\n",
        "                                label = tf.reshape(label,shape=(1,label.shape[0]*label.shape[1]))\n",
        "                            if label_walker.shape[0] != 1 and len(label_walker) != 0:\n",
        "                                # print('label walker',label_walker)\n",
        "                                label_walker = tf.reshape(label_walker,shape=(1,label_walker.shape[0]*label_walker.shape[1]))\n",
        "                            \n",
        "\n",
        "                            # if walker and agent vehicles are seen, then it is concatenated\n",
        "                            if label.shape[0] == 1 and label_walker.shape[0] == 1:\n",
        "                                final_label = tf.concat([label, label_walker],axis=1)\n",
        "                                # print(final_label)\n",
        "                            # if only agent vehicle, then final label is the agent label\n",
        "                            elif label.shape[0] == 1 and label_walker.shape[0] == 0:\n",
        "                                final_label = label\n",
        "                            # if only walker, then final label is the walker\n",
        "                            elif label.shape[0] == 0 and label_walker.shape[0] == 1:\n",
        "                                final_label = label_walker\n",
        "\n",
        "                            label_h, label_w = final_label.shape\n",
        "                            # label_walker_h, label_walker_w = label_walker.shape\n",
        "                            # print(label_h,label_w)\n",
        "                            # final_label = np.array([final_label])\n",
        "\n",
        "                            # if the final label has more than 2 objects, then it needs to reshape to (label_h * label_w, )\n",
        "                            if label_h != 1:\n",
        "                                final_label = tf.reshape(final_label,shape=(label_h * label_w))\n",
        "                                # label = label.numpy()\n",
        "                                final_label = np.array([final_label])\n",
        "\n",
        "                   \n",
        "                            # print(label)\n",
        "\n",
        "                            # Finally, it is filled in the label_array\n",
        "                            for i in range(len(final_label[0])):\n",
        "                                label_array[i] = final_label[0][i]\n",
        "\n",
        "                            \n",
        "                            # concat_label = tf.concat([label, label_agent],axis=-1)\n",
        "\n",
        "                            if os.path.isfile(path_to_rgb_front_img): #and os.path.isfile(path_to_rgb_left_img) and os.path.isfile(path_to_rgb_right_img) and os.path.isfile(path_to_rgb_back_img) and os.path.isfile(path_to_depth_front_img) and os.path.isfile(path_to_depth_left_img) and os.path.isfile(path_to_depth_right_img) and os.path.isfile(path_to_depth_back_img):\n",
        "\n",
        "                                # read rgb front images\n",
        "                                front_rgb_image = read_rgb_image(path_to_rgb_front_img)\n",
        "                                # tf.print('path to rgb img',path_to_rgb_front_img)\n",
        "                                # # read rgb left images\n",
        "                                # left_rgb_image = read_rgb_image(path_to_rgb_left_img)\n",
        "\n",
        "                                # # read rgb right images\n",
        "                                # right_rgb_image = read_rgb_image(path_to_rgb_right_img)\n",
        "\n",
        "                                # # read rgb back images\n",
        "                                # back_rgb_image = read_rgb_image(path_to_rgb_back_img)\n",
        "                           \n",
        "                                # read depth front images\n",
        "                                front_depth_image = read_depth_image(path_to_depth_front_img)\n",
        "\n",
        "                                # # read depth |left images\n",
        "                                # left_depth_image = read_depth_image(path_to_depth_left_img)\n",
        "\n",
        "                                # # read depth right images\n",
        "                                # right_depth_image = read_depth_image(path_to_depth_right_img)\n",
        "\n",
        "                                # # read depth back images\n",
        "                                # back_depth_image = read_depth_image(path_to_depth_back_img)\n",
        "\n",
        "                                # Concatenate rgb and depth\n",
        "                                concatennate_rgb_depth_front = tf.concat([front_rgb_image,front_depth_image],axis=-1)\n",
        "                                # concatennate_rgb_depth_right = tf.concat([right_rgb_image,right_depth_image],axis=-1)\n",
        "                                # concatennate_rgb_depth_left = tf.concat([left_rgb_image,left_depth_image],axis=-1)\n",
        "                                # concatennate_rgb_depth_back = tf.concat([back_rgb_image,back_depth_image],axis=-1)\n",
        "\n",
        "                                normalized_front = normalize_on_rgb_depth(concatennate_rgb_depth_front)\n",
        "                                # normalized_left = normalize_on_rgb_depth(concatennate_rgb_depth_left)\n",
        "                                # normalized_right = normalize_on_rgb_depth(concatennate_rgb_depth_right)\n",
        "                                # normalized_back = normalize_on_rgb_depth(concatennate_rgb_depth_back)\n",
        "\n",
        "                                label_array_included_conf_val = add_confident_value(label_array)\n",
        "                                yield normalized_front, label_array_included_conf_val \n",
        "                            else:\n",
        "                                # print('frame of depth image is not same to rgb image')\n",
        "                                pass                   \n",
        "                        else:\n",
        "                            pass\n",
        "                            \n",
        "    return fetch_data_pair       "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iZIZOVvFMZi"
      },
      "source": [
        "# Load data validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEWtBcYuFLdr"
      },
      "source": [
        "def load_4_side_data_validation():\n",
        "    def fetch_data_pair():\n",
        "        for i,(town) in enumerate(town_listdir): # use enumerate for debugging to check the loops\n",
        "            town_path = os.path.join(TRAIN_SET_PATH,town)\n",
        "\n",
        "            csv_ego_path = os.path.join(town_path,'location.csv')\n",
        "\n",
        "            csv_agent_path = os.path.join(town_path,'agent_vehicle_information.csv')\n",
        "            # print(csv_ego_path,csv_agent_path)\n",
        "            csv_walker_path = os.path.join(town_path,'walkers_information.csv')\n",
        "            rgb_path = os.path.join(town_path,'rgb_image')\n",
        "\n",
        "            front_rgb_path = os.path.join(rgb_path,'front_image')\n",
        "            \n",
        "            round_1_4 = os.listdir(front_rgb_path)\n",
        "            round_1_4.sort()\n",
        "\n",
        "            # SET_TRAIN_PATH = [os.path.join(front_rgb_path,round_1_4[i]) for i in range(0,4)]\n",
        "            SET_DEV_PATH = os.path.join(front_rgb_path,round_1_4[4])\n",
        "\n",
        "            # print(os.path.isdir(SET_DEV_PATH))\n",
        "\n",
        "            # # Not normalize input data\n",
        "            # df = pd.read_csv(csv_ego_path)\n",
        "            # df_walker = pd.read_csv(csv_walker_path)\n",
        "\n",
        "\n",
        "\n",
        "            # ----------------------------------------------\n",
        "            #----------NORMALIZE INPUT DATA-----------------\n",
        "            #-----------------------------------------------\n",
        "            df = normalize_relative_coord(csv_ego_path,type_object='ego_vehicle_validation')\n",
        "            # df_agent = normalize_relative_coord(csv_agent_path, type_object='agent_vehicle_validation')\n",
        "            df_walker = normalize_relative_coord(csv_walker_path, type_object='walker_validation')\n",
        "\n",
        "\n",
        "            #------------------------------------------------\n",
        "            #--------FILL THE MISSING VALUES-----------------\n",
        "            #------------------------------------------------\n",
        "            df['relative_x'] = df['relative_x'].fillna(0)\n",
        "            df['relative_y'] = df['relative_y'].fillna(0)\n",
        "            df['throttle'] = df['throttle'].fillna(0)\n",
        "            df['steer'] = df['steer'].fillna(0)\n",
        "\n",
        "            df_walker['relative_x'] = df_walker['relative_x'].fillna(0)\n",
        "            df_walker['relative_y'] = df_walker['relative_y'].fillna(0)\n",
        "            #-------------------------------------------------\n",
        "            \n",
        "            # df_agent = pd.read_csv(csv_agent_path)\n",
        "            # print('in validation',os.path.isfile(csv_agent_path))\n",
        "            \n",
        "            for subdir,_,name_images in os.walk(SET_DEV_PATH):\n",
        "                name_images.sort()\n",
        "                # print(subdir)\n",
        "                '''\n",
        "                subdir: /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town02/rgb_image/front_image/round1\n",
        "                '''\n",
        "\n",
        "                # split the directory to get the depth directory\n",
        "                '''\n",
        "                head1: /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town02/rgb_image/front_image\n",
        "                split_round: round1 -> round 4\n",
        "                '''\n",
        "                head1, split_round = os.path.split(subdir)\n",
        "                \n",
        "                '''\n",
        "                rgb_img_path_split: /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town02/rgb_image\n",
        "                '''\n",
        "                rgb_img_path_split, front_img_split = os.path.split(head1)\n",
        "                path_to_town = head1[:97]\n",
        "\n",
        "                # print(path_to_town)\n",
        "                left_path = 'left_image'\n",
        "                right_path = 'right_image'\n",
        "                back_path = 'back_image'\n",
        "                front_path = 'front_image'\n",
        "\n",
        "                path_to_depth = os.path.join(path_to_town,'depth_image')\n",
        "                subdir_depth_front = os.path.join(path_to_depth, front_img_split)\n",
        "                subdir_depth_round = os.path.join(subdir_depth_front, split_round)\n",
        "                # print(subdir_depth_front)\n",
        "\n",
        "                for name in name_images:\n",
        "                    # print(name)\n",
        "                    path_to_rgb_front_img = os.path.join(subdir,name)\n",
        "                    # print(subdir)\n",
        "\n",
        "                    # # path to rgb left image\n",
        "                    # path_to_rgb_left = os.path.join(rgb_img_path_split,left_path)\n",
        "                    # path_to_rgb_left_img = os.path.join(path_to_rgb_left,split_round)\n",
        "                    # path_to_rgb_left_img = os.path.join(path_to_rgb_left_img,name)\n",
        "                    # # print(os.path.isfile(path_to_rgb_left_img))\n",
        "                    # # print(path_to_rgb_left_img)\n",
        "\n",
        "                    # # path to rgb right image\n",
        "                    # path_to_rgb_right = os.path.join(rgb_img_path_split,right_path)\n",
        "                    # path_to_rgb_right_img = os.path.join(path_to_rgb_right,split_round)\n",
        "                    # path_to_rgb_right_img = os.path.join(path_to_rgb_right_img,name)\n",
        "                    # # print(os.path.isfile(path_to_rgb_right_img))\n",
        "\n",
        "                    # # path to rgb back image\n",
        "                    # path_to_rgb_back = os.path.join(rgb_img_path_split,back_path)\n",
        "                    # path_to_rgb_back_img = os.path.join(path_to_rgb_back,split_round)\n",
        "                    # path_to_rgb_back_img = os.path.join(path_to_rgb_back_img,name)\n",
        "                    # # print(os.path.isfile(path_to_rgb_back_img))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "                    # path to depth front image\n",
        "                    path_to_depth_front = os.path.join(subdir_depth_round,name)\n",
        "                    \n",
        "\n",
        "                    #-----------------------------------------------------------------------------\n",
        "                    #--------------------------- split the depth path ----------------------------\n",
        "                    #-----------------------------------------------------------------------------\n",
        "                    path_to_depth_split = path_to_depth_front[:109]\n",
        "\n",
        "                    \n",
        "                    \n",
        "                    path_from_depth_round_to_img = path_to_depth_front[122:] # round4/round4_024192.png\n",
        "                    test = path_to_depth_front[122:142]\n",
        "                    path_from_depth_round_to_img = test + '.png'\n",
        "                    # print(path_from_depth_round_to_img)\n",
        "\n",
        "                    # path to depth front image\n",
        "                    path_to_depth_front = os.path.join(path_to_depth_split,front_path)\n",
        "                    path_to_depth_front_img = os.path.join(path_to_depth_front,path_from_depth_round_to_img)\n",
        "                    # print(os.path.isfile(path_to_depth_front_img))\n",
        "                    # print(path_to_depth_front_img)\n",
        "\n",
        "                    # # path to depth right image\n",
        "                    # path_to_depth_right = os.path.join(path_to_depth_split,right_path)\n",
        "                    # path_to_depth_right_img = os.path.join(path_to_depth_right,path_from_depth_round_to_img)\n",
        "                    # # print(path_to_depth_right_img)\n",
        "                    # # print('path to depth right is ',os.path.isfile(path_to_depth_right_img))\n",
        "\n",
        "                    # # path to depth left image\n",
        "                    # path_to_depth_left = os.path.join(path_to_depth_split,left_path)\n",
        "                    # path_to_depth_left_img = os.path.join(path_to_depth_left,path_from_depth_round_to_img)\n",
        "                    # # print(os.path.isfile(path_to_depth_left_img))\n",
        "                    # # print(path_to_depth_left_img)\n",
        "\n",
        "                    # # path to depth back image\n",
        "                    # path_to_depth_back = os.path.join(path_to_depth_split,back_path)\n",
        "                    # path_to_depth_back_img = os.path.join(path_to_depth_back,path_from_depth_round_to_img)\n",
        "                    # # print(os.path.isfile(path_to_depth_back_img))\n",
        "\n",
        "                    # print(f'path to rgb front is {os.path.isfile(path_to_rgb_front_img)}\\n\n",
        "                    #         path to rgb left is {os.path.isfile(path_to_rgb_left_img)}\\n\n",
        "                    #         path to rgb right is {os.path.isfile(path_to_rgb_right_img)}\\n\n",
        "                    #         path to rgb back is {os.path.isfile(path_to_rgb_back_img)}\\n\n",
        "                    #         path to depth front is ')\n",
        "\n",
        "                    frame,round = get_dir_name(path_to_rgb_front_img)\n",
        "                    try:\n",
        "                        frame = int(frame)\n",
        "                    except:\n",
        "                        # print('Can not convert', str ,\"to int\")\n",
        "                        pass\n",
        "\n",
        "                    \n",
        "                    #------------------------------\n",
        "                    #------create nan array--------\n",
        "                    #------------------------------\n",
        "                    label_array = np.empty((21,))\n",
        "                    label_array[:] = np.NaN\n",
        "                    label_array = np.array(label_array)\n",
        "                    #-------------------------------\n",
        "\n",
        "                    label = []\n",
        "\n",
        "                    for index,row in df.iterrows():\n",
        "                        if row['frame'] == frame: \n",
        "                            cls = 1                  \n",
        "                            relative_x = row['relative_x']\n",
        "                            relative_y = row['relative_y']\n",
        "                            label.append([float(relative_x), float(relative_y),int(cls)])\n",
        "                    \n",
        "\n",
        "                    label_walker = []\n",
        "                    for index, row in df_walker.iterrows():\n",
        "                        if row['frame'] == frame:\n",
        "                            cls = 0\n",
        "                            relative_x = row['relative_x']\n",
        "                            relative_y = row['relative_y']\n",
        "                            label_walker.append([float(relative_x),float(relative_y),int(cls)])\n",
        "\n",
        "\n",
        "                \n",
        "\n",
        "                    label = np.array(label)\n",
        "                    label_walker = np.array(label_walker)\n",
        "\n",
        "                    # if label.shape[0] == 1 and label_agent.shape[0] == 1:\n",
        "                    if label.shape[0] != 0 or label_walker.shape[0] != 0:\n",
        "\n",
        "                        if label.shape[0] != 1 and len(label) != 0:\n",
        "                                # print(label)\n",
        "                                label = tf.reshape(label,shape=(1,label.shape[0]*label.shape[1]))\n",
        "                        if label_walker.shape[0] != 1 and len(label_walker) != 0:\n",
        "                            # print('label walker',label_walker)\n",
        "                            label_walker = tf.reshape(label_walker,shape=(1,label_walker.shape[0]*label_walker.shape[1]))\n",
        "\n",
        "                        if label.shape[0] == 1 and label_walker.shape[0] == 1:\n",
        "                            final_label = tf.concat([label, label_walker],axis=1)\n",
        "                            # print(final_label)\n",
        "                        elif label.shape[0] == 1 and label_walker.shape[0] == 0:\n",
        "                            final_label = label\n",
        "                        elif label.shape[0] == 0 and label_walker.shape[0] == 1:\n",
        "                            final_label = label_walker\n",
        "\n",
        "                        label_h, label_w = final_label.shape\n",
        "                        # label_walker_h, label_walker_w = label_walker.shape\n",
        "                        # print(label_h,label_w)\n",
        "                        # final_label = np.array([final_label])\n",
        "                        if label_h != 1:\n",
        "                            final_label = tf.reshape(final_label,shape=(label_h * label_w))\n",
        "                            # label = label.numpy()\n",
        "                            final_label = np.array([final_label])\n",
        "\n",
        "                \n",
        "                        # print(label)\n",
        "                        for i in range(len(final_label[0])):\n",
        "                            label_array[i] = final_label[0][i]\n",
        "\n",
        "                        if os.path.isfile(path_to_rgb_front_img): # and os.path.isfile(path_to_rgb_left_img) and os.path.isfile(path_to_rgb_right_img) and os.path.isfile(path_to_rgb_back_img) and os.path.isfile(path_to_depth_front_img) and os.path.isfile(path_to_depth_left_img) and os.path.isfile(path_to_depth_right_img) and os.path.isfile(path_to_depth_back_img):\n",
        "\n",
        "                            # read rgb front images\n",
        "                            front_rgb_image = read_rgb_image(path_to_rgb_front_img)\n",
        "\n",
        "                            # # read rgb left images\n",
        "                            # left_rgb_image = read_rgb_image(path_to_rgb_left_img)\n",
        "                            # # print(path_to_rgb_left_img)\n",
        "\n",
        "                            # # read rgb right images\n",
        "                            # right_rgb_image = read_rgb_image(path_to_rgb_right_img)\n",
        "\n",
        "                            # # read rgb back images\n",
        "                            # back_rgb_image = read_rgb_image(path_to_rgb_back_img)\n",
        "                         \n",
        "                            # read depth front images\n",
        "                            front_depth_image = read_depth_image(path_to_depth_front_img)\n",
        "\n",
        "                            # # read depth left images\n",
        "                            # left_depth_image = read_depth_image(path_to_depth_left_img)\n",
        "\n",
        "                            # # read depth right images\n",
        "                            # right_depth_image = read_depth_image(path_to_depth_right_img)\n",
        "\n",
        "                            # # read depth back images\n",
        "                            # back_depth_image = read_depth_image(path_to_depth_back_img)\n",
        "\n",
        "                            # Concatenate rgb and depth\n",
        "                            concatennate_rgb_depth_front = tf.concat([front_rgb_image,front_depth_image],axis=-1)\n",
        "                            # concatennate_rgb_depth_right = tf.concat([right_rgb_image,right_depth_image],axis=-1)\n",
        "                            # concatennate_rgb_depth_left = tf.concat([left_rgb_image,left_depth_image],axis=-1)\n",
        "                            # concatennate_rgb_depth_back = tf.concat([back_rgb_image,back_depth_image],axis=-1)\n",
        "\n",
        "                            normalized_front = normalize_on_rgb_depth(concatennate_rgb_depth_front)\n",
        "                            # normalized_left = normalize_on_rgb_depth(concatennate_rgb_depth_left)\n",
        "                            # normalized_right = normalize_on_rgb_depth(concatennate_rgb_depth_right)\n",
        "                            # normalized_back = normalize_on_rgb_depth(concatennate_rgb_depth_back)\n",
        "\n",
        "                            label_array_included_conf_val = add_confident_value(label_array)\n",
        "                            yield normalized_front, label_array_included_conf_val\n",
        "                        else:\n",
        "                            pass\n",
        "                            # print('frame of depth image is not same to rgb image') \n",
        "                            # print(path_to_rgb_front_img)                        \n",
        "                    else:\n",
        "                        pass\n",
        "                        # print('validation label is Null or has shape (2,2)')\n",
        "    return fetch_data_pair"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GZ63HegFwjU"
      },
      "source": [
        "# Test loading data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84s1UqHkFW9N"
      },
      "source": [
        "IMG_SHAPE = (50, 100, 4)\n",
        "LABEL_SHAPE = (28,)\n",
        "OUTPUT = 28 # same as label shape\n",
        "train_set = tf.data.Dataset.from_generator(load_4_side_data_train(), \n",
        "                                           output_types=(tf.float32, tf.float64),\n",
        "                                           output_shapes=(tf.TensorShape(IMG_SHAPE), tf.TensorShape(LABEL_SHAPE)\n",
        "                                            ) )\n",
        "\n",
        "validation_set = tf.data.Dataset.from_generator(load_4_side_data_validation(), output_types=(tf.float32, tf.float64),\n",
        "                                         output_shapes=(tf.TensorShape(IMG_SHAPE), tf.TensorShape(LABEL_SHAPE)\n",
        "                                         ))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_IHoEDtF2ss",
        "outputId": "5be727fd-8d8a-42dc-900b-e620dcc170e1"
      },
      "source": [
        "for i, (feature,labels) in enumerate(train_set.take(60)):\n",
        "    # print(i)\n",
        "    if i % 10 ==0:\n",
        "        # print(i)\n",
        "        print(feature[1])\n",
        "        # plot_images(feature[0])\n",
        "        print(labels)\n",
        "        break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[ 0.24531221  0.32773122  0.5659261   0.        ]\n",
            " [ 0.21106271  0.3102242   0.58335525  0.        ]\n",
            " [ 0.1768132   0.29271722  0.58335525  0.        ]\n",
            " [ 0.15968846  0.2752102   0.58335525  0.        ]\n",
            " [ 0.09118944  0.22268921  0.54849684  0.        ]\n",
            " [ 0.03981505  0.20518221  0.54849684  0.        ]\n",
            " [ 0.07406469  0.20518221  0.54849684  0.        ]\n",
            " [ 0.1425637   0.2577032   0.5659261   0.        ]\n",
            " [ 0.19393796  0.3102242   0.61821365  0.        ]\n",
            " [ 0.19393796  0.3102242   0.61821365  0.        ]\n",
            " [ 0.21106271  0.32773122  0.653072    0.        ]\n",
            " [ 0.22818747  0.3452382   0.653072    0.        ]\n",
            " [ 0.26243696  0.3802522   0.6879304   0.        ]\n",
            " [ 0.24531221  0.36274523  0.67050123  0.        ]\n",
            " [ 0.26243696  0.3802522   0.6879304   0.        ]\n",
            " [ 0.24531221  0.36274523  0.67050123  0.        ]\n",
            " [ 0.26243696  0.3802522   0.6879304   0.        ]\n",
            " [ 0.31381124  0.43277323  0.7227888   0.        ]\n",
            " [ 0.34806073  0.45028022  0.740218    0.        ]\n",
            " [ 0.4336845   0.5378152   0.7750764   0.        ]\n",
            " [ 0.53643304  0.59033626  0.7750764   0.        ]\n",
            " [ 0.53643304  0.57282925  0.7750764   0.        ]\n",
            " [ 0.53643304  0.57282925  0.79250556  0.        ]\n",
            " [ 0.53643304  0.59033626  0.80993474  0.        ]\n",
            " [ 0.55355775  0.59033626  0.80993474  0.        ]\n",
            " [ 0.55355775  0.6078432   0.80993474  0.        ]\n",
            " [ 0.53643304  0.6078432   0.80993474  0.        ]\n",
            " [ 0.53643304  0.6078432   0.80993474  0.        ]\n",
            " [ 0.5706825   0.62535024  0.82736397  0.        ]\n",
            " [ 0.5706825   0.64285725  0.84479314  0.        ]\n",
            " [ 0.5706825   0.64285725  0.84479314  0.        ]\n",
            " [ 0.5706825   0.62535024  0.84479314  0.        ]\n",
            " [ 0.5878073   0.62535024  0.84479314  0.        ]\n",
            " [ 0.5878073   0.64285725  0.84479314  0.        ]\n",
            " [ 0.5878073   0.62535024  0.84479314  0.        ]\n",
            " [ 0.5878073   0.64285725  0.8622223   0.        ]\n",
            " [ 0.5706825   0.64285725  0.84479314  0.        ]\n",
            " [ 0.5706825   0.64285725  0.84479314  0.        ]\n",
            " [ 0.5878073   0.62535024  0.84479314  0.        ]\n",
            " [ 0.5878073   0.62535024  0.84479314  0.        ]\n",
            " [ 0.5878073   0.62535024  0.84479314  0.        ]\n",
            " [ 0.5706825   0.62535024  0.84479314  0.        ]\n",
            " [ 0.5706825   0.64285725  0.84479314  0.        ]\n",
            " [ 0.55355775  0.62535024  0.82736397  0.        ]\n",
            " [ 0.55355775  0.62535024  0.84479314  0.        ]\n",
            " [ 0.55355775  0.6078432   0.84479314  0.        ]\n",
            " [ 0.53643304  0.6078432   0.80993474  0.        ]\n",
            " [ 0.51930827  0.59033626  0.80993474  0.        ]\n",
            " [ 0.53643304  0.6078432   0.82736397  0.        ]\n",
            " [ 0.53643304  0.6078432   0.84479314  0.        ]\n",
            " [ 0.55355775  0.57282925  0.7750764   0.        ]\n",
            " [ 0.5021835   0.57282925  0.7750764   0.        ]\n",
            " [ 0.5021835   0.57282925  0.7750764   0.        ]\n",
            " [ 0.5021835   0.57282925  0.80993474  0.        ]\n",
            " [ 0.5021835   0.57282925  0.79250556  0.        ]\n",
            " [ 0.48505875  0.55532223  0.79250556  0.        ]\n",
            " [ 0.48505875  0.55532223  0.7750764   0.        ]\n",
            " [ 0.41655976  0.5378152   0.75764716  0.        ]\n",
            " [ 0.39943498  0.50280124  0.740218    0.        ]\n",
            " [ 0.39943498  0.48529422  0.7227888   0.        ]\n",
            " [ 0.3651855   0.46778724  0.740218    0.        ]\n",
            " [ 0.31381124  0.41526622  0.7227888   0.        ]\n",
            " [ 0.26243696  0.3802522   0.6879304   0.        ]\n",
            " [ 0.21106271  0.3452382   0.67050123  0.        ]\n",
            " [ 0.22818747  0.3452382   0.67050123  0.        ]\n",
            " [ 0.19393796  0.3102242   0.6356428   0.        ]\n",
            " [ 0.1768132   0.29271722  0.61821365  0.        ]\n",
            " [ 0.1768132   0.29271722  0.61821365  0.        ]\n",
            " [ 0.1768132   0.29271722  0.6007844   0.        ]\n",
            " [ 0.21106271  0.32773122  0.6356428   0.        ]\n",
            " [ 0.31381124  0.45028022  0.67050123  1.5899857 ]\n",
            " [ 0.1768132   0.3102242   0.5659261   1.590202  ]\n",
            " [ 0.07406469  0.18767507  0.4439217   1.5904187 ]\n",
            " [ 0.55355775  0.57282925  0.740218    1.5817708 ]\n",
            " [ 0.29668647  0.2752102   0.4439217   1.5661343 ]\n",
            " [ 1.1700488   1.0980393   1.106231    1.5485084 ]\n",
            " [ 0.1425637   0.13515407  0.23477125  1.5481122 ]\n",
            " [ 0.7248053   0.6603643   0.6879304   1.5033635 ]\n",
            " [ 0.03981505  0.06512606  0.14762528  1.4885144 ]\n",
            " [-0.02868396  0.01260505  0.14762528  1.474138  ]\n",
            " [-1.0390444  -0.95028013 -0.688976    1.4721972 ]\n",
            " [-1.210292   -1.1253501  -0.88069713  1.4724162 ]\n",
            " [ 0.41655976  0.36274523  0.4264925   1.4694412 ]\n",
            " [ 0.9816766   0.8704483   0.8622223   1.4568976 ]\n",
            " [ 0.8446786   0.90546227  1.106231    1.4457867 ]\n",
            " [ 1.2042984   1.2030813   1.1410894   1.4330662 ]\n",
            " [-0.4568028  -0.407563   -0.23581696  1.4299995 ]\n",
            " [-0.8164226  -0.74019605 -0.28810453  1.4302225 ]\n",
            " [ 0.26243696  0.3802522   0.7750764   1.4304388 ]\n",
            " [-0.14855723 -0.02240895  0.06047932  1.4306549 ]\n",
            " [ 0.19393796  0.22268921  0.3219172   1.429886  ]\n",
            " [ 0.15968846  0.17016807  0.26962963  1.429985  ]\n",
            " [ 0.10831419  0.13515407  0.21734206  1.4302014 ]\n",
            " [-0.04580871 -0.03991595  0.07790852  1.4304177 ]\n",
            " [-0.19993149 -0.19747896 -0.09638342  1.4259228 ]\n",
            " [-0.11430772 -0.14495796 -0.1138126   1.3875966 ]\n",
            " [ 0.9474271   0.8529413   0.79250556  1.378405  ]\n",
            " [ 0.41655976  0.3452382   0.37420478  1.3734237 ]\n",
            " [-0.04580871 -0.03991595  0.06047932  1.3735003 ]\n",
            " [-0.16568197 -0.17997196 -0.00923745  1.3737204 ]], shape=(100, 4), dtype=float32)\n",
            "tf.Tensor(\n",
            "[-13.01042 -21.48643   0.        1.            nan       nan       nan\n",
            "   0.            nan       nan       nan   0.            nan       nan\n",
            "       nan   0.            nan       nan       nan   0.            nan\n",
            "       nan       nan   0.            nan       nan       nan   0.     ], shape=(28,), dtype=float64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CnB-Vj7Lc53"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "xrhEBhU5F8RR",
        "outputId": "6693f634-78ec-46b4-a4d3-322db047ef1d"
      },
      "source": [
        "plt.imshow(cv2.imread('/content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town01/rgb_image/front_image/round1/round1_006476.jpg'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f049a10cb90>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Ee2nkl2LesV"
      },
      "source": [
        "# Load resnet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqLB52VIRcTM"
      },
      "source": [
        "# Set hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hm4IEbQwRfNW"
      },
      "source": [
        "learning_rate = 0.001\n",
        "weight_decay = 0.0001\n",
        "batch_size = 256\n",
        "num_epochs = 100\n",
        "image_size = 12  # We'll resize input images to this size\n",
        "patch_size = 6  # Size of the patches to be extract from the input images\n",
        "num_patches = (image_size // patch_size) ** 2\n",
        "projection_dim = 64\n",
        "num_heads = 4\n",
        "transformer_units = [\n",
        "    projection_dim * 2,\n",
        "    projection_dim,\n",
        "]  # Size of the transformer layers\n",
        "transformer_layers = 8\n",
        "mlp_head_units = [2048, 1024]  # Size of the dense layers of the final classifier"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0a85dhpIZ8h"
      },
      "source": [
        "#identity_block\n",
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Add, MaxPooling2D, ZeroPadding2D, AveragePooling2D, Flatten\n",
        "from tensorflow.keras.initializers import glorot_uniform\n",
        "def identity_block(X, f, filters, stage, block, time):\n",
        "    \"\"\"\n",
        "    Implementation of the identity block as defined in Figure 3\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
        "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
        "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
        "    stage -- integer, used to name the layers, depending on their position in the network\n",
        "    block -- string/character, used to name the layers, depending on their position in the network\n",
        "    \n",
        "    Returns:\n",
        "    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)\n",
        "    \"\"\"\n",
        "    \n",
        "    # defining name basis\n",
        "    conv_name_base = 'res' + str(stage) + block + '_branch_' + f'{time}'\n",
        "    bn_name_base = 'bn' + str(stage) + block + '_branch_' + f'{time}'\n",
        "    \n",
        "    # Retrieve Filters\n",
        "    F1, F2, F3 = filters\n",
        "    \n",
        "    # Save the input value. You'll need this later to add back to the main path. \n",
        "    X_shortcut = X\n",
        "    \n",
        "    # First component of main path\n",
        "    X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
        "    X = Activation('relu')(X)\n",
        "    \n",
        "    # Second component of main path\n",
        "    X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
        "    X = Activation('relu')(X)\n",
        "\n",
        "    # Third component of main path\n",
        "    X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
        "\n",
        "    # Final step: Add shortcut value to main path, and pass it through a RELU activation\n",
        "    X = Add()([X,X_shortcut])\n",
        "    X = Activation('relu')(X)\n",
        "    \n",
        "    return X\n",
        "\n",
        "#convolutional_block\n",
        "\n",
        "def convolutional_block(X, f, filters, stage, block, s = 2):\n",
        "    \"\"\"\n",
        "    Implementation of the convolutional block as defined in Figure 4\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
        "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
        "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
        "    stage -- integer, used to name the layers, depending on their position in the network\n",
        "    block -- string/character, used to name the layers, depending on their position in the network\n",
        "    s -- Integer, specifying the stride to be used\n",
        "    \n",
        "    Returns:\n",
        "    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)\n",
        "    \"\"\"\n",
        "    \n",
        "    # defining name basis\n",
        "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
        "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
        "    \n",
        "    # Retrieve Filters\n",
        "    F1, F2, F3 = filters\n",
        "    \n",
        "    # Save the input value\n",
        "    X_shortcut = X\n",
        "\n",
        "\n",
        "    ##### MAIN PATH #####\n",
        "    # First component of main path \n",
        "    X = Conv2D(F1, (1, 1), strides = (s,s),padding = 'valid', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3)(X)\n",
        "    X = Activation('relu')(X)\n",
        "\n",
        "    # Second component of main path\n",
        "    X = Conv2D(F2, (f, f), strides = (1,1), padding = 'same', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3)(X)\n",
        "    X = Activation('relu')(X)\n",
        "\n",
        "    # Third component of main path\n",
        "    X = Conv2D(F3, (1, 1), strides = (1,1), padding = 'valid', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3)(X)\n",
        "\n",
        "    ##### SHORTCUT PATH ####\n",
        "    X_shortcut = Conv2D(F3, (1, 1), strides = (s,s), padding = 'valid', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)\n",
        "    X_shortcut = BatchNormalization(axis = 3)(X_shortcut)\n",
        "\n",
        "    # Final step: Add shortcut value to main path, and pass it through a RELU activation\n",
        "    X = Add()([X,X_shortcut])\n",
        "    X = Activation('relu')(X)\n",
        "    \n",
        "    return X\n",
        "# input_shape = (32, 32, 3)\n",
        "def ResNet50(time, input_shape, classes = 14):\n",
        "    \"\"\"\n",
        "    Implementation of the popular ResNet50 the following architecture:\n",
        "    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3\n",
        "    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER\n",
        "\n",
        "    Arguments:\n",
        "    input_shape -- shape of the images of the dataset\n",
        "    classes -- integer, number of classes\n",
        "\n",
        "    Returns:\n",
        "    model -- a Model() instance in Keras\n",
        "    \"\"\"\n",
        "    \n",
        "    # Define the input as a tensor with shape input_shape\n",
        "    # X_input = Input(input_shape)\n",
        "\n",
        "    \n",
        "    # Zero-Padding\n",
        "    X = ZeroPadding2D((3, 3))(input_shape)\n",
        "    \n",
        "    # Stage 1\n",
        "    X = Conv2D(32, (7, 7), strides = (1, 1), name = f'conv_{time}', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = f'bn_conv_{time}')(X)\n",
        "    X = Activation('relu')(X)\n",
        "    X = MaxPooling2D((3, 3))(X)\n",
        "\n",
        "    # Stage 2\n",
        "    X = convolutional_block(X, f = 3, filters = [32, 32, 128], stage = 2, block='a', s = 1)\n",
        "    X = identity_block(X, 3, [32, 32, 128], stage=2, block='b', time = time)\n",
        "    X = identity_block(X, 3, [32, 32, 128], stage=2, block='c', time = time)\n",
        "\n",
        "    # Stage 3\n",
        "    X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 3, block='a', s = 2)\n",
        "    X = identity_block(X, 3, [64, 64, 256], stage=3, block='b', time = time)\n",
        "    X = identity_block(X, 3, [64, 64, 256], stage=3, block='c', time = time)\n",
        "    # X = identity_block(X, 3, [64, 64, 256], stage=3, block='d', time = time)\n",
        "\n",
        "    # Stage 4 \n",
        "    X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 4, block='a', s = 2)\n",
        "    X = identity_block(X, 3, [128, 128, 512], stage=4, block='b', time = time)\n",
        "    X = identity_block(X, 3, [128, 128, 512], stage=4, block='c', time = time)\n",
        "    # X = identity_block(X, 3, [128, 128, 512], stage=4, block='d', time = time)\n",
        "    # X = identity_block(X, 3, [128, 128, 512], stage=4, block='e', time = time)\n",
        "    # X = identity_block(X, 3, [128, 128, 512], stage=4, block='f', time = time)\n",
        "\n",
        "    # Stage 5 \n",
        "    X = convolutional_block(X, f = 3, filters = [256,256, 1024], stage = 5, block='a', s = 2)\n",
        "    X = identity_block(X, 3, [256,256, 1024], stage=5, block='b', time = time)\n",
        "    X = identity_block(X, 3, [256,256, 1024], stage=5, block='c', time = time)\n",
        "\n",
        "    # AVGPOOL\n",
        "    X = AveragePooling2D(pool_size=(2,2), name=f'avg_pool_{time}')(X)\n",
        "    \n",
        "\n",
        "    # output layer\n",
        "    X = Dense(512)(X)\n",
        "    X = layers.Dropout(0.5)(X)\n",
        "    X = Dense(128)(X)\n",
        "    X = layers.Dropout(0.5)(X)\n",
        "    X = Dense(64)(X)\n",
        "    X = layers.Dropout(0.5)(X)\n",
        "    X = Dense(32)(X) # shape = (none, 12, 17, 32)\n",
        "    # X = Flatten()(X)\n",
        "    \n",
        "    # X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    \n",
        "    \n",
        "    # # Create model\n",
        "    # model = Model(inputs = X_input, outputs = X, name='ResNet50')\n",
        "\n",
        "    return X"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrqYCuLI-Kjr"
      },
      "source": [
        "# Load ViT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pI6-ViDpMSrI"
      },
      "source": [
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Add, MaxPooling2D, ZeroPadding2D, AveragePooling2D, Flatten\n",
        "from tensorflow.keras.initializers import glorot_uniform\n",
        "\n",
        "# TODO: add the config layer to each class, so they can be saved\n",
        "# --> done\n",
        "def mlp(x, hidden_units, dropout_rate):\n",
        "    for units in hidden_units:\n",
        "        x = layers.Dense(units, activation=tf.nn.gelu)(x)\n",
        "        x = layers.Dropout(dropout_rate)(x)\n",
        "    return x\n",
        "\n",
        "# Problem when loading the model: __init__() missing 1 required positional argument projection_dim\n",
        "#--------------------------------------------------------\n",
        "#---------UNCOMMENT THIS FOR TRAINING--------------------\n",
        "#--------------------------------------------------------\n",
        "class PatchEncoder(layers.Layer):\n",
        "    def __init__(self, num_patches, projection_dim, **kwargs):\n",
        "        super(PatchEncoder, self).__init__()\n",
        "        self.num_patches = num_patches\n",
        "        self.projection_dim = projection_dim\n",
        "        self.projection = layers.Dense(units=projection_dim)\n",
        "        self.position_embedding = layers.Embedding(\n",
        "            input_dim=num_patches, output_dim=projection_dim\n",
        "        )\n",
        "\n",
        "    def call(self, patch):\n",
        "        positions = tf.range(start=0, limit=self.num_patches, delta=1)\n",
        "        encoded = self.projection(patch) + self.position_embedding(positions)\n",
        "        # a = self.prj_dim\n",
        "        return encoded\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config().copy()\n",
        "        config.update({\n",
        "            'num_patches': self.num_patches,\n",
        "            'projection': self.projection,\n",
        "            'position_embedding': self.position_embedding,\n",
        "            'projection_dim': self.projection_dim   \n",
        "            })\n",
        "        return config\n",
        "        \n",
        "class Patches(layers.Layer):\n",
        "    def __init__(self, patch_size,**kwargs):\n",
        "        super(Patches, self).__init__()\n",
        "        self.patch_size = patch_size\n",
        "\n",
        "    def call(self, images):\n",
        "        images = tf.image.resize(images,size=(image_size,image_size))\n",
        "        batch_size = tf.shape(images)[0]\n",
        "        patches = tf.image.extract_patches(\n",
        "            images=images,\n",
        "            sizes=[1, self.patch_size, self.patch_size, 1],\n",
        "            strides=[1, self.patch_size, self.patch_size, 1],\n",
        "            rates=[1, 1, 1, 1],\n",
        "            padding=\"VALID\",\n",
        "        )\n",
        "        patch_dims = patches.shape[-1]\n",
        "        patches = tf.reshape(patches, [batch_size, -1, patch_dims])\n",
        "        return patches\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config().copy()\n",
        "        config.update({\n",
        "            'patch_size': self.patch_size,              \n",
        "            })\n",
        "        return config\n",
        "\n",
        "def ViT_feature(input, i):\n",
        "    # Create patches.\n",
        "    patches = Patches(patch_size)(input)\n",
        "    # Encode patches.\n",
        "    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)\n",
        "\n",
        "    # Create multiple layers of the Transformer block.\n",
        "    \n",
        "    for _ in range(transformer_layers):\n",
        "        # Layer normalization 1.\n",
        "        # append_inputs.append(input)\n",
        "        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
        "        # Create a multi-head attention layer.\n",
        "        attention_output = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=projection_dim, dropout=0.2\n",
        "        )(x1, x1)\n",
        "        # Skip connection 1.\n",
        "        x2 = layers.Add()([attention_output, encoded_patches])\n",
        "        # Layer normalization 2.\n",
        "        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n",
        "        # MLP.\n",
        "        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.2)\n",
        "        # Skip connection 2.\n",
        "        encoded_patches = layers.Add()([x3, x2])\n",
        "\n",
        "    # Create a [batch_size, projection_dim] tensor.\n",
        "    representation = layers.LayerNormalization(epsilon=1e-6,name=f'feature_layer_{i}')(encoded_patches) \n",
        "    # print(representation.shape) \n",
        "    return representation"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFCq1IH1MTrG"
      },
      "source": [
        "# Create model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAEKqr7oLgzB"
      },
      "source": [
        "def create_resnet_on_4_sides():\n",
        "    # shape = (None,50, 100, 4)\n",
        "    inputs = layers.Input(shape=IMG_SHAPE)\n",
        "\n",
        "    branch_outputs = []\n",
        "\n",
        "\n",
        "    resnet_feature = ResNet50(input_shape=inputs,time=1)\n",
        "    to_vit = ViT_feature(resnet_feature, '1') \n",
        "    # branch_outputs.append(to_vit)\n",
        "\n",
        "\n",
        "    # stack_output = Concatenate()(branch_outputs) # shape = (None, 4, 256) which is 4 patches and 256 features\n",
        "    # stack_output = Add()(branch_outputs)\n",
        "    # stack_input = Concatenate()(branch_input)\n",
        "    # # print(stack_output.shape)\n",
        "    # dropout_output = layers.Dropout(0.5)(stack_output)\n",
        "    stack_output = layers.Flatten()(to_vit)\n",
        "    stack_output = layers.Dense(512)(stack_output)\n",
        "    stack_output = layers.Dropout(0.2)(stack_output)\n",
        "    stack_output = layers.Dense(128)(stack_output)\n",
        "    stack_output = layers.Dense(64)(stack_output)\n",
        "    stack_output = layers.Dense(OUTPUT)(stack_output)\n",
        "    # stack_output = layers.Reshape(target_shape=(7,4))(stack_output)\n",
        "    model = tf.keras.Model(inputs=inputs, outputs=stack_output)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS4EcC-cM1H1"
      },
      "source": [
        "resnet_model = create_resnet_on_4_sides()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGHs5_JU-Ztb"
      },
      "source": [
        "inculded_vit = create_resnet_on_4_sides()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxdUxogsNiP4"
      },
      "source": [
        "# Test training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVAH30BaNysP"
      },
      "source": [
        "x_training_test = tf.random.uniform(shape=(1200,50,100,4))\n",
        "y_training_test = tf.random.uniform(shape = (1200,28,))\n",
        "# test_training_dataset = tf.data.Dataset.from_tensor_slices([x_training_test,y_training_test])\n",
        "\n",
        "x_validation_test = tf.random.uniform(shape=(200,50,100,4))\n",
        "y_validation_test = tf.random.uniform(shape = (200,28,))\n",
        "# test_validation_dataset = tf.data.Dataset.from_tensor_slices([x_validation_test,y_validation_test])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WoSbkRejAmoj"
      },
      "source": [
        "# Visualize result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddwiUy1FAovy"
      },
      "source": [
        "def visualize_mse_loss(history_):\n",
        "    history = history_\n",
        "    #  \"custome mse\"\n",
        "    plt.plot(history.history['custom_mse_hungarian_py_func'])\n",
        "    plt.plot(history.history['val_custom_mse_hungarian_py_func'])\n",
        "    plt.title('model custome mse')\n",
        "    plt.ylabel('custome mse')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'validation'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "    # \"Loss\"\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('model loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'validation'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "def visualize_prec_recall(save_precision_075,save_precision_05,save_recall_075,save_recall_05):\n",
        "    plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "    figure, axis = plt.subplots(1, 2,figsize=(10,10))\n",
        "\n",
        "    X = np.arange(len(save_precision_075))\n",
        "    # Plotting both the curves simultaneously\n",
        "    # axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "    axis[0].plot(X, save_precision_075, color='r', label='precision 0.75')\n",
        "    axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "    axis[0].set_xlabel(\"epochs\")\n",
        "    axis[0].legend()\n",
        "    axis[0].set_title(\"Precision at threshold 0.5 and 0.75\")\n",
        "\n",
        "    # axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "    axis[1].plot(X, save_recall_05, color='b', label='recall 0.5')\n",
        "    axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "    axis[1].set_xlabel(\"epochs\")\n",
        "    axis[1].legend()\n",
        "    axis[1].set_title(\"Recall at threshold 0.5 and 0.75\")\n",
        "\n",
        "def visualize_3_parts_hungarian_loss(reduce_mean_cls_loss_store, regression_loss_store,reduce_confident_loss_store):\n",
        "    plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "    figure, axis = plt.subplots(1, 3,figsize=(10,10))\n",
        "\n",
        "    step = 21\n",
        "    mean_regression_store = []\n",
        "    mean_cls_store = []\n",
        "    mean_conf_store = []\n",
        "    for i in range(0,len(reduce_mean_cls_loss_store),step):\n",
        "        mean_regression = tf.reduce_mean(regression_loss_store[i:i+step])\n",
        "        mean_cls = tf.reduce_mean(reduce_mean_cls_loss_store[i:i+step])\n",
        "        mean_conf = tf.reduce_mean(reduce_confident_loss_store[i:i+step])\n",
        "\n",
        "        mean_regression_store.append(mean_regression)\n",
        "        mean_cls_store.append(mean_cls)\n",
        "        mean_conf_store.append(mean_conf)\n",
        "        # print(mean_regression_store)\n",
        "\n",
        "        X = np.arange(len(mean_regression_store))\n",
        "        # Plotting both the curves simultaneously\n",
        "        # axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "        axis[0].plot(X, mean_cls_store, color='r', label='reduce_mean_cls_loss_store')\n",
        "        # axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "        axis[0].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "        axis[0].legend()\n",
        "        axis[0].set_title(\"reduce_mean_cls_loss_store\")\n",
        "\n",
        "        # axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "        axis[1].plot(X, mean_regression_store, color='b', label='regression_loss_store')\n",
        "        # axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "        axis[1].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "        axis[1].legend()\n",
        "        axis[1].set_title(\"c\")\n",
        "\n",
        "        axis[2].plot(X, mean_conf_store, color='b', label='reduce_confident_loss_store')\n",
        "        # axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "        axis[2].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "        axis[2].legend()\n",
        "        axis[2].set_title(\"reduce_confident_loss_store\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GcmR622NOpO"
      },
      "source": [
        "# Built-in training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmA4yjtXM336"
      },
      "source": [
        "def run_experiment(model, history_ = None):\n",
        "    optimizer_adamW = tfa.optimizers.AdamW(\n",
        "        learning_rate=learning_rate, weight_decay=weight_decay\n",
        "    )\n",
        "    optimizer_adam = tf.keras.optimizers.Adam(lr=0.5e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-6)\n",
        "    # 'mean_squared_error'\n",
        "    metrics = [custom_mse_hungarian_py_func]\n",
        "    model.compile(loss= hungarian_loss_fit_fixed, optimizer=optimizer_adam,\n",
        "                  metrics=metrics)\n",
        "\n",
        "    checkpoint_filepath = \"/content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/check_point/check_point_50_epochs\"\n",
        "    checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "        checkpoint_filepath,\n",
        "        monitor=\"val_custom_mse_hungarian_py_func\",\n",
        "        save_best_only=False,\n",
        "        save_weights_only=False,\n",
        "    )\n",
        "    early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
        "    # lr_scheduler_callback = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "    reduce_lr_callback = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_custom_mse_hungarian_py_func', factor=0.2,\n",
        "                              patience=5, min_lr=0.0001)\n",
        "\n",
        "    \n",
        "    # use recall+ precision in callback\n",
        "    batch_size = 32\n",
        "    myCallBack = Evaluation_callback(validation_data=validation_set.batch(batch_size))\n",
        "\n",
        "    \n",
        "    history = model.fit(\n",
        "                train_set.batch(batch_size),\n",
        "                validation_data=validation_set.batch(batch_size),\n",
        "                epochs=60,\n",
        "                verbose=1,\n",
        "                shuffle=True, callbacks = [myCallBack, reduce_lr_callback] )\n",
        "    \n",
        "    # history_ = model.fit(x_training_test,y_training_test,batch_size=32, epochs=50, validation_split=0.2, shuffle=True )\n",
        "    return history"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VclO7fhNRh3j"
      },
      "source": [
        "# Run experiment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CFpSnkA9QMW"
      },
      "source": [
        "## Only resnet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dB7re1QD1qok"
      },
      "source": [
        "### 50 epochs, optimzer adamW, not normalize input label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ywaHBHYNba-"
      },
      "source": [
        "test_data_resnet_model = run_experiment(resnet_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "XMs9YhDXRUR1",
        "outputId": "03567ee6-bab9-4695-bbcc-e801aeae1b9a"
      },
      "source": [
        "history = test_data_resnet_model\n",
        "#  \"custome mse\"\n",
        "plt.plot(history.history['custom_mse_hungarian_py_func'])\n",
        "plt.plot(history.history['val_custom_mse_hungarian_py_func'])\n",
        "plt.title('model custome mse')\n",
        "plt.ylabel('custome mse')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# \"Loss\"\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "FcqsBwhljOGP",
        "outputId": "e12a2af0-5a9e-4f40-c2a4-c66c83eb5156"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "figure, axis = plt.subplots(1, 2,figsize=(10,10))\n",
        "\n",
        "X = np.arange(len(save_precision_075))\n",
        "# Plotting both the curves simultaneously\n",
        "# axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "axis[0].plot(X, save_precision_075, color='r', label='precision 0.75')\n",
        "axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "axis[0].set_xlabel(\"epochs\")\n",
        "axis[0].legend()\n",
        "axis[0].set_title(\"Precision at threshold 0.5 and 0.75\")\n",
        "\n",
        "# axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "axis[1].plot(X, save_recall_05, color='b', label='recall 0.5')\n",
        "axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[1].set_xlabel(\"epochs\")\n",
        "axis[1].legend()\n",
        "axis[1].set_title(\"Recall at threshold 0.5 and 0.75\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Recall at threshold 0.5 and 0.75')"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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7tKRWkHS+BywFXsY3/T8fLEt+/DbgFPz+cCv+c+PuHsa7DPg0Pqh6Fx+QfjZ+vZndb2bfCP4cj9/fN+Cb6qcDJzrn2lPcbyP+S9Jd+H30x4B7MtjOuAvxJb938F/aftPDNrQBH8RnmurwByF8MFiOmX3DzO5PutnHSR007op//TXi+8EiwOl9GPeQs64lXRERERHpi50+MyUiIiIyEAqmRERERAZAwZSIiIjIACiYEhERERmAnJ2sdsyYMW769Om5engRyYHnnntus3NubK7HMVDaf4nsfHraf+UsmJo+fTpLly7N1cOLSA6YWU8zKIeG9l8iO5+e9l8q84mIiIgMgIIpERERkQFQMCUiIiIyAAqmRERERAZAwZSIFDQz+7WZvWtm/05zvZnZT83sTTN72czmDvUYRSTcFEyJSKG7BX9i23QW4k8wPQu4APjlEIxJRAqIgikRKWjOuceArT2scjJwm/OWALVmNmFoRicihUDBlIjs7CYBaxP+Xhcs68LMLjCzpWa2dNOmTUM2OBHJfwqmREQy4Jy7yTl3oHPuwLFjQz+Ju4hkkYIpEdnZrQemJPw9OVgmIpIRBVMisrO7Bzg7OKpvPlDvnHs714MSkfDI2bn5RESGgpndASwAxpjZOuDbQCmAc+4G4D7gBOBNoBk4NzcjFZGwUjAlIgXNOXd6L9c74HNDNBwRKUAq84mIiIgMgIIpERERkQFQMCUiIiIyAAqmRERERAZAwZSIiIjIACiYEhERERkABVMiIiIiA6BgSkRERGQAFEyJiIiIDIBmQJeMLXt3GWfcfQaRWKTL8tqKWg6YcAAHTjyQAyYcwJyxcygp0ktLZCBuugmuuabrsqIi+MEP4KSTBnbfzc1w/PGwadPA7iefVFXBhz4EZ54J06blejSys9EnnmTs1pduZdmmZZwy55Quyzc2beS2l27j+mevB2BY6TCuP+F6ztnvnByMUqQwPPAAbNjgg564v/4VHnxw4MHUf/4Djz8OhxwCkycP7L7yxfr18M1v+suRR8LZZ8Opp0JNTa5HJjsDBVOSscUrFvO+ae/jDx/+Q7frOlwHK7asYOmGpfz4qR9z6SOXctY+Z1FcVJyDkYqEXywGM2bAHxLebrvuCvX1A7/vhgb/89JL4bjjBn5/+WLlSvjd7+C22+Dcc+Eb34A77oD3vS/XI5NCp54pycjKbSt5ddOrLJq1KOX1RVbE7mN254x9zuCSwy9hbcNaHvzPg0M8SpHCEY1CcdJ3kdpaqKsb+H3Hg6kRIwZ+X/lkxgz41rdg+XL45z99Vuqoo+CKK6CjI9ejk0KmYEoysnjFYgBOnH1ir+uetPtJjB02lpuev2mwhyVSsGIxKEmqHWQ7mBo+fOD3lY/M4IgjYOlS+OhH4ZJL4IQTCqtHTPKLynySkcUrFjN79GxmjZ7V67plxWWcs985/OSpn/B249tMqJkwBCMUKSyxWPfM1IgRvt9poAo9mIqrqYHbb/dlvs9/Hvbf3/dRme1Yp7wcTj4Z3vverstF+kKZKenV9rbtPLLykbQlvlTOm3seMRfjlhdvGbyBiRSwaFSZqWwwg099Cp56CsaMgVtvhVtu2XG55ho49FDYe2+47jrYujXHA5ZQUjAlvXpo5UNEYpGMSnxxs0fPZsH0Bdz8ws10ODUriPRVusxUNhrQ4/dRXT3w+wqL/feHF1/0wWjiZcsW+NWvYNgw+O//hokT4Ywz4OGH1WclmVMwJb362/K/UVNWw2FTD+vT7c6fez5vbXuLR1Y+MkgjEylcqYKp2lqfVYrFBnbfDQ2+BFakTwBqauC88+CZZ3ywdd55sHgxHH00zJoF3/++n3ZBpCcZvZXM7Hgze8PM3jSzr6e4/hwz22RmLwaX87I/VMkF5xyLVyzmuJnHUVZc1qfbnjLnFEZVjuJXz/9qkEYnUrjSlflgR5muvxoaCu9IvmzYd1/4+c/h7bf9FAvTpvl5q6ZO9b1Wzz+f6xFKvuo1mDKzYuB6YCGwJ3C6me2ZYtU/OOf2Cy43Z3mckiMvvvMiGxo39KlfKq6ipIKz9jmLP7/+ZzY3bx6E0YkUrnRlPhh4qa+hYefol+qvysodpb4334SvfhUeeggOOABOPNH3X4kkyuRovnnAm865twDM7E7gZODVwRyY5IfFKxZjGAtnLuzX7c+fez7XPX0dt710G1987xezPDqRwpVunikYeBO6gqnM7babP4XP178O118PP/mJnzl+wQKYPTvXo8vM3nvDOeek7pFzzs+q/9e/9q98XF4OCxfCscd2z6TuTDLZ9EnA2oS/1wEHp1jvVDM7AlgOfME5tzZ5BTO7ALgAYOrUqX0frQy5vy3/G/MmzWN89fh+3f49497DIVMO4abnbuIL87+A6dhjkYykm2cKBh5M1dfvuC/JzIgRfkb1z38ebrwRbrgBXn8916PqXSzmz/N46aXw2c/C//t/MH48tLfDnXfCj38ML7/se8eqqvp+/42N8LOfwbhxcPrpcNZZMHfuzjfNRLbiyHuBO5xzETP7FHArcFTySs65m4CbAA488ECXpceWQfLu9nd5Zv0zfGfBdwZ0P+fPPZ9z/3ouh/76UI6cfiTvm/4+DplyCNVlO9GhRAXo0VWP8vc3/84Pjv6BguRBMNhlPn2f7Z+qKvjiF/0lLJ58En70Iz8T/I9/7E8I/cQTsG4dvOc98JvfwMc+BmV9a4sFIBKB++/3PWa//KWfXmL0aCgtzf52DIbbbvNZtYHKJJhaD0xJ+HtysKyTc25Lwp83A1cNfGiSa/evuB+H69OUCKmcsfcZrK5bzf1v3s8P//VDrnjiCkqKSjhm12P4/Sm/Z2TlyCyNWIbKv9b8ixN/fyLTaqdx8WEXM6JC3czZNthlPjWg7zwOOQT+/Gd/mp2rr4bf/hbmzfPZtYULB3ZUZ3k5fPCD/rJtG/zxj/Dcc9kb+2Ab37+iSzeZBFPPArPMbAY+iDoN+FjiCmY2wTn3dvDnScBr2Rme5NLiFYuZWDOR/XbZb0D3U1pcyrcXfJtvL/g2TW1NPLn2SR5e+TDXLLmGhbcv5MGzHmR4uRo4wuLpdU+z8PaFTBo+iYfOfkiB1CBJVeZTA7oMxOzZvkR5442Dc/8jR8IFFwzOfee7XuNR51wUuBB4AB8k3eWcW2Zml5vZScFqF5nZMjN7CbgIOGewBixDI9oR5YH/PMCiWYuyWsKpLqvm/bu9nyuPuZK7PnwXz739HIt+v4jtbduz9hgyeJ7b8BzH/e44xlWN4+GzH2aX6l1yPaSC1VOZbyCZqVgMmpoUTIlkU0Y9U865+4D7kpZdmvD7xcDF2R2a5NL6hvU0RBo4aOJBg/YYJ+9xMrefcjun/+l0Tr7zZO49/V4qSyuzdv+t0VZeeucllm5YyrMbnmXZpmVEO6Jd1ulwHcQ6YkQ7osRcjFhHDEfXdr5iK2af8ftw+NTDOXza4ey3y36UFJWwtWUrT659ksdXP84Ta59gQ+OGfo1zdOVoFkxfwJHTj+TwaYfnbZbupXde4tjfHkttRS0Pf/xhJg2flOshFbRU80yVlPgjsgYSTDU2+p8KpkSyZyc+kFF6srp+NQDTa6cP6uN85D0foTXayjl/OYcP//HD/Pmjf+51ctD2WDuvb36dF955gRfefoEX3nmBjds3dlkn1hFjZd3KzuBpXNU49h2/b7dgzTBKikooLiqm2IopLiqmyLombFujrSzdsJQ/v/5nAKpKq5g8fDJvbHkDgNKiUg6adBCHTz28X1m81XWr+fkzP+fqp66m2Io5YOIBTB4+ucs6RVbEpJpJTK+dzozaGcwYOYPh5cNZXbealXUrWbltJavqV9HU1tTlds452mJtRGIR/zMaob2jPeU44tuf7nl46Z2XqCqr4uGPP8zUEepeHmypMlMw8FPK7Ezn5RMZKgqmJKXVdT6YmlY7bdAf6+x9z6Y12sqn/vYpRv5wJCVFPb8sW9pbOgOCypJK9hm/D3uP27tbIHPqnFM5aNJBHDTxICYPnzzgcuX6hvU8seYJHl/zOGvq13DmPmdy+NTDmTdp3oAzai3tLSxZt4RHVj3CP1f/k+Vblne5vj3Wzv0r7md7e+pyqGFMGj6J2orux7uXFZdRXlxOWXEZIypGUFpU2u25cM51ZubiP5PPqXjo1EP5yft/wq4jdx3QtkpmUjWgw8BPdhwPptSALpI9CqYkpXhmaqgyEBcccAGjK0fzxJonel23vKScvcftzf4T9mf26Nm9Bl/ZMmn4JD6610f56F4fzfp9V5ZWcuSMIzlyxpFp13HOsaVlCyu3rWRl3UoaIg1MGzGNGSNnMGX4FMpLyrM+LsmdVA3o4IOgbARTykyJZI+CKUlpdd1qxleNp6KkYsge89Q9T+XUPU8dsscLGzNjzLAxjBk2hoMmDV4vm+SHdGW+2lrYuLH78kwpmBLJPp0zXFJaXb96SEp8IpLaYJf5FEyJZI+CKUlpVd0qpo1QMCWSKz2V+QbSgB6/rYIpkexRMCXddLgO1tSvUTAlkkM9lfnq6vwJavtDDegi2adgSrp5d/u7RGKRQZ8WQUTSSzXPFPhgKhqF5ub+3W88mKrWqTFFskbBlHQzlNMiiEh3HR0+85Runinof6mvoQFqagZ2PjYR6UpvJ+kmPi2CynwiuRGL+Z/pynzQ/yZ0nZdPJPsUTEk3ykyJ5FY8mErXgA79D6bq6xVMiWSbginpZnX9amoravP2HHEihS6TzNRAynxqPhfJLgVT0o2mRRDJrWhwPm6V+UTCQcGUdKMJO0VyK5My30AyUwqmRLJLwZR04Zxjdd1qpo+YnuuhiOy01IAuEi4KpqSLutY6GtsalZkSyaF4mS9VZqqiAkpLFUyJ5BMFU9KFpkUQyb2eMlNmPjvVnzJfLAaNjWpAF8k2BVPShaZFEMm9nhrQof8nO25q8j+VmRLJLgVT0oUyUyK511MDOvjMUn+CqfipZBRMiWSXginpYnXdaipLKhkzbEyuhyKy0+qpzAf9L/MpmBIZHAqmpItV9auYVjsNM8v1UER2WoNV5lMwJTI4FExJF6vrVjO9dnquhyGyU8ukzNefzFT8NgqmRLJLwZR0sbp+tfqlRHIskzLfQDJTOppPJLsUTGVRrCNGrCOW62H02/a27Wxu3qxgSiTHeppnCnww1NwM7e19u1+V+UQGh4KpLDrlrlP47OLP5noY/bamfg2gaRFEci2TzBT0vdSnYEpkcKT53iP98da2t2hub871MPpN0yKI5IdMGtDBl/rG9OHA23gwVV3d/7GJSHfKTGVRJBqhpb0l18PoN03YKZIfMmlAh773TdXX+0AqXZAmIv2jYCqLWqOttEZbcz2Mfltdv5qSohImVE/I9VBEdmqDWeZT87lI9imYyqLWaCst0fBmplbVrWLqiKkUF+lrq0gu9Vbm629mSic5FhkcCqayqBAyU+qXEsm93sp8A8lMKZgSyT4FU1nUGm0Nfc+U+qVEci/TMp8yUyL5QcFUlnS4Dto72kObmWqLtbGhcYMyUyJ5oLd5pmpqwEzBlEi+UDCVJZFoBCC0PVPrGtbhcAqmRPJAb5mpoiIfFPW1zFdfrwZ0kcGgYCpL4hmp1mgrzrkcj6bvNC2CSP7orQEd+ndKGWWmRAaHgqksSSzvRWKRHI6kfzRhp0j+6K0BHXyGqS/BVEcHNDYqmBIZDAqmsiQxmApj39SqulUYxpQRU3I9FJGdXm9lPvCZqb6U+Zqa/E8FUyLZp2AqS8IeTK2uX83EmomUFZfleigiO71Mynx9zUzpvHwig0fBVJYkBlBhnB5hXcM6ZaVE8kQmZb6+Zqbi66oBXST7FExlSWKfVBgzU3WtdYyqHJXrYYgImZf5lJkSyQ8KprKkS2YqhNMjNEYaqSmryfUwRITe55kCn2Gqr/eN5ZlQMCUyeBRMZUnYe6YaIg0ML9deViQfZJqZcm5HY3lvFEyJDB4FU1kS9p4pBVMi+SPTBnTIvNSnYEpk8CiYypIwZ6ZiHTG2t29XmU8kT2TagA59D6bUgC6SfQqmsiTMPVNNbb5OoMyUSH7ItMwHmR/RF1+vurr/4xKR1BRMZUn83HwQvsxUQ8R/ZVUwJZIfBqvMV13d832KSP8omMqSMN0kg1oAACAASURBVPdMxYOpmnKV+UTyQV/KfJlmpnRePpHBo2AqS8LcM9XY1ggoMyWSL+LBVFEPe+j+9EwpmBIZHAqmsiTMPVMq84nkl2i056wU9K/Mp2BKZHAomMqS1mgrxVbc+XuYKJgSyS+xWO+9TWVlUFnZtwZ0HcknMjgUTGVJa7SVqrIqSotKw9szpakRRPJCNJpZo3hfTnaszJTI4OklkSyZisQilBeXA+HLTDVG1DMlkk9isd7LfNC38/MpmBIZPAqmsqQ12kpFSQVmFtqeKR3NJ5IfMinzgQ+mdDSfSO4pmMqSeDBVZEWhy0w1RBqoLKmkpEgvB5F80Jcy39atva/X0QGNjQqmRAaLeqayJB5MVZRUhC6YamxrVIlPJI/0pcyXSWZq+3Z/UmQ1oIsMDqUisiQeTBUXFYeyzKdgSiR/ZFrmy7QBPR5wKTMlMjgUTGVJZzDVURy6zFRDpEH9UiJ5JNMyX6YN6PGTHCuYEhkcKvNlSSQWobyknMqSytBNjaAyn0h+6UuZr60NWnv5/qZgSmRwKZjKkjD3TKnMJ5Jf+tKADr1npxRMiQwuBVNZkhhMqWdKRAaiL5kpyDyYUgO6yOBQz1SWxIOpDtcRysyUZj8XyR99mWcKYMuWntdTA7rI4FJmKktao61UFFdQUVwRvp6piHqmRPJJpmW+ffeFigq45pqe11OZT2RwKZjKknhmqrK0MlSZqUg0QiQWUTAlkkcyLfNNmgTf+hb86U9w333p14sHUzVKQIsMCgVTWRKJ+qP5wtYz1djmz8unMp9I/si0zAfw5S/DHnvAhRdCc3PqdRoaoKoq8/sUkb5RMJUlnZmpEp+Zcs7lekgZ0UmORfJPpmU+gLIy+MUvYOVKuOKK1Os0NKj5XGQwKZjKgmhHlJiLdR7N1+E6aO9oz/WwMhI/ybGCKZH8kWmZL+7II+Gss+Cqq+D117tfX1+vfimRwaRgKgviPVLxnqnEZfkuHkxpBnSR/NGXzFTcj3/sS3mf+Yw/D1+ihgYFUyKDScFUFiQGUxUlFQChOaJPmSmR/NPXzBTAuHHwgx/Ao4/Cb37j+6diMX+dgimRwaVgKgu6ZKZKwpWZijegK5gSyR99aUBPdMEFMG8efPKTPktVUgKlpfDUU+qZEhlMmrQzCyLRCADlxeWUFPmnNCxH9CkzJZJ/+lPmAygqgnvugT/8AVpaIBLxl9ZW+MhHsj9OEfEUTGVBYmaqtLi0y7J819kzpakRpECZ2fHAdUAxcLNz7sqk66cCtwK1wTpfd871MGvT4OtPmS9u/Hi46KLsjkdEeqYyXxak6pkKSzDVGGnEMKrKqnI9FJGsM7Ni4HpgIbAncLqZ7Zm02jeBu5xz+wOnAb8Y2lF2198yn4jkhoKpLEjVMxWmBvSa8hqKTC8FKUjzgDedc28559qAO4GTk9ZxQLzOPQLYMITjS6m/ZT4RyQ2V+bIgMZgqKy7rsizf6STHUuAmAWsT/l4HHJy0zmXAg2b2/4Aq4JhUd2RmFwAXAEydOjXrA000kDKfiAw9pSOyIOXUCCFpQG9s00mOZad3OnCLc24ycALwW7PuqVrn3E3OuQOdcweOHTt2UAekzJRIuCiYyoJILDiar6Q8lJN2KpiSArYemJLw9+RgWaJPAncBOOeeAiqAMUMyujSUmRIJFwVTWRD2STs1+7kUsGeBWWY2w8zK8A3m9yStswY4GsDM5uCDqU1DOsokakAXCRcFU1kQ5kk7lZmSQuaciwIXAg8Ar+GP2ltmZpeb2UnBal8Czjezl4A7gHNcjs9UrjKfSLgokZwF6pkSyV/BnFH3JS27NOH3V4FDh3pcPVGZTyRclJnKgrCf6Hh4mYIpkXyiMp9IuCiYyoLEYKqkqIRiKw5Fz5RzTj1TInlIZT6RcFEwlQXxYKq0yJ9KprK0MhSZqZZoCx2uQ2U+kTyjMp9IuCiYyoJINEJFSQVmBvgMVRh6pnSSY5H8pMyUSLgomMqC1mhrZ+M5QGVJODJTOsmxSH5SZkokXBRMZUFyMKXMlIgMhBrQRcJFwVQWtMaSMlMh6ZlqjDQCCqZE8klHBzinYEokTBRMZUGqzFQYgqnOMp+O5hPJG7GY/6kyn0h4KJjKgtZoK+XF5Z1/V5RUhGJqBJX5RPJPPJhSZkokPBRMZUH8aL64sDSgN7apzCeSb6JR/1PBlEh4KJjKAjWgi0i2qMwnEj4KprKg29QIIWlAb4g0UFJU0qVEKSK5pcyUSPgomMqClJmpEPRMNUb8SY7jk42KSO4pMyUSPgqmsiC0k3a2NajEJ5Jn1IAuEj4KprKgNdpKeUnS0Xwh6ZnS7Oci+UVlPpHwySiYMrPjzewNM3vTzL7ew3qnmpkzswOzN8T8F4lFqCgOYWYqosyUSL5RmU8kfHoNpsysGLgeWAjsCZxuZnumWK8G+DzwdLYHme9S9UxFO6JEO6I5HFXv4j1TIpI/VOYTCZ9MMlPzgDedc28559qAO4GTU6z3XeCHQP6nZLIs1dF88eX5rCHSoNnPRfKMynwi4ZNJMDUJWJvw97pgWSczmwtMcc4t7umOzOwCM1tqZks3bdrU58HmI+dcyswUkPdH9DVEGhhepsyUSD5RmU8kfAbcgG5mRcBPgC/1tq5z7ibn3IHOuQPHjh070IfOC22xNoBuR/NB/memGttU5hPJN8pMiYRPJsHUemBKwt+Tg2VxNcBewKNmtgqYD9yzszShxwOm5KP5gLw+oi/WEaOprUllPpE8o8yUSPhkEkw9C8wysxlmVgacBtwTv9I5V++cG+Ocm+6cmw4sAU5yzi0dlBHnmUgsAhC6nqmmtiZAp5IRyTdqQBcJn16DKedcFLgQeAB4DbjLObfMzC43s5MGe4D5Lh4wha1nSuflE8lPKvOJhE9GiWTn3H3AfUnLLk2z7oKBDys8egqm8jkz1djWCCiYEsk3KvOJhI9mQB+gVMFUGBrQ45kpzYAukl9U5hMJHwVTA9RjmS+PG9BV5hPJTyrziYSPgqkB6jyar3jH0XxhaEBvjKjMJ5KPVOYTCR8FUwMUiXY/mi9MDeiaGkEkvygzJRI+CqYGKOw9U8pMieQX9UyJhI+CqQEKa89U/Gg+NaCL5BeV+UTCR8HUAKXMTIWgZ6oh0kBFSQWlxaW5HoqIJFCZTyR8FEwNUKpgqrSoFMPyvmdKJT6R/KPMlEj46O2awjG3HcOL77zIlBFTmDJ8CpOHT2bmqJl85sDPdGad4lKdm8/MqCytzPvMlIIpkfyjnimR8FEwlcLT659m2ohpTKqZxOr61Ty+5nHqWuuYNWoWH9j9A13WTXVuvvjf+d4zpWBKJP+ozCcSPgqmknS4Dpramjh1zql858jvALCqbhUzrpvB5ubN3dZPVeYDf0Rfvmem1Hwukn9U5hMJH/VMJWlubwa6zr80qnIUAFtbtnZbvzXaSrEVU1LUdc+X75kplflE8pMyUyLho2AqSVNbEwDVZdWdy2rKaii24rTBVHJWCnwwlc+ZqcaIynwi+Ug9UyLho2AqSfw0K4klMDNjVOWoPgVTlaWVeX80n8p8IvlHZT6R8FEwlSRVZgp8qW9ra+pgKvFIvrh8z0ypzCeSn1TmEwkfBVNJegymUmSmIrFI6sxUHjegt8XaiMQiCqZE8pAyUyLho2AqSedpVpJOANzXMl8+N6B3ljJ1kmORvKOeKZHwUTCVpK+ZqZ56pvI1M6WTHIvkL5X5RMJHwVSSeNZmoMFURUlF3jagK5gSyV8q84mEj4KpJPHMVPKRbqMqR9EQaaA91t5ledrMVB73TMVLmQqmRPKPMlMi4aNgKkk8mKoqq+qyPD5xZ11rXZflrdFWyotTH82Xrz1T8cyUpkYQyT/xzFSR9s4ioaG3a5LGtkbKissoKy7rsnxkxUig+yzokWj4juZTmU8kf8ViKvGJhI2CqSRNbU0pMzbpTinTU89UW6yNWEdscAY6APG+MAVTIvknGlWJTyRsFEwlaWpr6tZ8Dn0PpipLKwE/D1W+2dKyBYARFSNyPBIRSabMlEj4KJhK0tjWmJVgKr4sH4/oe3Prm4yrGpdyO0Ukt2IxZaZEwkbBVJKmtqaUk1nGg6ltrdu6LO/paL749flmxdYVzB49O9fDEJEUVOYTCR8FU0nSlflqK2qB1JmpdEfzAXl5RN/yLcuZPUrBlEg+UplPJHwUTCVpjKQu8xUXFVNbUdslmOpwHbR3tPdY5su3zFRDpIF3mt5RZkokTykzJRI+CqaSpDuaD7rPgh6J+ubynhrQ861nasWWFQDMGj0rxyMRkVTUMyUSPgqmkqRrQIfuwVQ86xSmzNSKrT6YUmZKJD+pzCcSPgqmkqTrmYK+BVPxBvR865lavmU5hrHbyN1yPRQRSUFlPpHwUTCVINoRpTXamnGZL4yZqeVbljN1xNTOMqSI5BdlpkTCR8FUgvh5+dJmpipSB1PlJd2P5osHK/kYTKlfSiR/qWdKJHwUTCWIB1Op5pkCn5na1rqNDtcB7JjdPCyTdjrn/BxTmhZBJG+pzCcSPgqmEvSamaocRYfr6DxRcCY9U/mUmdrcvJm61jo1n4vkMZX5RMJHwVSC+AmAewqmYMfEnZn0TOVTA/ryLcsBHcknks+UmRIJHwVTCTrLfD00oENmwVQ+9kzFgyn1TInkL/VMiYSPgqkEmZT5ILNgKn6KmXzqmVqxdQUlRSVMr52e66GISBoq84mEj4KpBI1t/SvzpTo3n5lRUVKRd5mp3UbuRkmR9tQi+UplPpHwUTCVoLej+UZWjgR2BFM9nU4mvjzfeqbULyWS35SZEgkfBVMJemtAH1nRNZjqqcwXX54vmakO18GKrSuYNUr9UiL5TD1TIuGjYCpBPDNVVVqV8vryknKqSqsyDqYqSyrzJjO1vmE9rdFWZaZE8pzKfCLho2AqQVNbE8NKh1FclH5PlnhKmTBlpjQtgkg4qMwnEj4KphI0tjWmLfHFpQqmUp1OBvz0CPlyNJ+CKZFwUGZKJHwUTCVoamvqczBVWlRKkaV+GvMtMzWsdBgTaybmeigi0gP1TImEj4KpBE1tTWkn7IxLDKYisUjaEh/kV8/U8q3LmTVqFmaW66GISA9U5hMJHwVTCfpT5uspmMqnzNSKLStU4hMJAZX5RMJHwVSCvpT5nHO9BlOVpZV5EUy1x9p5a9tbmhZBJASUmRIJHwVTCZramtJO2Bk3qnIU7R3tNLc3Z5SZyocG9JV1K4m5mDJTIiGgnimR8FEwlaAxklmZD/zEnb1mpkryIzOlI/lEwkNlPpHwUTCVoKmtierSvgVT6aZFgPw5ncyKLSsABVMiYaAyn0j4KJgKOOdobGvMqMwHPpjK5Gi+fMlMjawYyehho3M9FBHphTJTIuGjYCrQFmsj2hHNapkvfjSfcy6rY+2r5Vt1gmORsFDPlEj4KJgKxM/Ll8k8U5Bhz1RpJeDno8ql5VsUTImEhcp8IuGjYCrQ2NYIkPXMFJDTI/qa25tZ17BOwZRISKjMJxI+CqYC8cxUdVk1XHMNPPpoyvUqSyopLy7vUzCVy76pe9+4F4B9xu+TszGISOaUmRIJHwVTgc4yX3kNXHopXH11yvXMrHPiztZoK+XF6Y/mqyzxZb5cHdEXiUa4+KGL2Wf8PiyatSgnYxCRvlHPlEj46PtPoDESlPmsHJqaYMkScA5SnMtuZOVItrZuJRLt+Wi+XGemrn/2elbWreTBMx+kuEh7Z5EwUJlPJHyUmQp0lvlaYn7B5s3w1lsp103MTGXSgJ6LnqktzVv47mPf5fiZx3PsbscO+eOLSN91dPifKvOJhIuCqUBnmW97+46FS5akXDfTYCqXmanvPfY9GiIN/OjYHw35Y4tI/0Sj/qcyUyLhomAq0Hk0X2PCNAY9BFPvbn+XmIv1OmknDH3P1Jtb3+T6Z6/nE/t9gr3G7TWkjy0i/RcLEuMKpkTCRcFUoLPMVx9kkUaNSh9MVYxiY9NGgLzMTF380MWUFpdy+ZGXD+njisjAxIMplflEwkXBVKAx0ohhDNvmgyqOPx5efBFaumeVRlWOwuFnNe/xaL4c9Ew9ufZJ/vfV/+Wrh3yVCTUThuxxRWTgVOYTCScFU4Gmtiaqy6qxbdv8gkWL/J7t+ee7rRufuBPyKzNV11rHZxd/lgnVE/jyIV8ekscUkexRmU8knBRMBeLBFFu2QGkpHH20vyJFqS/TYCreM7W5efOAx9fhOnh548t0uI6U129u3sxRtx7Fq5te5Vcf+BVVZVUDfkwRGVoq84mEk4KpQGNbo5+wc8sWGD0axo+H6dPh6ae7rZtpMDWhZgL77bIfVzxxBRsaN/R7bO9uf5dFv1/EvjfsyxG/OYKXN77c5fq3G9/mfbe8j9c2v8Y9p9/DotmaoFMkjFTmEwknBVOBzszU1q0+mAKYP39AmakiK+KOU++gub2ZM+8+k1hHrM/jeuith9j3hn15ZOUjXDTvIt7Y8gZzb5zLFx/4Ig2RBtbUr+GIW45gdd1q7j/jfo6feXyfH0NE8oMyUyLhpGAq0KXMNyoIlubPh7VrYf36LutmGkwB7DFmD3628Gc8suoRrvrXVRmPpz3WziUPXcKxvz2WkRUjeeb8Z7hu4XW8ceEbnDf3PK5dci17/HwPDvv1YWzavol/nP0PFkxfkPH9i0j+UWZKJJwUTAUa2xqpKUso84EPpqBbqS8xmCovSX80X9y5+53LaXudxrce+RZPrX2q1/X/ueqfHPrrQ7niiSv4xP6f4Nnzn+08UfGoylHccOINLDlvCRNrJtIabeXhjz/M/MnzM9xSEclXakAXCScFU4GUZb799oOysm6lvuHlwyk2v7frLTMF/uTINyy6gSkjpnD6n06nrrUu5XpPr3ua9//2/Sy4dQHrG9dzx6l3cPNJN6dsJp83aR7Pnv8sa7+wlrkT5vZxa0UkH6nMJxJOessGfDBV1bXMV14Oc+d2C6bMjJGVI9ncvDmjYApgRMUI7jj1Dg779WGcefeZfHjPD3de55zjz6//mXuX38uYYWO4+v1X85kDP9M5T1U6ZpZRZkxEwkFlPpFwUjAVaIw0UlNUCZHIjswU+FLfjTdCe7ufMiEwqnJUn4IpgPmT53PlMVfylf/7CotXLO5yXW1FLd8/6vtcdPBFPkMmIjsdlflEwknBFD4z1NTWRHU02ION2tETxcEHw7XXwiuv+CxVIN431ZdgCuDLh3yZM/Y+g0gs0mX5uKpxDCsd1r8NEJG0zOx44DqgGLjZOXdlinU+AlwGOOAl59zHhnSQAZX5RMJJb1mgub0Zh6Om3fyC5MwU+Cb0LARTgE7zIjJEzKwYuB44FlgHPGtm9zjnXk1YZxZwMXCoc26bmY3LzWhV5hMJKzWgk3CS44g/316XYGraND+BZ1LfVDyY6uncfDQ0wIwZcN99WR2viGRsHvCmc+4t51wbcCdwctI65wPXO+e2ATjn3h3iMXZSZkoknAr6LfvEmidYXbe6y7LailpOmHUCZta5rDOYagn2ZIllPrOUk3eOqsggM7VkCaxaBffcAyec0P8NEZH+mgSsTfh7HXBw0jqzAczsX/hS4GXOub8n35GZXQBcADB16tRBGawyUyLhVLDBVCQa4chbjyTaEe123QufeoH9dtmv8+/GtkYAarYH6yZmpsAHU3/9a5c5qPYYswcTqidQVlyWfhDxACzFLOp5pa0NLrwQ3nkHvvAFWLDAB5EiO4cSYBawAJgMPGZmezvnusxh4py7CbgJ4MADD3SDMRA1oIuEU8EGU83tzUQ7olxy+CV8fN+PA7CybiXH/e44/v3uv7sEU52ZqaY2vyAxMwU7+qaWLIFF/rx3nzrwU5y7/7ldMlzdxIOoV16BpiaozsOj9Jqa4NRT4cEH/Xbfey8ccAB85St+ueoNhWXbNvjjH30AnWjmTHj/+6Go4Cr/64EpCX9PDpYlWgc87ZxrB1aa2XJ8cPXs0AxxB5X5RMKpYN+yrdFWAKYMn8Ks0bMAmFY7jWIr5vXNr3dZtzOYamiBYcOgIql0N2+en7zzn//sDKaKrKjnEp9zPpiaMsWfkmbpUp/xySebN/vtWboUfv1rOP10uO02+PGP4bTTfL/Xd78LH/uYMlWFYN06HzC99lrq6+fMgS9/Gc44w8+xVhieBWaZ2Qx8EHUakHyk3l+A04HfmNkYfNnvrSEdZUBlPpFwKvhgKjHgKSsuY7dRu/Ha5q4fJo2RoMxX19K9xAc+wDr4YHjkkcwHsGKFzwJ8/evwta/5wCqfgqm1a/0H68qVcPfdcHLQk3vBBfDJT/o+r+9/H848E/7nf+D66/2HbaFbvhzWrOm6rKTEZyeTg+xEa9b42/bHlCmw++79u22m3njD/7+3bYMHHuhyZCrOwf/9H/zoR/5//81vwkUXwYEHdr2Pigr/PIQobeKci5rZhcAD+H6oXzvnlpnZ5cBS59w9wXXvN7NXgRjwFefcllyMV2U+kXAKz16xj1qiLUD3BvE5Y+akz0xtbUodTAEceSR873tQXw8jRvQ+gHiJb9EiuPnm/Ombcs4Hheec47flwQfhiCO6rlNcDB/6EJx0EvzqV3DxxbDvvj5r8c1v+uCykDjns45XXQX33596nV128QHGpz8NI0fuWP7ccz4I+eMfoaOj/2M4/nj46lcHp1/t2Wf9ARBFRX4799+/+zof+5jPTP7jH357Lr449X3tuit88Ytw7rmheR045+4D7ktadmnC7w74YnDJKZX5RMKpYN+yqTJT4BvH71txH9GOKCVFfvM7g6nNDd37peIWLIDLL4fHH4cTT+x9AEuWwPDhPpszf77/5u9c7spl0Sj86U8+YHj+eZg0CR57zAdJ6RQX++DhlFP8B/0PfuADw3FJ0/BUV8Ps2T67svvusMce/pJvnwjvvtu9V+ipp3zw8OyzMHas/x8nBzRbtsAvfgHf+AZccQWcfz4cfrjP1j30ENTU+ADjAx/oe8+Rc/7/8NOfwlFH7ehXO/TQAW8uAC++6Eu248b5wHnmzPTrmsGxx/rL8uX++Uq0bh1cd50/WOHb3/Y/P/c5/7xJVqjMJxJOefZplz09BVPtHe28te0tZo+eDSQczbepHt4zPfUdvve9vo/k0UczD6bmzfMfrvPnw29/60tB06b1b4MiEVi8GI4+OrPMWNz27b4f6ic/8dM0zJ4NN90EZ53Vc9kq0bhxcMst8IlPwA03dA9Itm2Dhx/22xg3fboPMD7xCajqfqLmIeOcL2tddVX6Mu3MmX67zj4bKtOcD/Hkk+Gll3w/2U9/CtdcAxMn+vu94IK+/U+SHX44fOlLvl/t6qt98JNN++wDf/87TOjDZLGzZ/tLso9+FP71L7/d3/mOD67Wrcvt/7iAKDMlEk4F+5aNB1PJJwueM8b3/by++fXOYKqprYmSohLKNm1Nn5mK94s8+mjvD97cDC+/vKNUkng0YH+CqcZG+OAHfcBSUwOf+hR8/vMweXL627z7Lvz85z57snUrHHKIDwBOOqn/R2wdcUT3kmCipiaf0XjlFR+wXXQRXHaZz15ceGH3jNZgam+HO+/0WadXXvGBz+WXdw8oJk6E447LLBWw774+YPz+9+Hf/4ZjjvEHJmRDRYUPys47z2eQ1q3Lzv2WlflAcCDBXiIzOOwwf3ntNXjmGQVSWaTMlEg4FXwwlSozBT6YOmn3kwDfgF5dVo1t3Za+Zwp839Tll0NdHdTWpl/vuef8V8yDg7kB997bf1guWeK/2ffFpk2wcKEv11x1Fbzwgg+Krr3W97kkZ5ja2+Guu3wmKRLxH6Rf+YoPpgZbdbVvbJ47Fz7+cZ/B+NGPfK/ZVVf58tHJJ/vM3i67DOyxOjr883zPPf7yn/90vb693WfQ9trLPxenn569wGfqVH8ZDEVFvn8qDObM2TkOShhCakAXCaeCDaZa2lM3oI+oGMGE6gldjuhram+iprQaYnU9B1MLFvhMy+OP+/6YdOLN5vFgqrTUHxnV1yb01av9EVhr1/pJQ4NpGbjiCh9Q3XyzLw0lKy/3JasvfWnwjxLryaGH+ssbb8Avf+m34W9/89cdfLDPcvUnwNm40Z+iZ8MGH3wcdpjP6iRm3Mx8D9Lxx2taBwkNlflEwqlg37LpMlPgs1OJR/Q1tTVRXRSsl67MBz4AqKjwvTe9BVO77da1MXf+fN9rE4lkNofPsmU+kGpu9s3riQ3J06f7XpVvf9tnqlzSZMx77+3PJ5gvdt/dZ9KuucaXx+LZpGuu6T72TFRV+efmpJP8UWo9BcAiIaIyn0g47bTB1B3/vgPnHGbmy3wEGZKePpgrKnwjek99U875I8SOOqrr8vnzffPySy/5xvSe1NX5kmJJiT/Sa++9U683apRvSA8LM78te+8Nl1yS69GI5B2V+UTCqeDOHRHX2YBe0v3orDlj5lDXWsfG7RsBn5mqcRkEU+CDnBdf9E3dqaxbB2+/vaPpPC6xCb03V17pZydfvDh9ICUiBUdlPpFwKvhgKl1mCugs9TW1NVEdC56Knsp84PumnPN9U6nEg6XkYGrSJH/0XW/B1Nq1viR21lmpJ1cUkYKlMp9IOBVsMJVuBnToHkw1tjVS3R48Fb1lpubN83MRpZuzaMkSXw7cZ5/u182f33swdWkwMfN3v9vzeiJScJSZEgmngg2mWqOtFFlR5yzniSYPn0xVaRWvbfJH9DW1NVHTFjRCJ54qJJXycj/NQLq+qSVL/CzWqY5Smz/fnwtv48bUt335Zbj1Vj8/02Adei8ieUuZKZFwKuhgqrKkEktxWLyZ+SP6tiSU+Vo6/MSGmXwlPPJI30i+JelcqG1t/lQtySW+uPjyp59Off3Xvubnr0p3XjQRKWhqQBcJp4yCKTM73szeMLM3zezrKa7/tJm9YmYvmtkTZrZn9ofaN63R1pQlvrj49AixjhjN7c3UNEczP8R+wQL/87HHui5/+WVotuqswgAAIABJREFUbd0xv1SyuXN9sJYqmHroIX/Kj0su6T07JiIFSWU+kXDqNZgys2LgemAhsCdweopg6ffOub2dc/sBVwE/yfpI+6i3YGrOmDmsqV/TeURfdVNb783ncQcdBMOGde+bStd8HldZ6U9Jktw31dHhTyQ8bZo/9YqI7JRU5hMJp0y+/8wD3nTOvQVgZncCJwOvxldwzjUkrF8F9GMmxuxqibZQEYn605kkqq2FT36yswn9uQ3PAVDdEIHR0zO787IyP4nmPffAlCk7lt99tz/XW0/nzJs/35/eJHFcq1b58uBvf5v5yYdFpOCozCcSTpkEU5OAtQl/rwO61bHM7HPAF4Ey4Kjk64N1LgAuAJg6yA3WrZHtVKzbCL/4avcr3/Me9pjpg6mlG5YCUFPXDOP7MJP2KafAZz7jM0qJzj+/59OXLFwIv/hF99sddZQ/156I7LRiMb/76O+5yEUkN7L2lnXOXe+c2w34GvDNNOvc5Jw70Dl34NjEU60MgtZIM5Xt+AxQU5O/rFjhr3z+eWaOmkmxFfPshmcBqN62PfMyH8CnP+1P9RK/7/jlxht7vt2iRalv93//pz2oyE4uGlVWSiSMMslMrQcSallMDpalcyfwy4EMKhta25upiOKP0Kuq8gvj58t7/nnKS8rZdeSuXYOpvp7jrbL77OoZUSlPRFKIxdR8LhJGmaRCngVmmdkMMysDTgPuSVzBzGYl/LkIWJG9IfZPS1sQTCUGLmZ+VvEXXgBgztg5bG7eDEBNBJ0wV0RySpkpkXDqNZhyzkWBC4EHgNeAu5xzy8zscjM7KVjtQjNbZmYv4vumPj5oI85Qa7TVB1PJ2aO5c2HZMohE2GP0Hp2Lq9voW5lPRCTLYjEFUyJhlFFC2Tl3H3Bf0rJLE37/fJbHNWCdwVRySW3//aG9HZYt6zyiD4JgSpkpEckhlflEwqlgO55bo61UpspMxU8e/MILzBk7p3NxjYIpEckxlflEwqlwg6lYJHVmarfdoKYGXniB3Ufv3rm4SmU+EckxlflEwqlgg6mWjkjqnqmiIthvP3jhBUZWjmR81XjKKaG0A2WmRCSnVOYTCaeCDaZaO9pSB1PgS30vvgixGHPGzqHGlfkga/jwIR+niEicynwi4VSQ34Gcc7S69tRlPvDBVHMzrFjBolmLGPn6ahjVqEkzRSSnlJkSCaeCjB7aYm0Afgb0VJmpuXP9zxde4MuHfJm73zpIJT4RyTllpkTCqSCDqdZoK0D6zNScOVBe7k8uDLBli5rPRSTn1IAuEk4FGUy1RFsA0vdMlZbCXnt1zoTO1q3KTIlIzqnMJxJOBRlMdWamYkBZWeqV4qeVcc5nphRMiUiOqcwnEk4FHUxVWqk/H18qc+f6jNSaNf6nynwikmMq84mEU0EHUxWWJisFO2ZCf/ppaGpSZkpEck5lPpFwKshgqqU96JkqLk+/0j77+KkQ/vEP/7eCKRHJMZX5RMKpIIOpzsxUT8HUsGGwxx47gimV+UQkx1TmEwmngkwodwZTJSmmRUi0//5w++3+d2WmRCTHolF/sDHAmh+uYfUPVne53oqMmdfOZJezdxnY4zREee6A52jb1Dag+8knRRVFDJ83nBGHjWDE4SOoOaCGorKCzBdIHiroYKqyJMW0CIkSgyllpkQkx2KxHVPjNSxpoKi0iHFnjOu8fuNvN7L1wa0DDqZa17TS8mYLoxaNonJmL/vJkIjVx6h/sp4t924BfHA18piRTPnKFEYcPgJLdzCSSBYUdDBVUZpBZipOmSkRybHEBvSOtg4qplcw69pZndc3L2umZUXLwB+nKQbApM9NYvTCwtr3tb3bRv2/6ql/rJ6Nt2/kxfe9yPBDhjP161MZvWg0VqSgSrKvIIOpzkk7y4b1vKKCKRHJI4kN6K7NYWVdP/grZ1Xy7h/eHfDjxIOp4urCa9AqG1fG2A+NZeyHxjLjihm88+t3WPOjNfz7pH9TtVcVNfNquqxvpUbN3BpGHDaCYXOGKYMl/VKQwdSOzFQvwdTIkTB9OmzY4BvSRURyKLEBvaOtg6Lyrj0/lTMriW6N0r61ndJRpf1/nAIOphIVVxYz6XOTmHDBBDbdtYl1161j24PbuqwT2x7j7RvfBqBkdAkjDh3h+64OU9+VZK6wg6nyDAKkAw7wXwf1bUREcqxLmS/SQUlt1110vL+p5T8tAwqmOrZ3AIUfTMUVlRYx/ozxjD9jfLfrnHO0/KeF+sfrqX/CX7bcs6PvquZgn7Ua+6Gx1BxQ0+32IlDgwVRlWVXvK//whz4zJSKSY8llvuSsSGcwtaKF4QcN7/fj7CyZqUyYGcNmDmPYzGFMOHcCEPRdPbEjuFpz5RrWfH8NI48ZydSLp1J7ZK3KgdJFwQZT5qC0MoNgarfd/EVEJMeSy3zJPVMVu1aAQcubA2tCVzDVs7JxZYw9ZSxjTxkLQLQ+yoYbN7DumnW8dPRL1MyrYcpXpjBsVjjaQypmVFAyPP3HvYs5ml9vxkVdn+/bSo3K2ZUUlezc5dCCDKZa2luoiIJVFMYhvyKyc4hGd5T5UmWmiiuKKZ9Snr1gapiCqUyUjChh6lenMumiSWy8dSNrrlrDq//1aq6HlbHimmImfnoik78wmfIJOyaz7mjrYOPv/Pa0vNH/11RRVRHD5w+n9vBaRhw2gqq9q7DicGTuimuKs9IXV5DBVGu0lYooUKlgSkTCo7fMFPhSXzaCqaLKotB84OWL4opiJn5qIrt8chfqHq0j1hjL9ZB65WKOzXdvZu3Va1l33Tp2OWcXJn1uEtse2sbaq9fStr6N6v2r2f3m3SkZ1feQILY9RuPTjdQ/Uc+q76yCvie3cmqve/dizIljBnw/hRlMtbdQ2Q6MUDAlIuGR2IDuIq7b0Xzgg6nNd28e2ONsj6nENwBFJUWMOiY8Ez2P+/A4Znx3Bmt/vJa3f/M2b9/kj16sXVDLHr/eg5HHjhxQD9guZ/pJZKP1Ueqfqs/KXGhDpeo9GbQDZaAwg6m27T4zVdHLpJ0iInkksQG9o60jZfmhcmYl7Zvbaa9rp7S2f0f0xZoUTO1sKnerZPYvZzPt0mlsumuTP0px/oisPkbJiBJGHz8ajs/q3YZCYQZTkWaV+UQkdBLLfKkm7QQ/cSdA639aKT1AwZT0TfmEciZ/fnKuh1FwCrL9vkWZKREJoeTTyaTLTMHAjuiLNcUorlIwJZItBRlMtQZH8ykzJSJhEi/zOefSZ6Z29fu15hXN/X4cZaZEsqtgg6lKZaZEJGTiZb74fD+pMlPFw4opm1Q28MyUgimRrCnYYEqZKREJm/g8Ux0Rf7oXK099hNWwWcMGFkzpaD6RrCrIYKolPs+UMlMiEiKdmam29Jkp+P/t3X2Q1NWd7/H3d56HGUAZiavAgorBJxCXUeRiYjQ+JildDdb4kI1310oqejFGsyVauTEkS9UmWYpoakmtVjSoi4rx3pTES258vDGmYuTBIYgIjAYRZAOOiExPT/d097l//Lp7umd6Znpmuuk+7edVZc30r390n4H5HT/9Ped3ztjXmlJlSqSwKjJM9cQjqkyJiFec65uAnogmK1M55kxBcnmEv/YS+zg2qvdSmBIprMoMU4mIKlMi4pVEkJ/yrkwBhN8eeXXKxR2J7gRVTRXZ/YuUREVeTT3xaLACuipTIuKJeHJnkurq/CpTMLrlEeLd2uRYpNAqM0y5qIb5RMQrqTBVUxNsJQPk3E4GxhimQgpTIoVWcWHKOUfY9WqYT0S8EktOf8qsTA02zFfdVE3dcXWj2gMt3qUwJVJoFRemehO9OJwqUyLilcxhvtScqcGG+WD0d/QpTIkUXsWFqZ5YD4AW7RQRr6QqU5l38w1WmYIChCltJyNSMBUbphpiQF1daRsjIpKnEVemTm4kui+angOV9/uoMiVScJUbpqwWbPCOSESknGROQM+3MgUjn4SuMCVSeBUXpsK9QcfSUKWqlIj4I2sC+jDbycDow1QiFLy2wpRI4VRcmEpXphSmRMQjuYb5VJkS8UPFhqnG6voSt0REJH+5hvmGmjNVM76G2mNrFaZEykDFhqmGGt3JJyL+yBzmy6cyBUF1qntn94jeJ94VB4Oqhorr/kVKpuKupnAsOWeqWmFKRPwxku1kUkazPEJqk2PTDToiBVNxYSpdmarVgp0i4o/MdabSlalBtpNJaZzZSHRvNL3fXj7iobiG+EQKrILDlCpTIuKPrMpUZPilESBjEvrb+VenUpUpESmcig1TjXVNJW6JiEj+sjY6zmPRToBxJ48DGNEefQpTIoVXsWFKw3wi4pNcGx1bzTBzpj4d9HPd2/OfhB7vimsrGZECq7gwlV60s6G5xC0REcltxw7o7c0+1n+dKauzYSeJ14yvoe74upGHKVWmRAqq4sJUujJVN67ELRERGejQIZg9G1avzj7ef52p4eZLpYybNY7wdg3ziZRSxYap+kZVpkSk/Hz0EUSjsHdv9vH+28kMtZVMpnGzxtG9vRvnXF7n624+kcKryDDV0AvWqMqUiJSfcLKI9PHH2cf7D/PlW5lq/HQjsYMxej/oHf5kVJkSKYbKC1O9YRpiQIOWRhCR8jNYmMpcZyoRTQx7J1/KuFnJO/p25DfUF++KU9VUcV2/SElV3BUVjhwOwlSj7uYTkfJT6MpUKkzlMwk9EU3gok6VKZECq7gw1RPpVmVKRMrWcGFqpJWphhkNWJ3lFabiIW1yLFIMlRemot00qjIlImVquGG+kVamrNqCDY8VpkRKpiLDlCpTIlKu8hnmS0QSw+7Ll2ncp/NbHiHepTAlUgyVF6ZSE9BVmRKRMpTPMF9q0c58Nc5qJPx2mEQsMeR5ClMixVFxYSrcq8qUiJSv7uRo3FDDfCNZtBOCSeiu19Gzq2fI89JhStvJiBRUxYWpnliPKlMiUrbyvZtvJJWp9PIIwwz1qTIlUhwVGKYiNPaiypSIlKVUmIpGIRLpO95/namRVqZg+OURFKZEiqPywlQ8osqUiJStcEbx6NChvu8HVKby3E4GoLallppJNcOGqUQomFOlMCVSWBUXpsIJhSkRKV+ZYSpzqC9rnanIyCpT0LdH31BUmRIpjooLUz2JqCagi0jZGixM9Z+APpI5UxCEqbznTGkCukhBVV6Ycr1atFNEytZwlamRLtqZ0jirkeh/RYl9HBv0nHhXHKu1Eb+2iAyt4q6oHterypSIlK28hvlGWZkC6N4x+FBfvCuuIT6RIqioMBVLxIiT0JwpESlb4XBf9zTYMN9oKlP5LI8QDylMiRRDRYWpcG/QiTTEgNra0jZGRCSH7m449tjg+8GG+Ua6nQxA40mNUDX08giqTIkUR0WFqZ5YsPpvg9WCjaxELiJyJITD8Dd/E3yfqzJVhYMEIx7mq6qvomFGg8KUSAlUZJhqtLoSt0REJLdwGCZNCuZG9a9MmQHJ/fVGM0l8uOUR4l1x3cknUgQVGaYaqhSmRKQ8peZMTZgwMEyl5kvByCtTkFweYWcYl3A5n1dlSqQ4KjNMVdeXuCUiIrkNFqZisb47+WB0lanGWY0kuhNE9kZyPq8wJVIcFRWmwrHkBHSFKREpU0WtTH166D36dDefSHFUVJhKV6ZqtMaUiATM7DIz225mHWZ21xDnfdnMnJm1FrM9qTA1ceLAMJXaSgYY8d18MPzyCKpMiRRHRYapRoUpEQHMrBpYCVwOnAZcZ2an5ThvPHAb8Kdit2moYb7UVjIwuspU3fF1VDdX56xMOeeId8Wpaqqobl+kLFTUVZWuTNVqwU4RAeAcoMM5945zLgo8AVyZ47x/AX4E9BSzMfE4RKN9YerQoeznMof5RjNnysxo/HRjzjCV6ElAQpscixSDl2Hq6beeZsa9M9LhKSW9aKfClIgEpgDvZTzekzyWZmZ/B0xzzv2foV7IzL5uZhvMbMOBAwdG1ZjUVjL5TEAfTWUKBl8eIb3JscKUSMF5Gabe2P8G7x56l92HdmcdT1em6saVolki4hkzqwJWAN8e7lzn3APOuVbnXOvkyZNH9X6pMDVu3PAT0Ee7GXHjyY1E3oukQ1n69UMKUyLF4mWY6op2AbDn4z1ZxxWmRKSfvcC0jMdTk8dSxgNnAP/PzHYB5wJrizUJvX9lqqcnGPaDvjA1lgnoAA0zGiABkT3ZyyOoMiVSPF6GqVBvCBg8TDXWNx/xNolIWVoPnGxmJ5hZHXAtsDb1pHPukHPuGOfcDOfcDOBV4Arn3IZiNKZ/mAI4fDj4mhrmG8vSCJAMU0DPruxpEOkwpRXQRQrOyzCVqkzt/Xhv1vF0Zaq+6Yi3SUTKj3MuBiwGfgtsA550zm01sx+Y2RVHuj25wlRqqC9dmRrDop2QR5hSZUqk4GpK3YDRGKwylVq0s75RlSkRCTjn1gHr+h27Z5BzP1fMtmSGKZfc8SUzTBWiMlU/tR6qFKZEjiQ/w1Q0GaYO9xvmi3ZTF4OqRs2ZEpHykxmmapK9bypM9V9narSVqaraKuqn1itMiRxBXoapQSegR0I0xoAJWrRTRMpPZphqTK7g0n+Yb6yVKQiG+vqHqUQoCGkKUyKF5+WcqdQw34A5U5EQDTH6eikRkTIy1Jyp9DpTY7ybD3KHKVWmRIrHyzCVqkz9NfRXovFo+ng6TDWoMiUi5ac7uZZmPhPQx1qZiuzNXmsqHabGKUyJFJqXYSoUDVFtQYfw/uH308fDUVWmRKR8ZS7aOXFi8P1gw3yjnTMFudeainfFqWqswqpHH9JEJDcvw1RXtIsTjz4RyJ431RMNqzIlImUrc5hv3DioqsoxzFegyhRk39EX74priE+kSLwMU6HeELOOmQVkz5vq6Q3T2IsqUyJSljLDlFn2ZscDKlO1Y6xM0S9MhRSmRIrFuzDVG+8lGo8yqyUIU1mVqViPKlMiUrbC4SBE1dcHjzP350utM5WIJqCaMQ3H5VprSpUpkeLxLkyl7uQ7fvzxNNc15w5TqkyJSBkKh4PPepbMSZlhKrXOlIu4Md3JB7nXmop3xbWVjEiR5HXFmtllZrbdzDrM7K4cz99hZm+a2Z/N7AUzm174pgZSC3Y21zUzZfyUrIU7w3GFKREpX+FwdvfUvzKVuptvLJPPU/ovj6DKlEjxDHvFmlk1sBK4HDgNuM7MTut32utAq3NuDvAU8ONCNzQltSxCU20TUydMza5MxSMa5hORsjVUmMrc6Hgsk89TFKZEjpx8Pv6cA3Q4595xzkWBJ4ArM09wzr3knEuuoMKrwNTCNrNPapivua6ZqROmZk9AT0SDFdBVmRKRMnSkK1OZa00pTIkUTz5X7BTgvYzHe5LHBnMT8JtcT5jZ181sg5ltOHDgQP6tzJCuTNUFlan3D79PPBEsRteTiKoyJSJlq7t7+DBVyMpU5lpTuptPpHgKOgHdzL4CtAL/lut559wDzrlW51zr5MmTR/Ue/edMxV2cv4b+CkDY9WrOlIiUrXyG+RKRxJgnoMPA5RFUmRIpnnyu2L3AtIzHU5PHspjZRcB3gCucc5H+zxdKapgvNWcK+pZH6KFXlSkRKVvhcLBYZ8qECRAKBVWpzGG+glWmCMKUizsS3Qmqmry7gVvEC/lcWeuBk83sBDOrA64F1maeYGZnAfcTBKn9hW9mn9QwX2rOFAQLd8YSMWIkaIwZ1NYWswkiIqOSqzIFcPhw3zpTLuoKMmcqc62peLc2ORYppprhTnDOxcxsMfBboBp4yDm31cx+AGxwzq0lGNZrBn5pwQIqu51zVxSjwalhvqa6JprrmoGgMhWJBcWwBqvpW8RFRKSM9A9TmfvzpdaZKlRlKnOtqfQmxwpTIkUxbJgCcM6tA9b1O3ZPxvcXFbhdg8pcGmFc7TjqquvY8/EeemLBvIAGU1VKRMrTYJWpjz/OnoBeiMoUJJdHeLeHeEhhSqSYvBtAD/WGMIzG2kbMLL1wZzgWbHrVYHUlbqGISG5DhanMjY4LUZmCvrWmVJkSKS7vwlRXtItxVfVULfhvEImk15pKV6aqFaZEpDzlVZkqwHYyKQ0zGojsiRA7GAPQdjIiReJdmApFQzQnauBPf4KOjvQq6Kkw1VitO/lEpDwNFqYOHSr8op3Qt9ZU91vBmsqqTIkUh39hqjdEE8l5UTt3BsN8H+8h3Jsc5quuL2HrRERyi8chEhl+mK9Qi3ZC3/IIoTeCG3cUpkSKw7sw1RXtojmRDFM7djB1wlQi8Qh7DwdLXzXUqDIlIuWnJ7lN3nDDfAWvTKEwJVJs3oWpUG+IJpfsEHbuTK811fFhB6AwJSLlKRwUz7MW7WwOVndJh6lCV6ZSa00pTIkUl3dhqivaRVM8uaJDrjBVq61kRKT8pMJUZmWqqgrGjy/enKnUWlOxD5MT0BWmRIrCuzAVioZojiebvWMHUyYEey6nwlRj7bjB/qiISMnkClMQDPV99FHwfXV1sDef1Rdu4eHUUB8GVQ3edfkiXvDuyuqKdtEUSzZ73z7+hmaqrKqvMlWvMCUi5WeoMPXhh8H3hdxOJiUVpqqbqzHtDiFSFN6FqVBviObevg6h5p1dHNd8HLsP7QagoU5hSkTKz1Bh6uDB4PvqKofrLdycKcgOUyJSHN6FqaAyZcFHOEjPm3I4ABoamkvYOhGR3PIJUzXJfqxYlSkRKQ6vwlTCJeju7aY5Cpx8cnAwY94UQGPD+NI0TkRkCN3BupkDwtTEiX3DfLWWAFBlSsQzXoWp1MKcTVEHkybB8ccHlanxU9Pn1Nc3lap5IiKDyqsy5ZKVqQJtJwMZYUpbyYgUjVdhqivaBUBTxEF9PXz601nLI9TGoXqcwpSIlJ+hwlRqQc9agspUIYf5UmtNqTIlUjxehalQb7DwXHNPMkydfHJWmGqIAQ1atFNEyk+uRTuhbxV0gOpkZaqQw3xVtVXUT6uneqLClEix1JS6ASORrkz1xKExGaYOHGBKVdAbNcQY+LFPRKQMDFWZSqlxha9MAZz6yKnUHlNb0NcUkT5ehalQNFmZCsfhqPr0JPSpH/QC0NiLKlMiUpbyClMUvjIFcNRnjyro64lINq+G+dKVqXAsCE2f/jQAx78XLB+sypSIlKtUmOr/eS9rmC9RnMqUiBSXV1dses5UdyyYM3XiiWBGQ8cuJtcdrTlTIlK2wuGge+q/CHmuMFXI7WREpPj8ClPJYb6mUG8Qphoa4G//FnbuZEpdiypTIlK2wuHc3VP2nKnCL9opIsXn1RWbHuZLhSlI39H3D5Mu4MvbUGVKRMpSd/fwYaoqUfhFO0Wk+LwKU+lhvsM9fWEqudbUHUddzp1/QJUpESlL+VSmqhOqTIn4yKsrNl2ZipJdmfroI3jvveCxwpSIlKG8wpRTZUrER16FqVA0RH11PTUJssMUwJYtwVcN84lIGQqHBy7YCTA+YzvRKlWmRLzk1RXbFe2iqSbZG/UPU3/+c/BVlSkRKUODVaZqaqApuQtWdTy5NEIB9+YTkeLz6ooN9YZoru0Xpk44Aaqr4Y03gseqTIlIGRosTEHfUF+qMqVhPhG/eBemmqqTvVEqTNXWBoGquzt4rMqUiJShvMJUXIt2ivjIqyu2K9pFc/8wBX1DfVVVQc1cRKTMqDIlUrm8ClOhaIimqmSIyhWmGhsHLi8sIlIG8glTpsqUiJe8umK7ol00WY4wldyjT/OlRKRcDbZoJ2RUpmLaTkbER16FqVBviOZUmMoMTpmVKRGRMpRfZcqBgVUrTIn4xKsw1RXtoom64MFgw3wiImUmkYBIJL9hPqszTNMVRLziVZgKRUM0Uxs8yAxTf/u3UFenYT4RKUs9PcHXXIt2QubdfE7zpUQ85M1V65wLlkZwOcJUdTWcdJIqUyJSlsLh4OtwlSliCd3JJ+Ihb9YRiMajxBIxmhPJJmeGKYCvfhVisSPfMBGRYeQbpiymypSIj7wJU6HeEABN8ergQP8wddddR7hFIiL5GS5MXXQR3HADjLMEh7WVjIh3vLlqu6JdADTFk03uH6ZERMrUcGFq5kz4z/8MKlMa5hPxjzdhKhQNKlPNg1WmRETK1HBhKiURTWiYT8RD3ly16cpULPmpTWFKRDyR79ahLqrKlIiPvAlTqTlTzb0KUyLiF1WmRCqbN1dtapivqZdg/z1taCwinsg7TEW0NIKIj7wJU6lhvuYIQVVKKwSLiCdSYWqwRTtTXNRRpbv5RLzjzVWbXhoh6jTEJyJeGckwnypTIv7xJkylJ6BHnLaNERGv5BumXFSLdor4yJurNr00Qk9ClSkR8YoqUyKVzZsw1RXtosqqqO/pVZgSEa+kwlRDQzDJfMNZGzj44sEB56kyJeInb67aUG+I5rpmLBJVmBIRr4TDQbdVVQXRA1G62rv4+I8fDzgvEUloArqIh7y5akPREE21TRCJKEyJiFe6u/uG+BLhBAC9nb0DztOinSJ+8iZMdfV20VzXDD09ClMi4pVwOCNMdSfD1AcDw5QW7RTxkzdXbSgaoqlOlSkR8U9mmIp3xwFVpkQqiTdhqivapWE+EfFSONy3YGd6mE+VKZGK4c1Vm5qArjAlIr7JpzLl4g4SqDIl4iFvwlRXtEvDfCLipaw5U8nKVKwzlnVOIhIc1918Iv7x5qoNRVWZEhE/5ZqAHvsoRiKWSJ+TiAbfqzIl4h9/wlSvlkYQET9lDfOF4+njsQ/7qlMu6gA0Z0rEQ95ctV3RLlWmRMRLuSpTkD0JXZUpEX95EabiiTg9sZ6+ypQ2OhYRj+RatBOyJ6GrMiXiLy+u2lBvsMmxJqCLiI9y3c0H/SpTEVWmRHzlR5iKBmGquWYcxOMKUyLilVx388EglSndzSfiHS+u2q5oFwBNVhccUJgSEU84F+yClVq0M94dp6op6Hozl0dIzZnSMJ+If7y4alPDfM2WDFEKUyLiiZ6e4GtmZar2mFqqGqqyhvlSlSkN84n4p6bUDchHapivydUGBxSmRMQteoyDAAAgAElEQVQT4XDwNfNuvupx1XBM9jCfKlMi/vIiTKWG+ZoVpkTEM/3DVDwcp6qxCqszVaZEKoQXYSp9N18i2VyFKRHxxGCVKauz7MqUtpMR8ZYXV216AnqiOjigMCUinhgQpsIJqhqrqD2mNucwnypTIv7xIkyll0ZQmBIRz3R3B18z15mqGldFbUttzmE+zZkS8Y8XV226MhVTmBIRv+Qc5muspvaYWmIHY7h4EKJUmRLxlxdhKjVnalxqSRaFKRHxRM4J6MnKFAmIfRR0bKpMifjLi6s2FA3RWNNIdTSZphSmRMQTqTCVWrQz0R3MmappCW6oSc2bUmVKxF9ehKmuaBfNdc3BvnygjY5FxBu5JqBXjwuG+aAvTLmItpMR8ZUXV22oN9S3yTGoMiUi3sgMUy7hSPQk7+ZrSYapD7IrUxrmE/GPF1dtV7SLplqFKRHxT2aYSvQEgSlnZSq1aGethvlEfONFmAr1hrKH+RSmRMQTmWEq3h0H6JuATnZlymoMq1KYEvGNF2GqK9qlYT4R8VJWZSqcHMprrKJ6fDVWa8Q6++7m0+RzET95EaZCUVWmRMRP3d1QVwdVVcGdfBAM85lZ1sKdiUhC86VEPOXFlZueM9XTExxQmBIRT4TD2WtMQVCZAqhpqclaGsHqVZkS8ZEXYWrAnKna2tI2SEQkT5lhKrMyBWTtz+eiTpUpEU95ceWGoqG+u/nq68H06U1E/BAOZyzYmTFnCsge5osmNGdKxFNlH6acc9kT0DXEJyIjYGaXmdl2M+sws7tyPH+Hmb1pZn82sxfMbHoh3z9rmC/jbj5QZUqkUpT9ldsT68Hh+ob5FKZEJE9mVg2sBC4HTgOuM7PT+p32OtDqnJsDPAX8uJBtyBrmS1amqhuTw3wttcQ6YzjnVJkS8VjZh6muaBdA9jCfiEh+zgE6nHPvOOeiwBPAlZknOOdecs51Jx++CkwtZAOGrEy11OJijvjHcVzEaSsZEU+V/ZUb6g0BqDIlIqMxBXgv4/Ge5LHB3AT8JtcTZvZ1M9tgZhsOHDiQdwNyVabSc6YyVkFPRLU0goivakrdgOEkXII5x87h2OZjgzClTY5FpAjM7CtAK3B+ruedcw8ADwC0tra6fF/3+9/vuwG5/918NS1BF9z7Qa8W7RTxWNmHqROPPpHN39gcPIj8TJUpERmJvcC0jMdTk8eymNlFwHeA851zkUI24NJL+75PD/MNUpmqbdayLyI+8qumrGE+ERmZ9cDJZnaCmdUB1wJrM08ws7OA+4ErnHP7i9mYRDjYf6+qtm/OFKgyJeI7hSkRqVjOuRiwGPgtsA140jm31cx+YGZXJE/7N6AZ+KWZtZvZ2kFebszi3fH05HPoV5nSdjIi3ir7Yb4skQgcfXSpWyEiHnHOrQPW9Tt2T8b3Fx2ptiTCifQQH0DNxBqoCipT2k5GxF9+fQxSZUpEPJboTqQnnwNYlVE7KbnWlBbtFPGWX1euwpSIeCwejmdVpqBvFXQt2iniL7/CVE+PwpSIeKt/ZQqC5RFSE9BVmRLxk19XripTIuKx/nOmQJUpkUqgMCUicoT0v5sPguURej/oDbaTUWVKxEt+XbkKUyLisUQ4kd7kOKX2mGSYimlvPhFf5XXlmtllZrbdzDrM7K4cz3/WzDaZWczMFhW+mUkKUyLiscEqUy4a7E6jYT4RPw0bpsysGlgJXA6cBlxnZqf1O2038N+BxwrdwLREAmIxhSkR8VbOOVMtfVvIaJhPxE/5LNp5DtDhnHsHwMyeAK4E3kyd4JzblXwuUYQ2BiLJ7bK00bGIeCrX3XypVdBBlSkRX+XzMWgK8F7G4z3JYyNmZl83sw1mtuHAgQMj+8OpMKXKlIh4Kh4eOMxX09L3mVaVKRE/HdEr1zn3gHOu1TnXOnny5JH9YYUpEfGYiztcxOWcgJ6i7WRE/JRPmNoLTMt4PDV57MhSmBIRj8XDcYCcE9BTVJkS8VM+V+564GQzO8HM6oBrgaLtqj4ohSkR8VgiHEwp7T8BvebovmE+zZkS8dOwYco5FwMWA78FtgFPOue2mtkPzOwKADM728z2ANcA95vZ1oK3VGFKRDyW6A7CVP8J6FU1VelApcqUiJ/yuZsP59w6YF2/Y/dkfL+eYPiveBSmRMRj6WG+xoGBqballtjBmCpTIp7y52NQT0/wVWFKRDw0WGUK+iahqzIl4id/rlxVpkTEY4PNmYK+5RG0nYyIn/y5chWmRMRj8e7cd/NBX2VKw3wiflKYEhE5AlKVqf7rTEHf8gga5hPxkz9XrsKUiHhsyMpUiypTIj5TmBIROQKGmjNVOzlZmdKcKREv5bU0QlnQRsci4rGh7uabvGgyiXCChhPUv4n4yL8wpcqUiHhosO1kAGqPrmXqN4u7VJ+IFI8/NWWFKRHxWLoylWMCuoj4TWFKROQISIQTWJ1h1ZpkLlJp/AtTdXWlbYeIyCjEu+M5J5+LiP/8ubIjkSBImT7ViYh/EuFEzsnnIuI/v8KUhvhExFOqTIlULn+ubIUpEfFYoluVKZFK5U+Y6ulRmBIRbyXCCVWmRCqUP1e2KlMi4rF4dzznGlMi4j9/rmyFKRHxWCKc0BpTIhVKYUpE5AhQZUqkcvlzZStMiYjHtDSCSOXyK0xpk2MR8VSiWxPQRSqVP1e2KlMi4rF4WMN8IpXKnytbYUpEPJbo1gR0kUqlMCUiUmSJWALX61SZEqlQ/lzZClMi4qlEOAGgOVMiFcqfK1thSkQ8lQpTuptPpDIpTImIFFm8Ow6oMiVSqfy5shWmRMRTqkyJVDZ/wpQ2OhYRTyW6NWdKpJL5cWUnEtDbqzAlIl6Kh5PDfLqbT6Qi+XFlR6PBV4UpEfFQqjKlYT6RyuRHmIpEgq8KUyLiIU1AF6lsflzZClMi4jFNQBepbH6FKW10LCIe0gR0kcrmx5WtypSIeEwT0EUqmx9XtsKUiHgsPQFdGx2LVCSFKRGRItPefCKVzY8rW2FKRDwW745j9YZVWambIiJFoDAlIlJkiXBCd/KJVDCFKRGRIot3xzXEJ1LB/Li6FaZExGOqTIlUNoUpEZEiS3QnVJkSqWB+XN09PcFXhSkR8VA8HNcaUyIVzI+rW5UpEfFYolvDfCKVTGFKRKTIEmEN84lUMj+uboUpEfFYvDuuypRIBfMrTGmjYxHxkCpTIpXNj6s7Fabq6krbDhGRUYh3awK6SCXz4+qORKC2Fqr8aK6ISKZEd0KbHItUMD/SSSSi+VIi4q1EOKHKlEgF8+PqVpgSEU8lehO4mNOcKZEK5sfVrTAlIp5KhBMAuptPpIIpTImIFFG8Ow6gypRIBfPj6laYEhFPqTIlUvkUpkREiijRHYQpTUAXqVx+XN09PQpTIuKleFjDfCKVzo+rW5UpEfFUqjKlYT6RyqUwJSJSRKk5U6pMiVSumlI3IC+RCLS0lLoVUgZ6e3vZs2cPPT09pW6KDKGhoYGpU6dSW1tb6qaUXOpuPlWmpFDUDxbXaPovf8KUNjkWYM+ePYwfP54ZM2ZgZqVujuTgnKOzs5M9e/ZwwgknlLo5JafKlBSa+sHiGW3/5cfVrWE+Serp6aGlpUUdSBkzM1paWvSpOSm9zpTu5pMCUT9YPKPtv/y4uhWmJIM6kPKnf6M+6XWmtNGxFJCuseIZzd+twpSISBFpnSmRyufH1a0wJRVuw4YNfPOb3xz0+ffff59FixYV5L3+8pe/MH/+fGbOnElbWxvRaHTAOatXr2bu3Lnp/6qqqmhvbwfgc5/7HLNmzUo/t3///oK0q1Klh/ka/OhuRUpl1apVLF68GIClS5eyfPnyAedEIhHa2tqYOXMm8+fPZ9euXTlfa8aMGcyePZu5c+fS2tpazGYDClMiRRGPx0d0fmtrKz/96U8Hff7444/nqaeeGmuzAFiyZAm33347HR0dHH300Tz44IMDzrnhhhtob2+nvb2dRx99lBNOOIG5c+emn1+9enX6+U996lMFaVelSoQTVDVWaVhGKpJzjkQiccTe78EHH+Too4+mo6OD22+/nSVLlgx67ksvvUR7ezsbNmwoervK/24+5xSmJLdvfQuS1ZKCmTsX7r130Kd37drFZZddxrx589i0aROnn346jzzyCOPGjWPGjBm0tbXx3HPPceeddzJp0iS+973vEYlEOOmkk/jFL35Bc3Mz69ev57bbbiMUClFfX88LL7zAxo0bWb58Oc888wy/+93vuO2224Bg7P7ll1+ms7OTL33pS7zxxhv09PRw8803s2HDBmpqalixYgUXXHABq1atYu3atXR3d/P2229z1VVX8eMf/zir/c45XnzxRR577DEAbrzxRpYuXcrNN9886M/8+OOPc+211xbgL/eTKd4d1xCfFE0JukF27drFpZdeyvz589m4cSPr1q3jySef5MknnyQSiXDVVVfx/e9/H4BHHnmE5cuXY2bMmTOHRx99lF//+tcsW7aMaDRKS0sLq1ev5thjj82rbU8//TRLly4FYNGiRSxevBjnXMk/rJT/Fd7bG3xVmJIysX37dm655Ra2bdvGhAkT+NnPfpZ+rqWlhU2bNnHRRRexbNkynn/+eTZt2kRraysrVqwgGo3S1tbGfffdx+bNm3n++edpbGzMev3ly5ezcuVK2tvb+f3vfz/g+ZUrV2JmbNmyhccff5wbb7wxfedJe3s7a9asYcuWLaxZs4b33nsv6892dnZy1FFHUVMTfI6aOnUqe/fuHfLnXbNmDdddd13WsX/8x39k7ty5/Mu//AvOuZH9BX7CJMIJTT6XirNz505uueUWtm7dyvbt29m5cyevvfYa7e3tbNy4kZdffpmtW7eybNkyXnzxRTZv3sx9990HwHnnncerr77K66+/zrXXXjvgQ99Q9u7dy7Rp0wCoqalh4sSJdHZ2DjjPzLjkkkuYN28eDzzwQGF+6CGUf2UqEgm+KkxJf0N9dCqiadOmsXDhQgC+8pWv8NOf/pR//ud/BqCtrQ2AV199lTfffDN9XjQaZcGCBWzfvp3jjjuOs88+G4AJEyYMeP2FCxdyxx13cMMNN3D11VczderUrOdfeeUVbr31VgBOOeUUpk+fzo4dOwD4/Oc/z8SJEwE47bTTePfdd9Mdz2j86U9/Yty4cZxxxhnpY6tXr2bKlCkcPnyYL3/5yzz66KN89atfHfV7VLpEd0KVKSmaEnWDTJ8+nXPPPReAZ599lmeffZazzjoLgK6uLnbu3MnmzZu55pprOOaYYwCYNGkSEKyT1dbWxr59+4hGo0VZj+6VV15hypQp7N+/n4svvphTTjmFz372swV/n5Tyv8IVpqTM9C8nZz5uamoCguG0iy++OD2v6M0338w5NymXu+66i5///OeEw2EWLlzIW2+9lXfb6jOuk+rqamKxWNbzLS0tfPTRR+nje/bsYcqUKYO+3hNPPDGgKpU6f/z48Vx//fW89tprebfvkygejmvBTqk4qb4Ogv7u7rvvTvd3HR0d3HTTTYP+2VtvvZXFixezZcsW7r///hGt6TRlypR0xT0Wi3Ho0CFacuyQkuqnPvWpT3HVVVcVvZ8q/ys89ZesMCVlYvfu3fzxj38E4LHHHuO8884bcM65557LH/7wBzo6OgAIhULs2LGDWbNmsW/fPtavXw/A4cOHBwSet99+m9mzZ7NkyRLOPvvsAWHqM5/5DKtXrwZgx44d7N69m1mzZuXVdjPjggsuSE9mf/jhh7nyyitznptIJHjyySez5kvFYjE++OADINjS4plnnsmqWslAie6EtpKRinbppZfy0EMP0dXVBQRDcfv37+fCCy/kl7/8ZXoY7sMPPwTg0KFD6bDz8MMPj+i9rrjiivSfeeqpp7jwwgsHfMANhUIcPnw4/f2zzz5b9H6q/MOUKlNSZmbNmsXKlSs59dRTOXjwYM7J25MnT2bVqlVcd911zJkzhwULFvDWW29RV1fHmjVruPXWWznzzDO5+OKLB3wqu/feeznjjDOYM2cOtbW1XH755VnP33LLLSQSCWbPnk1bWxurVq3KqkgN50c/+hErVqxg5syZdHZ2pj9Brl27lnvuuSd93ssvv8y0adM48cQT08cikQiXXnopc+bMYe7cuUyZMoWvfe1reb/3J1Hqbj6RSnXJJZdw/fXXs2DBAmbPns2iRYs4fPgwp59+Ot/5znc4//zzOfPMM7njjjuAYNmDa665hnnz5qWHAPN100030dnZycyZM1mxYgU//OEPgWD5mC984QsA/PWvf+W8887jzDPP5JxzzuGLX/wil112WWF/6H6sVJNHW1tbXV63K77zDnzxi/Bv/wZf+lLxGyZlbdu2bZx66qkle/9du3al76qToeX6tzKzjc654i/6UmR591/A1rat1EysYdYD+VUPRYZT6n7wk2Ck/Vf5T0A/8UTYtq3UrRARGZXT15xe6iaISJGp9iwyAjNmzFBVSkREsihMiYiIiIyBwpSIiIjIGChMiYiIiIyBwpSIiIjIGChMiZSBDRs28M1vfnPQ599//30WLVpUkPf6y1/+wvz585k5cyZtbW1Eo9EB5+zatYvGxkbmzp3L3Llz+cY3vlGQ9xYRGcyqVatYvHgxEKxFtXz58gHnRCIR2tramDlzJvPnz2fXrl0Dztm+fXu675o7dy4TJkzg3uS+O0uXLmXKlCnp59atW1eQtpf/0ggiHorH41RX57/qdWtrK62tgy+/dPzxx6dXLR+rJUuWcPvtt3PttdfyjW98gwcffDDnwqMnnXQS7YXejl5EKopzDuccVVVHpjbz4IMPcvTRR9PR0cETTzzBkiVLWLNmTdY5s2bNSvdd8XicKVOmcNVVV6Wfv/3229P7qRaKwpR461v/91u0/1dh/2c/92/mcu9lg+8cumvXLi677DLmzZvHpk2bOP3003nkkUcYN24cM2bMoK2tjeeee44777yTSZMm8b3vfY9IJMJJJ53EL37xC5qbm1m/fj233XYboVCI+vp6XnjhBTZu3Mjy5ct55pln+N3vfsdtt90GBNu/vPzyy3R2dqYXC+3p6eHmm29mw4YN1NTUsGLFCi644AJWrVrF2rVr6e7u5u233+aqq64asBu7c44XX3yRxx57DIAbb7yRpUuX5gxTIlL+dn5rJ13tXQV9zea5zZx878mDPr9r1y4uvfRS5s+fz8aNG1m3bh1PPvkkTz75JJFIhKuuuorvf//7ADzyyCMsX74cM2POnDk8+uij/PrXv2bZsmVEo1FaWlpYvXo1xx57bF5te/rpp1m6dCkAixYtYvHixTjnBmwpk/LCCy9w0kknMX369JH9JYyQhvlERmj79u3ccsstbNu2jQkTJvCzn/0s/VxLSwubNm3ioosuYtmyZTz//PNs2rSJ1tZWVqxYQTQapa2tjfvuu4/Nmzfz/PPP09jYmPX6y5cvZ+XKlbS3t/P73/9+wPMrV67EzNiyZQuPP/44N954Y3pLmvb2dtasWcOWLVtYs2ZNekPQlM7OTo466ihqaoLPUVOnTmXv3r05f86//OUvnHXWWZx//vn8/ve/H/PfW6mY2WVmtt3MOszsrhzP15vZmuTzfzKzGUe+lSJ+2blzJ7fccgtbt25l+/bt7Ny5k9dee4329nY2btzIyy+/zNatW1m2bBkvvvgimzdv5r777gPgvPPO49VXX+X111/n2muvHfChbyh79+5l2rRpANTU1DBx4sT03n+55Nqs/d///d+ZM2cO//RP/8TBgwdH8dMPpMqUeGuoClIxTZs2jYULFwLwla98hZ/+9KfpknFbWxsAr776Km+++Wb6vGg0yoIFC9i+fTvHHXccZ599NgATJkwY8PoLFy7kjjvu4IYbbuDqq69m6tSpWc+/8sor3HrrrQCccsopTJ8+nR07dgDw+c9/nokTJwJw2mmn8e6776Y7npE47rjj2L17Ny0tLWzcuJG///u/Z+vWrTnbW87MrBpYCVwM7AHWm9la59ybGafdBBx0zs00s2uBHwFtR761IiM3VAWpmKZPn865554LwLPPPsuzzz7LWWedBUBXVxc7d+5k8+bNXHPNNen99yZNmgTAnj17aGtrY9++fUSjUU444YSitDEajbJ27Vr+9V//NX3s5ptv5rvf/S5mxne/+12+/e1v89BDD435vVSZEhmh/uXkzMdNTU1AMJx28cUX097eTnt7O2+++SYPPvhgXq9/11138fOf/5xwOMzChQt566238m5b5obH1dXVxGKxrOdbWlr46KOP0sf37NmT3r29/+u0tLQAMG/ePE466aR0YPPMOUCHc+4d51wUeAK4st85VwKpreufAj5vg40ZiAjQ19dB0N/dfffd6f6uo6MjvYF6LrfeeiuLFy9my5Yt3H///QM2ex/KlClT0hX3WCzGoUOH0n1Vf7/5zW/4u7/7u6whxGOPPZbq6mqqqqr42te+xmuvvZb3ew9FYUpkhHbv3s0f//hHAB577DHOO++8Aeece+65/OEPf6CjowOAUCjEjh07mDVrFvv27WP9+vUAHD58eEDgefvtt5k9ezZLlizh7LPPHhCmPvOZz7B69WoAduzYwe7du5k1K79NdM2MCy64ID2Z/eGHH+bKK/tnCzhw4ADxeByAd955h507d3LiiSfm9R5lZgqQOda5J3ks5znOuRhwCBjQO5vZ181sg5ltOHDgQJGaK+KfSy+9lIceeoiurmDu1t69e9m/fz8XXnghv/zlL9PDcB9++CEAhw4dSn+Ie/jhh3O/6CCuuOKK9J956qmnuPDCCwedL/X4448PGOLbt29f+vtf/epXnHHGGSN6/8EoTImM0KxZs1i5ciWnnnoqBw8ezDl5e/LkyaxatYrrrruOOXPmsGDBAt566y3q6upYs2YNt956K2eeeSYXX3zxgE9l9957L2eccQZz5syhtraWyy+/POv5W265hUQiwezZs2lra2PVqlVZFanh/OhHP2LFihXMnDmTzs7O9CfItWvXcs899wDw8ssvM2fOHObOncuiRYv4j//4j3SJ/pPKOfeAc67VOdc6efLkUjdHpGxccsklXH/99SxYsIDZs2ezaNEiDh8+zOmnn853vvMdzj//fM4880zuuOMOIFie4JprrmHevHnpIcB83XTTTXR2djJz5kxWrFjBD3/4QyBYPuYLX/hC+rxQKMRzzz3H1VdfnfXn77zzTmbPns2cOXN46aWX+MlPfjLGnz5gzrmCvNBItba2ug0bNpTkvcVf27Zt49RTTy3Z++/atSt9V50MLde/lZltdM4NvgZEgZnZAmCpc+7S5OO7AZxz/5pxzm+T5/zRzGqA/wImuyE6R/VfUkql7gc/CUbaf6kyJSKVbD1wspmdYGZ1wLXA2n7nrAVuTH6/CHhxqCAlItKf7uYTGYEZM2aoKuUR51zMzBYDvwWqgYecc1vN7AfABufcWuBB4FEz6wA+JAhcIiJ5U5gS7wy1QJuUh3Iq7Djn1gHr+h27J+P7HuCaI90ukbFQP1g8o+m/NMwnXmloaKCzs7Os/mct2ZxzdHZ20tDQUOqmiFQk9YPFM9r+S5Up8crUqVPZs2cPujW9vDU0NAxYbFRECkP9YHGNpv9SmBKv1NbWFm21XBERH6gfLD8a5hMREREZA4UpERERkTFQmBIREREZg5KtgG5mB4B3R/BHjgE+KFJzik1tLw21vTSGavt055z3e7Go//KG2l4aldr2QfuvkoWpkTKzDUdyG4pCUttLQ20vDZ/bXiw+/52o7aWhtpfGaNuuYT4RERGRMVCYEhERERkDn8LUA6VuwBio7aWhtpeGz20vFp//TtT20lDbS2NUbfdmzpSIiIhIOfKpMiUiIiJSdhSmRERERMag7MOUmV1mZtvNrMPM7ip1e4ZiZg+Z2X4zeyPj2CQze87Mdia/Hl3KNg7GzKaZ2Utm9qaZbTWz25LHy779ZtZgZq+Z2eZk27+fPH6Cmf0p+buzxszqSt3WwZhZtZm9bmbPJB970XYz22VmW8ys3cw2JI+V/e/MkeJT/wXqw0rF9z7M1/4LCteHlXWYMrNqYCVwOXAacJ2ZnVbaVg1pFXBZv2N3AS84504GXkg+Lkcx4NvOudOAc4H/kfy79qH9EeBC59yZwFzgMjM7F/gR8BPn3EzgIHBTCds4nNuAbRmPfWr7Bc65uRlrs/jwO1N0HvZfoD6sVHzvw3zuv6AQfZhzrmz/AxYAv814fDdwd6nbNUybZwBvZDzeDhyX/P44YHup25jnz/E0cLFv7QfGAZuA+QSr2Nbk+l0qp/+AqckL9kLgGcA8avsu4Jh+x7z6nSni3413/VeynerDSttur/own/uvZPsK0oeVdWUKmAK8l/F4T/KYT451zu1Lfv9fwLGlbEw+zGwGcBbwJzxpf7LM3A7sB54D3gY+cs7FkqeU8+/OvcCdQCL5uAV/2u6AZ81so5l9PXnMi9+ZI6AS+i/w8N9TfdgR5XP/BQXqw2qK1ToZyDnnzKys16Iws2bgfwHfcs59bGbp58q5/c65ODDXzI4CfgWcUuIm5cXMvgTsd85tNLPPlbo9o3Cec26vmX0KeM7M3sp8spx/Z2TkfPj3VB925FRA/wUF6sPKvTK1F5iW8Xhq8phP/mpmxwEkv+4vcXsGZWa1BJ3Qaufc/04e9qb9AM65j4CXCErLR5lZ6gNDuf7uLASuMLNdwBMEpfL78KPtOOf2Jr/uJ/gfwDl49jtTRJXQf4FH/57qw444r/svKFwfVu5haj1wcvLOgDrgWmBtids0UmuBG5Pf30gwjl92LPj49iCwzTm3IuOpsm+/mU1OfprDzBoJ5klsI+iQFiVPK8u2O+fuds5Ndc7NIPj9ftE5dwMetN3MmsxsfOp74BLgDTz4nTlCKqH/Ak/+PdWHHXk+919Q4D6s1JO/8pgc9gVgB8H48XdK3Z5h2vo4sA/oJRgnvolg/PgFYCfwPDCp1O0cpJkHzvcAAALASURBVO3nEYwd/xloT/73BR/aD8wBXk+2/Q3gnuTxE4HXgA7gl0B9qds6zM/xOeAZX9qebOPm5H9bU9enD78zR/DvyJv+K9le9WGlabv3fZhv/VdGOwvSh2k7GREREZExKPdhPhEREZGypjAlIiIiMgYKUyIiIiJjoDAlIiIiMgYKUyIiIiJjoDAlZcPMPpfadVxExCfqvz7ZFKZERERExkBhSkbMzL5iZq+ZWbuZ3Z/coLPLzH5iZlvN7AUzm5w8d66ZvWpmfzazX5nZ0cnjM83seTPbbGabzOyk5Ms3m9lTZvaWma1OrmqMmf3QzN5Mvs7yEv3oIuI59V9SDApTMiJmdirQBix0zs0F4sANQBOwwTl3OvA74HvJP/IIsMQ5NwfYknF8NbDSOXcm8N8IVl2GYKf3bwGnEaxOu9DMWoCrgNOTr7OsuD+liFQi9V9SLApTMlKfB+YB682sPfn4RCABrEme85/AeWY2ETjKOfe75PGHgc8m90Ka4pz7FYBzrsc515085zXn3B7nXIJgO4gZwCGgB3jQzK4GUueKiIyE+i8pCoUpGSkDHnbOzU3+N8s5tzTHeaPdpyiS8X0cqHHOxQh28n4K+BLwf0f52iLyyab+S4pCYUpG6gVgkZl9CsDMJpnZdILfpdQu4dcDrzjnDgEHzewzyeP/APzOOXcY2GNmf598jXozGzfYG5pZMzDRObcOuB04sxg/mIhUPPVfUhQ1pW6A+MU596aZ/U/gWTOrIthd/n8AIeCc5HP7CeYlANwI/Eeys3kH+Mfk8X8A7jezHyRf45oh3nY88LSZNRB8sryjwD+WiHwCqP+SYjHnRlvNFOljZl3OueZSt0NEZKTUf8lYaZhPREREZAxUmRIREREZA1WmRERERMZAYUpERERkDBSmRERERMZAYUpERERkDBSmRERERMbg/wPGVrNVPcAYKwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vx1ix5ea1KYG",
        "outputId": "bf379d43-7f8e-4f46-d17d-b7dcae9aed82"
      },
      "source": [
        "len(reduce_mean_cls_loss_store)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1450"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "B37M8TvdjSDZ",
        "outputId": "9dacf19e-47cf-4fc2-a97e-c6846ad2b657"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "figure, axis = plt.subplots(1, 3,figsize=(10,10))\n",
        "\n",
        "step = 21\n",
        "mean_regression_store = []\n",
        "mean_cls_store = []\n",
        "mean_conf_store = []\n",
        "for i in range(0,len(reduce_mean_cls_loss_store),step):\n",
        "    mean_regression = tf.reduce_mean(regression_loss_store[i:i+step])\n",
        "    mean_cls = tf.reduce_mean(reduce_mean_cls_loss_store[i:i+step])\n",
        "    mean_conf = tf.reduce_mean(reduce_confident_loss_store[i:i+step])\n",
        "\n",
        "    mean_regression_store.append(mean_regression)\n",
        "    mean_cls_store.append(mean_cls)\n",
        "    mean_conf_store.append(mean_conf)\n",
        "# print(mean_regression_store)\n",
        "\n",
        "X = np.arange(len(mean_regression_store))\n",
        "# Plotting both the curves simultaneously\n",
        "# axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "axis[0].plot(X, mean_cls_store, color='r', label='reduce_mean_cls_loss_store')\n",
        "# axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "axis[0].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[0].legend()\n",
        "axis[0].set_title(\"reduce_mean_cls_loss_store\")\n",
        "\n",
        "# axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "axis[1].plot(X, mean_regression_store, color='b', label='regression_loss_store')\n",
        "# axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[1].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[1].legend()\n",
        "axis[1].set_title(\"c\")\n",
        "\n",
        "axis[2].plot(X, mean_conf_store, color='b', label='reduce_confident_loss_store')\n",
        "# axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[2].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[2].legend()\n",
        "axis[2].set_title(\"reduce_confident_loss_store\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'reduce_confident_loss_store')"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUixFns-r2uv"
      },
      "source": [
        "### 75 epochs. optimizer Adam, weight decay = 1e-7, not normalize input label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6bnk4u10tHz"
      },
      "source": [
        "test_data_resnet_model = run_experiment(resnet_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "nJwzA3T7uGSQ",
        "outputId": "f49bf282-eaa1-496b-ad3c-1ef5d6f45cff"
      },
      "source": [
        "history = test_data_resnet_model\n",
        "#  \"custome mse\"\n",
        "plt.plot(history.history['custom_mse_hungarian_py_func'])\n",
        "plt.plot(history.history['val_custom_mse_hungarian_py_func'])\n",
        "plt.title('model custome mse')\n",
        "plt.ylabel('custome mse')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# \"Loss\"\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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5CCGEg9mtSJvW+tp6nhpmr/esKSLYl6LSCnIKSwlp385Rb1spIMzk9POOQ+zwyuPh/cxj2g4IOKvua4UQwk5abXXOpogM9gPgWE6RcwI/QP8rah87Hfh3QncJ/EIIx2rTJRsiQ8zGIsdzipzckhoCwsGvowzwCiGcom0H/mAT+I+5WuBXyvT6JfALIZygTQf+sAAfPBQczyl0dlNq69zPpHpcZetIIYTbaNOB38vTg/BAX466Wo8fzJTOklOQk+zslggh3EybDvxgZva4XI4fqg/wCiGEA7X5wB8Z7MsxV0z1hPc1jyectMBMCOG22nzgjwj25VhOkfMWcdXHNxiCYqTHL4RwuDYf+CODfSkoKSevuMzZTalNSjcIIZygzQf+CMsiLtfM8/eFjN1QXurslggh3EibD/xRrjqXH6BzfygvgawDzm6JEMKNtPnAHxFsXb3rwgO8spBLCOFAbT7whwf6opSL9vhDe4PykDy/EMKh2nzgb+flQWiAj2vm+L39oGN3mdIphHCoNh/4wczsccnVu2Cp2SM9fiGE47hF4I8I8nXNHD+YAd6sA5Cf4eyWCCHchFsE/kjLIi6X1H+qeVz9qnPbIYRwG24R+COC/cgrKuOUKy7iCusN/S6HNbOh8KSzWyOEcANuEfgjg110QxarCQ9DSR6sfdPZLRFCuAEJ/K4gYiD0vtCke4pPVX/u+FZ47zL4Ypb5YDi6Ccpd8M5FCNFquEngt+6966IDvGB6/YUnIWlO5bG0XfD+5XBiG+xfDN89DLPPgn/FmZ+FEKIF3CLwhwf5AC7c4weISYTuE2HVf6G0EDL3m6Dv4QUzf4aH98B9W+DKtyEgDL7/g/T8hRAt4haB39fbk07+7TiW68KBH2DCI5CfBov/YdI7FaVw01fQqYfZp7dDVxh4FZz3N8jYAxs/cHaLhRCtkN0Cv1JqjlIqTSm1rcbx3yuldimltiulnrXX+9cUEezLsWwXTvUAxI2FLmNg1ctmsPfGLyvr+VTV52KIHQlL/gkl+Y5vpxCiVbNnj/9dYHLVA0qpicDlQILWuj/wvB3fvxqXnstf1blPQnh/uOELiEyo+xylTK//1An4Teb/CyGax26BX2u9DMiqcfgu4BmtdbHlnDR7vX9NEcG+HHf1VA9Al1Hwu1Um59/geSOhzyWw8iU4le6Ytgkh2gRH5/h7A+OVUmuUUkuVUsPrO1EpNUsplaSUSkpPP/PAFhnsR3ZBKYUl5Wf8Wi7j3L+YgeBlDsuYCSHaAEcHfi+gIzAKeASYp5RSdZ2otZ6ttU7UWieGhYWd8RtHBFnm8reGXn9ThfaCoTeZKaCZ+53dGiFEK+HowJ8CfKGNtUAFEOqIN44Mse7E5eIDvM119mOgPGXVrxCiyRwd+BcAEwGUUr2BdoBDylJGuvLeu2cisDN0Pxv2fA9aO7s1QohWwJ7TOecCvwHxSqkUpdRMYA7Q3TLF8xPgZq0dE62sqZ5WMbOnuXpfACcPQcZeZ7dECNEKeNnrhbXW19bz1A32es+G+LXzJKS9d9vr8YMJ/Asxvf6w3s5uTeOK8+Cru6FTT5j0f85ujRBuxy1W7lpFBLWSufzNFRwDnQfCnh/t/16lRbDkGXg+Ho5tbv71BVmmFMWOr2Db57ZvnxCiUW4V+M0irjY2uGsVPxmOrDaB1V72LYJXR5kVw6dOwJZ5zbs+9xi8cxEc3wZx4+HkYTMdVQjhUG4V+COC/dpmqgeg92TQ5bDvF9u/dlEuzLsJPrwSPDzhxgXQ81zYtbDpA8pZB2DOBZCTDDfMh8QZgIbMfbZvrxCiQW4V+KNDfMnML+Fkfomzm2J7UUOhfSjs+cH2r73kGdj5DZzzJ7hrFfSYCPEXwsmDkL678eu1ho+vhuJcuPlr6DYBQi1jEU25XghhU24V+M/p0xmABZtSndwSO/DwMIO8+362bbnmk4dh3Zsw+HpTPdTLlLimt6UM0+7vGn+N1A2mmuh5f4PoYeZYp56gPMxxIYRDuVXg7xcVxMDoYD5dl4yDZpE6Vu/JUJQDyWts95qL/2EC9NmPVT8eHA2Rg2H3942/xvYvwMMb+l5aeczbF0K6So9fCCdwq8APMH14LLuO57H9aK6zm2J7PSaaALunCcG4KY5vhS2fwsg7TaCvqc/FkLIOTjVQa6+iArZ9YcYE/EKqPxcWL2sPhHACtwv8lyVE4ePlwafrkp3dFNvzCYS4cbab1rnor+AbBOPur/v5+AsB3fC4QvJqyDsKA66s/VxobzO4W9GGCucJ0Qq4XeAP9vNm8oAIvtqUSlFpGww4vSebvPmZFm07uNyMF4x/CPw61H1O5wEQHNtwumfbF+DlZ/mQqCEsHsqLzapjIYTDuF3gB5ieGEtuURk/bj/u7KbYXu8LzOO6t6CkoGWvoTUsehKComHErPrPU8oE9P2L636v8jLYscC0ySeg9vOh8eZRBniFcCi3DPyju3cipoMf85LaYLqnYzeTT1/9Kvw7Hr65D1KSmlfAbefXkLoeJj4O3n4Nnxt/IZQVwsGltZ87tBzy02HA1LqvDZMpnUI4g1sGfg8PxbRhsazcl0lyVgt7xa7sus/gloVm8HXzp/DWJPOVsr7xa8vL4JenIKwPJNRXbqmKruPAJ6juaZ3bv4B2AdDr/Lqv9Q2GgIjW0+NPegdeHiJjEqLVc8vAD3BVYgxKwfz1Kc5uiu15eJhB3imvw8N74OIXICcF3jrHFEdraKvGjR+YAddJT5pVuo3xamfuMHb/YGbwWJWVwI6vzYdPQ3cNob1aT4//0HKzAlnGJEQr57aBPzrEj3E9Q5m/PoXyijY4p9/KNwiGz4R7kmDM72HzJ/DfYbD+vdrnlhSYVbqxo+oejK1P/EWQnwab50J5qTl2YDEUZUP/etI8VmHxpsffGtZVpFvuTI5vdW47hDhDbhv4Aa4eHktqdiFLdjtsz3fn8Q2C85+Gu36DqAT45l5Y93b1c9a8DqeOm718694Rs269zjOze776nRlX+PZBWP2aSeX0OKfha0PjTSmHUyea+xs5VkUFZFrWHJzY7ty2CHGG3DrwX9A/goggX+asPOjspjhOWG+44Qsz7XPhQ7DlM3O8IAtWvGSOdx3dvNf0C4Hfr4erPzJ1eDZ9bHr8fS8zqaDG2gONp3sy98NPf3Zefj3nCJRZCvxJ4BetnN02YmkNvD09uGlMV579YTc7j+XSNzLI2U1yDE9vmPYufDQNvrwD2vnDkVWm593SjVG8fKDvJearOA8OLoPYkY1fV3VKZ/ez6j9v2fOw+WMYdDVEDGhZG8+ENc0TFA0nJNUjWje37vEDXDeiC77eHrzjTr1+MAOu186FqMHw2S2wZjYkXAOd+5/5a/sEmkFd/9DGzw2MMLOCGurxlxSYKabQss1fbME686j/FMg+YmoiCdFKNSnwK6XuU0oFKeNtpdQGpVQ9c/Ral5D27bhyaAwLNh0l41Sxs5vjWD6BcP186NTD/Dzxcce3QSlTuiGjgcC/ayGUnDLfOy3w7zZlr+PGm59P7HBOO4Swgab2+GdorXOB84EOwI3AM3ZrlYPdOrYbJWUVfLT6iLOb4njtO8KMH02d/ZAuzmlDWHxlKqUum+dCcBeIGeG8wJ++x3xAWdNMJ7Y5px1C2EBTA791isdFwAda6+1VjrV6PcMDODs+jA9WH6a4zA0X5/gGQWhP571/aC8zm6iu9EnecTNQPGg6RA0xUymrrhdwlIw9ZiA6KNrMVpIBXtGKNTXwr1dK/YQJ/D8qpQIBJ/zfZz8zx3Uj41Qx32w+5uymuB/rAG9dvf6tn4GuMOMPkQlQmg9ZZ1iArrnyM6Awy7RTKbOxvfT4RSvW1MA/E3gUGK61LgC8gVvt1ionGNczlN6dA3h7xUF2H89j1b4MvtqUysdrjpBfbMMdrURtYQ0Ua9v8qdm1K7SXCfzg+HSPtV3W7SI79zc5fmfcedia1rD0uYZTbaLNaWrgHw3s1lpnK6VuAP4ENDitQSk1RymVppSq1TVSSj2klNJKqSZM+3AMpRQzxnZj57FcLnhpGde9tYb7PtnE419uZe5aN8z9O1JIV/BsV3uA9/g2M3Vy0DXm57B48PSBY5sc2z7rjKOwKoG/NB+yDzm2HfaQuQ8WPw3fP+LslrifPT/Cq2OgtMjhb93UwP8aUKCUSgAeAvYD7zdyzbvA5JoHlVKxmEFil4umVw2L4flpCfz32iHMvX0Uix6cQLdQf1buy3B209o2Ty+zB2/NXueWT8DDq3ITF09vE3Sd0eP3bg9BMeZn6wDv8TaQ7rFu03lgCRyx4ZadonGHlkPadqeMFzU18Jdps0nt5cD/tNavAIENXaC1XgZk1fHUi8AfAJcrzuLl6cFVw2K4NCGK0T060TM8kLE9O7H2YBal5W3gtt6VRQyCvT/BZ7dC8jqzQnfrfFPZ079T5XmRCSbwO7K2T8Ye88HkYfnfJayv2Ye4LQzwJq8Fn2AzVXVpm5mo1zpkW8rCH3f8TLWmBv48pdRjmGmcC5VSHpg8f7MopS4HUrXWjf6mSqlZSqkkpVRSenoD1STtbFzPUPJLytmcnO20NriFC/4Bo38H+36Bt8+FV0ZC3jGzUreqyAQz+yf7sOPalr6nchwCoF176NijbQzwJq+F2BGmgN/+X82HrnCMbEvS49gWh791UwP/1UAxZj7/cSAGeK45b6SUag88DjSpJoDWerbWOlFrnRgWFtact7KpUd07oRSskHSPffl3MkXkHtwBFz0PaAiMMrWDqoocZB4d9T9LSYGp02Md2LXq3L/1B/7CbEjfaQL/8NvAryMs/deZv67WlVVaRf1OB34X7fFbgv1HQLBS6hKgSGvdWI6/ph5AN2CzUuoQ5sNjg1Iqopmv41Ah7dsxICqYVfsynd0U9+ATACNuh7vXwX2bwdu3+vPh/UF5Ou5/FmtFzlqBf4Cpy1+c55h22ENqknmMHWH+7mPuMfsspzZhw56GrHsLXujrlEHLVqOkAAoyzKSGtB1mAyQHamrJhunAWmAaMB1Yo5S6qjlvpLXeqrUO11rHaa3jgBRgqOVDxaWN7RnKxuSTMq3TkTw86q7s6e0L4X0dF/jTa0zltDq9grcVl25IXmvGKqKHmZ9HzAK/DrD02TN73U0fmy0309rAGIi95Fjy+93OMlVfHbwLXVNTPU9g5vDfrLW+CRgB/LmhC5RSc4HfgHilVIpSauaZNdV5xvbsRGm5Zu2husaqhcNFJpgpnY4Y4M3YY4KjtZ6RlbWYXWtO9ySvNXdQPpZ5Gj6BMPpu2PMDHN3YstfMSYGjG8z3LX0Nd2BN8/S9xDwed2yev6mB30NrXXW3kszGrtVaX6u1jtRae2utY7TWb9d4Pk5r3SoS54ldO9LO04NVkud3DZEJpkeZ54CbxYzd0KGbKTtdVXCsmQ3TnMCvNax9E3JSbdvGlqgoh5Qkk+apasQdptf/7QMtS9XsWmgePX3qD/zWGVvuvHexNfD3mARefg7P8zc18P+glPpRKXWLUuoWYCFQx+7abZNfO0+Gde3ASsnzu4a6VvCWl8Hen5ueK83PgNWvw+FVDZ9nLc5Wk1KWAd5mpDO2fArfPQy/Pt30a+wlfReU5NUO/L5BcPkrJmj/8Mfmv+7Ob0xpi7hxcLSeYLbrW/h8pvn3clfZR8DD29R+6tzf4TN7mjq4+wgwGxhk+ZqttW7BfxWt19iendhxLJes/BJnN0V0HgCoysBfWgTzboKProINdewlbKW12SDms1vh331MYPtoOmTsrfv88jJTFyisjsAPzSvdUJgNP/3JtHvb5+aDx5msC7dqBn4weymMewDWvwsbP2z6a+Znmg/SvpeYgnppO6C0sPZ5h1aYR0evwHYl2UcgOMaMZUUOMsUHHbg2pckbsWitP9daP2j5+tKejXJFY3qa6hKr9ku6x+l8AkztnuNbzKyaj6fB7oWmaua2L+q+pqIc5kyG9y6F/b+Y6Ys3LjADyJ/eUPfsnOzDUF5Sd48fIGKg6TU3pWjckn+aYD/lDSgvbvgDyhGS15pFWx261f38xD+ZbTQXPtT0NMSe70GXQ99LzQY/urzu1c2HVprHoy4U+MtLYf17ZraNI+QkV5ZBj0yA4hwzS8xBGgz8SmAvODEAACAASURBVKk8pVRuHV95SqlcRzXSFQyKDibQx0vSPa4iMsHkqN+/3ASSKbNh1N1weCXkHq19/v5fIXm1CWgP7YYLn4EeE+Gqd8wA7ld31+5xnS7OFl/79QC6WPYmbixddHwrrJ0NiTMg4Wozk2PdHIdP4asmea3ZGlPVU13d0wuunGPm9n96IxSebPw1d35rxj4iB5seP9TO8xdkmdk+ysN5eyvUZc8P8M298NMTjnm/7CMQEmu+j7CuTXHc36OxAdpArXVQHV+BWms32aDW8PL0YGT3TtLjdxWRCaaG//FtcPWHJqAOmApo2L6g9vnr3zU93LH3mW0nrbqfBef+BXZ8Bav+W/0aa3G20F51tyG0F/iHNRz4Kypg4cNmwHSSZSLcyDsgN8X0kJ0hP8PcpcQOb/i8gDCY/r75IP3xTw2fW3zKfLj2udh8mARFm79NzXSO9W8VfxHkHYVTabVfyxmsdyFJc2DPT/Z9r9IiOHXCFCcECO9n1qY4cGaP2++52xxje3bicGYByVkOuh0U9es92fQsb5gPfS4yx0J7md7TtvnVz807YXp0g6+te23AmHuh3+Ww6En45W/wxSx4ZRT88lcIjAS/kLrboBR0HdNw4N/yibnTOO8pE/ytbQ+OhTVvNP/3toUUS1mG2JGNnxs73GyCs+ubhlfj7vvZpLD6WKYnKmV6/TV7/IdXgpevufsBp5QrqNPhlRA7ykxv/epuM15hLzkp5tGa6vH2hbA+Dv1bSOBvhrGWPP+Md9dx/VurmfHuOu76cD0r9spdgMOF9oI7lpo8dFUDrjQrT7MOVh7b/DFUlMGQm+p+LaXMTJbQeFj+vBkADukC4x+Cqz9quB1dx5qSDtbpeVUV5cJPfzZbRiZcV3ncwxOGz7RUZ9xZ/Zr8TNN7tqfkNabqqTUd05jek019pCOr6z9n57fQvlNl+gvMB3P6LijJrzx2aAXEDIeYRPOzvQZ4j22B3U28oyrMNum4HhNh6mwoyjZpH3sNtlrrTAXHVh6LHCQ9flfVKzyAG0d1JTzIh+LSCtLyili1P5M/LdhKeYXLFRt1TwOmmsftlkFerWHD+9BlTP2zc8AsXrr9F3h4Hzy0C66fB+f8CWKGNfx+XceYx7p6/bu+Ncvyz3uqsrKn1dCbTc937Wzzc/Ep+OUpeKEPfHt/47+nldbNHytIXmvujKqmvBrSY6IpLbDnh7qfLys2lVXjLzRjA1ZRQ8zuadYBXmuAjRtnBuI7drd9Xru8zGws8+ZEmHstpG5o/JrkNYA2H+IRA8y/+65vzV7P9mBdtVt1j+vIBJP+ccTaFCTwN4tSir9dMYCPbhvF/LvG8O3vx/PM1IEcyizgh20uX3nCPYR0MSkM6+yeQ8sh6wAMu7nxa9v5m7x2c4T3M0Hs8Mraz23/0vTquoyq/Vz7jjDgKtj8iZlN8r9EWP5v8A0xFUqburvX8udNXZyy4qadX1pogmFT0jxWPoEmWNfXgz64DIpzoc+l1Y/XHOA9sprTARYqV2DbStYBeOdCs7FMv8vNGMN3Dzf+tzy80sypt96FjL7HtPG7P9R9J3emso+YO67AyMpjEY4tPiiB/wyd3z+C7qH+vLZ0H9qRNeJF/QZcaVbUpu0yQdU32AQCe/DwNHcTNXv8hSfNYGf/K+qfOTPidigtMGmFgM4w4yc490mzv2/N3cjqkncclr8A+WmNT40sPGk+WP6TAGWF0HNS034/q94XmgHhjH21n9s6H9oFQPezqx8PijS/lzXwH15h7hysATYywQTBAhuUQtn9Pbw+3gzIX/k2XDUHzv+bSfttamQtwqGVpl6R9Q7IwxOueM3U0Fn9+pm3rabsIxAUVf3uKGKgeXRQbX4J/GfI00Nxx1nd2Zaay3LJ9buGfleY6YLr3oKdX5ua/k1Na7RE1zFmC8O8E5XHdi004wr9p9R/XdRgmPR/Znzh9sXQZWRlb9i6yKkhS/9l1hkAHKlngLm8FH58Al4cYFJJ4f3M+oVe5zXtd7OKt5THrjkTKSfFDKYPvr52JVWoPsB7aCVEJ1b+W1hXYDeW266oMGswVv2v/nMW/90E07tWwkBL/chBV5sxh0V/qf/DpfiUueuIG1v9eIeuZobS5rlNv5tqquzkyhk9Vr5BZk2F9PhbjyuGRBMR5MtrS5qwkEfYX2Bnk5pY96YJjEObkOY5E9ZgXTXds32BSTtFDW342vEPwZAbKscAOsSZqZB1pY6qythr7mYSZ0CnXnD4t7rP2/Y5/PY/6H0B3LkCblpgcvbNFdLFzHjZXSPP/9urZpxhzD11Xxc1xKyHyDtu8vlVA2zkYPPYWJ7/wK9w5DeTFqtLfqYZOxg4vXJuPJg7rYueM3c7i/9e97Up68wHtHWspqqhN5m7r13fNtw+MH+Dxf+Ety+Ady6G9y6DD6bWniIMljn8XWofd+AArwR+G/Dx8uS28d347UAmG480YaGLsD/rPr1RQytLKNtL5CDw9q9M9xRkwYHFljuPetI89ak6RbSh1OEvT5me84Q/QNfRZspoXbnsfYtMrnvqW5XphJaKn2wCsHUxV0GWWR8x8Kq6AxlY8vzaDGLr8soPSTDjHMFdGg/8Se+YxxNb6573f2iZeex+Vu3nIgaaVdpJc+p+n8MrzRz6usY8uk807dvQhK1HVv7HbF1pvQMrKzYfeL8+XX1WU1mJ2Vmu6oweq8gEs3q36p2jnUjgt5FrRnQh2M+b15dKr98l9L0MAiLq74nakqe3SdNYA39T0jwN6TrWzPDIrOe/peR1JoU15vdmMLrLGDPdMq3G3gAV5WaguMek2rOKWqL3ZBO89/1ifl73NpTmm0Vx9bH26te9bQY0a9YGihzUcODPSTX5++5nm58PLKl9zoGl0C6w/ruriU+YFcgL6xjoPbzKBFyfOrYQ9/CAoTea96w6Pbimnd+YdFL/qXD7r3DrQpj5Y+U4wb5FlefmpgC67g/KnueZMZA559t9nwcJ/DYS4OPFzaO78uP2E+xLa8W7MrUV7TvCw7sre/721nWMKUVQkGVm84R0bfo8+VqvVUfqyEprs9DMP8zUzgfT4wfTG6/q2CaTquh5bsvaUVP0MLP6eff3pqbNmteh1/mVexPUJbCz2UKzKNsE5nb+1Z+PHGzGR4rqqQCz8QPzYXPxCyZ4719c+5yDy8zfv+pgaVV+IWagN2UtrHmt8nhpkSn7UVeax2rw9Wa8qL5idUc3wue3mwHrK16tfofXZbRp885vKo9ZZwnVl+q5ZaGZefXWudWvszEJ/DZ085g4fL09ePLr7eQUyJ6jbsUarHd/BweXmt5+c9M8VqG9wD+87sC/92dz/Kw/VvZSQ7qa4FpzZtG+XwDVspx+XTw8zVjBvp9NkbmCDBjbhDUH1g/AmgOoUGWAd2vt58rLzDhGj0lmI5zuZ5sUWtUUWE6KmW1UV5qnqoRrzR3Lor9WluJIXW9WG3eto11WwdGmJ77po9rrJXKPmrUC/qFwzce1JxB4epnSFHt+MikeqBL460j1gLkjmrUEwuJN8cAlzzR9am8zSOC3oU4BPjx5aX/WHMjiopeXs0Hy/e4jaqjZfOTXp88szQOVef5DK6sHuYoK+PUpM/tj2C01zh9tevxVz9+3yARd/9CWt6Um6yreRX81K3Ab6i1bWQN/13F1PNfAAO/eH009H2t5hx4TTX48fVflOQeWmsdujQR+peDSl80dx5d3mNlOh1cBqvKOqT5DbzLvu6/K/gEndsAHU8ysoOs+hYDwuq/te4mpvGkdh8hONncQQdH1v19QFNz6vVntveSflYsRbUgCv41dO6ILn905GqVg+uu/8frS/VTIqt62z9vXBMK8Y2ZmjrUn21Jdx5p8cNUFRLu+MT3jsx814wpVdRlt3tta2rfwpJmxYqs0j5V1FW9ZoanZ35S7mgFTTRCrq8cfEG4WMtUV+JPmmOd6W6aSdrfcuVRN9xxcZtJP4f0ab0dgZ7jkRZOeWf5vc+fUuX9lDaX69L7ArEfY8L4ZN1n5Msw+Cwoy4dqPG051dZ9oBv53WmYGZR8xQb/mv19N3r4mdXTNXDN2YGMS+O1gSJcOLLx3POf168wz3+9i1gdJFJa48TZz7sLa+z2TNI9VXI08f0WFmS7YqRcMnFb/e1vz/AeWmHIJtg78PoEm9RHe3yzqaopOPWDKa/WvpYhMqB34sw6aVNXQmytz9yGx5vff/6v5WWuTVus2vumD1/2vMNM+lz5revwNpXmsPL1h8HWw50d45yL4+c9mbON3q2vXiqrJ2xd6nWtSgBUVlg1Y6knz1KSUKUBoi4H5GiTw20mwnzevXj+UJy/txy+70rhpzhpyCiXv36bFTza9u0HXnPlrhfU1PVFr4N/+BaTvNL19D8+6z/cNqczz71tkVixHN1JrqCWufAtm/GC7gBSZYFYqV90EZcN7JvANrVFYr8dE8zcpKzZrGfKONZ7mqemiZ00Pvry4aakqgCE3mkHmE9vNbJ2rP2x6Cq3PpWaWVsq66huwOFE9w+DCFpRS3Dq2G2GBPjzw6Saunb2a92aMICzQp/GLResTPQweTz3z3j6YoNrFkucvLzODfOH96r/t9/AwNYGsef59v5g0Q30zXc5Eu/a2fb3Iwebu5Ovfm/GRU2lmRlLvC83galU9zjFrApLXVA7SNjawW5NfB5j6Bvz8ZNOv7dQDbv7GFJYLjmne+/U+39QC2v4l5Ka6ROCXHr8DXDIoijdvSuRAximmv/EbKSelnn+bZYugbxU3Fk4ehFUvQ+ZeOPuxhnvZXUabqZEHlpiesK3TPPYSO8Lcrez92fSolYep9HlOHbthxY0z6wH2LzZpnuDY+rePbEi3CTBrceP5/ZrXNDfog7nz6jYBNn1sPuDqm9HjQHbr8Sul5gCXAGla6wGWY88BlwIlwH7gVq11tr3a4ErOjg/nw5kjmfHuOqa//huf3TWG6BA71o8RrZ81DfHr02YFqnWTk3rPH1t5PjS/EJuz+IfCHw817UPTJ9Dsb7BvkcmX97nEth+29tL3ErPXM7T5Hv+7wOQax34GBmitBwF7gMfs+P4uJzGuIx/fPoq84jJufHsNmadsXPxJtC0Rg8AnyOSWJz7ReE49MgG8/CA1yQy+BkU5pp220Jzg3WOiqWlTlN38NI+zxF8MWH7Hthz4tdbLgKwax37SWltXQawGWnDf1LoNiA7m7ZuHk3qykFveWcepYiduuC1cm4enmT3SZUzllMaGeFUpedxaevst0eOcyu8bm1XjKgI7W8pVKAhyfthzZo5/BlDv3mhKqVlKqSSlVFJ6eroDm2V/I7p15NXrh7LjWC6z3k+iqFSmeop6TH3TDCo2tUdsTQ+1lvx+S0QNMXnz0HgIjHB2a5pu/EMw6q669312MGXPzUOUUnHAt9Ycf5XjTwCJwFTdhAYkJibqpKQku7TRmb7YkMKD8zZzbt9wnr0qgY7+zv8PQrRyOSmmINrEJ+wzo8dVbPrYpMH6NjLu4eaUUuu11ok1jzv8vwyl1C2YQd9JTQn6bdnUoTHkFpby1Lc7OOu5xdwzsael3k8d87SFaIrgGLOLV1s3+LrGzxH1cmiqRyk1GfgDcJnWWuY0AreM7cYP908gsWsH/vn9Lib9eynfbD7q7GYJIdowuwV+pdRc4DcgXimVopSaCfwPCAR+VkptUkrZYUPL1qd350DeuXUEH902kmA/b34/dyM/bZfN24UQ9mHXHL+ttNUcf11Kyyu4+OXlFJSUs+jBsyTtI4Rosfpy/LJy18V4e3rw1OUDSDlZyKuyh68Qwg4k8LugUd07cVlCFK8v3c/hzPzGLxBCiGaQwO+inri4L94eiqe+se/em0II9yOB30V1DvLlvnN78cuuNBbtOHH6uNaanIJSWsPYjBDCNbXhFR6t361juzEvKYUnv97O4t1p7D1xit0n8sgpLKVzkA9n9Q5jQu8wxvUMJaS9LP4SQjSNzOpxcav2Z3Dj22vxb+dJ786B9I4IJLZDe7YdzWH5nnRyi8rwUHD9yK48cXFfmQUkhDjNZVbuiuYZ0yOUbX+5AF9vD1SNei1l5RVsTslhwcZUPlh9mHWHsvjfdUPoGR7opNYKIVoDyfG3An7tPGsFfQAvTw+Gde3A364YwLu3Dic9r5hL/ruCT9YekTEAIUS9JPC3EWfHh/P9feMZ1rUDj36xlRveXsOyPenyASCEqEUCfxsSHuTLBzNG8n+X9GPviVPcNGctF/5nOV9sSKGkrMLZzRNCuAgJ/G2Mh4dixrhuLP/jRJ67ahAVWvPgvM3MfG+d9P6FEIAE/jbLx8uTaYmx/Hj/BB6/qA/L92bwtVT9FEIggb/NU0oxc1x3EmJD+Nu3O8ktKnV2k4QQTiaB3w14eiievnwAWfnFvPDTHmc3RwjhZBL43cTAmGBuHNWV9387xLbUnDN6rbLyCjYcOUl5hYwZCNEayQIuN/Lg+fEs3HqcJxZs48u7zKbcSYdP8uXGVDYeOUlxWQVFpeUUl1Xg5+3JlUOjuW5kVyKCfQEor9B8s/ko//llLwcz8rl9fDeeuLifM38lIUQLSMkGN7NgYyr3f7qJyf0j2HY0h5SThfh5ezKqe0cCfL3x9fLAx9uD1JOFLNmTjqdSXDAggtHdO/HeqkPsTTtFn4hA4jr588P247wwPYGpQ2Oc/WsJIeogJRsEAJcPjuKz9cn8tOM443qF8fD58ZzXrzP+PrX/UziSWcAHqw/x6bpkFm45Rq/wAF69fiiT+0dQrjU3vLWGR7/YSs/wAAbFhDjhtxFCtIT0+N1QUWk5hSXldPBvWkXPwpJydp/IY2B0MJ4elaUjMk8Vc9n/VlKhNV/fM46wQB97NVkI0QKy9aI4zdfbs8lBH0ytoMGxIdWCPkCnAB/euHEYJwtKuOvD9RSXldu6qUIIO5DAL87IgOhgnrsqgaTDJ7nk5RWs2Jvh7CYJIRohgV+csUsTonjzpkSKysq54e013P5+EkcyC04/X16hZeGYEC5EcvzCZopKy3l7xUFeWbyPsnJNWKAPuYWl5BWXAXDVsBien5bg5FYK4T4cPqtHKTUHuARI01oPsBzrCHwKxAGHgOla65P2aoNwLF9vT+6e2JMrh8bw+tL95BWVEeTnRZCvN0eyCpi/PoWLBkZwTp/Ozm6qEG7Nbj1+pdQE4BTwfpXA/yyQpbV+Rin1KNBBa/3Hxl5LevytX0lZBZf8dzn5xeX89MCEOqePCiFsy+GzerTWy4CsGocvB96zfP8ecIW93l+4lnZeHvxjykBSswt58WepFySEMzl6cLez1vqY5fvjQL33/EqpWUqpJKVUUnp6umNaJ+wqMa4j143swpyVB8+4XpAQouWcNqtHmxxTvXkmrfVsrXWi1joxLCzMgS0T9vTHC/rQ0d+Hx7/cKkXehHASRwf+E0qpSADLY5qD3184WXB7b568tB9bUnJ48uttJB3KoqhUFn4J4UiOHmH7GrgZeMby+JWD31+4gEsGRbJo5wk+XH2ED1cfwctD0S8qiMsSorhtfPcmvUZRaTnbj+YwtEsHlFKNX9BMp4rLWLAxlXZeHkxPjLX56wvhTPaczjkXOBsIVUqlAE9iAv48pdRM4DAw3V7vL1yXUor/XDOEP13cj03J2Ww8cpJV+zN5euFO/Np5cv3IrvVeq7Xm+23H+cd3O0k5WcizVw5i+nDbBeadx3L5cPVhFmxMJb+kHKVgcGwIvTsH2uw9hHA2WcAlXEJ5hWbGu+tYuS+DD28byajunWqdsy01h6e+3cHag1n0iQjE29ODI1kFLHrwrDMqEFdeoVm08wRvLz/I2kNZ+Hh5cMmgKC4fHMXvPtrAuJ6hvH7jsDP59YRwivqmc0rgFy4jp7CUKa+u5GR+CV/fM47Yju0BSMst4vmfdvPZ+hQ6tm/HQ+fHc/XwWA5mnOKi/6xg8oAIXr52SLPfr6CkjPnrU5iz4iCHMguIDvHjljFxTEuMIaS9KWL30qI9vLRoL1/fM1ZKT4tWRwK/aBUOZuRz+f9WEBXix0e3jWTu2iO8umQ/peUV3Dq2G/ec05MgX+/T51sD8zu3DmdifHiT3ye7oIQpr67iYEY+g2NDuH18dy7o3xkvz+rzHfKKSpnw7GIGxoTw/owRNvs9hXAECfyi1Vi+N52b56zFy8ODkvIKLujfmccu7EtcqH+tc4vLyrn45RUUljR9RXBZeQW3vLOOtQezmH3TMM5u5APjjaX7+ef3u/h01ihG1pGCEsJVST1+0WqM7xXGP6YMZGjXEObePoo3bkysM+gD+Hh58s+plSuCk7MK+HD1Ye74IIlR//iFp7/dUWufgH98t4sV+zJ4esqARoM+wE2j4wgL9OH5n3ZTV0dJa82RzALmrUvm2R92UVBS1rJfXAgHkYIpwiVdM6IL14zo0qRzh1tWBL+14iBvrTgIQHSIH/ERgby14iCr9mfy8rWD6RkeyGdJycxZeZBbx8Y1eZqmXztP7j2nJ3/+ajtL96Qzpkcou47nsjklh42HT7L6QCZHc4pOnx8W6MOtY7s1/5cWwkEk1SPahNyiUp7/cTfdQ/0Z3zuM7qH+KKX4ZecJHpm/hYKSMmaM7cZbyw+SGNeB92eMqJXPb0hJWQXn/HsJOYWlFJdWUFJeAUBoQDtGduvEqO4dGd2jE3+Yv4Ws/BJ+fehsPDxsv76gJq01haXltG8nfThRm+T4hdtKyy3ioc82s3xvBrEd/fj67nHN2nrS6tddJ3hn5SH6RQYxKCaEhNhgokP8qi0g+2bzUX4/dyNv35zIpL72LT9dUaF5ZP4Wvtl8lBtGdeXuiT3oFFB9Wuu+tDzWHjzJxD5hRAb71fk6R7ML6RzkW2trTdH6SeAXbq2iQvPt1mMMiQ05PU3UHkrLKxj/r8X06hzABzNH2u19AP753U7eWHaAEXEdSTqchZ+3J7eN7860xBgW70pj/oZUNidnA+DlobhoYCS3je/GoJgQsgtK+GrTUeavT2Frag73ntOTB8+Pt2t7heNJ4BfCQV5ZvI/nftzNzw9MoFeVFb/JWQXc8cF6rhoWw4xxZzYG8OayA/z9u53cPLorf7msP/vTT/Hvn/bw/bbjp8/pExHIVcNiGNW9Ews2pvLJumROFZfRJyKQA+n5lJRX0C8yCG8vD/aeyGPFH8+hYwvuhITrksAvhINknipm9DO/Mm1YDH+fMhAwYxBXvbaKPSdOAfDohX2486weLXr9Lzem8MCnm7l4YCQvXzukWopmc3I2y/emc3Z8OP2jgqqlofKKSpmXlMK3W46SEBPCtMQY+kcFs/dEHue/tIxZE7rz2IV9z+A3F67G4VsvCuGuOgX4cMXgKL7YkMofLuiDv48nv/94IwfS83l/xgg+W5/CM9/vorxCc/fEns167cW703jksy2M6dGJF65OqJWXT4gNISG27hXGgb7ezBzXjZk17jZ6dQ7ksoQo3l91mNvHdyc0oOXlL0TrIPP4hbCDW8Z0o7C0nE/WHeFv3+5g6Z50/nbFACb0DuPF6QlMGRLNcz/u5j+L9jb5NRfvSuOO99cTHxHIGzcOw8fL02btvXdSL4rLynlj6X6bvaZwXdLjF8IO+kUFMbJbR15atJfC0nJuH9+Nay3rErw8PXh+WgIeSvHioj1sO5rD9SO7ML5XWL0zaxbtOMFdH5mg/+HMkQRWKVthCz3CArhiSDTv/2Z6/eFBvjZ9feFapMcvhJ3cOjaOwtJyzu3bmUdr5M49PRTPXTWI+yb1Yv3hk9zyzjrG/+tXXvx5D9uP5lRbbfzDtuPc+eF6+kUG8dHMUacLyNnavef0oqxC85r0+ts8GdwVwk601izbm8GIuI74tas/LVNSVsGinSf4ZF0yy/emo7WZftkjLIAe4f78uP0Eg2KCeW/GiGoF6uzhD/M3s2DTUZY9MpGI4MZ7/dkFJcxLSqagpJxZE7rLQjIXI7N6hGgFjmYXsv7wSXYey2XnsVx2Hc+jT0QgL187xObpnbokZxUw8fkldA7yZVpiDNMSY4kOqb3wa8+JPN5ddYgvNqRQVGpWMXcP9eelawZL+WoXIoFfCNEkS/ek8+ayA6zYl4FSMK5nKD3DA0jLKyY9r5i03CIOZRbg4+XBlCHR3DwmjpMFJTw0bzPpecU8cF5v7jyrh6wEdgES+IUQzZKcVcD89Sl8viGFk/klhAf5EhboQ3igDwOig5meGFttwVdOQSmPf7mVhVuPMap7R167fliLSmO4i5STBfyw7Tg3j4nDuxl1o5pDAr8Qwu601sxfn8ITC7YRE+LHezNG2LVERms24911/LorjYsGRvDyNUOaVTSwqaQevxDC7pRSTEuM5cOZI8nMN7ucbU3JcXazXM6WlGx+3ZXGkC4hfLf1OA/O20x5heM64TIEL4SwuRHdOvL5XaO5ec46rp79G89cOQg/b0/2pZ1iX9opsvKLufOsHjbd0UxrXa1ERXOlZhcSFex7Rq/RVC//spdgP2/enzGCD1cf4V8/7MLLU/H8VQkOKectgV8IYRc9wwP58ndjuOWdddw7d+Pp4+GBpiTENW+u5s6zevDAub1p59Xy5MOB9FM8OG8zhzLzuaBfBJckRDK6e6cmp04qKjT/+G4nb604yKQ+4fx9ysAmTWVtqW2pOSzamcZD5/Um0Nebu87uQVl5Bf/+eQ/eHh78c+pAuwd/CfxCCLsJD/Jl3p2jWbE3nc5BvnQPCyDYz5v84jKe+mYHry3Zz4q9Gbx49WDat/NkzcFMVu/PYnNKNoNigpk5rjvxEYF1vrbWms+SUnjy6+34eHswrmcoC7ce49OkZDr5t2Nk9474envSztMDb08PwgJ9uHZEF8ICK2sRlZRV8Mj8zXy16SgT48NYuT+D815cyp8v6ce0YTF26f2/tGgvQb5e3Dw27vSx30/qRWl5BS//uo+isnKen5ZgtwFfcNLgrlLqAeA2QANbgVu11kX1dVFSsgAACZFJREFUnS+Du0K0TT9sO85jX2whp7AUa4o7yNeLgTHBrD98kqLSCsb1DGXmuG4Mjg1BU7nr2D+/38XCLcdMwbrpg4kI9qWotJwlu9P5dstRth/NpbS8wvKlOVlQgo+XBzeNjmPWhO74enty14frWb43g0cuiOd3Z/fgcGYBf5i/hbWHsjirdxhPXtqP7mEBNvt9t6XmcMl/V/DAub2579xe1Z7TWvPqkv089+NuzukTzivXDW1w4V9TuMysHqVUNLAC6Ke1LlRKzQO+01q/W981EviFaLvScot4e8VBOgf5MrJ7R/pEBOHpoTiZX8LHa4/w/m+HOJFbXOs6Lw/FQ+fHM2tC9yatGdiffor//bqPrzal4uPlSUSwL0eyCnhm6kCmVdl/uaJC88Hqw/zrh10UlZZzxeBo7jmnp00+AGa9n8TqA5ks/+M5BPvVvSDv4zVHeGLBVhK7duCtm4fXe15TuFrgXw0kALnAAuBlrfVP9V0jgV8I92UtaXEitwiFmTmkFAyP60jfyKBmv96+tFP879e9LN2Tzr+nJ3BOn7q3yMw4VczsZQd4/7dDlJRVcPngaKJCfDmaXURqdiHHcgrpGxHEHWd1Z1jXjo2+78YjJ5ny6iruP7cX95/bu8FzF245xv2fbqRneCDvzRhOeGDLxhxcJvBbGnMf8HegEPhJa319HefMAmYBdOnSZdjhw4cd20ghhADS84p5c7n5ACgt10QE+RId4kdYoA8r92eQXVBKYtcO3HFWDyb1Ca82MJtTUMp3247x5cZU1h7MokN7b5Y8MrFJvfhle9K55+MNvHbDMMb2DG1R210m8CulOgCfA1cD2cBnwHyt9Yf1XSM9fiGEsxWXlePl4VEtrZRfXMa8pGTeWn6Q1OxCPBT4eXvi186L9u08OZ5TREl5Bd3D/Jk6JJqpQ2OIqqP2UX1yi0rPqDCfK+3AdS5wUGudDqCU+gIYA9Qb+IUQwtnq2vjG38eLW8d244ZRXflh23H2nMijoKScgpJyCkvKCA3w4fLB0QyIDmrRDCF7VWN1RuA/AoxSSrXHpHomAdKdF0K0Wt6eHlyaEOXsZjSZw0s2aK3XAPOBDZipnB7AbEe3Qwgh3JVTFnBprZ8EnnTGewshhLuTIm1CCOFmJPALIYSbkcAvhBBuRgK/EEK4GQn8QgjhZiTwCyGEm2kVe+4qpdKBlhbrCQUybNgce5A22k5raKe00TakjY3rqrUOq3mwVQT+M6GUSqqrVoUrkTbaTmtop7TRNqSNLSepHiGEcDMS+IUQws24Q+BvDXWApI220xraKW20DWljC7X5HL8QQojq3KHHL4QQogoJ/EII4WbadOBXSk1WSu1WSu1TSj3q7PYAKKXmKKXSlFLbqhzrqJT6WSm11/LYwcltjFVKLVZK7VBKbbfskexS7VRK+Sql1iqlNlva+FfL8W5KqTWWf/NPlVLtnNXGKm31VEptVEp964ptVEodUkptVUptUkolWY65zL+1pT0hSqn5SqldSqmdSqnRLtjGeMvf0PqVq5S639XaCW048CulPIFXgAuBfsC1Sql+zm0VAO8Ck2scexT4RWvdC/jF8rMzlQEPaa37AaOAuy1/O1dqZzFwjtY6ARgMTFZKjQL+Bbyote4JnARmOrGNVvcBO6v87IptnKi1Hlxlzrkr/VsD/Af4QWvdB0jA/D1dqo1a692Wv+FgYBhQAHyJi7UTAK11m/wCRgM/Vvn5MeAxZ7fL0pY4YFuVn3cDkZbvI4Hdzm5jjfZ+BZznqu0E2mN2dBuJWSXpVdd/A05qWwzmf/ZzgG8B5YJtPASE1jjmMv/WQDBwEMtkFFdsYx1tPh9Y6artbLM9fiAaSK7yc4rlmCvqrLU+Zvn+ONDZmY2pSqn/b+/uXqyqwjiOf39hic2EU2EQTWRWVASiXsxFWgheJSFdGL2YSATdeOOVIb1Bf0AvF1FCEUaDhaUhXZVTDHiRr01mCmURNGJORFYGRY1PF+s5djq9KNG4V7N/Hzicvdfes3kOa85z9l77nGdpLrAQ2EVlceYQyhgwAbwDfAaciIhfc5ca+vxpYD1wKtcvpb4YA3hb0j5JD2ZbTX19NfA18FIOmb0gqY+6Yux1N7A5l6uLczon/v+lKKcFVXzHVlI/8AawLiK+795WQ5wRMRnlsnoQGAJuaDKeXpJuByYiYl/TsZzBkohYRBkWXSvp1u6NFfT1DGAR8FxELAR+pGe4pIIYT8t7NiuALb3baolzOif+o8CVXeuD2Vaj45IuB8jniYbjQdL5lKQ/HBFbs7m6OAEi4gTwHmXYZEBSZy7ppvt8MbBC0hfAq5ThnmeoK0Yi4mg+T1DGpIeoq6/HgfGI2JXrr1M+CGqKsdttwP6IOJ7r1cU5nRP/HuC6/AbFBZRLr+0Nx/R3tgNrcnkNZUy9MZIEvAgcjognuzZVE6ekOZIGcnkW5R7EYcoHwMrcrdEYI2JDRAxGxFzK/9+7EbGKimKU1Cfpos4yZWz6IBX1dUR8BXwp6fpsWgYcoqIYe9zD78M8UGOcTd9kmOIbLMuBTyhjvw83HU/GtBk4BvxCOZN5gDLuOwJ8CuwALmk4xiWUy9EDwFg+ltcUJzAf+CBjPAg8lu3zgN3AEcql9sym+zzjWgq8VVuMGcuH+fi48z6pqa8zngXA3uzvN4GLa4sx4+wDvgFmd7VVF6dLNpiZtcx0HuoxM7O/4MRvZtYyTvxmZi3jxG9m1jJO/GZmLePEbzbFJC3tVOY0q4ETv5lZyzjxmyVJ92WN/zFJG7MI3ElJT2XN/xFJc3LfBZLel3RA0rZOjXVJ10rakfME7Jd0TR6+v6ue/HD+OtqsEU78ZoCkG4G7gMVRCr9NAqsov8TcGxE3AaPA4/knLwMPRcR84KOu9mHg2SjzBNxM+ZU2lAqn6yhzQ8yj1PExa8SMM+9i1grLKJNn7MmT8VmUYlqngNdyn1eArZJmAwMRMZrtm4AtWfPmiojYBhARPwHk8XZHxHiuj1HmZNg59S/L7M+c+M0KAZsiYsMfGqVHe/b7tzVOfu5ansTvPWuQh3rMihFgpaTL4PScs1dR3iOdSpr3Ajsj4jvgW0m3ZPtqYDQifgDGJd2Rx5gp6cJz+irMzoLPOsyAiDgk6RHKTFTnUaqnrqVM+jGU2yYo9wGglNd9PhP758D92b4a2CjpiTzGnefwZZidFVfnNPsHkk5GRH/TcZj9lzzUY2bWMj7jNzNrGZ/xm5m1jBO/mVnLOPGbmbWME7+ZWcs48ZuZtcxvs69Xp4svfMAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HpmJKqGZfen"
      },
      "source": [
        "### 150 epochs, optimizer Adam, weight decay = 1e-7, not normalize input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "XsCTTB2QZpQu"
      },
      "source": [
        "test_data_resnet_model = run_experiment(resnet_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9GmMarOZpbo"
      },
      "source": [
        "history = test_data_resnet_model\n",
        "#  \"custome mse\"\n",
        "plt.plot(history.history['custom_mse_hungarian_py_func'])\n",
        "plt.plot(history.history['val_custom_mse_hungarian_py_func'])\n",
        "plt.title('model custome mse')\n",
        "plt.ylabel('custome mse')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# \"Loss\"\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbCo4puluLeU"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "figure, axis = plt.subplots(1, 2,figsize=(10,10))\n",
        "\n",
        "X = np.arange(len(save_precision_075))\n",
        "# Plotting both the curves simultaneously\n",
        "# axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "axis[0].plot(X, save_precision_075, color='r', label='precision 0.75')\n",
        "axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "axis[0].set_xlabel(\"epochs\")\n",
        "axis[0].legend()\n",
        "axis[0].set_title(\"Precision at threshold 0.5 and 0.75\")\n",
        "\n",
        "# axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "axis[1].plot(X, save_recall_05, color='b', label='recall 0.5')\n",
        "axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[1].set_xlabel(\"epochs\")\n",
        "axis[1].legend()\n",
        "axis[1].set_title(\"Recall at threshold 0.5 and 0.75\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gxIBvjFuPNr"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "figure, axis = plt.subplots(1, 3,figsize=(10,10))\n",
        "\n",
        "step = 21\n",
        "mean_regression_store = []\n",
        "mean_cls_store = []\n",
        "mean_conf_store = []\n",
        "for i in range(0,len(reduce_mean_cls_loss_store),step):\n",
        "    mean_regression = tf.reduce_mean(regression_loss_store[i:i+step])\n",
        "    mean_cls = tf.reduce_mean(reduce_mean_cls_loss_store[i:i+step])\n",
        "    mean_conf = tf.reduce_mean(reduce_confident_loss_store[i:i+step])\n",
        "\n",
        "    mean_regression_store.append(mean_regression)\n",
        "    mean_cls_store.append(mean_cls)\n",
        "    mean_conf_store.append(mean_conf)\n",
        "# print(mean_regression_store)\n",
        "\n",
        "X = np.arange(len(mean_regression_store))\n",
        "# Plotting both the curves simultaneously\n",
        "# axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "axis[0].plot(X, mean_cls_store, color='r', label='reduce_mean_cls_loss_store')\n",
        "# axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "axis[0].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[0].legend()\n",
        "axis[0].set_title(\"reduce_mean_cls_loss_store\")\n",
        "\n",
        "# axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "axis[1].plot(X, mean_regression_store, color='b', label='regression_loss_store')\n",
        "# axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[1].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[1].legend()\n",
        "axis[1].set_title(\"c\")\n",
        "\n",
        "axis[2].plot(X, mean_conf_store, color='b', label='reduce_confident_loss_store')\n",
        "# axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[2].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[2].legend()\n",
        "axis[2].set_title(\"reduce_confident_loss_store\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xU6jIvDdXGT"
      },
      "source": [
        "### 60 epochs, optimizer Adam. Normalized input labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVsVOafvddzK"
      },
      "source": [
        "test_data_resnet_model = run_experiment(resnet_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "M9eaVrjHdgar",
        "outputId": "1494dfb7-b14c-476b-b3bf-5ef559d4d17f"
      },
      "source": [
        "history = test_data_resnet_model\n",
        "#  \"custome mse\"\n",
        "plt.plot(history.history['custom_mse_hungarian_py_func'])\n",
        "plt.plot(history.history['val_custom_mse_hungarian_py_func'])\n",
        "plt.title('model custome mse')\n",
        "plt.ylabel('custome mse')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# \"Loss\"\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "ckXfv1qzdmRj",
        "outputId": "277626c7-95b4-4994-b62f-1e72b564ef4f"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "figure, axis = plt.subplots(1, 2,figsize=(10,10))\n",
        "\n",
        "X = np.arange(len(save_precision_075))\n",
        "# Plotting both the curves simultaneously\n",
        "# axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "axis[0].plot(X, save_precision_075, color='r', label='precision 0.75')\n",
        "axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "axis[0].set_xlabel(\"epochs\")\n",
        "axis[0].legend()\n",
        "axis[0].set_title(\"Precision at threshold 0.5 and 0.75\")\n",
        "\n",
        "# axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "axis[1].plot(X, save_recall_05, color='b', label='recall 0.5')\n",
        "axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[1].set_xlabel(\"epochs\")\n",
        "axis[1].legend()\n",
        "axis[1].set_title(\"Recall at threshold 0.5 and 0.75\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Recall at threshold 0.5 and 0.75')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "JWF4aEkwdmmk",
        "outputId": "b84e947f-a5e7-4fb4-f80a-f7ae9987088d"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "figure, axis = plt.subplots(1, 3,figsize=(10,10))\n",
        "\n",
        "step = 21\n",
        "mean_regression_store = []\n",
        "mean_cls_store = []\n",
        "mean_conf_store = []\n",
        "for i in range(0,len(reduce_mean_cls_loss_store),step):\n",
        "    mean_regression = tf.reduce_mean(regression_loss_store[i:i+step])\n",
        "    mean_cls = tf.reduce_mean(reduce_mean_cls_loss_store[i:i+step])\n",
        "    mean_conf = tf.reduce_mean(reduce_confident_loss_store[i:i+step])\n",
        "\n",
        "    mean_regression_store.append(mean_regression)\n",
        "    mean_cls_store.append(mean_cls)\n",
        "    mean_conf_store.append(mean_conf)\n",
        "# print(mean_regression_store)\n",
        "\n",
        "X = np.arange(len(mean_regression_store))\n",
        "# Plotting both the curves simultaneously\n",
        "# axis[0].figure(figsize=(8, 6), dpi=80)\n",
        "axis[0].plot(X, mean_cls_store, color='r', label='reduce_mean_cls_loss_store')\n",
        "# axis[0].plot(X, save_precision_05, color='g', label='precision 0.5')\n",
        "axis[0].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[0].legend()\n",
        "axis[0].set_title(\"reduce_mean_cls_loss_store\")\n",
        "\n",
        "# axis[1].figure(figsize=(8, 6), dpi=80)\n",
        "axis[1].plot(X, mean_regression_store, color='b', label='regression_loss_store')\n",
        "# axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[1].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[1].legend()\n",
        "axis[1].set_title(\"c\")\n",
        "\n",
        "axis[2].plot(X, mean_conf_store, color='b', label='reduce_confident_loss_store')\n",
        "# axis[1].plot(X, save_recall_075, color='m', label='recall 0.75')\n",
        "axis[2].set_xlabel(\"value of 21 steps in each epoch in 50 epochs\")\n",
        "axis[2].legend()\n",
        "axis[2].set_title(\"reduce_confident_loss_store\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'reduce_confident_loss_store')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgAxx0C_9bAj"
      },
      "source": [
        "## Model includes ViT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdJw7zkI-sYb"
      },
      "source": [
        "### 50 epochs, optimizer Adam, weight decay = 1e-6, normalized input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hzBpsd7do3i",
        "outputId": "7b1d2445-5b9b-43a4-ed4a-f33127c73b58"
      },
      "source": [
        "test_data_resnet_model = run_experiment(inculded_vit)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/60\n",
            "      3/Unknown - 77s 19s/step - loss: 3.5555 - custom_mse_hungarian_py_func: 2.8853Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 668s 31s/step - loss: 2.7548 - custom_mse_hungarian_py_func: 3.0167 - val_loss: 2.5021 - val_custom_mse_hungarian_py_func: 1.8718\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 0 - score: 0.217512\n",
            "interval recall evaluation - epoch: 0 - score: 0.460673\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 0 - score: 0.122881\n",
            "interval self-implemented recall evaluation - epoch: 0 - score: 0.156780\n",
            "Epoch 2/60\n",
            " 3/21 [===>..........................] - ETA: 2:10 - loss: 2.3655 - custom_mse_hungarian_py_func: 2.8833Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 395s 19s/step - loss: 2.2285 - custom_mse_hungarian_py_func: 3.2074 - val_loss: 1.7638 - val_custom_mse_hungarian_py_func: 1.8149\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 1 - score: 0.570009\n",
            "interval recall evaluation - epoch: 1 - score: 0.795876\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 1 - score: 0.305085\n",
            "interval self-implemented recall evaluation - epoch: 1 - score: 0.360169\n",
            "Epoch 3/60\n",
            " 3/21 [===>..........................] - ETA: 2:10 - loss: 2.1370 - custom_mse_hungarian_py_func: 2.9691Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 396s 19s/step - loss: 2.0235 - custom_mse_hungarian_py_func: 2.8394 - val_loss: 1.9260 - val_custom_mse_hungarian_py_func: 1.4324\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 2 - score: 0.466966\n",
            "interval recall evaluation - epoch: 2 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 2 - score: 0.463983\n",
            "interval self-implemented recall evaluation - epoch: 2 - score: 0.521186\n",
            "Epoch 4/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.9813 - custom_mse_hungarian_py_func: 2.4955Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 396s 19s/step - loss: 1.9401 - custom_mse_hungarian_py_func: 3.1985 - val_loss: 1.8238 - val_custom_mse_hungarian_py_func: 2.1146\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 3 - score: 0.647682\n",
            "interval recall evaluation - epoch: 3 - score: 0.827890\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 3 - score: 0.404661\n",
            "interval self-implemented recall evaluation - epoch: 3 - score: 0.444915\n",
            "Epoch 5/60\n",
            " 3/21 [===>..........................] - ETA: 2:12 - loss: 1.9651 - custom_mse_hungarian_py_func: 3.0307Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.8884 - custom_mse_hungarian_py_func: 2.7677 - val_loss: 1.9905 - val_custom_mse_hungarian_py_func: 2.4319\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 4 - score: 0.459475\n",
            "interval recall evaluation - epoch: 4 - score: 0.966883\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 4 - score: 0.444915\n",
            "interval self-implemented recall evaluation - epoch: 4 - score: 0.500000\n",
            "Epoch 6/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.8880 - custom_mse_hungarian_py_func: 2.7999Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 396s 19s/step - loss: 1.8586 - custom_mse_hungarian_py_func: 2.6382 - val_loss: 1.9828 - val_custom_mse_hungarian_py_func: 2.0618\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 5 - score: 0.466845\n",
            "interval recall evaluation - epoch: 5 - score: 0.999515\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 5 - score: 0.461864\n",
            "interval self-implemented recall evaluation - epoch: 5 - score: 0.516949\n",
            "Epoch 7/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.8570 - custom_mse_hungarian_py_func: 2.3932Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 395s 19s/step - loss: 1.8577 - custom_mse_hungarian_py_func: 2.6293 - val_loss: 1.6378 - val_custom_mse_hungarian_py_func: 1.2690\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 6 - score: 0.485733\n",
            "interval recall evaluation - epoch: 6 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 6 - score: 0.463983\n",
            "interval self-implemented recall evaluation - epoch: 6 - score: 0.521186\n",
            "Epoch 8/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.8644 - custom_mse_hungarian_py_func: 2.5369Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 394s 19s/step - loss: 1.8115 - custom_mse_hungarian_py_func: 2.7002 - val_loss: 1.7970 - val_custom_mse_hungarian_py_func: 2.7571\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 7 - score: 0.679318\n",
            "interval recall evaluation - epoch: 7 - score: 0.935891\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 7 - score: 0.396186\n",
            "interval self-implemented recall evaluation - epoch: 7 - score: 0.449152\n",
            "Epoch 9/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.8011 - custom_mse_hungarian_py_func: 2.7409Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.7706 - custom_mse_hungarian_py_func: 2.8422 - val_loss: 1.8323 - val_custom_mse_hungarian_py_func: 2.7069\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 8 - score: 0.466845\n",
            "interval recall evaluation - epoch: 8 - score: 0.999515\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 8 - score: 0.461864\n",
            "interval self-implemented recall evaluation - epoch: 8 - score: 0.516949\n",
            "Epoch 10/60\n",
            " 3/21 [===>..........................] - ETA: 2:10 - loss: 1.8743 - custom_mse_hungarian_py_func: 2.8172Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.7546 - custom_mse_hungarian_py_func: 2.8560 - val_loss: 1.7951 - val_custom_mse_hungarian_py_func: 2.8069\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 9 - score: 0.466845\n",
            "interval recall evaluation - epoch: 9 - score: 0.999515\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 9 - score: 0.461864\n",
            "interval self-implemented recall evaluation - epoch: 9 - score: 0.516949\n",
            "Epoch 11/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.8235 - custom_mse_hungarian_py_func: 2.8993Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.7862 - custom_mse_hungarian_py_func: 2.9418 - val_loss: 1.7574 - val_custom_mse_hungarian_py_func: 2.8763\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 10 - score: 0.480218\n",
            "interval recall evaluation - epoch: 10 - score: 0.999515\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 10 - score: 0.461864\n",
            "interval self-implemented recall evaluation - epoch: 10 - score: 0.516949\n",
            "Epoch 12/60\n",
            " 3/21 [===>..........................] - ETA: 2:10 - loss: 1.8031 - custom_mse_hungarian_py_func: 2.5718Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.7238 - custom_mse_hungarian_py_func: 2.9623 - val_loss: 1.9188 - val_custom_mse_hungarian_py_func: 2.8600\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 11 - score: 0.607239\n",
            "interval recall evaluation - epoch: 11 - score: 0.597938\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 11 - score: 0.347458\n",
            "interval self-implemented recall evaluation - epoch: 11 - score: 0.347458\n",
            "Epoch 13/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.7206 - custom_mse_hungarian_py_func: 3.0106Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.6741 - custom_mse_hungarian_py_func: 3.1004 - val_loss: 1.6984 - val_custom_mse_hungarian_py_func: 2.9039\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 12 - score: 0.537291\n",
            "interval recall evaluation - epoch: 12 - score: 0.500842\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 12 - score: 0.330508\n",
            "interval self-implemented recall evaluation - epoch: 12 - score: 0.326271\n",
            "Epoch 14/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.8591 - custom_mse_hungarian_py_func: 2.9456Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.6994 - custom_mse_hungarian_py_func: 3.0174 - val_loss: 1.7912 - val_custom_mse_hungarian_py_func: 2.6885\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 13 - score: 0.315246\n",
            "interval recall evaluation - epoch: 13 - score: 0.849316\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 13 - score: 0.235876\n",
            "interval self-implemented recall evaluation - epoch: 13 - score: 0.411017\n",
            "Epoch 15/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.7643 - custom_mse_hungarian_py_func: 2.7745Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 399s 19s/step - loss: 1.6216 - custom_mse_hungarian_py_func: 2.9146 - val_loss: 1.8363 - val_custom_mse_hungarian_py_func: 2.7745\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 14 - score: 0.372428\n",
            "interval recall evaluation - epoch: 14 - score: 0.985087\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 14 - score: 0.319209\n",
            "interval self-implemented recall evaluation - epoch: 14 - score: 0.463983\n",
            "Epoch 16/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.9195 - custom_mse_hungarian_py_func: 2.6542Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.5791 - custom_mse_hungarian_py_func: 2.7628 - val_loss: 2.0575 - val_custom_mse_hungarian_py_func: 2.2363\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 15 - score: 0.383322\n",
            "interval recall evaluation - epoch: 15 - score: 0.994825\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 15 - score: 0.358757\n",
            "interval self-implemented recall evaluation - epoch: 15 - score: 0.466102\n",
            "Epoch 17/60\n",
            " 3/21 [===>..........................] - ETA: 2:10 - loss: 1.8527 - custom_mse_hungarian_py_func: 2.8598Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 400s 20s/step - loss: 1.5776 - custom_mse_hungarian_py_func: 2.8239 - val_loss: 1.9584 - val_custom_mse_hungarian_py_func: 2.1516\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 16 - score: 0.369200\n",
            "interval recall evaluation - epoch: 16 - score: 0.938039\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 16 - score: 0.363701\n",
            "interval self-implemented recall evaluation - epoch: 16 - score: 0.447034\n",
            "Epoch 18/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.6999 - custom_mse_hungarian_py_func: 2.8272Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 396s 19s/step - loss: 1.5185 - custom_mse_hungarian_py_func: 2.5896 - val_loss: 2.1140 - val_custom_mse_hungarian_py_func: 2.0158\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 17 - score: 0.346308\n",
            "interval recall evaluation - epoch: 17 - score: 0.928685\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 17 - score: 0.312147\n",
            "interval self-implemented recall evaluation - epoch: 17 - score: 0.421610\n",
            "Epoch 19/60\n",
            " 3/21 [===>..........................] - ETA: 2:10 - loss: 1.7899 - custom_mse_hungarian_py_func: 2.7857Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.5895 - custom_mse_hungarian_py_func: 2.8288 - val_loss: 1.8434 - val_custom_mse_hungarian_py_func: 2.5402\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 18 - score: 0.343455\n",
            "interval recall evaluation - epoch: 18 - score: 0.999901\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 18 - score: 0.338277\n",
            "interval self-implemented recall evaluation - epoch: 18 - score: 0.516949\n",
            "Epoch 20/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.7855 - custom_mse_hungarian_py_func: 3.2677Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.4969 - custom_mse_hungarian_py_func: 3.2947 - val_loss: 1.9826 - val_custom_mse_hungarian_py_func: 3.0026\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 19 - score: 0.371197\n",
            "interval recall evaluation - epoch: 19 - score: 0.976665\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 19 - score: 0.324859\n",
            "interval self-implemented recall evaluation - epoch: 19 - score: 0.457627\n",
            "Epoch 21/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.6859 - custom_mse_hungarian_py_func: 3.1567Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.4539 - custom_mse_hungarian_py_func: 3.2278 - val_loss: 1.9804 - val_custom_mse_hungarian_py_func: 2.7640\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 20 - score: 0.360211\n",
            "interval recall evaluation - epoch: 20 - score: 0.989040\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 20 - score: 0.311441\n",
            "interval self-implemented recall evaluation - epoch: 20 - score: 0.480932\n",
            "Epoch 22/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.6411 - custom_mse_hungarian_py_func: 3.0822Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.4118 - custom_mse_hungarian_py_func: 3.0908 - val_loss: 2.2039 - val_custom_mse_hungarian_py_func: 2.2882\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 21 - score: 0.338167\n",
            "interval recall evaluation - epoch: 21 - score: 0.999529\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 21 - score: 0.297316\n",
            "interval self-implemented recall evaluation - epoch: 21 - score: 0.512712\n",
            "Epoch 23/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.6342 - custom_mse_hungarian_py_func: 3.3403Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.3953 - custom_mse_hungarian_py_func: 3.1234 - val_loss: 2.0361 - val_custom_mse_hungarian_py_func: 2.7903\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 22 - score: 0.341804\n",
            "interval recall evaluation - epoch: 22 - score: 0.999529\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 22 - score: 0.307203\n",
            "interval self-implemented recall evaluation - epoch: 22 - score: 0.512712\n",
            "Epoch 24/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.6840 - custom_mse_hungarian_py_func: 2.7984Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.3941 - custom_mse_hungarian_py_func: 2.8288 - val_loss: 2.3651 - val_custom_mse_hungarian_py_func: 2.7636\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 23 - score: 0.394847\n",
            "interval recall evaluation - epoch: 23 - score: 0.999664\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 23 - score: 0.341102\n",
            "interval self-implemented recall evaluation - epoch: 23 - score: 0.512712\n",
            "Epoch 25/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.5464 - custom_mse_hungarian_py_func: 3.4023Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.4046 - custom_mse_hungarian_py_func: 2.9943 - val_loss: 2.1038 - val_custom_mse_hungarian_py_func: 1.5496\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 24 - score: 0.373890\n",
            "interval recall evaluation - epoch: 24 - score: 0.999934\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 24 - score: 0.324859\n",
            "interval self-implemented recall evaluation - epoch: 24 - score: 0.516949\n",
            "Epoch 26/60\n",
            " 3/21 [===>..........................] - ETA: 2:10 - loss: 1.6568 - custom_mse_hungarian_py_func: 2.8817Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.3930 - custom_mse_hungarian_py_func: 3.2628 - val_loss: 2.4039 - val_custom_mse_hungarian_py_func: 2.6592\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 25 - score: 0.373852\n",
            "interval recall evaluation - epoch: 25 - score: 0.999323\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 25 - score: 0.321328\n",
            "interval self-implemented recall evaluation - epoch: 25 - score: 0.508475\n",
            "Epoch 27/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.5381 - custom_mse_hungarian_py_func: 3.2191Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 395s 19s/step - loss: 1.3131 - custom_mse_hungarian_py_func: 2.9396 - val_loss: 2.3061 - val_custom_mse_hungarian_py_func: 1.6257\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 26 - score: 0.367832\n",
            "interval recall evaluation - epoch: 26 - score: 0.998947\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 26 - score: 0.317797\n",
            "interval self-implemented recall evaluation - epoch: 26 - score: 0.504237\n",
            "Epoch 28/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.5258 - custom_mse_hungarian_py_func: 2.9292Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 399s 19s/step - loss: 1.3279 - custom_mse_hungarian_py_func: 3.1928 - val_loss: 2.5745 - val_custom_mse_hungarian_py_func: 1.8230\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 27 - score: 0.381385\n",
            "interval recall evaluation - epoch: 27 - score: 0.998816\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 27 - score: 0.326271\n",
            "interval self-implemented recall evaluation - epoch: 27 - score: 0.500000\n",
            "Epoch 29/60\n",
            " 3/21 [===>..........................] - ETA: 2:11 - loss: 1.5064 - custom_mse_hungarian_py_func: 3.3879Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.2863 - custom_mse_hungarian_py_func: 3.4208 - val_loss: 2.6763 - val_custom_mse_hungarian_py_func: 1.8742\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 28 - score: 0.402774\n",
            "interval recall evaluation - epoch: 28 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 28 - score: 0.353814\n",
            "interval self-implemented recall evaluation - epoch: 28 - score: 0.521186\n",
            "Epoch 30/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.4953 - custom_mse_hungarian_py_func: 3.4532Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.2746 - custom_mse_hungarian_py_func: 3.1791 - val_loss: 2.7213 - val_custom_mse_hungarian_py_func: 1.8945\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 29 - score: 0.401549\n",
            "interval recall evaluation - epoch: 29 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 29 - score: 0.353107\n",
            "interval self-implemented recall evaluation - epoch: 29 - score: 0.521186\n",
            "Epoch 31/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.5099 - custom_mse_hungarian_py_func: 3.9335Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.3160 - custom_mse_hungarian_py_func: 3.3971 - val_loss: 2.9891 - val_custom_mse_hungarian_py_func: 2.6290\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 30 - score: 0.434499\n",
            "interval recall evaluation - epoch: 30 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 30 - score: 0.391243\n",
            "interval self-implemented recall evaluation - epoch: 30 - score: 0.521186\n",
            "Epoch 32/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.4573 - custom_mse_hungarian_py_func: 3.4882Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 399s 19s/step - loss: 1.2432 - custom_mse_hungarian_py_func: 3.2647 - val_loss: 2.7116 - val_custom_mse_hungarian_py_func: 2.8717\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 31 - score: 0.438249\n",
            "interval recall evaluation - epoch: 31 - score: 0.991982\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 31 - score: 0.391243\n",
            "interval self-implemented recall evaluation - epoch: 31 - score: 0.512712\n",
            "Epoch 33/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.4016 - custom_mse_hungarian_py_func: 4.0888Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 397s 19s/step - loss: 1.2264 - custom_mse_hungarian_py_func: 3.4353 - val_loss: 2.5144 - val_custom_mse_hungarian_py_func: 2.2843\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 32 - score: 0.436022\n",
            "interval recall evaluation - epoch: 32 - score: 0.976352\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 32 - score: 0.380650\n",
            "interval self-implemented recall evaluation - epoch: 32 - score: 0.504237\n",
            "Epoch 34/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.4816 - custom_mse_hungarian_py_func: 3.3461Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 395s 19s/step - loss: 1.1983 - custom_mse_hungarian_py_func: 3.3119 - val_loss: 2.4705 - val_custom_mse_hungarian_py_func: 2.6209\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 33 - score: 0.458210\n",
            "interval recall evaluation - epoch: 33 - score: 0.994567\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 33 - score: 0.425141\n",
            "interval self-implemented recall evaluation - epoch: 33 - score: 0.504237\n",
            "Epoch 35/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.3968 - custom_mse_hungarian_py_func: 3.1891Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 398s 19s/step - loss: 1.2032 - custom_mse_hungarian_py_func: 3.2182 - val_loss: 2.4538 - val_custom_mse_hungarian_py_func: 2.0206\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 34 - score: 0.452901\n",
            "interval recall evaluation - epoch: 34 - score: 0.975861\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 34 - score: 0.387712\n",
            "interval self-implemented recall evaluation - epoch: 34 - score: 0.491525\n",
            "Epoch 36/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.4817 - custom_mse_hungarian_py_func: 3.8444Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 392s 19s/step - loss: 1.1923 - custom_mse_hungarian_py_func: 3.4868 - val_loss: 2.5657 - val_custom_mse_hungarian_py_func: 2.6222\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 35 - score: 0.445784\n",
            "interval recall evaluation - epoch: 35 - score: 0.984451\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 35 - score: 0.372881\n",
            "interval self-implemented recall evaluation - epoch: 35 - score: 0.504237\n",
            "Epoch 37/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.4336 - custom_mse_hungarian_py_func: 3.9972Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 392s 19s/step - loss: 1.1930 - custom_mse_hungarian_py_func: 3.3907 - val_loss: 2.5446 - val_custom_mse_hungarian_py_func: 1.7181\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 36 - score: 0.420715\n",
            "interval recall evaluation - epoch: 36 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 36 - score: 0.381356\n",
            "interval self-implemented recall evaluation - epoch: 36 - score: 0.521186\n",
            "Epoch 38/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.3656 - custom_mse_hungarian_py_func: 3.7314Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 390s 19s/step - loss: 1.1799 - custom_mse_hungarian_py_func: 3.7443 - val_loss: 2.3851 - val_custom_mse_hungarian_py_func: 3.0996\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 37 - score: 0.429902\n",
            "interval recall evaluation - epoch: 37 - score: 0.999599\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 37 - score: 0.384181\n",
            "interval self-implemented recall evaluation - epoch: 37 - score: 0.508475\n",
            "Epoch 39/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.4353 - custom_mse_hungarian_py_func: 4.3526Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 394s 19s/step - loss: 1.2474 - custom_mse_hungarian_py_func: 3.7046 - val_loss: 2.9792 - val_custom_mse_hungarian_py_func: 1.7414\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 38 - score: 0.395625\n",
            "interval recall evaluation - epoch: 38 - score: 0.999934\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 38 - score: 0.366525\n",
            "interval self-implemented recall evaluation - epoch: 38 - score: 0.516949\n",
            "Epoch 40/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.4192 - custom_mse_hungarian_py_func: 3.4015Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 393s 19s/step - loss: 1.1863 - custom_mse_hungarian_py_func: 3.6484 - val_loss: 2.5770 - val_custom_mse_hungarian_py_func: 2.7359\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 39 - score: 0.475196\n",
            "interval recall evaluation - epoch: 39 - score: 0.944678\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 39 - score: 0.389124\n",
            "interval self-implemented recall evaluation - epoch: 39 - score: 0.474576\n",
            "Epoch 41/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.5233 - custom_mse_hungarian_py_func: 4.7597Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 393s 19s/step - loss: 1.2337 - custom_mse_hungarian_py_func: 3.8160 - val_loss: 2.6636 - val_custom_mse_hungarian_py_func: 2.1952\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 40 - score: 0.442114\n",
            "interval recall evaluation - epoch: 40 - score: 0.984477\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 40 - score: 0.383475\n",
            "interval self-implemented recall evaluation - epoch: 40 - score: 0.504237\n",
            "Epoch 42/60\n",
            " 3/21 [===>..........................] - ETA: 2:04 - loss: 1.4672 - custom_mse_hungarian_py_func: 4.2274Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 394s 19s/step - loss: 1.2232 - custom_mse_hungarian_py_func: 3.7396 - val_loss: 2.8976 - val_custom_mse_hungarian_py_func: 1.9614\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 41 - score: 0.435707\n",
            "interval recall evaluation - epoch: 41 - score: 0.999659\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 41 - score: 0.409604\n",
            "interval self-implemented recall evaluation - epoch: 41 - score: 0.516949\n",
            "Epoch 43/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.4287 - custom_mse_hungarian_py_func: 4.2185Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 395s 19s/step - loss: 1.1799 - custom_mse_hungarian_py_func: 3.7081 - val_loss: 2.7482 - val_custom_mse_hungarian_py_func: 2.0997\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 42 - score: 0.426077\n",
            "interval recall evaluation - epoch: 42 - score: 0.999695\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 42 - score: 0.399011\n",
            "interval self-implemented recall evaluation - epoch: 42 - score: 0.516949\n",
            "Epoch 44/60\n",
            " 3/21 [===>..........................] - ETA: 2:05 - loss: 1.4602 - custom_mse_hungarian_py_func: 4.0603Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 390s 19s/step - loss: 1.2075 - custom_mse_hungarian_py_func: 3.5647 - val_loss: 2.4247 - val_custom_mse_hungarian_py_func: 2.8255\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 43 - score: 0.462456\n",
            "interval recall evaluation - epoch: 43 - score: 0.999291\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 43 - score: 0.394774\n",
            "interval self-implemented recall evaluation - epoch: 43 - score: 0.508475\n",
            "Epoch 45/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.4203 - custom_mse_hungarian_py_func: 3.8453Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 395s 19s/step - loss: 1.1808 - custom_mse_hungarian_py_func: 3.8314 - val_loss: 2.7010 - val_custom_mse_hungarian_py_func: 2.0033\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 44 - score: 0.429117\n",
            "interval recall evaluation - epoch: 44 - score: 0.996705\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 44 - score: 0.379237\n",
            "interval self-implemented recall evaluation - epoch: 44 - score: 0.508475\n",
            "Epoch 46/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.3613 - custom_mse_hungarian_py_func: 4.0264Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 393s 19s/step - loss: 1.1362 - custom_mse_hungarian_py_func: 3.6550 - val_loss: 2.9011 - val_custom_mse_hungarian_py_func: 1.9365\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 45 - score: 0.399476\n",
            "interval recall evaluation - epoch: 45 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 45 - score: 0.370763\n",
            "interval self-implemented recall evaluation - epoch: 45 - score: 0.521186\n",
            "Epoch 47/60\n",
            " 3/21 [===>..........................] - ETA: 2:05 - loss: 1.4007 - custom_mse_hungarian_py_func: 4.2466Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 393s 19s/step - loss: 1.1894 - custom_mse_hungarian_py_func: 3.4187 - val_loss: 2.5000 - val_custom_mse_hungarian_py_func: 2.1121\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 46 - score: 0.453993\n",
            "interval recall evaluation - epoch: 46 - score: 0.996796\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 46 - score: 0.429378\n",
            "interval self-implemented recall evaluation - epoch: 46 - score: 0.512712\n",
            "Epoch 48/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.4073 - custom_mse_hungarian_py_func: 3.7949Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 392s 19s/step - loss: 1.1934 - custom_mse_hungarian_py_func: 4.1038 - val_loss: 2.7854 - val_custom_mse_hungarian_py_func: 2.2757\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 47 - score: 0.446816\n",
            "interval recall evaluation - epoch: 47 - score: 0.999515\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 47 - score: 0.425141\n",
            "interval self-implemented recall evaluation - epoch: 47 - score: 0.516949\n",
            "Epoch 49/60\n",
            " 3/21 [===>..........................] - ETA: 2:06 - loss: 1.2611 - custom_mse_hungarian_py_func: 3.0008Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 393s 19s/step - loss: 1.1587 - custom_mse_hungarian_py_func: 3.1729 - val_loss: 2.5951 - val_custom_mse_hungarian_py_func: 2.0246\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 48 - score: 0.442155\n",
            "interval recall evaluation - epoch: 48 - score: 0.963817\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 48 - score: 0.406780\n",
            "interval self-implemented recall evaluation - epoch: 48 - score: 0.500000\n",
            "Epoch 50/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.3171 - custom_mse_hungarian_py_func: 3.4503Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 394s 19s/step - loss: 1.0740 - custom_mse_hungarian_py_func: 3.1808 - val_loss: 2.5487 - val_custom_mse_hungarian_py_func: 3.8190\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 49 - score: 0.456316\n",
            "interval recall evaluation - epoch: 49 - score: 0.999515\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 49 - score: 0.436441\n",
            "interval self-implemented recall evaluation - epoch: 49 - score: 0.516949\n",
            "Epoch 51/60\n",
            " 3/21 [===>..........................] - ETA: 2:09 - loss: 1.3134 - custom_mse_hungarian_py_func: 3.7160Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 388s 19s/step - loss: 1.0750 - custom_mse_hungarian_py_func: 3.0082 - val_loss: 2.9026 - val_custom_mse_hungarian_py_func: 2.6519\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 50 - score: 0.443036\n",
            "interval recall evaluation - epoch: 50 - score: 0.992663\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 50 - score: 0.413842\n",
            "interval self-implemented recall evaluation - epoch: 50 - score: 0.510593\n",
            "Epoch 52/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.3123 - custom_mse_hungarian_py_func: 4.2784Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 392s 19s/step - loss: 1.0699 - custom_mse_hungarian_py_func: 3.5017 - val_loss: 2.7164 - val_custom_mse_hungarian_py_func: 4.4564\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 51 - score: 0.439694\n",
            "interval recall evaluation - epoch: 51 - score: 0.999659\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 51 - score: 0.411017\n",
            "interval self-implemented recall evaluation - epoch: 51 - score: 0.516949\n",
            "Epoch 53/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.2679 - custom_mse_hungarian_py_func: 3.3278Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 389s 19s/step - loss: 1.0686 - custom_mse_hungarian_py_func: 3.1888 - val_loss: 2.1990 - val_custom_mse_hungarian_py_func: 2.0209\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 52 - score: 0.457879\n",
            "interval recall evaluation - epoch: 52 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 52 - score: 0.409604\n",
            "interval self-implemented recall evaluation - epoch: 52 - score: 0.521186\n",
            "Epoch 54/60\n",
            " 3/21 [===>..........................] - ETA: 2:08 - loss: 1.2977 - custom_mse_hungarian_py_func: 4.0848Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 395s 19s/step - loss: 1.0733 - custom_mse_hungarian_py_func: 3.5926 - val_loss: 2.4749 - val_custom_mse_hungarian_py_func: 1.8123\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 53 - score: 0.408066\n",
            "interval recall evaluation - epoch: 53 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 53 - score: 0.382768\n",
            "interval self-implemented recall evaluation - epoch: 53 - score: 0.521186\n",
            "Epoch 55/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.3395 - custom_mse_hungarian_py_func: 3.6144Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 392s 19s/step - loss: 1.0973 - custom_mse_hungarian_py_func: 3.3868 - val_loss: 2.9890 - val_custom_mse_hungarian_py_func: 2.1022\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 54 - score: 0.396637\n",
            "interval recall evaluation - epoch: 54 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 54 - score: 0.381356\n",
            "interval self-implemented recall evaluation - epoch: 54 - score: 0.521186\n",
            "Epoch 56/60\n",
            " 3/21 [===>..........................] - ETA: 2:07 - loss: 1.2932 - custom_mse_hungarian_py_func: 4.6500Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "21/21 [==============================] - 389s 19s/step - loss: 1.0569 - custom_mse_hungarian_py_func: 3.2677 - val_loss: 3.1261 - val_custom_mse_hungarian_py_func: 4.4221\n",
            "Threshold = 0.5\n",
            "interval precision evaluation - epoch: 55 - score: 0.415031\n",
            "interval recall evaluation - epoch: 55 - score: 1.000000\n",
            "\n",
            "Threshold = 0.75\n",
            "interval self-implemented precision evaluation - epoch: 55 - score: 0.399717\n",
            "interval self-implemented recall evaluation - epoch: 55 - score: 0.521186\n",
            "Epoch 57/60\n",
            " 3/21 [===>..........................] - ETA: 2:06 - loss: 1.3481 - custom_mse_hungarian_py_func: 3.7736Can not convert <class 'str'> to int\n",
            "056406 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056406 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056408 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056408 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056410 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056410 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056412 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056412 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056414 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056414 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056416 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056416 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056418 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056418 (1).jpg\n",
            "Can not convert <class 'str'> to int\n",
            "056422 (1)     /content/drive/Shareddrives/Behavior vehicles prediction_BME/carla_dataset/carla_data_test/Town04/rgb_image/front_image/round4/round4_056422 (1).jpg\n",
            "17/21 [=======================>......] - ETA: 59s - loss: 1.1672 - custom_mse_hungarian_py_func: 3.8844 "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "9qRbkeJm-4Ur",
        "outputId": "aa8e8aec-5ff4-466b-f05d-21a1c4c5d73f"
      },
      "source": [
        "visualize_mse_loss(test_data_resnet_model)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-279932e799cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvisualize_mse_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data_resnet_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'visualize_mse_loss' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "X0eps0z88VmN",
        "outputId": "31d76fa2-1624-4f85-8af8-4b88c24a1822"
      },
      "source": [
        "visualize_prec_recall(save_precision_075,save_precision_05,save_recall_075,save_recall_05)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-2a089d9a69b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvisualize_prec_recall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_precision_075\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msave_precision_05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msave_recall_075\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msave_recall_05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'visualize_prec_recall' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "me_TV-4W8Zib",
        "outputId": "efa55420-d1b6-4e14-f9cc-0e6878ca5cb2"
      },
      "source": [
        "visualize_3_parts_hungarian_loss(reduce_mean_cls_loss_store, regression_loss_store,reduce_confident_loss_store)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-00c3b5991216>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvisualize_3_parts_hungarian_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduce_mean_cls_loss_store\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregression_loss_store\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreduce_confident_loss_store\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'visualize_3_parts_hungarian_loss' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxuGNzN78d7s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}